diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index e7f80c3..48c77b0 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -20,7 +20,7 @@ jobs: matrix: version: - '1.10' - - '1' + - '1.11' os: - ubuntu-latest arch: @@ -54,3 +54,23 @@ jobs: - uses: codecov/codecov-action@v5 with: files: lcov.info + docs: + name: Documentation + runs-on: ubuntu-latest + timeout-minutes: 40 + steps: + - uses: actions/checkout@v4 + - uses: julia-actions/setup-julia@v2 + - uses: julia-actions/cache@v2 + - uses: julia-actions/julia-buildpkg@v1 + - uses: julia-actions/julia-docdeploy@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} + - run: | + julia --project=docs -e ' + using Documenter: DocMeta, doctest + using AtmosphericModels + DocMeta.setdocmeta!(AtmosphericModels, :DocTestSetup, :(using AtmosphericModels); recursive=true) + doctest(AtmosphericModels)' + diff --git a/.gitignore b/.gitignore index d32a974..201094d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,11 @@ /Manifest.toml .vscode/settings.json +data/*.npz +examples/Manifest.toml +examples/Manifest-v1.10.toml +examples/Manifest-v1.11.toml +docs/build +docs/Manifest.toml +Manifest-v1.11.toml +Manifest-v1.10.toml +cspell.json diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..f406696 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,8 @@ +## Unreleased + +## Changed +- BREAKING: When constructing an atmospheric model, you MUST pass the parameter set::Settings. This ensures that all parts of the simulation use the same settings struct, and that you can run different simulations with different settings in parallel. + +## Added +- The function `get_wind(am, x, y, z, t)` which returns a wind vector for the given position and time. It creates a 3D wind field if it does not exist in the data folder. The parameters of this wind field are configured in `settings.yaml`. +- Documenter generated documentation. \ No newline at end of file diff --git a/Manifest-v1.10.toml.default b/Manifest-v1.10.toml.default new file mode 100644 index 0000000..fd339eb --- /dev/null +++ b/Manifest-v1.10.toml.default @@ -0,0 +1,843 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.10.10" +manifest_format = "2.0" +project_hash = "9bb49c098248f08091074f5c420965bb57cb87fc" + +[[deps.AbstractFFTs]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef" +uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" +version = "1.5.0" + + [deps.AbstractFFTs.extensions] + AbstractFFTsChainRulesCoreExt = "ChainRulesCore" + AbstractFFTsTestExt = "Test" + + [deps.AbstractFFTs.weakdeps] + ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" + Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" + +[[deps.Accessors]] +deps = ["CompositionsBase", 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"8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.59.0+0" + +[[deps.oneTBB_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "d5a767a3bb77135a99e433afe0eb14cd7f6914c3" +uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e" +version = "2022.0.0+0" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+2" diff --git a/Project.toml b/Project.toml index ba2a2d8..c8a91ef 100644 --- a/Project.toml +++ b/Project.toml @@ -4,18 +4,36 @@ authors = ["Uwe Fechner and contributors"] version = "0.2.5" [deps] +FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341" HypergeometricFunctions = "34004b35-14d8-5ef3-9330-4cdb6864b03a" KiteUtils = "90980105-b163-44e5-ba9f-8b1c83bb0533" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MeshGrid = "ebf956a0-ef5e-43be-9fb1-27952996e635" +NPZ = "15e1cf62-19b3-5cfa-8e77-841668bca605" +Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" [compat] +ControlPlots = "0.2.7" +Documenter = "1.13.0" +FFTW = "1.9.0" HypergeometricFunctions = "0.3" -KiteUtils = "0.6, 0.7, 0.8, 0.9, 0.10" +KiteUtils = "0.10.14" +LinearAlgebra = "1.10, 1.11" +MeshGrid = "1.0.3" +NPZ = "0.4.3" +Printf = "1.10, 1.11" +Random = "1.10, 1.11" +Statistics = "1.10, 1.11" julia = "1.10, 1.11" [extras] BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" +ControlPlots = "23c2ee80-7a9e-4350-b264-8e670f12517c" +Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" Remez = "2e7db186-766a-50e7-8928-5c30181fb135" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [targets] -test = ["Test", "BenchmarkTools", "Remez"] +test = ["Test", "BenchmarkTools", "Remez", "ControlPlots", "Documenter"] diff --git a/README.md b/README.md index 4ca9c33..ee1ba4a 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ at the Julia prompt. ## Exported types ```julia AtmosphericModel -@enum ProfileLaw EXP=1 LOG=2 EXPLOG=3 FAST_EXP=4 FAST_LOG=5 FAST_EXPLOG=6 +@enum ProfileLaw EXP=1 LOG=2 EXPLOG=3 ``` ## Exported functions @@ -25,10 +25,31 @@ calc_wind_factor(am::AM, height, profile_law::Int64=am.set.profile_law) ``` ## Wind profile -

+

The EXPLOG profile law is the fitted linear combination of the exponential and the log law. +## Running the tests +Launch Julia using this project and run the tests: +```julia +julia --project +using Pkg +Pkg.test("AtmosphericModels") +``` + +## Running the examples +If you check out the project using git, you can more easily run the examples: +``` +git clone https://github.com/OpenSourceAWE/AtmosphericModels.jl +cd AtmosphericModels.jl +``` +Launch Julia using this project and run the example menu: +```julia +julia --project +include("examples/menu.jl") +``` +The first time will take some time, because the graphic libraries will get installed, the second time it is fast. + ## Usage ```julia using AtmosphericModels @@ -41,50 +62,33 @@ wf = calc_wind_factor(am, height, profile_law) The result is the factor with which the ground wind speed needs to be multiplied to get the wind speed at the given height. -## Plot a wind profile -```julia -using AtmosphericModels, Plots -am = AtmosphericModel() - -heights = 6:1000 -wf = [calc_wind_factor(am, height, Int(EXPLOG)) for height in heights] - -plot(heights, wf, legend=false, xlabel="height [m]", ylabel="wind factor") +## Using the turbulent wind field +You can get a wind vector as function of x,y,z and time using the following code: ``` +using AtmosphericModels, KiteUtils -```julia -using AtmosphericModels, ControlPlots -am = AtmosphericModel() -AtmosphericModels.se().alpha = 0.234 # set the exponent of the power law - -heights = 6:200 -wf = [calc_wind_factor(am, height, Int(EXP)) for height in heights] - -plot(heights, wf, xlabel="height [m]", ylabel="wind factor") -``` +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) -## Benchmark -```julia -using AtmosphericModels, BenchmarkTools +@info "Ground wind speed: $(am.set.v_wind) m/s" -am = AtmosphericModel() -@benchmark calc_wind_factor(am, height, Int(EXPLOG)) setup=(height=Float64((6.0+rand()*500.0))) +wf::WindField = WindField(am, am.set.v_wind) +x, y, z = 20.0, 0.0, 200.0 +t = 0.0 +vx, vy, vz = get_wind(wf, am, x, y, z, t) +@time get_wind(am, x, y, z, t) +@info "Wind at x=$(x), y=$(y), z=$(z), t=$(t): v_x=$(vx), v_y=$(vy), v_z=$(vz)" +@info "Wind speed: $(sqrt(vx^2 + vy^2 + vz^2)) m/s" ``` -|Profile law|time [ns]| -| --- |:---:| -|EXP |12 | -|LOG |16 | -|EXPLOG |33 | -|FAST_EXP|6.6 | -|FAST_LOG|6.6 | -|FAST_EXPLOG|6.6| - -The FAST versions are an approximations with an error of less than $1.5 \cdot 10^{-5}$ and are correct only for the default values of h_ref, z0 and alpha. +It is suggested to check out the code using git before executing this example, +because it requires that a data directory with the correct files `system.yaml` +and `settings.yaml` exists. See below how to do that. ## Air density ```julia -using AtmosphericModels, BenchmarkTools -am = AtmosphericModel() +using AtmosphericModels, BenchmarkTools, KiteUtils +am = AtmosphericModel(se()) @benchmark calc_rho(am, height) setup=(height=Float64((6.0+rand()*500.0))) ``` This gives 4.85 ns as result. Plot the air density: @@ -93,25 +97,7 @@ heights = 6:1000 rhos = [calc_rho(am, height) for height in heights] plot(heights, rhos, legend=false, xlabel="height [m]", ylabel="air density [kg/m³]") ``` -

- -## Running the test scripts -First, add TestEnv to your global environment. -```julia -julia -using Pkg -Pkg.add("TestEnv") -exit() -``` -Then you can run Julia using this project and run the tests: -```julia -julia --project -using TestEnv -TestEnv.activate() -include("test/bench.jl") -include("calc_approximations.jl") -include("runtests.jl") -``` +

## Further reading These models are described in detail in [Dynamic Model of a Pumping Kite Power System](http://arxiv.org/abs/1406.6218). diff --git a/data/settings.yaml b/data/settings.yaml index df7f6ae..6e3af4b 100644 --- a/data/settings.yaml +++ b/data/settings.yaml @@ -1,129 +1,22 @@ -system: - log_file: "data/log_8700W_8ms" # filename without extension [replay only] - # use / as path delimiter, even on Windows - log_level: 2 # 0: no logging - time_lapse: 1.0 # relative replay speed - sim_time: 100.0 # simulation time [sim only] - segments: 6 # number of tether segments - sample_freq: 20 # sample frequency in Hz - zoom: 0.03 # zoom factor for the system view - kite_scale: 3.0 # relative zoom factor for the 4 point kite - fixed_font: "" # name or filepath+filename of alternative fixed pitch font, e.g. Liberation Mono - -initial: - l_tether: 150.0 # initial tether length [m] - elevation: 70.7 # initial elevation angle [deg] - v_reel_out: 0.0 # initial reel out speed [m/s] - depower: 25.0 # initial depower settings [%] - -solver: - abs_tol: 0.0006 # absolute tolerance of the DAE solver [m, m/s] - rel_tol: 0.001 # relative tolerance of the DAE solver [-] - solver: "DFBDF" # DAE solver, IDA or DFBDF or DImplicitEuler - linear_solver: "GMRES" # can be GMRES or LapackDense or Dense (only for IDA) - max_order: 4 # maximal order, usually between 3 and 5 (IDA and DFBDF) - max_iter: 200 # max number of iterations of the steady-state-solver - -steering: - c0: 0.0 # steering offset -0.0032 [-] - c_s: 2.59 # steering coefficient one point model; 2.59 was 0.6; TODO: check if it must be divided by kite_area - c2_cor: 0.93 # correction factor one point model - k_ds: 1.5 # influence of the depower angle on the steering sensitivity - delta_st: 0.02 # steering increment (when pressing RIGHT) - max_steering: 16.83 # max. steering angle of the side planes for four point model [degrees] - -depower: - alpha_d_max: 31.0 # max depower angle [deg] - depower_offset: 23.6 # at rel_depower=0.236 the kite is fully powered [%] - -kite: - model: "data/kite.obj" # 3D model of the kite - physical_model: "KPS4" # name of the kite model to use (KPS3 or KPS4) - version: 1 # version of the model to use - mass: 6.2 # kite mass incl. sensor unit [kg] - area: 10.18 # projected kite area [m²] - rel_side_area: 30.6 # relative side area [%] - height: 2.23 # height of the kite [m] - alpha_cl: [-180.0, -160.0, -90.0, -20.0, -10.0, -5.0, 0.0, 20.0, 40.0, 90.0, 160.0, 180.0] - cl_list: [ 0.0, 0.5, 0.0, 0.08, 0.125, 0.15, 0.2, 1.0, 1.0, 0.0, -0.5, 0.0] - alpha_cd: [-180.0, -170.0, -140.0, -90.0, -20.0, 0.0, 20.0, 90.0, 140.0, 170.0, 180.0] - cd_list: [ 0.5, 0.5, 0.5, 1.0, 0.2, 0.1, 0.2, 1.0, 0.5, 0.5, 0.5] - -kps4: - width: 5.77 # width of the kite [m] - alpha_zero: 4.0 # should be 4 .. 10 [degrees] - alpha_ztip: 10.0 # [degrees] - m_k: 0.2 # relative nose distance; increasing m_k increases C2 of the turn-rate law - rel_nose_mass: 0.47 # relative nose mass - rel_top_mass: 0.4 # mass of the top particle relative to the sum of top and side particles - -kps4_3l: - radius: 2.0 # the radius of the circle shape on which the kite lines, viewed - # from the front [m] - bridle_center_distance: 4.0 # the distance from point the center bridle connection point of - # the middle line to the kite [m] - middle_length: 1.5 # the cord length of the kite in the middle [m] - tip_length: 0.62 # the cord length of the kite at the tips [m] - min_steering_line_distance: 1.0 # the distance between the left and right steering bridle [m] - # line connections on the kite that are closest to each other [m] - width_3l: 4.1 # width of the kite [m] - aero_surfaces: 3 # the number of aerodynamic surfaces to use per mass point [-] - -bridle: - d_line: 2.5 # bridle line diameter [mm] - l_bridle: 33.4 # sum of the lengths of the bridle lines [m] - h_bridle: 4.9 # height of bridle [m] - rel_compr_stiffness: 0.25 # relative compression stiffness of the kite springs [-] - rel_damping: 6.0 # relative damping of the kite spring (relative to main tether) [-] - -kcu: - kcu_mass: 8.4 # mass of the kite control unit [kg] - power2steer_dist: 1.3 # [m] - depower_drum_diameter: 0.069 # [m] - tape_thickness: 0.0006 # [m] - v_depower: 0.075 # max velocity of depowering in units per second (full range: 1 unit) - v_steering: 0.2 # max velocity of steering in units per second (full range: 2 units) - depower_gain: 3.0 # 3.0 means: more than 33% error -> full speed - steering_gain: 3.0 - -tether: - d_tether: 4 # tether diameter [mm] - cd_tether: 0.958 # drag coefficient of the tether - damping: 473.0 # unit damping coefficient [Ns] - c_spring: 614600.0 # unit spring constant coefficient [N] - rho_tether: 724.0 # density of Dyneema [kg/m³] - e_tether: 55000000000.0 # axial tensile modulus of Dyneema (M.B. Ruppert) [Pa] - # SK75: 109 to 132 GPa according to datasheet - -winch: - winch_model: "AsyncMachine" # or TorqueControlledMachine - max_force: 4000 # maximal (nominal) tether force; short overload allowed [N] - v_ro_max: 8.0 # maximal reel-out speed [m/s] - v_ro_min: -8.0 # minimal reel-out speed (=max reel-in speed) [m/s] - drum_radius: 0.1615 # radius of the drum [m] - gear_ratio: 6.2 # gear ratio of the winch [-] - inertia_total: 0.204 # total inertia, as seen from the motor/generator [kgm²] - f_coulomb: 122.0 # coulomb friction [N] - c_vf: 30.6 # coefficient for the viscous friction [Ns/m] - environment: - v_wind: 9.51 # wind speed at reference height [m/s] - v_wind_ref: [9.51, 0.0] # wind speed vector at reference height [m/s] + v_wind: 5.324 # wind speed at reference height [m/s] + upwind_dir: -90.0 # upwind direction [deg] temp_ref: 15.0 # temperature at reference height [°C] height_gnd: 0.0 # height of groundstation above see level [m] h_ref: 6.0 # reference height for the wind speed [m] rho_0: 1.225 # air density at zero height and 15 °C [kg/m³] - alpha: 0.08163 # exponent of the wind profile law + alpha: 0.234 # exponent of the wind profile law z0: 0.0002 # surface roughness [m] - profile_law: 3 # 1=EXP, 2=LOG, 3=EXPLOG, 4=FAST_EXP, 5=FAST_LOG, 6=FAST_EXPLOG + profile_law: 1 # 1=EXP, 2=LOG, 3=EXPLOG # the following parameters are for calculating the turbulent wind field using the Mann model - use_turbulence: 0.0 # turbulence intensity relative to Cabauw, NL + use_turbulence: 1.0 # turbulence intensity relative to Cabauw, NL v_wind_gnds: [3.483, 5.324, 8.163] # wind speeds at ref height for calculating the turbulent wind field [m/s] avg_height: 200.0 # average height during reel out [m] rel_turbs: [0.342, 0.465, 0.583] # relative turbulence at the v_wind_gnds i_ref: 0.14 # is the expected value of the turbulence intensity at 15 m/s. v_ref: 42.9 # five times the average wind speed in m/s at hub height over the full year [m/s] # Cabauw: 8.5863 m/s * 5.0 = 42.9 m/s + grid: [4050, 100, 500, 70] # grid size nx, ny, nz and minimal height z_min [m] height_step: 2.0 # use a grid with 2m resolution in z direction [m] grid_step: 2.0 # grid resolution in x and y direction [m] diff --git a/data/settings_nearshore.yaml b/data/settings_nearshore.yaml new file mode 100644 index 0000000..c510e95 --- /dev/null +++ b/data/settings_nearshore.yaml @@ -0,0 +1,12 @@ +environment: + v_wind: 10.35 # wind speed at reference height [m/s] + upwind_dir: -90.0 # upwind direction [deg] + temp_ref: 15.0 # temperature at reference height [°C] + height_gnd: 0.0 # height of groundstation above see level [m] + h_ref: 6.0 # reference height for the wind speed [m] + + rho_0: 1.225 # air density at zero height and 15 °C [kg/m³] + alpha: 0.08163 # exponent of the wind profile law + z0: 0.0002 # surface roughness [m] + profile_law: 3 # 1=EXP, 2=LOG, 3=EXPLOG + use_turbulence: 0.0 # turbulence intensity diff --git a/data/system.yaml b/data/system.yaml index 5a7736b..f4f753f 100644 --- a/data/system.yaml +++ b/data/system.yaml @@ -1,3 +1,3 @@ system: - project: "settings.yaml" # simulator settings + project: "settings.yaml" # model settings \ No newline at end of file diff --git a/data/system_nearshore.yaml b/data/system_nearshore.yaml new file mode 100644 index 0000000..965e7cd --- /dev/null +++ b/data/system_nearshore.yaml @@ -0,0 +1,3 @@ +system: + project: "settings_nearshore.yaml" # model settings + \ No newline at end of file diff --git a/docs/Project.toml b/docs/Project.toml new file mode 100644 index 0000000..c2770b8 --- /dev/null +++ b/docs/Project.toml @@ -0,0 +1,3 @@ +[deps] +AtmosphericModels = "c59cac55-771d-4f45-b14d-1c681463a295" +Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" diff --git a/docs/make.jl b/docs/make.jl new file mode 100644 index 0000000..b10a9c9 --- /dev/null +++ b/docs/make.jl @@ -0,0 +1,36 @@ +using AtmosphericModels +using Pkg +if ("TestEnv" ∈ keys(Pkg.project().dependencies)) + if ! ("Documenter" ∈ keys(Pkg.project().dependencies)) + using TestEnv; TestEnv.activate() + end +end +using Documenter + +DocMeta.setdocmeta!(AtmosphericModels, :DocTestSetup, :(using AtmosphericModels); recursive=true) + +makedocs(; + modules=[AtmosphericModels], + authors="Uwe Fechner and contributors", + repo="https://github.com/OpenSourceAWE/KiteUtils.jl/blob/{commit}{path}#{line}", + sitename="AtmosphericModels.jl", + checkdocs=:none, + format=Documenter.HTML(; + repolink = "https://github.com/OpenSourceAWE/AtmosphericModels.jl", + prettyurls=get(ENV, "CI", "false") == "true", + canonical="https://OpenSourceAWE.github.io/AtmosphericModels.jl", + assets=String[], + ), + pages=[ + "Home" => "index.md", + "API" => "api.md", + "Settings" => "settings.md", + "3D Wind Fields" => "wind_field.md", + ], +) + +deploydocs(; + repo="github.com/OpenSourceAWE/AtmosphericModels.jl", + devbranch="main", + push_preview=true, +) diff --git a/doc/airdensity.png b/docs/src/airdensity.png similarity index 100% rename from doc/airdensity.png rename to docs/src/airdensity.png diff --git a/docs/src/api.md b/docs/src/api.md new file mode 100644 index 0000000..28a785c --- /dev/null +++ b/docs/src/api.md @@ -0,0 +1,43 @@ +```@meta +CurrentModule = AtmosphericModels +``` + +## Introduction +Most functions need an instance of the struct `AtmosphericModel` as first parameter, which can be created using the following code: +```julia +using AtmosphericModels, KiteUtils + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) +``` +This requires that the files `system.yaml` and `settings.yaml` exist in the folder `data`. See also [Settings](@ref). The parameter `relax=true` allows loading a yaml file that does not contain all sections needed to run a kite power system simulation. This is useful if you want to use this package for +other purposes than simulating kite power systems. + +## Types + +### Exported types +```@docs +ProfileLaw +AtmosphericModel +AtmosphericModel(set::Settings; nowindfield::Bool=false) +``` +### Private types +```@docs +WindField +``` + +## Functions + +### Wind shear and air density calculation +```@docs +calc_rho +calc_wind_factor +``` +### Wind turbulence calculation +```@docs +get_wind +rel_turbo +new_windfield +new_windfields +``` \ No newline at end of file diff --git a/docs/src/index.md b/docs/src/index.md new file mode 100644 index 0000000..c6ec240 --- /dev/null +++ b/docs/src/index.md @@ -0,0 +1,129 @@ +```@meta +CurrentModule = AtmosphericModels +``` +# AtmosphericModels +This package provides functions for modelling the influence of the atmosphere on wind energy systems. It models the air density, the vertical wind profile and the wind turbulence. Further functions to import measured data are planned. + +## Installation +Install [Julia 1.10](http://www.julialang.org) or later, if you haven't already. You can add AtmosphericModels from Julia's package manager, by typing +```julia +using Pkg +pkg"add AtmosphericModels" +``` +at the Julia prompt. + +### Running the tests +Launch Julia using this project and run the tests: +```julia +julia --project +using Pkg +Pkg.test("AtmosphericModels") +``` + +### Running the examples +If you check out the project using git, you can more easily run the examples: +``` +git clone https://github.com/OpenSourceAWE/AtmosphericModels.jl +cd AtmosphericModels.jl +``` +Launch Julia using this project and run the example menu: +```julia +julia --project +include("examples/menu.jl") +``` +The first time will take some time, because the graphic libraries will get installed, the second time it is fast. + +## Usage +### Calculate the height dependant wind speed +```julia +using AtmosphericModels +am = AtmosphericModel() + +const profile_law = Int(EXPLOG) +height = 100.0 +wf = calc_wind_factor(am, height, profile_law) +``` +The result is the factor with which the ground wind speed needs to be multiplied +to get the wind speed at the given height. + +![Wind Profile](wind_profile.png) + +The `EXPLOG` profile law is the fitted linear combination of the exponential and the log law. + +### Using the turbulent wind field +You can get a wind vector as function of x,y,z and time using the following code: +```julia +using AtmosphericModels, KiteUtils + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) + +@info "Ground wind speed: $(am.set.v_wind) m/s" + +wf::WindField = WindField(am, am.set.v_wind) +x, y, z = 20.0, 0.0, 200.0 +t = 0.0 +vx, vy, vz = get_wind(wf, am, x, y, z, t) +@time get_wind(am, x, y, z, t) +@info "Wind at x=$(x), y=$(y), z=$(z), t=$(t): v_x=$(vx), v_y=$(vy), v_z=$(vz)" +@info "Wind speed: $(sqrt(vx^2 + vy^2 + vz^2)) m/s" +``` +It is suggested to check out the code using git before executing this example, +because it requires that a data directory with the correct files `system.yaml` +and `settings.yaml` exists. See below how to do that. + +### Plot a wind profile +```julia +using AtmosphericModels, KiteUtils, ControlPlots +am = AtmosphericModel(se()) + +heights = 6:1000 +wf = [calc_wind_factor(am, height, Int(EXPLOG)) for height in heights] + +plot(heights, wf, xlabel="height [m]", ylabel="wind factor") +``` + +```julia +using AtmosphericModels, ControlPlots, KiteUtils +am = AtmosphericModel(se()) +AtmosphericModels.se().alpha = 0.234 # set the exponent of the power law + +heights = 6:200 +wf = [calc_wind_factor(am, height, Int(EXP)) for height in heights] + +plot(heights, wf, xlabel="height [m]", ylabel="wind factor") +``` + +### Air density +```julia +using AtmosphericModels, BenchmarkTools, KiteUtils +am = AtmosphericModel(se()) +@benchmark calc_rho(am, height) setup=(height=Float64((6.0+rand()*500.0))) +``` +This gives 4.85 ns as result. Plot the air density: +```julia +heights = 6:1000 +rhos = [calc_rho(am, height) for height in heights] +plot(heights, rhos, legend=false, xlabel="height [m]", ylabel="air density [kg/m³]") +``` +![Airdensity](airdensity.png) + +## Further reading +These models are described in detail in [Dynamic Model of a Pumping Kite Power System](http://arxiv.org/abs/1406.6218). You can find a summary in the section [Wind Fields](@ref). + +## Licence +This project is licensed under the MIT License. Please see the below WAIVER in association with the license. + +## WAIVER +Technische Universiteit Delft hereby disclaims all copyright interest in the package “AtmosphericModels.jl” (models for airborne wind energy systems) written by the Author(s). + +Prof.dr. H.G.C. (Henri) Werij, Dean of Aerospace Engineering + +## See also +- [Research Fechner](https://research.tudelft.nl/en/publications/?search=Uwe+Fechner&pageSize=50&ordering=rating&descending=true) +- The application [KiteViewer](https://github.com/ufechner7/KiteViewer) +- the package [KiteUtils](https://github.com/ufechner7/KiteUtils.jl) +- the packages [KiteModels](https://github.com/ufechner7/KiteModels.jl) and [WinchModels](https://github.com/aenarete/WinchModels.jl) and [KitePodModels](https://github.com/aenarete/KitePodModels.jl) +- the packages [KiteControllers](https://github.com/aenarete/KiteControllers.jl) and [KiteViewers](https://github.com/aenarete/KiteViewers.jl) + diff --git a/docs/src/settings.md b/docs/src/settings.md new file mode 100644 index 0000000..8ba798d --- /dev/null +++ b/docs/src/settings.md @@ -0,0 +1,45 @@ +```@meta +CurrentModule = AtmosphericModels +``` + +## Settings +The parameters af the atmospheric model can be configured in the section `environment` of the `settings.yaml` file in the `data` folder. + +The file `system.yaml` specifies which `yaml` files are used to configure +the current project. + +### Example for system.yaml +```yaml +system: + project: "settings.yaml" # simulator settings +``` +Often additional `yaml` files, for example for the controller settings are used. + +### Example for settings.yaml, scenario Cabauw, NL +```yaml +environment: + v_wind: 5.324 # wind speed at reference height [m/s] + upwind_dir: -90.0 # upwind direction [deg] + temp_ref: 15.0 # temperature at reference height [°C] + height_gnd: 0.0 # height of groundstation above see level [m] + h_ref: 6.0 # reference height for the wind speed [m] + + rho_0: 1.225 # air density at zero height and 15 °C [kg/m³] + alpha: 0.234 # exponent of the wind profile law + z0: 0.0002 # surface roughness [m] + profile_law: 1 # 1=EXP, 2=LOG, 3=EXPLOG + # the following parameters are for calculating the turbulent wind field using the Mann model + use_turbulence: 1.0 # turbulence intensity relative to Cabauw, NL + v_wind_gnds: [3.483, 5.324, 8.163] # wind speeds at ref height for calculating the turbulent wind field [m/s] + avg_height: 200.0 # average height during reel out [m] + rel_turbs: [0.342, 0.465, 0.583] # relative turbulence at the v_wind_gnds + i_ref: 0.14 # is the expected value of the turbulence intensity at 15 m/s. + v_ref: 42.9 # five times the average wind speed in m/s at hub height over the full year [m/s] + # Cabauw: 8.5863 m/s * 5.0 = 42.9 m/s + grid: [4050, 100, 500, 70] # grid size nx, ny, nz and minimal height z_min [m] + height_step: 2.0 # use a grid with 2m resolution in z direction [m] + grid_step: 2.0 # grid resolution in x and y direction [m] +``` + +## Remarks +- If the parameter `use_turbulence` is zero, no windfield is loaded. \ No newline at end of file diff --git a/docs/src/wind_field.md b/docs/src/wind_field.md new file mode 100644 index 0000000..975e52f --- /dev/null +++ b/docs/src/wind_field.md @@ -0,0 +1,118 @@ +```@meta +CurrentModule = AtmosphericModels +``` +# Wind Fields + +## Wind shear, scenario Maasvlakte, NL +Maasvlakte is a location close to Rotterdam, The Netherlands, very close to the sea (near-shore). + +To determine the wind speed $v_\mathrm{w}$ at the height of the kite and at +the height of each tether segment, the power law and the log +law are used. Input parameters are the ground wind speed +$v_\mathrm{w,ref}$ and the current height $z$ of the kite or tether segment. The +ground wind speed used in this paper was measured at $z_\mathrm{ref} = 6.0~m$. +The power law establishes the relationship between $v_\mathrm{w}$ and $v_\mathrm{w,ref}$ as + +$v_\mathrm{w} = v_\mathrm{w,ref}\left(\frac{z}{z_\mathrm{ref}}\right)^{\alpha}$ + +with the exponent $\alpha$ as fitting parameter. The logarithmic law can be written in the following form + +$v_\mathrm{w,log} = v_\mathrm{w,ref}\left( \frac{\mathrm{log}(z/z_0)}{\mathrm{log}(z_\mathrm{ref}/z_0)}\right)$ + +where $z_\mathrm{ref}$ is the reference height and $z_0$ is the roughness length. For +the paper **Fechner (2015)** not only the ground wind speed $v_\mathrm{w,ref}$ is measured, but +once per flight additionally the wind speed at two more heights, $z_1$ +and $z_2$. Then, a wind profile is fitted to these three wind speeds. To +make a fit with three (speed, height) pairs possible, Eqs. (2) and (1) +are combined in the following way + +$v_\mathrm{w} = v_\mathrm{w,log} + K (v_\mathrm{w,log} - v_\mathrm{w,exp})$ + +The fit is done by varying the surface roughness $z_0$ and $K$ until $v_\mathrm{w}$ according to Eq. (3) matches the measured wind speed at all three heights. The exponent $\alpha$ is chosen according to + +$\alpha = \frac{\mathrm{log} (v_\mathrm{w,exp}(z_1)) / v_\mathrm{w,ref}}{\mathrm{log}(z) - \mathrm{log}(z_\mathrm {ref})}$ + +which results in $v_\mathrm{w,exp}(z_1) = v_\mathrm{w,log}(z_1)$. + + +The following result was achieved: + +| Parameter| Value | +|:--------:|:------:| +| K | 1.0 | +|$\alpha$ | 0.08163| +|$z_0$ | 0.0002 | + +To use this atmospheric model, execute: +```julia +set_data_path("data") +set = load_settings("system_nearshore.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) +``` + +## Wind shear, scenario Cabauw, NL +Wind data from Royal Netherlands Meteorological Institute (KNMI 2011) at the +inland location Cabauw, The Netherlands was used and the wind profile fitted, using the power law according to the following equation: + +$v_\mathrm{w} = v_\mathrm{w,ref}\left(\frac{z}{z_\mathrm{ref}}\right)^{\mathrm{\alpha}}$ + +where z is the height and $\alpha$ the power coefficient. + +A coefficient **$\alpha$ = 0.234** was obtained, which is +significantly larger than for the location Maasvlakte. This is to be expected because Cabauw is a lot further away from the shore than Maasvlakte. + +## Wind turbulence, scenario Cabauw +A one year measurement time series from the wind measurement tower in Cabauw was used to calibrate +the wind speed, wind shear and wind turbulence of this scenario. + +The relative turbulence intensity I, the ratio of the standard deviation of the wind speed +within 10 minute intervals and the 10 minute wind speed average ws calculated, based on +the Cabauw data set from Royal Netherlands Meteorological Institute **KNMI (2011)**. +Three scenarios are chosen for the simulation: The average ground wind speed of +**3.483 m/s**, the wind speed for nominal power of **5.324 m/s** and the maximal wind speed +for operation without the need to depower the kite of **8.163 m/s**. + +| $v_{w,g}$ | $I_{99}$ |$I_{197}$| Description | +|:----------:|:--------:|:-------:|:-----------------| +| 3.483 m/s | 8.5% | 6.3% |Average wind speed| +| 5.324 m/s | 9.7% | 7.2% |Nominal wind speed| +| 8.163 m/s | 9.8% | 7.9% |High wind speed | + +The reference height for the ground wind speed is 6m. The reason for choosing 6m instead of the standard height of 10m is that during the flight tests only a 6m mast was available. + +A correction factor was used to adapt the turbulence from the IEC model to the measured values. +In `settings.yaml`, this line defines the correction factors: `rel_turbs: [0.342, 0.465, 0.583]`. + +As you see, in this scenario the turbulence increases with the average wind speed and decreases +with the height. + +The turbulence is modelled in three dimensions, using the Mann model as described +by **Mann (1994)** and **Mann (1998)**. The resulting homogeneous velocity field is obtained +by a 3D FFT (Fast Fourier Transformation) of the spectral tensor. Randomization is done by a white noise vector to give the wave numbers a random phase and amplitude. +A wind field of 4050 x 100 x 500 points with 2 m resolution is pre-calculated. To obtain the 3D wind speed vector at any given position a 3rd order spline interpolation is +used. To take the time dependency of the wind into account, the product of the simulation time and the average wind speed at the height of the kite is added to the x-coordinate before the wind vector lookup. The turbulent component of the wind field is periodic in all directions, because it is generated using reverse FFT. The size of the wind field is chosen such that the wind speed sequence repeats every 13.5 minutes at a wind speed of 10 m/s. + +To use this atmospheric model, execute: +```julia +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) +``` +If the file `windfield_4050_100_500_70_1.0_5.3.npz`, which contains the required wind field does not exist it will be created automatically. This might take 30s, but is required only once. + +**Known limitation:** Only three of the parameters that determine the wind field +are encoded in the filename. Therefore, if you change one of the other parameters the wrong file might be loaded. Workaround: Delete all `*.npz` files in the data folder before changing another parameter than `grid`, `use_turbulence` or `v_wind_gnds`. + +**References** + +**Fechner 2015** Fechner, U., Vlugt, R. V. D., Schreuder, E. & Schmehl, R. (2015). Dynamic Model of +a Pumping Kite Power System. Renewable Energy, 83, 705–716. [doi:10.1016/j.renene.2015.04.028](https://doi.org/10.1016/j.renene.2015.04.028) + +**KNMI 2011** KNMI, The Royal Netherlands Meteorological Institute. (2011). Cesar Tower Meteoro +logical Profiles (Wind Data from Cabauw, The Netherlands), validated. Retrieved +from [www.cesar-database.nl](http://www.cesar-database.nl) + +**Mann 1994** Mann, J. (1994, April). The spatial structure of neutral atmospheric surface-layer turbulence. Journal of Fluid Mechanics, 273, 141. [doi:10.1017/S0022112094001886](https://doi.org/10.1017/S0022112094001886) + +**Mann 1998** Mann, J. (1998, October). Wind field simulation. Probabilistic Engineering Mechanics, 13(4), 269–282. [doi:10.1016/S0266-8920(97)00036-2](https://doi.org/10.1016/S0266-8920(97)00036-2) + diff --git a/doc/wind_profile.png b/docs/src/wind_profile.png similarity index 100% rename from doc/wind_profile.png rename to docs/src/wind_profile.png diff --git a/examples/Project.toml b/examples/Project.toml new file mode 100644 index 0000000..5c9da44 --- /dev/null +++ b/examples/Project.toml @@ -0,0 +1,32 @@ +[deps] +AtmosphericModels = "c59cac55-771d-4f45-b14d-1c681463a295" +BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" +ControlPlots = "23c2ee80-7a9e-4350-b264-8e670f12517c" +FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341" +GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" +HypergeometricFunctions = "34004b35-14d8-5ef3-9330-4cdb6864b03a" +KiteUtils = "90980105-b163-44e5-ba9f-8b1c83bb0533" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MeshGrid = "ebf956a0-ef5e-43be-9fb1-27952996e635" +NPZ = "15e1cf62-19b3-5cfa-8e77-841668bca605" +Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +Timers = "21f18d07-b854-4dab-86f0-c15a3821819a" + +[sources] +AtmosphericModels = {path = ".."} + +[compat] +ControlPlots = "0.2.7" +FFTW = "1.9.0" +GLMakie = "0.13.2" +HypergeometricFunctions = "0.3" +KiteUtils = "0.10.14" +LinearAlgebra = "1.10, 1.11" +MeshGrid = "1.0.3" +NPZ = "0.4.3" +Printf = "1.10, 1.11" +Random = "1.10, 1.11" +Statistics = "1.10, 1.11" +julia = "1.10, 1.11" diff --git a/examples/bench_get_wind.jl b/examples/bench_get_wind.jl new file mode 100644 index 0000000..34e9184 --- /dev/null +++ b/examples/bench_get_wind.jl @@ -0,0 +1,22 @@ +using Pkg +if ! ("BenchmarkTools" ∈ keys(Pkg.project().dependencies)) + Pkg.activate("examples") +end +using AtmosphericModels, KiteUtils, BenchmarkTools, Timers + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) + +@info "Ground wind speed: $(set.v_wind) m/s" +tic() +am::AtmosphericModel = AtmosphericModel(set) +toc() +x, y, z = 20.0, 25.0, 200.0 +t = 0.0 +vx, vy, vz = get_wind(am, x, y, z, t) +@btime get_wind(am, x, y, z, t) +@info "Wind speed: $(round(sqrt(vx^2 + vy^2 + vz^2), digits=1)) m/s at $z m height." +# 319.979 ns (11 allocations: 192 bytes) on laptop on battery (without lookup) +# 284.933 ns (19 allocations: 368 bytes) on desktop +# 360.128 ns (19 allocations: 368 bytes) on laptop on battery + diff --git a/examples/create_windfield.jl b/examples/create_windfield.jl new file mode 100644 index 0000000..2347373 --- /dev/null +++ b/examples/create_windfield.jl @@ -0,0 +1,16 @@ +using AtmosphericModels, KiteUtils + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set; nowindfield=true) + +v_wind_gnd = 5.324 + +x = range(0, 50, length=25) +y = range(0, 800, length=400) +z = range(0, 200, length=100) + +u, v, w = AtmosphericModels.create_windfield(x, y, z; sigma1=1.2) +am = AtmosphericModel(set) +am.set.v_wind = v_wind_gnd +AtmosphericModels.addWindSpeed(am, z, u) \ No newline at end of file diff --git a/examples/get_wind.jl b/examples/get_wind.jl new file mode 100644 index 0000000..50af547 --- /dev/null +++ b/examples/get_wind.jl @@ -0,0 +1,15 @@ +using AtmosphericModels, KiteUtils + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) + +@info "Ground wind speed: $(am.set.v_wind) m/s" + +x, y, z = 20.0, 0.0, 200.0 +t = 0.0 +vx, vy, vz = get_wind(am, x, y, z, t) +@time get_wind(am, x, y, z, t) +@info "Wind at x=$(x), y=$(y), z=$(z), t=$(t): v_x=$(vx), v_y=$(vy), v_z=$(vz)" +@info "Wind speed: $(sqrt(vx^2 + vy^2 + vz^2)) m/s" + diff --git a/examples/load_windfield.jl b/examples/load_windfield.jl new file mode 100644 index 0000000..b016416 --- /dev/null +++ b/examples/load_windfield.jl @@ -0,0 +1,10 @@ +using AtmosphericModels, KiteUtils + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) + +x,y,z,u,v,w,param = AtmosphericModels.load_windfield(am, 5.324) + +@info "Windfield dimensions: $(size(x)), $(size(y)), $(size(z))" +@info "Parameters: $param" \ No newline at end of file diff --git a/examples/menu.jl b/examples/menu.jl new file mode 100644 index 0000000..54188ed --- /dev/null +++ b/examples/menu.jl @@ -0,0 +1,38 @@ +using REPL.TerminalMenus, Pkg + +if ! isfile("examples/Manifest.toml") + using Pkg + Pkg.activate("examples") + Pkg.instantiate() +end + +options = ["bench_get_wind = include(\"bench_get_wind.jl\")", + "get_wind_ = include(\"get_wind.jl\")", + "load_windfield = include(\"load_windfield.jl\")", + "plot_windshear = include(\"plot_windshear.jl\")", + "plot_windshear_cabauw = include(\"plot_windshear_cabauw.jl\")", + "plot_wind_vs_time_ = include(\"plot_wind_vs_time.jl\")", + "plot_windfield_ = include(\"plot_windfield.jl\")", + "new_windfields_ = include(\"new_windfields.jl\")", + "show_grid_ = include(\"show_grid.jl\")", + "test_all = include(\"test_all.jl\")", + "delete_windfields = foreach(rm, filter(endswith(\".npz\"), readdir(\"data\",join=true)))", + "quit"] + +function example_menu() + active = true + while active + menu = RadioMenu(options, pagesize=8) + choice = request("\nChoose function to execute or `q` to quit: ", menu) + + if choice != -1 && choice != length(options) + eval(Meta.parse(options[choice])) + else + println("Left menu. Press to quit Julia!") + active = false + end + end +end + +example_menu() +Pkg.activate(".") \ No newline at end of file diff --git a/examples/new_windfields.jl b/examples/new_windfields.jl new file mode 100644 index 0000000..aee4990 --- /dev/null +++ b/examples/new_windfields.jl @@ -0,0 +1,8 @@ +using AtmosphericModels, KiteUtils + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set; nowindfield=true) + +new_windfields(am) +nothing diff --git a/examples/plot_wind_vs_time.jl b/examples/plot_wind_vs_time.jl new file mode 100644 index 0000000..e6dc8f5 --- /dev/null +++ b/examples/plot_wind_vs_time.jl @@ -0,0 +1,43 @@ +using Pkg +if ! ("ControlPlots" ∈ keys(Pkg.project().dependencies)) + Pkg.activate("examples") +end + +using ControlPlots, Statistics, KiteUtils, AtmosphericModels + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) + +function plot_wind_vs_time(am, x=0.0, y=0.0; z=197.3) + rel_turb = rel_turbo(am) + @info "Relative turbulence: $rel_turb" + TIME = Float64[] + v_wind_x = Float64[] + v_wind_norm = Float64[] + for t in range(0.0, stop=600.0, length=600*20) + push!(TIME, t) + v_x, v_y, v_z = get_wind(am, x, y, z, t) + v_wind = sqrt(v_x^2 + v_y^2 + v_z^2) + push!(v_wind_norm, v_wind) + push!(v_wind_x, v_x) + if v_wind < 0.1 + println("Error for x, y, z, t: ", x, y, z, t) + end + end + su = std(v_wind_x) + mean_val = round(mean(v_wind_x), digits=1) + turbulence_intensity = round(su / mean_val * 100.0, digits=1) + println("Mean wind x: $(mean_val) m/s, turbulence intensity: $(turbulence_intensity) %") + fig = plt.figure("Wind speed at I = $(turbulence_intensity) %, z= $(z) m") + plt.plot(TIME, v_wind_x, label = "Abs. wind speed at $z m [m/s]", color="black") + plt.grid(true, color=(0.25, 0.25, 0.25), linestyle="--", linewidth=0.5) + plt.xlabel("Time [s]") + plt.ylabel("Abs. wind speed at $z m height [m/s]") + plt.legend(loc="upper right") +end + +plot_wind_vs_time(am; z=197.3) +plot_wind_vs_time(am; z=100.0) +nothing + diff --git a/examples/plot_windfield.jl b/examples/plot_windfield.jl new file mode 100644 index 0000000..fc7994a --- /dev/null +++ b/examples/plot_windfield.jl @@ -0,0 +1,146 @@ +using Pkg +if ! ("GLMakie" ∈ keys(Pkg.project().dependencies)) + Pkg.activate("examples") +end +using GLMakie, AtmosphericModels, KiteUtils, Statistics + +num_points = 100 +V_MIN::Float64 = -100.0 +V_MAX::Float64 = 100.0 +I::Float64 = 0.0 # turbulence intensity +V_WIND_ABS::Vector{Float64} = zeros(num_points) +V_WIND_X::Vector{Float64} = zeros(num_points) +V_WIND_Y::Vector{Float64} = zeros(num_points) +V_WIND_Z::Vector{Float64} = zeros(num_points) + +function turbulence_intensity(vec::Vector) + TURB=vec .- mean(vec) + v_mean = mean(vec) + rms = sqrt(mean(TURB .^ 2)) + I = round(100*rms/v_mean; digits=1) +end +function turbulence_intensity(am::AtmosphericModel, height::Number) + pos_z = height + pos_x = 0.0 + y = 0.0 + V_ABS = zeros(601) + for time in 0:600 + vx, vy, vz = get_wind(am, pos_x, y, pos_z, time) + v_abs = sqrt(vx^2+vy^2+vz^2) + V_ABS[time+1] = v_abs + end + turbulence_intensity(V_ABS) +end +function wind_speed(am::AtmosphericModel, height::Number) + pos_z = height + pos_x = 0.0 + y = 0.0 + V_ABS = zeros(601) + for time in 0:600 + vx, vy, vz = get_wind(am, pos_x, y, pos_z, time) + v_abs = sqrt(vx^2+vy^2+vz^2) + V_ABS[time+1] = v_abs + end + round(mean(V_ABS), digits=1) +end + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) +@info "Wind speed at reference height: $(am.set.v_wind) m/s" + + +# Create the figure and axis +fig = Figure() +ax = Axis(fig[1, 1], xlabel = "pos_y [m]", ylabel = "v_wind [m/s]", + title = "Wind Field") + +function v_wind(pos_x, pos_z, time, num_points) + global I + pos_y = range(V_MIN, V_MAX, length=num_points) + for (i, y) in pairs(pos_y) + vx, vy, vz = get_wind(am, pos_x, y, pos_z, time) + V_WIND_X[i] = vx + V_WIND_Y[i] = vy + V_WIND_Z[i] = vz + V_WIND_ABS[i] = sqrt(vx^2+vy^2+vz^2) + end + turb=V_WIND_ABS .- mean(V_WIND_ABS) + v_mean = mean(V_WIND_ABS) + rms = sqrt(mean(turb .^ 2)) + I = round(100*rms/v_mean; digits=1) + v_mean= round(v_mean; digits=1) + ax.title="Wind Field, I = $I %, v_mean = $v_mean m/s" + return V_WIND_ABS +end + +pos_x = Observable(20.0) # x-position +pos_z = Observable(200.0) # height +time = Observable(0.0) # time + + +function v_wind_abs(pos_x, pos_z, time) + v_abs = v_wind(pos_x, pos_z, time, num_points) + return v_abs +end +function v_wind_x(pos_x, pos_z, time) + v_wind(pos_x, pos_z, time, num_points) + return V_WIND_X +end +function v_wind_y(pos_x, pos_z, time) + v_wind(pos_x, pos_z, time, num_points) + return V_WIND_Y +end +function v_wind_z(pos_x, pos_z, time) + v_wind(pos_x, pos_z, time, num_points) + return V_WIND_Z +end + + +v_abs = @lift(v_wind_abs($pos_x, $pos_z, $time)) +v_x = @lift(v_wind_x($pos_x, $pos_z, $time)) +v_y = @lift(v_wind_y($pos_x, $pos_z, $time)) +v_z = @lift(v_wind_z($pos_x, $pos_z, $time)) +x = range(V_MIN, V_MAX, length=num_points) + +# Plot wind speed +line_abs = lines!(ax, x, v_abs) +line_x = lines!(ax, x, v_x) +line_y = lines!(ax, x, v_y) +line_z = lines!(ax, x, v_z) +ylims!(ax, -5, 20) +xlims!(ax, V_MIN, V_MAX) + +Legend(fig[1, 2], + [line_abs, line_x, line_y, line_z], + ["v_abs", "v_x", "v_y", "v_z"]) + +# Create sliders for amplitude and frequency +sg = SliderGrid( + fig[2, 1], + (label = "pos_x", range = 0:1:500.0, startvalue = 20.0, update_while_dragging=true), + (label = "pos_z", range = 5:1:500.0, startvalue = 200.0, update_while_dragging=true), + (label = "time", range = 0.0:1:1000.0, startvalue = 0.0, update_while_dragging=true), +) + +# Connect sliders to observables +on(sg.sliders[1].value) do val + pos_x[] = val +end + +on(sg.sliders[2].value) do val + pos_z[] = val +end + +on(sg.sliders[3].value) do val + time[] = val +end + +# rms = sqrt(mean(A .^ 2)) +@info "Wind speed at 10m height: $(wind_speed(am,10)) m/s" +@info "Wind speed at 100m height: $(wind_speed(am,100)) m/s" +@info "Wind speed at 200m height: $(wind_speed(am,200)) m/s" +@info "Turbulence intensity at 100m height: $(turbulence_intensity(am, 100)) %" +@info "Turbulence intensity at 200m height: $(turbulence_intensity(am, 200)) %" + +display(fig) diff --git a/examples/plot_windshear.jl b/examples/plot_windshear.jl new file mode 100644 index 0000000..05759ad --- /dev/null +++ b/examples/plot_windshear.jl @@ -0,0 +1,17 @@ +using Pkg +if ! ("ControlPlots" ∈ keys(Pkg.project().dependencies)) + Pkg.activate("examples") +end +using AtmosphericModels, KiteUtils, ControlPlots + +set_data_path("data") +set = load_settings("system_nearshore.yaml"; relax=true) + +@info "Ground wind speed: $(set.v_wind) m/s" +am::AtmosphericModel = AtmosphericModel(set) + +heights = 6:1000 +v_w = set.v_wind .* [calc_wind_factor(am, height) for height in heights] + +p=plot(v_w, heights, xlabel="v_wind [m/s]", ylabel="height [m]", fig="Near shore, EXPLOG law") +display(p) diff --git a/examples/plot_windshear_cabauw.jl b/examples/plot_windshear_cabauw.jl new file mode 100644 index 0000000..069fded --- /dev/null +++ b/examples/plot_windshear_cabauw.jl @@ -0,0 +1,18 @@ +using Pkg +if ! ("ControlPlots" ∈ keys(Pkg.project().dependencies)) + Pkg.activate("examples") +end +using AtmosphericModels, KiteUtils, ControlPlots + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +set.v_wind = 3.483*1.085 # wind speed at reference height, average wind speed at Cabauw, NL [m/s] + +@info "Ground wind speed: $(set.v_wind) m/s" +am::AtmosphericModel = AtmosphericModel(set) + +heights = 10:200 +v_w = set.v_wind .* [calc_wind_factor(am, height) for height in heights] + +p=plot(heights, v_w, ylabel="v_wind [m/s]", xlabel="height [m]", fig="Cabauw, NL, EXP law") +display(p) diff --git a/examples/show_grid.jl b/examples/show_grid.jl new file mode 100644 index 0000000..4a71e2e --- /dev/null +++ b/examples/show_grid.jl @@ -0,0 +1,28 @@ +using Pkg +if ! ("ControlPlots" ∈ keys(Pkg.project().dependencies)) + Pkg.activate("examples") +end +using ControlPlots, AtmosphericModels, KiteUtils + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) + +function show_grid(x, y, z) + """ + x: downwind direction + z: up + y: orthogonal to x and z + """ + fig = plt.figure("Show Grid") + ax = fig.add_subplot(111, projection="3d") + ax.scatter(x, y, z; s=2, c="blue", alpha=0.1) + ax.set_xlabel("X Label") + ax.set_ylabel("Y Label") + ax.set_zlabel("Height [m]") + plt.show() +end +ny=50; nx=100; nz=50; z_min=25 +am.set.grid= [nx, ny, nz, z_min] +y, x, z = AtmosphericModels.create_grid(am) +show_grid(x, y, z) diff --git a/examples/test_all.jl b/examples/test_all.jl new file mode 100644 index 0000000..6544ec4 --- /dev/null +++ b/examples/test_all.jl @@ -0,0 +1,43 @@ +# Expected results for low, medium and high wind speed: +# vw_10m I 99 I197 +# 4.26 m/s 8.5 % 6.3 % +# 6.00 m/s 9.7 % 7.2 % +# 9.20 m/s 9.8 % 7.9 % + +using Statistics, KiteUtils, AtmosphericModels, Test + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) + +I99::Vector{Float64} = [8.5, 9.7, 9.8] # Turbulence intensity at 99 m height +I197::Vector{Float64} = [6.3, 7.2, 7.9] # Turbulence intensity at 197 m height + +function analyze_windfield(am::AtmosphericModel; z=197.3) + x = 0.0; y = 0.0 + v_wind_x = Float64[] + v_wind_norm = Float64[] + for y in -50:5:50 + for t in range(0.0, stop=600.0, length=601) + v_x, v_y, v_z = get_wind(am, x, y, z, t) + v_wind = sqrt(v_x^2 + v_y^2 + v_z^2) + push!(v_wind_norm, v_wind) + push!(v_wind_x, v_x) + end + end + su = std(v_wind_norm) + v_mean = round(mean(v_wind_norm), digits=1) + turbulence_intensity = round(su / mean(v_wind_norm) * 100.0, digits=2) + return v_mean, turbulence_intensity +end + +for (i, v_wind_gnd) in pairs(am.set.v_wind_gnds) + local v_mean, ti, am + set.v_wind = v_wind_gnd + am::AtmosphericModel = AtmosphericModel(set) + v_mean, ti = analyze_windfield(am) + v_mean_99, ti_99 = analyze_windfield(am; z=99.0) + @test ti_99 ≈ I99[i] rtol=0.06 + @test ti ≈ I197[i] rtol=0.06 + @info "v_mean_99: $v_mean_99 m/s, ti_99: $ti_99 %" + @info "v_mean_197: $v_mean m/s, ti_197: $ti %" +end \ No newline at end of file diff --git a/mwes/mwe_01.jl b/mwes/mwe_01.jl new file mode 100644 index 0000000..79634e7 --- /dev/null +++ b/mwes/mwe_01.jl @@ -0,0 +1,45 @@ +using LinearAlgebra +using FFTW +using KiteUtils +using AtmosphericModels + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) + +const GRID_STEP = 2.0 # Grid resolution in x/y (meters) +const HEIGHT_STEP = 2.0 # Z-resolution (meters) + +function create_grid(am, res=GRID_STEP) + res = Int(res) + y_range = range(-ny÷2, ny÷2, length=ny÷res + 1) + x_range = range(0, nx, length=nx÷res + 1) + z_range = range(z_min, z_min+nz, length=nz÷Int(HEIGHT_STEP) + 1) + return meshgrid(x_range, y_range, z_range) +end + +function meshgrid(x, y, z) + X = [i for i in x, _ in y, _ in z] + Y = [j for _ in x, j in y, _ in z] + Z = [k for _ in x, _ in y, k in z] + return (X, Y, Z) +end + +# Create grid +x, y, z = create_grid(am) + +# Get dimensions +nx, ny, nz = size(x, 1), size(y, 2), size(z, 3) + +# Wave number discretization +m1_range = range(-nx/2, nx/2 - 1, length=nx) +m2_range = range(-ny/2, ny/2 - 1, length=ny) +m3_range = range(-nz/2, nz/2 - 1, length=nz) + +m1, m2, m3 = meshgrid(m1_range, m2_range, m3_range) + +# Apply ifftshift with epsilon offset +m1 = ifftshift(m1 .+ 1e-6) +m2 = ifftshift(m2 .+ 1e-6) +m3 = ifftshift(m3 .+ 1e-6) +println("--> $(size(m1)), $(size(m2)), $(size(m3))") \ No newline at end of file diff --git a/mwes/mwe_01.py b/mwes/mwe_01.py new file mode 100644 index 0000000..cab02ac --- /dev/null +++ b/mwes/mwe_01.py @@ -0,0 +1,43 @@ +import numpy as np +from math import pi, sqrt + +GRID_STEP = 2.0 # Resolution of the grid in x and y direction in meters +HEIGHT_STEP = 2.0 # Resolution in z direction in meters + +def create_grid(ny=50, nx=100, nz=50, z_min=25, res=GRID_STEP): + """ + res: resolution of the grid in x and y direction in meters + ny: number of meters in y direction + nx: number of meters in x direction (downwind) + nz: number of meters in z direction (up) + z_min: minimal height in m + """ + res = int(res) + y_range=np.linspace(-ny//2, ny//2, num=ny//res+1) + x_range=np.linspace(0, nx, num=nx//res+1) + z_range=np.linspace(z_min, z_min+nz, num=nz//int(HEIGHT_STEP)+1) + # returns three 3-dimensional arrays with the components of the position of the grid points + y, x, z = np.meshgrid(y_range, x_range, z_range) + return y, x, z + +y, x, z = create_grid(100, 4050, 500, 70) + +# Number of elements in each direction +nx = x.shape[0] #size(x,2); +ny = y.shape[1] #size(y,1); +nz = z.shape[2] #size(z,3); + +X, Y, Z = x, y, z + +# Wave number discretization +# Shifted integers +y_range=np.linspace(-ny/2., ny/2. - 1, num=ny) +x_range=np.linspace(-nx/2., nx/2. - 1, num=nx) +z_range=np.linspace(-nz/2., nz/2. - 1, num=nz) +m2, m1, m3 = np.meshgrid(y_range, x_range, z_range) +print(m2.shape, " ", m1.shape, " ", m3.shape) + +m1 = np.fft.ifftshift(m1 + 1e-6) +m2 = np.fft.ifftshift(m2 + 1e-6) +m3 = np.fft.ifftshift(m3 + 1e-6) +print(m2.shape, " ", m1.shape, " ", m3.shape) diff --git a/mwes/mwe_02.jl b/mwes/mwe_02.jl new file mode 100644 index 0000000..d9ce6e7 --- /dev/null +++ b/mwes/mwe_02.jl @@ -0,0 +1,8 @@ +using BenchmarkTools +const A=rand(2026,51,251) +@btime maximum(A) +# 6.946 ms (0 allocations: 0 bytes) # 4.569 ms (0 allocations: 0 bytes) +# @btime treduce(max, A) +# 3.589 ms (126 allocations: 9.86 KiB) +@btime @fastmath maximum(A) +# 4.444 ms on battery \ No newline at end of file diff --git a/mwes/mwe_02.py b/mwes/mwe_02.py new file mode 100644 index 0000000..24124b9 --- /dev/null +++ b/mwes/mwe_02.py @@ -0,0 +1,5 @@ +import numpy as np + +A = np.random.rand(2026,51,251) +# %timeit np.max(A) +4.49 ms ± 89.2 μs per loop (mean ± std. dev. of 7 runs, 100 loops each) \ No newline at end of file diff --git a/mwes/mwe_03.jl b/mwes/mwe_03.jl new file mode 100644 index 0000000..4841ca9 --- /dev/null +++ b/mwes/mwe_03.jl @@ -0,0 +1,185 @@ +using Random +using HypergeometricFunctions:_₂F₁ +using LinearAlgebra +using FFTW, Statistics +using Test + +const HEIGHT_STEP = 2.0 +const GRID_STEP = 2.0 + +Random.seed!(1234) # reproducible wind fields + +function pfq(z) + _₂F₁(1. /3 , 17. /6, 4. /3, z) +end + +function ndgrid(xs, ys, zs) + X = reshape(xs, :, 1, 1) + Y = reshape(ys, 1, :, 1) + Z = reshape(zs, 1, 1, :) + X = repeat(X, 1, length(ys), length(zs)) + Y = repeat(Y, length(xs), 1, length(zs)) + Z = repeat(Z, length(xs), length(ys), 1) + return X, Y, Z +end + +function createWindField(x, y, z; sigma1=nothing, gamma=3.9, ae=0.1, length_scale=33.6) + # Validate sigma1 + if sigma1 !== nothing + if !(isa(sigma1, Number) || (isa(sigma1, AbstractVector) && length(sigma1) == 3)) + throw(ArgumentError("The parameter 'sigma1' must be a single value or a 3-component vector.")) + end + end + + # Check monotonicity + if x[1] > x[end] || y[1] > y[end] || z[1] > z[end] + throw(ArgumentError("Values of x, y, and z must be monotonically increasing.")) + end + + # If sigma1 is a scalar, convert to vector for component-wise use + if sigma1 === nothing + sigma1_vec = [1.0, 1.0, 1.0] # default if no sigma1 provided + elseif isa(sigma1, Number) + sigma1_vec = [sigma1, sigma1, sigma1] + else + sigma1_vec = sigma1 + end + + sigma_iso = 0.55 .* sigma1_vec + sigma2 = 0.7 .* sigma1_vec + sigma3 = 0.5 .* sigma1_vec + + nx, ny, nz = size(x) + # Domain lengths + Lx = x[end,1,1] - x[1,1,1] + Ly = y[1,end,1] - y[1,1,1] + Lz = z[1,1,end] - z[1,1,1] + + # Wave number discretization + y_range = range(-ny/2, stop=ny/2 - 1, length=ny) + x_range = range(-nx/2, stop=nx/2 - 1, length=nx) + z_range = range(-nz/2, stop=nz/2 - 1, length=nz) + + # meshgrid equivalent in Julia: use broadcasting + m1 = reshape(x_range, nx, 1, 1) .+ 1e-6 + m2 = reshape(y_range, 1, ny, 1) .+ 1e-6 + m3 = reshape(z_range, 1, 1, nz) .+ 1e-6 + + # fftshift equivalent: use fftshift from FFTW.jl + m1 = fftshift(m1, 1) + m2 = fftshift(m2, 2) + m3 = fftshift(m3, 3) + + k1 = 2pi .* m1 .* (length_scale / Lx) + k2 = 2pi .* m2 .* (length_scale / Ly) + k3 = 2pi .* m3 .* (length_scale / Lz) + + k = sqrt.(k1.^2 .+ k2.^2 .+ k3.^2) + + pfq_term = pfq.(-k.^(-2)) + beta = gamma ./ (k.^(2/3) .* sqrt.(pfq_term)) + + k30 = k3 .+ beta .* k1 + k0 = sqrt.(k1.^2 .+ k2.^2 .+ k30.^2) + + E0 = ae * length_scale^(5/3) .* k0.^4 ./ (1 .+ k0.^2).^(17/6) + + # Avoid division by zero by adding a small epsilon + eps = 1e-14 + denom = k.^2 .* (k1.^2 .+ k2.^2) .+ eps + + C1 = (beta .* k1.^2 .* (k1.^2 .+ k2.^2 .- k3 .* (k3 .+ beta .* k1))) ./ denom + arctan_arg = (beta .* k1 .* sqrt.(k1.^2 .+ k2.^2)) + arctan_denom = k0.^2 .- (k3 .+ beta .* k1) .* k1 .* beta + C2 = (k2 .* k0.^2) ./ (k1.^2 .+ k2.^2).^(3/2) .* atan.(arctan_arg, arctan_denom) + + zeta1 = C1 .- k2 ./ k1 .* C2 + zeta2 = C2 .+ k2 ./ k1 .* C1 + + B = sigma_iso[1] * sqrt.(2pi^2 * length_scale^3 .* E0 ./ (Lx * Ly * Lz .* k0.^4)) + + # Initialize correlation matrix C with dimensions (3,3,nx,ny,nz) + C = zeros(ComplexF64, 3, 3, nx, ny, nz) + + C[1,1,:,:,:] = B .* k2 .* zeta1 + C[1,2,:,:,:] = B .* (k30 .- k1 .* zeta1) + C[1,3,:,:,:] = B .* -k2 + C[2,1,:,:,:] = B .* (k2 .* zeta2 .- k30) + C[2,2,:,:,:] = B .* -k1 .* zeta2 + C[2,3,:,:,:] = B .* k1 + C[3,1,:,:,:] = B .* k2 .* k0.^2 ./ k.^2 + C[3,2,:,:,:] = B .* -k1 .* k0.^2 ./ k.^2 + # C[3,3,:,:,:] remains zero + + # Generate white noise vector n with shape (3, 1, nx, ny, nz) + n_real = randn(3, 1, nx, ny, nz) + n_imag = randn(3, 1, nx, ny, nz) + n = complex.(n_real, n_imag) + + # Compute stochastic field dZ (3, nx, ny, nz) + dZ = zeros(ComplexF64, 3, nx, ny, nz) + for i in 1:nx, j in 1:ny, k_ in 1:nz + # Extract 3x3 matrix C[:,:,i,j,k] and 3x1 vector n[:,1,i,j,k] + C_mat = reshape(C[:,:,i,j,k_], (3,3)) + n_vec = reshape(n[:,1,i,j,k_], 3) + dZ[:,i,j,k_] = C_mat * n_vec + end + + u = nx * ny * nz * real.(ifft(dZ[1,:,:,:])) + v = nx * ny * nz * real.(ifft(dZ[2,:,:,:])) + w = nx * ny * nz * real.(ifft(dZ[3,:,:,:])) + + if sigma1 !== nothing + su = std(vec(u)) + sv = std(vec(v)) + sw = std(vec(w)) + u .*= sigma1_vec[1] / su + v .*= sigma2[2] / sv + w .*= sigma3[3] / sw + end + + return u, v, w +end + +function create_grid(ny=50, nx=100, nz=50, z_min=25; res=GRID_STEP, height_step=HEIGHT_STEP) + y_range = range(-ny/2, ny/2, length=Int(ny/res)+1) + x_range = range(0, nx, length=Int(nx/res)+1) + z_range = range(z_min, z_min+nz, length=Int(nz/height_step)+1) + + # Create meshgrid (Julia's ndgrid returns in order (x, y, z)) + X, Y, Z = ndgrid(x_range, y_range, z_range) + + return Y, X, Z # To match the Python (y, x, z) order +end + +# Example usage +y, x, z = create_grid(10, 20, 10, 5) +u, v, w = createWindField(x, y, z, sigma1=1.0) + +@testset "createWindField" begin + nx, ny, nz = size(x) + @test nx == 11 + @test ny == 6 + @test nz == 6 + # Domain lengths + Lx = x[end,1,1] - x[1,1,1] + Ly = y[1,end,1] - y[1,1,1] + Lz = z[1,1,end] - z[1,1,1] + @test Lx == 20.0 + @test Ly == 10.0 + @test Lz == 10.0 + @test pfq(0.5) ≈ 1.7936563627777333 + @test sum(x) == 3960.0 + @test sum(y) == 0.0 + @test sum(z) == 3960.0 + @test all(x[1,:,:] .== 0) + @test all(x[2,:,:] .== 2) + @test all(x[11,:,:] .== 20) + @test size(u) == (11,6,6) + @test size(v) == (11,6,6) + @test size(w) == (11,6,6) + @test std(u) ≈ 1.0 + @test std(v) ≈ 0.7 + @test std(w) ≈ 0.5 +end +nothing \ No newline at end of file diff --git a/mwes/mwe_03.py b/mwes/mwe_03.py new file mode 100644 index 0000000..4c3c8e8 --- /dev/null +++ b/mwes/mwe_03.py @@ -0,0 +1,155 @@ +import numpy as np +import numpy.random as rd +from math import pi +from scipy.special import hyp2f1 + +HEIGHT_STEP = 2.0 # use a grid with 2m resolution in z direction +GRID_STEP = 2.0 # grid resolution in x and y direction +np.random.seed(1234) # do this only if you want to have reproducible wind fields + +def pfq(z): + return np.real(hyp2f1(1./3., 17./6., 4./3., z)) + +def createWindField(x, y, z, sigma1 = None, gamma= 3.9, ae= 0.1, length_scale=33.6): + """ + sigma1: Target value(s) for the turbulence + intensity. This can either be a single value, in + which case the statistics are corrected based on + the longitudinal component only, or a vector where + each u,v,w-component can be defined separately. + gamma: wind shear (zero for isotropic turbulence, 3.9 for IEC wind profile) + ae: Coefficient of the inertial cascade, ae = a*e^(2/3), + where a = 1.7 is the three-dimensional Kolmogorov + constant and e = the mean TKE dissipation rate [m/s]. + Performance: Python: 17 seconds for a field of 50x800x200 m with 2 m resolution + Matlab: 17 seconds + """ + if sigma1 is not None: + if type(sigma1) != int and type(sigma1) != float and type(sigma1) != np.float64: + if len(sigma1) != 3: + raise Exception("The parameter 'sigma' must either be a single value or a 3-component vector.") + if (x.ravel()[0] > x.ravel()[-1]) or (y.ravel()[0] > y.ravel()[-1]) or (z.ravel()[0] > z.ravel()[-1]): + raise Exception("The values of x, y, and z must be monotonically increasing.") + + # Standard deviations + sigma_iso = 0.55 * sigma1 + sigma2 = 0.7 * sigma1 + sigma3 = 0.5 * sigma1 + + # Domain size + # Number of elements in each direction + nx = x.shape[0] #size(x,2); + ny = y.shape[1] #size(y,1); + nz = z.shape[2] #size(z,3); + + X, Y, Z = x, y, z + + Lx = X.ravel()[-1] - X.ravel()[0] + Ly = Y.ravel()[-1] - Y.ravel()[0] + Lz = Z.ravel()[-1] - Z.ravel()[0] + + # Wave number discretization + # Shifted integers + y_range=np.linspace(-ny/2., ny/2. - 1, num=ny) + x_range=np.linspace(-nx/2., nx/2. - 1, num=nx) + z_range=np.linspace(-nz/2., nz/2. - 1, num=nz) + m2, m1, m3 = np.meshgrid(y_range, x_range, z_range) + + m1 = np.fft.ifftshift(m1 + 1e-6) + m2 = np.fft.ifftshift(m2 + 1e-6) + m3 = np.fft.ifftshift(m3 + 1e-6) + + # Wave number vectors + k1 = 2 * pi * m1 * (length_scale / Lx) + k2 = 2 * pi * m2 * (length_scale / Ly) + k3 = 2 * pi * m3 * (length_scale / Lz) + + k = np.sqrt(k1**2 + k2**2 + k3**2) + + # Non-dimensional distortion time + pfq_term = pfq(-k**-2) + beta = gamma / (k**(2./3.) * np.sqrt(pfq_term)) + + # Initial wave vectors + k30 = k3 + beta * k1 + k0 = np.sqrt(k1**2 + k2**2 + k30**2) + + # Non-dimensional von Karman isotropic energy spectrum + E0 = ae * length_scale**(5./3.) * k0**4 / (1 + k0**2)**(17./6.) + + # Correlation matrix + C1 = (beta * k1**2 * (k1**2 + k2**2 - k3 * (k3 + beta * k1))) / (k**2 * (k1**2 + k2**2)) + C2 = (k2 * k0**2) / (k1**2 + k2**2)**(3./2.) * np.arctan2((beta * k1 * np.sqrt(k1**2 + k2**2)).real, (k0**2 \ + - (k3 + beta * k1) * k1 * beta).real) + + zeta1 = C1 - k2 / k1 * C2 + zeta2 = C2 + k2 / k1 * C1 + B = sigma_iso * np.sqrt(2.0 * pi**2 * length_scale**3 * E0 / (Lx * Ly *Lz * k0**4)) + # B = sigma_iso * sqrt(2*pi^2 * L^3 * E0 ./ (Lx*Ly*Lz * k0.^4)); + + C = np.zeros((3, 3,) + X.shape) + C[0,0] = B * k2 * zeta1 + C[0,1] = B * (k30 - k1 * zeta1) + C[0,2] = B * -k2 + C[1,0] = B * (k2 * zeta2 - k30) + C[1,1] = B * -k1 * zeta2 + C[1,2] = B * k1 + C[2,0] = B * k2 * k0**2 / k**2 + C[2,1] = B * -k1 * k0**2 / k**2 + + # Set up stochastic field + # White noise vector + n = rd.normal(0, 1, [3, 1] + list(k.shape)) + 1j * rd.normal(0, 1, [3, 1] + list(k.shape)) + + # Stochastic field + dZ = np.zeros(((3,) + k.shape), dtype=np.dtype(np.complex128)) + for i in range(X.shape[0]): + for j in range(Y.shape[1]): + for k in range(Z.shape[2]): + dZ[:, i, j, k] = (np.dot(C[:, : , i, j, k] , n[:, :, i, j, k])).reshape((3,)) + + # Reconstruct time series + u = nx * ny * nz * (np.fft.ifftn(np.squeeze(dZ[0,:,:,:]))).real + v = nx * ny * nz * (np.fft.ifftn(np.squeeze(dZ[1,:,:,:]))).real + w = nx * ny * nz * (np.fft.ifftn(np.squeeze(dZ[2,:,:,:]))).real + + if sigma1 is not None: + su = np.std(u) + sv = np.std(v) + sw = np.std(w) + u = sigma1/su * u + v = sigma2/sv * v + w = sigma3/sw * w + return u, v, w + +def createGrid(ny=50, nx=100, nz=50, z_min=25, res=GRID_STEP): + """ + res: resolution of the grid in x and y direction in meters + ny: number of meters in y direction + nx: number of meters in x direction (downwind) + nz: number of meters in z direction (up) + z_min: minimal height in m + """ + res = int(res) + y_range=np.linspace(-ny//2, ny//2, num=ny//res+1) + x_range=np.linspace(0, nx, num=nx//res+1) + z_range=np.linspace(z_min, z_min+nz, num=nz//int(HEIGHT_STEP)+1) + # returns three 3-dimensional arrays with the components of the position of the grid points + y, x, z = np.meshgrid(y_range, x_range, z_range) + return y, x, z + +y, x, z = createGrid(10, 20, 10, 5) +u, v, w = createWindField(x, y, z, sigma1=1) +nx = x.shape[0] #size(x,2); +ny = y.shape[1] #size(y,1); +nz = z.shape[2] #size(z,3); +X, Y, Z = x, y, z +Lx = X.ravel()[-1] - X.ravel()[0] +Ly = Y.ravel()[-1] - Y.ravel()[0] +Lz = Z.ravel()[-1] - Z.ravel()[0] +print(u.shape) +print(v.shape) +print(w.shape) +print(np.std(u)) +print(np.std(v)) +print(np.std(w)) diff --git a/mwes/mwe_04.jl b/mwes/mwe_04.jl new file mode 100644 index 0000000..701ab8b --- /dev/null +++ b/mwes/mwe_04.jl @@ -0,0 +1,21 @@ +using Pkg +if ! ("ControlPlots" ∈ keys(Pkg.project().dependencies)) + using TestEnv; TestEnv.activate() +end +using ControlPlots + +function meshgrid(x, y) + X = [i for i in x, _ in y] + Y = [j for _ in x, j in y] + return (X, Y) +end + +X = -10:1:9 +Y = -10:1:9 +U, V = meshgrid(X, Y) + +fig, ax = plt.subplots() +q = ax.quiver(X, Y, U, V) +# ax.quiverkey(q, X=0.3, Y=1.1, U=10, label="Quiver key, length = 10", labelpos="E") + +plt.show() diff --git a/mwes/mwe_05.jl b/mwes/mwe_05.jl new file mode 100644 index 0000000..9a84629 --- /dev/null +++ b/mwes/mwe_05.jl @@ -0,0 +1,31 @@ +using Pkg +if ! ("ControlPlots" ∈ keys(Pkg.project().dependencies)) + using TestEnv; TestEnv.activate() +end +using ControlPlots, AtmosphericModels, KiteUtils + +function meshgrid(x, y) + X = [i for i in x, _ in y] + Y = [j for _ in x, j in y] + return (X, Y) +end + +set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am = AtmosphericModel(set) + +x,y,z,u,v,w,param = AtmosphericModels.load_windfield(am, 5.324) + +x1 = x[:, 1, :][1:51,:] +y1 = y[1, :, :] +u1 = u[:, 1, :][1:51,:] +v1 = v[1, :, :] + +X = -10:1:9 +Y = -10:1:9 +U, V = meshgrid(X, Y) + +fig, ax = plt.subplots() +# ax.quiver(X, Y, U, V) +ax.quiver(x1, y1, u1, v1) +plt.show() diff --git a/scripts/build_docu.jl b/scripts/build_docu.jl new file mode 100644 index 0000000..f88f437 --- /dev/null +++ b/scripts/build_docu.jl @@ -0,0 +1,24 @@ +# build and display the html documentation locally +# you must have installed the package LiveServer in your global environment + +using Pkg + +function globaldependencies() + projectpath = Pkg.project().path + basepath, _ = splitdir(projectpath) + Pkg.activate() + globaldependencies = keys(Pkg.project().dependencies) + Pkg.activate(basepath) + globaldependencies +end + +if !("LiveServer" in globaldependencies()) + println("Installing LiveServer globally!") + run(`julia -e 'using Pkg; Pkg.add("LiveServer")'`) +end + +if !("Documenter" ∈ keys(Pkg.project().dependencies)) + using TestEnv + TestEnv.activate() +end +using LiveServer; servedocs(launch_browser=true) diff --git a/src/AtmosphericModels.jl b/src/AtmosphericModels.jl index fadaa32..57e843a 100644 --- a/src/AtmosphericModels.jl +++ b/src/AtmosphericModels.jl @@ -2,26 +2,111 @@ module AtmosphericModels using KiteUtils using HypergeometricFunctions:_₂F₁ +using NPZ, Printf +using FFTW, LinearAlgebra, Random, Statistics export AtmosphericModel, ProfileLaw, EXP, LOG, EXPLOG, FAST_EXP, FAST_LOG, FAST_EXPLOG -export clear, calc_rho, calc_wind_factor +export clear, calc_rho, calc_wind_factor, rel_turbo + +export new_windfield, new_windfields, get_wind const ABS_ZERO = -273.15 +const SRL = StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64} + +""" + struct WindField + +Struct that is storing a 3D model of wind vectors of the atmosphere. The Fields +x, y and z store the grid coordinates, the fields u, v and w the wind turbulence +vectors. + +# Fields +- x_max::Float64 = NaN +- x_min::Float64 = NaN +- y_max::Float64 = NaN +- y_min::Float64 = NaN +- z_max::Float64 = NaN +- z_min::Float64 = NaN +- last_speed::Float64 = 0.0 +- valid::Bool = false +- x::Union{SRL, Array{Float64, 3}} +- y::Union{SRL, Array{Float64, 3}} +- z::Union{SRL, Array{Float64, 3}} +- u::Array{Float64, 3} +- v::Array{Float64, 3} +- w::Array{Float64, 3} +- param::Vector{Float64} = [0, 0] # [alpha, `v_wind_gnd`] +""" +Base.@kwdef struct WindField + x_max::Float64 = NaN + x_min::Float64 = NaN + y_max::Float64 = NaN + y_min::Float64 = NaN + z_max::Float64 = NaN + z_min::Float64 = NaN + last_speed::Float64 = 0.0 + valid::Bool = false + x::Union{SRL, Array{Float64, 3}} + y::Union{SRL, Array{Float64, 3}} + z::Union{SRL, Array{Float64, 3}} + u::Array{Float64, 3} + v::Array{Float64, 3} + w::Array{Float64, 3} + param::Vector{Float64} = [0, 0] # [alpha, v_wind_gnd] +end + """ mutable struct AtmosphericModel -Stuct that is storing the settings and the state of the atmosphere. +Struct that is storing the settings and the state of the atmosphere. + +# Fields +- set::Settings: The Settings struct +- `rho_zero_temp` +- wf::Union{WindField, Nothing}: The 3D [`WindField`](@ref) or `nothing` """ Base.@kwdef mutable struct AtmosphericModel - set::Settings = se() - turbulence::Float64 = 0.0 - rho_zero_temp::Float64 = (15.0 - ABS_ZERO) / (se().temp_ref - ABS_ZERO) * se().rho_0 + set::Settings + rho_zero_temp::Float64 = (15.0 - ABS_ZERO) / (set.temp_ref - ABS_ZERO) * set.rho_0 + wf::Union{WindField, Nothing} = nothing +end + +""" + AtmosphericModel(set::Settings; nowindfield::Bool=false) + +Constructs an `AtmosphericModel` using the provided `Settings`. + +# Arguments +- `set::Settings`: The settings object containing configuration parameters for the atmospheric model. +- `nowindfield::Bool=false`: Optional keyword argument. If `true`, the wind field will not be loaded. + +# Returns +- An instance of `AtmosphericModel` configured according to the provided settings. +""" +function AtmosphericModel(set::Settings; nowindfield::Bool=false) + am = AtmosphericModel(set=set) + if set.use_turbulence > 0 && !nowindfield + am.wf = WindField(am, am.set.v_wind) + end + am end const AM = AtmosphericModel +""" + clear(s::AM) + +Clears or resets the state of the given `AM` (Atmospheric Model) instance `s`. + +# Arguments +- `s::AM`: An instance of the `AM` (Atmospheric Model) struct containing atmospheric parameters. + +# Returns +- nothing +""" function clear(s::AM) s.rho_zero_temp = (15.0 - ABS_ZERO) / (s.set.temp_ref - ABS_ZERO) * s.set.rho_0 + nothing end @inline function fastexp(x) @@ -31,7 +116,24 @@ end y end -# Calculate the air densisity as function of height +""" + calc_rho(s::AM, height) + +Calculates the air density at a given height above ground level. + +# Arguments +- `s::AM`: An instance of the `AM` (Atmospheric Model) struct containing atmospheric parameters. +- `height`: The height above ground level (in meters) at which to calculate the air density. + +# Returns +- The air density at the specified height (in kg/m³). + +# Notes +- The calculation assumes an exponential decrease of air density with altitude. +- `s.rho_zero_temp` is the reference air density at ground level. +- `s.set.height_gnd` is the ground height offset. +- The scale height used is 8550.0 meters. +""" calc_rho(s::AM, height) = s.rho_zero_temp * fastexp(-(height+s.set.height_gnd) / 8550.0) """ @@ -112,6 +214,20 @@ function calc_wind_factor6(s::AM, height) evalpoly(1/height, (1.0, 1735.2333827029918, 279373.0012683715)) end +""" + calc_wind_factor(am::AM, height; profile_law::Int64=am.set.profile_law) + +Calculates the wind factor at a given `height` using the specified wind profile law. + +# Arguments +- `am::AM`: An instance of the `AM` type containing atmospheric model parameters. +- `height`: The height (in meters) at which to calculate the wind factor. +- `profile_law::Int64`: (Optional) The wind profile law to use for the calculation. + Defaults to `am.set.profile_law`. + +# Returns +- The wind factor at the specified height as determined by the chosen profile law. +""" @inline function calc_wind_factor(am::AM, height, profile_law::Int64=am.set.profile_law) if profile_law == 1 calc_wind_factor1(am, height) diff --git a/src/windfield.jl b/src/windfield.jl index e2ac1e0..91da20e 100644 --- a/src/windfield.jl +++ b/src/windfield.jl @@ -13,16 +13,64 @@ The code is based on the following papers: Engineering Mechanics 13(4), pp. 269-282. """ +function Base.getproperty(wf::WindField, sym::Symbol) + if sym == :y_range + getproperty(wf, :y_max) - getproperty(wf, :y_min) + elseif sym == :x_range + getproperty(wf, :x_max) - getproperty(wf, :x_min) + else + getfield(wf, sym) + end +end +function WindField(am, speed; prn=true) + try + last_speed = 0.0 + prn && @info "Loading wind field... $speed m/s" + x, y, z, u, v, w, param = load_windfield(am, speed) + valid = true + x_max = maximum(x) + x_min = minimum(x) + y_max = maximum(y) + y_min = minimum(y) + z_max = maximum(z) + z_min = minimum(z) + return WindField(x_max, x_min, y_max, y_min, z_max, z_min, last_speed, valid, x, y, z, u, v, w, param) + catch + @error "Error reading wind field!" + return nothing + end +end + function pfq(z) _₂F₁(1. /3 , 17. /6, 4. /3, z) end function calc_sigma1(am, v_wind_gnd) - v_height = v_wind_gnd * calc_wind_factor(am, am.set.avg_height, Val{Int(EXP)}) + v_height = v_wind_gnd * calc_wind_factor(am, am.set.avg_height, Val{Int(EXP)}) am.set.i_ref * (0.75 * v_height + 5.6) end """ + rel_turbo(am::AtmosphericModel, v_wind = am.set.v_wind) + +Find the closest relative turbulence value for a given ground wind speed. + +# Arguments +- `am::AtmosphericModel`: The atmospheric model instance containing relevant parameters. +- `v_wind`: (Optional) The wind velocity to use for the calculation. Defaults to `am.set.v_wind`. + +# Returns +- The computed relative turbulence value. +""" +function rel_turbo(am::AtmosphericModel, v_wind = am.set.v_wind) + # Find the closest relative turbulence value for a given ground wind speed + min_dist, idx = findmin(abs.(am.set.v_wind_gnds .- v_wind)) + return am.set.rel_turbs[idx] +end + +""" + nextpow2(i) + Find 2^n that is equal to or greater than i. """ function nextpow2(i) @@ -33,425 +81,326 @@ function nextpow2(i) n end -# def calcFullName(v_wind_gnd, basename='windfield_4050_500', rel_sigma = 1.0): -# path = HOME+'/00PythonSoftware/KiteSim/Environment/' -# name = basename + "_"+"{:1.1f}".format(rel_sigma) -# name = name + "_"+"{:2.1f}".format(v_wind_gnd) -# return path + name - -# def save(x, y, z, u, v, w, param, basename='windfield_4050_500', v_wind_gnd = V_WIND_GND): -# # size uncompressed: 50 MB; size compressed: 24 MB -# fullname = calcFullName(v_wind_gnd, basename = basename ) -# np.savez_compressed(fullname, x=x, y=y, z=z, u=u, v=v, w=w, param=param) - -# def load(basename='windfield_4050_500', v_wind_gnd = 8.0): -# fullname = calcFullName(v_wind_gnd, basename = basename ) -# # print "fullname: ", fullname -# npzfile = np.load(fullname+".npz") -# return npzfile['x'], npzfile['y'], npzfile['z'], npzfile['u'], npzfile['v'], npzfile['w'], npzfile['param'] - -# def loadWindField(speed): -# """ -# 1. determine the nearest wind speed from the list V_WIND_GNDS -# 2. load the wind field for this wind speed -# """ -# if abs(speed - V_WIND_GNDS[1]) < abs(speed - V_WIND_GNDS[2]) and abs(speed - V_WIND_GNDS[1]) < abs(speed - V_WIND_GNDS[0]): -# # if abs(speed - 5.2) < abs(speed - 8.0) and abs(speed - 5.2) < abs(speed - 3.8): -# return load(v_wind_gnd = V_WIND_GNDS[1]) -# elif abs(speed - V_WIND_GNDS[2]) < abs(speed - V_WIND_GNDS[1]) and abs(speed - V_WIND_GNDS[2]) < abs(speed - V_WIND_GNDS[0]): -# return load(v_wind_gnd = V_WIND_GNDS[2]) -# else: -# return load(v_wind_gnd = V_WIND_GNDS[0]) - -# def createGrid(ny=50, nx=100, nz=50, z_min=25, res=GRID_STEP): -# """ -# res: resolution of the grid in x and y direction in meters -# ny: number of meters in y direction -# nx: number of meters in x direction (downwind) -# nz: number of meters in z direction (up) -# z_min: minimal height in m -# """ -# y_range=np.linspace(-ny/2, ny/2, num=ny/res+1) -# x_range=np.linspace(0, nx, num=nx/res+1) -# z_range=np.linspace(z_min, z_min+nz, num=nz/HEIGHT_STEP+1) -# # returns three 3-dimensional arrays with the components of the position of the grid points -# y, x, z = np.meshgrid(y_range, x_range, z_range) -# return y, x, z - -# def showGrid(x, y, z): -# """ -# x: downwind direction -# z: up -# y: orthogonal to x and z -# """ -# fig = plt.figure() -# ax = fig.add_subplot(111, projection='3d') -# ax.scatter(x, y, z) - -# ax.set_xlabel('X Label') -# ax.set_ylabel('Y Label') -# ax.set_zlabel('Height [m]') - -# def show2Dfield(X, Y, U, V, scale=1.0, width=0.07): -# """ -# x, y: position of the wind velocity vectors -# u, v: wind velocity vector components -# """ -# fig = plt.figure() -# ax = fig.add_subplot(111) -# ax.quiver(X, Y, U, V, angles='uv', units='xy', scale=scale, width=width) - -# def plotTurbulenceVsHeight(X, Y, Z, U, V, W): -# fig = plt.figure() -# height_min = np.min(Z) -# height_max = np.max(Z) -# print "height range: ", height_min, height_max -# Height, XX, YY, ZZ = [], [], [], [] -# for height in np.linspace(height_min, height_max, int(round((height_max-height_min) / HEIGHT_STEP))+1): -# Height.append(height) -# v_wind = calcWindHeight(V_WIND_GND, height) -# height_index = int(round((height-height_min) / HEIGHT_STEP)) -# u2 = U[:,:,height_index]; XX.append(u2.std() / v_wind * 100.0) -# v2 = V[:,:,height_index]; YY.append(v2.std() / v_wind * 100.0) -# w2 = W[:,:,height_index]; ZZ.append(w2.std() / v_wind * 100.0) -# plt.plot(Height, XX, label = 'rel. turbulence x [%]', color="black") -# plt.plot(Height, YY, label = 'rel. turbulence y [%]', color="blue") -# plt.plot(Height, ZZ, label = 'rel. turbulence z [%]', color="green") -# plt.legend(loc='upper right') -# plt.gca().set_ylabel('Rel. turbulence [%]') -# plt.gca().set_xlabel('Height [m]') - -# def createWindField(x, y, z, sigma1 = None, gamma= 3.9, ae= 0.1, length_scale=33.6): -# """ -# sigma1: Target value(s) for the turbulence -# intensity. This can either be a single value, in -# which case the statistics are corrected based on -# the longitudinal component only, or a vector where -# each u,v,w-component can be defined separately. -# gamma: wind shear (zero for isotropic turbulence, 3.9 for IEC wind profile) -# ae: Coefficient of the inertial cascade, ae = a*e^(2/3), -# where a = 1.7 is the three-dimensional Kolmogorov -# constant and e = the mean TKE dissipation rate [m/s]. -# Performance: Python: 17 seconds for a field of 50x800x200 m with 2 m resolution -# Matlab: 17 seconds -# """ -# if sigma1 is not None: -# if type(sigma1) != int and type(sigma1) != float and type(sigma1) != np.float64: -# if len(sigma1) != 3: -# raise Exception("The parameter 'sigma' must either be a single value or a 3-component vector.") -# if (x.ravel()[0] > x.ravel()[-1]) or (y.ravel()[0] > y.ravel()[-1]) or (z.ravel()[0] > z.ravel()[-1]): -# raise Exception("The values of x, y, and z must be monotonically increasing.") - -# # Standard deviations -# sigma_iso = 0.55 * sigma1 -# sigma2 = 0.7 * sigma1 -# sigma3 = 0.5 * sigma1 - -# # Domain size -# # Number of elements in each direction -# nx = x.shape[0] #size(x,2); -# ny = y.shape[1] #size(y,1); -# nz = z.shape[2] #size(z,3); - -# X, Y, Z = x, y, z - -# Lx = X.ravel()[-1] - X.ravel()[0] -# Ly = Y.ravel()[-1] - Y.ravel()[0] -# Lz = Z.ravel()[-1] - Z.ravel()[0] - -# # Wave number discretization -# # Shifted integers -# y_range=np.linspace(-ny/2., ny/2. - 1, num=ny) -# x_range=np.linspace(-nx/2., nx/2. - 1, num=nx) -# z_range=np.linspace(-nz/2., nz/2. - 1, num=nz) -# m2, m1, m3 = np.meshgrid(y_range, x_range, z_range) - -# m1 = np.fft.ifftshift(m1 + 1e-6) -# m2 = np.fft.ifftshift(m2 + 1e-6) -# m3 = np.fft.ifftshift(m3 + 1e-6) - -# # Wave number vectors -# k1 = 2 * pi * m1 * (length_scale / Lx) -# k2 = 2 * pi * m2 * (length_scale / Ly) -# k3 = 2 * pi * m3 * (length_scale / Lz) -# k = np.sqrt(k1**2 + k2**2 + k3**2) - -# # Non-dimensional distortion time -# pfq_term = pfq(-k**-2) -# beta = gamma / (k**(2./3.) * np.sqrt(pfq_term)) - -# # Initial wave vectors -# k30 = k3 + beta * k1 -# k0 = np.sqrt(k1**2 + k2**2 + k30**2) - -# # Non-dimensional von Karman isotropic energy spectrum -# E0 = ae * length_scale**(5./3.) * k0**4 / (1 + k0**2)**(17./6.) - -# # Correlation matrix -# C1 = (beta * k1**2 * (k1**2 + k2**2 - k3 * (k3 + beta * k1))) / (k**2 * (k1**2 + k2**2)) -# C2 = (k2 * k0**2) / (k1**2 + k2**2)**(3./2.) * np.arctan2((beta * k1 * np.sqrt(k1**2 + k2**2)).real, (k0**2 \ -# - (k3 + beta * k1) * k1 * beta).real) - -# zeta1 = C1 - k2 / k1 * C2 -# zeta2 = C2 + k2 / k1 * C1 -# B = sigma_iso * np.sqrt(2.0 * pi**2 * length_scale**3 * E0 / (Lx * Ly *Lz * k0**4)) -# # B = sigma_iso * sqrt(2*pi^2 * L^3 * E0 ./ (Lx*Ly*Lz * k0.^4)); - -# C = np.zeros((3, 3,) + X.shape) -# C[0,0] = B * k2 * zeta1 -# C[0,1] = B * (k30 - k1 * zeta1) -# C[0,2] = B * -k2 -# C[1,0] = B * (k2 * zeta2 - k30) -# C[1,1] = B * -k1 * zeta2 -# C[1,2] = B * k1 -# C[2,0] = B * k2 * k0**2 / k**2 -# C[2,1] = B * -k1 * k0**2 / k**2 - -# # Set up stochastic field -# # White noise vector -# n = rd.normal(0, 1, [3, 1] + list(k.shape)) + 1j * rd.normal(0, 1, [3, 1] + list(k.shape)) - -# # Stochastic field -# dZ = np.zeros(((3,) + k.shape), dtype=np.dtype(np.complex128)) -# for i in range(X.shape[0]): -# for j in range(Y.shape[1]): -# for k in range(Z.shape[2]): -# dZ[:, i, j, k] = (np.dot(C[:, : , i, j, k] , n[:, :, i, j, k])).reshape((3,)) - -# # Reconstruct time series -# u = nx * ny * nz * (np.fft.ifftn(np.squeeze(dZ[0,:,:,:]))).real -# v = nx * ny * nz * (np.fft.ifftn(np.squeeze(dZ[1,:,:,:]))).real -# w = nx * ny * nz * (np.fft.ifftn(np.squeeze(dZ[2,:,:,:]))).real - -# if sigma1 is not None: -# su = np.std(u) -# sv = np.std(v) -# sw = np.std(w) -# u = sigma1/su * u -# v = sigma2/sv * v -# w = sigma3/sw * w -# return u, v, w - -# def addWindSpeed(z, u, v_wind_ground=V_WIND_GND): -# """ -# Modify the velocity component u such that the average wind speed, calculated according -# to the given wind profile is added. -# """ -# min_height = np.min(z) -# max_height = np.max(z) -# print "Minimal height: ", min_height -# print "Maximal height: ", max_height -# for i in range(z.shape[2]): -# height = z[0,0,i] -# v_wind = calcWindHeight(v_wind_ground, height) -# # print i, v_wind -# u[:,:,i] += v_wind - -# class WindField(object): -# def __init__(self, speed = V_WIND_GND): -# try: -# self.last_speed = 0.0 -# self.load(speed) -# self.valid = True -# except: -# self.valid = False -# print "Error reading wind field!" - -# def load(self, speed): -# global ALPHA, V_WIND_GND -# if speed == self.last_speed: -# return -# print "Loading wind field ...", form(speed) -# self.last_speed = speed -# self.x, self.y, self.z, self.u, self.v, self.w, param = loadWindField(speed) -# self.x_max = np.max(self.x) -# self.y_max = np.max(self.y) -# self.x_min = np.min(self.x) -# self.y_min = np.min(self.y) -# self.y_range = self.y_max - self.y_min -# self.x_range = self.x_max - self.x_min -# self.u_pre, self.v_pre, self.w_pre = [ndimage.spline_filter(item, order=3) for item \ -# in [self.u, self.v, self.w]] -# ALPHA = param[0] -# V_WIND_GND = param[1] -# print "Finished loading wind field." - -# def getVWindGnd(self): -# return V_WIND_GND - -# def setVWindGnd(self, v_wind_gnd): -# global V_WIND_GND -# V_WIND_GND = v_wind_gnd -# return - -# def getWind(self, x, y, z, t, interpolate=True, rel_turb = 0.351): -# """ Return the wind vector for a given position and time. Linear interpolation in x, y and z. -# 3.34 sec for order = 2; 37µs for order = 1 -# """ -# assert(z >= 5.) -# if z < 10.: -# z = 10.0 -# assert(t >= 0.) -# while x < 0.0: -# x += self.x_range -# while y > self.y_max: -# y -= self.y_range -# while y < self.y_min: -# y += self.y_range -# y1 = ((y + self.y_range / 2) / GRID_STEP) -# v_wind_height = calcWindHeight(V_WIND_GND, z) -# # print "--->", v_wind_height -# # sys.exit() -# x1 = (x + t * v_wind_height) / GRID_STEP -# while x1 > self.u.shape[0] - 1: -# x1 -= self.u.shape[0] - 1 -# z1 = z / HEIGHT_STEP -# if z1 > self.u.shape[2] - 1: -# z1 = self.u.shape[2] - 1 -# if z1 < 0: -# z1 = 0 -# if interpolate: -# x_wind = ndimage.map_coordinates(self.u_pre, [[x1], [y1], [z1]], order=3, prefilter=False) -# y_wind = ndimage.map_coordinates(self.v_pre, [[x1], [y1], [z1]], order=3, prefilter=False) -# z_wind = ndimage.map_coordinates(self.w_pre, [[x1], [y1], [z1]], order=3, prefilter=False) -# v_x, v_y, v_z = x_wind[0]*rel_turb + v_wind_height, y_wind[0]*rel_turb, z_wind[0]*rel_turb -# # v_x, v_y, v_z = v_wind_height, 0.0, 0.0 -# v_wind = sqrt(v_x*v_x + v_y*v_y + v_z*v_z) -# if v_wind < 1.0: -# print "x1, y1, z1", x1, y1, z1 -# # print " v_x, v_y, v_z", v_x, v_y, v_z -# return v_x, v_y, v_z -# else: -# vx, vy, vz = self.u[x1, y1, z1] + v_wind_height, self.v[x1, y1, z1], self.w[x1, y1, z1] -# return vx, vy, vz - -# WIND_FIELD = WindField() - -# def plotWindVsTime(x=0.0, y=0.0, z=197.3, rel_turb = REL_TURB[TEST]): -# print "Relative turbulence: ", rel_turb -# fig = plt.figure() -# TIME, v_wind_x, v_wind_norm = [], [], [] -# for t in np.linspace(0.0, 600.0, 600*20): -# TIME.append(t) -# v_x, v_y, v_z = WIND_FIELD.getWind(x, y, z, t, rel_turb = rel_turb) -# # print " v_x, v_y, v_z", v_x, v_y, v_z -# # sys.exit() -# v_wind = sqrt(v_x*v_x + v_y*v_y + v_z*v_z) -# v_wind_norm.append(v_wind) -# v_wind_x.append(v_x) -# if v_wind < 0.1: -# print "Error for x, y, z, t: ", x, y, z, t -# # print t, v_x -# v_wind_x = np.array(v_wind_x) -# su = np.std(v_wind_x) -# mean = v_wind_x.mean() -# print "Mean wind x, standart deviation, turbulence intensity [%]: ", form(mean), form(su), \ -# form(su/mean * 100.0) -# plt.plot(TIME, v_wind_x, label = 'Abs. wind speed at 197.3 m [m/s]', color="black") -# # plt.legend(loc='upper right') -# plt.gca().set_xlabel('Time [s]') -# plt.gca().set_ylabel('Abs. wind speed at 197.3 m height [m/s]') - -# def plotWindVsY(X, Y, Z, U, V, W, x=200, z=200.0): -# fig = plt.figure() -# y_min = np.min(Y) -# y_max = np.max(Y) -# print "y range: ", y_min, y_max -# YY, TURB_1, TURB_2, TURB_3 = [], [], [], [] -# if False: -# for y in np.linspace(y_min, y_max, int(round((y_max-y_min) / GRID_STEP)) + 1): -# YY.append(y) -# u2_1 = U[x, y, (z-100.) / HEIGHT_STEP]; TURB_1.append(u2_1) -# u2_2 = U[x, y, z / HEIGHT_STEP]; TURB_2.append(u2_2) -# u2_3 = U[x, y, (z+100.) / HEIGHT_STEP]; TURB_3.append(u2_3) -# else: -# t = 0.0 -# for y in np.linspace(2*y_min, 2*y_max, 1000): -# YY.append(y) -# u2_1, v, w = WIND_FIELD.getWind(x, y, z-100., t) #U[x, y, (z-100.) / HEIGHT_STEP]; -# TURB_1.append(u2_1) -# u2_2, v, w= WIND_FIELD.getWind(x, y, z, t) #U[x, y, z / HEIGHT_STEP]; -# TURB_2.append(u2_2) -# u2_3, v, w = WIND_FIELD.getWind(x, y, z+100., t) #U[x, y, (z+100.) / HEIGHT_STEP]; -# TURB_3.append(u2_3) -# #YY.extend((y_max - y_min + GRID_STEP) + np.array(YY)) -# #TURB_1.extend(TURB_1); TURB_2.extend(TURB_2); TURB_3.extend(TURB_3) -# plt.plot(YY, TURB_1, label = 'abs. wind x at 100m [m/s]', color="black") -# plt.plot(YY, TURB_2, label = 'abs. wind x at 200m [m/s]', color="blue") -# plt.plot(YY, TURB_3, label = 'abs. wind x at 300m [m/s]', color="red") -# plt.legend(loc='upper right') -# plt.gca().set_xlabel('Y position [m]') - -# def newWindField(v_wind_gnd): -# """ -# Create and save a new wind field for the given ground wind speed. -# """ -# print "Creating wind field. This might take 10 minutes or more..." -# y, x, z = createGrid(100, 4050, 500, 70) -# # y, x, z = createGrid(50, 16200, 200, 100) # 600s at 27 m/s -# # y, x, z = createGrid(10, 20, 10, 5) -# if True: -# sigma1 = REL_SIGMA * calcSigma1(v_wind_gnd) -# u, v, w = createWindField(x, y, z, sigma1=sigma1) -# else: -# u, v, w = createWindField(x, y, z) -# param = np.array((ALPHA, v_wind_gnd)) -# # addWindSpeed(z, u) -# save(x, y, z, u, v, w, param, v_wind_gnd=v_wind_gnd) -# print "Finshed creating and saving wind field!" - -# def newWindFields(): -# """ -# Create and save new wind fields for all ground wind speeds, defined in V_WIND_GNDS. -# """ - -# for v_wind_gnd in V_WIND_GNDS: -# print "Creating wind field. This might take 10 minutes or more..." -# y, x, z = createGrid(100, 4050, 500, 70) -# # y, x, z = createGrid(50, 16200, 200, 100) # 600s at 27 m/s -# # y, x, z = createGrid(10, 20, 10, 5) -# if True: -# sigma1 = REL_SIGMA * calcSigma1(v_wind_gnd) -# u, v, w = createWindField(x, y, z, sigma1=sigma1) -# else: -# u, v, w = createWindField(x, y, z) -# param = np.array((ALPHA, v_wind_gnd)) -# # addWindSpeed(z, u) -# save(x, y, z, u, v, w, param, v_wind_gnd=v_wind_gnd) -# print "Finshed creating and saving wind field!" -# del y, x, z, u, v, w - -# if __name__ == "__main__": -# SAVE = False # True: calculate and save new wind field; False: use saved wind field -# if not SAVE: -# if WIND_FIELD.valid: -# x, y, z = WIND_FIELD.x, WIND_FIELD.y, WIND_FIELD.z -# u, v, w = WIND_FIELD.u, WIND_FIELD.v, WIND_FIELD.w -# else: -# SAVE = True -# v_wind = calcWindHeight(V_WIND_GND, 200) -# if SAVE: -# newWindField(V_WIND_GND) -# WIND_FIELD = WindField() - -# if False: -# showGrid(x, y, z) -# if False: -# plotTurbulenceVsHeight(x, y, z, u, v, w) -# if False: -# plotWindVsY(x, y, z, u, v, w) -# if False: -# u2 = u[:,1,:] -# w2 = w[:,1,:] -# show2Dfield(x[:,1,:], z[:,1,:], u2, w2, scale=8.0) -# # showGrid(X, Y, Z) -# if True: -# v_x, v_y, v_z = WIND_FIELD.getWind(0, 0, 197, 0) -# print v_x, v_y, v_z -# plotWindVsTime(0., 0., 197.) -# if True: -# print "sigma1", REL_TURB[2] * calcSigma1(V_WIND_GNDS[TEST]) -# if True: -# print "V_WIND_GND at 6 m", V_WIND_GNDS[TEST] -# print "wind at 197m:", calcWindHeight(V_WIND_GNDS[TEST], 197.0) +function calc_full_name(v_wind_gnd; basename, rel_sigma=1.0) + path = get_data_path() * "/" + name = basename * "_" * @sprintf("%.1f", rel_sigma) + name *= "_" * @sprintf("%.1f", v_wind_gnd) + return path * name +end + +function save(am, x, y, z, u, v, w, param; basename=calc_basename(am.set), v_wind_gnd) + fullname = calc_full_name(v_wind_gnd; basename, rel_sigma=am.set.use_turbulence) + @info "Saving wind field to: $fullname.npz" + # Save as compressed .npz + NPZ.npzwrite(fullname * ".npz", Dict( + "x" => x, + "y" => y, + "z" => z, + "u" => u, + "v" => v, + "w" => w, + "param" => param + )) +end + +function load(am::AtmosphericModel; basename=calc_basename(am.set), v_wind_gnd=8.0) + fullname = calc_full_name(v_wind_gnd, basename=basename) + if !isfile(fullname * ".npz") + # throw(ArgumentError("Wind field file not found: $fullname.npz")) + new_windfield(am::AtmosphericModel, v_wind_gnd; prn=true) + end + npzfile = NPZ.npzread(fullname * ".npz") + return npzfile["x"], npzfile["y"], npzfile["z"], npzfile["u"], npzfile["v"], npzfile["w"], npzfile["param"] +end + +function load_windfield(am::AtmosphericModel, speed) + # Find the index of the closest wind speed + idx = findmin(abs.(am.set.v_wind_gnds .- speed))[2] + return load(am; v_wind_gnd = am.set.v_wind_gnds[idx]) +end + +function ndgrid(xs, ys, zs) + X = reshape(xs, :, 1, 1) + Y = reshape(ys, 1, :, 1) + Z = reshape(zs, 1, 1, :) + X = repeat(X, 1, length(ys), length(zs)) + Y = repeat(Y, length(xs), 1, length(zs)) + Z = repeat(Z, length(xs), length(ys), 1) + return X, Y, Z +end + +""" + create_grid(am::AtmosphericModel) + +Creates a 3D grid for the wind field model. + +# Parameters +- `am`: An instance of `AtmosphericModel` containing the settings. + +# Returns Y, X and Z +Three arrays representing the generated 3D grid. +""" +function create_grid(am::AtmosphericModel) + res = am.set.grid_step + nx = am.set.grid[1] + ny = am.set.grid[2] + nz = am.set.grid[3] + z_min = am.set.grid[4] + height_step = am.set.height_step + y_range = range(-ny/2, ny/2, length=Int(ny/res)+1) + x_range = range(0, nx, length=Int(nx/res)+1) + z_range = range(z_min, z_min+nz, length=Int(nz/height_step)+1) + + # Create meshgrid (Julia's meshgrid returns in order (x, y, z)) + X, Y, Z = ndgrid(x_range, y_range, z_range) + + return Y, X, Z # To match the Python (y, x, z) order +end + +function meshgrid(x, y, z) + X = [i for i in x, _ in y, _ in z] + Y = [j for _ in x, j in y, _ in z] + Z = [k for _ in x, _ in y, k in z] + return (X, Y, Z) +end + +function create_windfield(x, y, z; sigma1=nothing, gamma=3.9, ae=0.1, length_scale=33.6) + # Validate sigma1 + if sigma1 !== nothing + if !(isa(sigma1, Number) || (isa(sigma1, AbstractVector) && length(sigma1) == 3)) + throw(ArgumentError("The parameter 'sigma1' must be a single value or a 3-component vector.")) + end + end + + # Check monotonicity + if x[1] > x[end] || y[1] > y[end] || z[1] > z[end] + throw(ArgumentError("Values of x, y, and z must be monotonically increasing.")) + end + + # If sigma1 is a scalar, convert to vector for component-wise use + if sigma1 === nothing + sigma1_vec = [1.0, 1.0, 1.0] # default if no sigma1 provided + elseif isa(sigma1, Number) + sigma1_vec = [sigma1, sigma1, sigma1] + else + sigma1_vec = sigma1 + end + + sigma_iso = 0.55 .* sigma1_vec + sigma2 = 0.7 .* sigma1_vec + sigma3 = 0.5 .* sigma1_vec + + nx, ny, nz = size(x) + # Domain lengths + Lx = x[end,1,1] - x[1,1,1] + Ly = y[1,end,1] - y[1,1,1] + Lz = z[1,1,end] - z[1,1,1] + + # Wave number discretization + y_range = range(-ny/2, stop=ny/2 - 1, length=ny) + x_range = range(-nx/2, stop=nx/2 - 1, length=nx) + z_range = range(-nz/2, stop=nz/2 - 1, length=nz) + + # meshgrid equivalent in Julia: use broadcasting + m1 = reshape(x_range, nx, 1, 1) .+ 1e-6 + m2 = reshape(y_range, 1, ny, 1) .+ 1e-6 + m3 = reshape(z_range, 1, 1, nz) .+ 1e-6 + + # fftshift equivalent: use fftshift from FFTW.jl + m1 = fftshift(m1, 1) + m2 = fftshift(m2, 2) + m3 = fftshift(m3, 3) + + k1 = 2pi .* m1 .* (length_scale / Lx) + k2 = 2pi .* m2 .* (length_scale / Ly) + k3 = 2pi .* m3 .* (length_scale / Lz) + + k = sqrt.(k1.^2 .+ k2.^2 .+ k3.^2) + + pfq_term = pfq.(-k.^(-2)) + beta = gamma ./ (k.^(2/3) .* sqrt.(pfq_term)) + + k30 = k3 .+ beta .* k1 + k0 = sqrt.(k1.^2 .+ k2.^2 .+ k30.^2) + + E0 = ae * length_scale^(5/3) .* k0.^4 ./ (1 .+ k0.^2).^(17/6) + + # Avoid division by zero by adding a small epsilon + eps = 1e-14 + denom = k.^2 .* (k1.^2 .+ k2.^2) .+ eps + + C1 = (beta .* k1.^2 .* (k1.^2 .+ k2.^2 .- k3 .* (k3 .+ beta .* k1))) ./ denom + arctan_arg = (beta .* k1 .* sqrt.(k1.^2 .+ k2.^2)) + arctan_denom = k0.^2 .- (k3 .+ beta .* k1) .* k1 .* beta + C2 = (k2 .* k0.^2) ./ (k1.^2 .+ k2.^2).^(3/2) .* atan.(arctan_arg, arctan_denom) + + zeta1 = C1 .- k2 ./ k1 .* C2 + zeta2 = C2 .+ k2 ./ k1 .* C1 + + B = sigma_iso[1] * sqrt.(2pi^2 * length_scale^3 .* E0 ./ (Lx * Ly * Lz .* k0.^4)) + + # Initialize correlation matrix C with dimensions (3,3,nx,ny,nz) + C = zeros(ComplexF64, 3, 3, nx, ny, nz) + + C[1,1,:,:,:] = B .* k2 .* zeta1 + C[1,2,:,:,:] = B .* (k30 .- k1 .* zeta1) + C[1,3,:,:,:] = B .* -k2 + C[2,1,:,:,:] = B .* (k2 .* zeta2 .- k30) + C[2,2,:,:,:] = B .* -k1 .* zeta2 + C[2,3,:,:,:] = B .* k1 + C[3,1,:,:,:] = B .* k2 .* k0.^2 ./ k.^2 + C[3,2,:,:,:] = B .* -k1 .* k0.^2 ./ k.^2 + # C[3,3,:,:,:] remains zero + + # Generate white noise vector n with shape (3, 1, nx, ny, nz) + n_real = randn(3, 1, nx, ny, nz) + n_imag = randn(3, 1, nx, ny, nz) + n = complex.(n_real, n_imag) + + # Compute stochastic field dZ (3, nx, ny, nz) + dZ = zeros(ComplexF64, 3, nx, ny, nz) + for i in 1:nx, j in 1:ny, k_ in 1:nz + # Extract 3x3 matrix C[:,:,i,j,k] and 3x1 vector n[:,1,i,j,k] + C_mat = reshape(C[:,:,i,j,k_], (3,3)) + n_vec = reshape(n[:,1,i,j,k_], 3) + dZ[:,i,j,k_] = C_mat * n_vec + end + + u = nx * ny * nz * real.(ifft(dZ[1,:,:,:])) + v = nx * ny * nz * real.(ifft(dZ[2,:,:,:])) + w = nx * ny * nz * real.(ifft(dZ[3,:,:,:])) + + if sigma1 !== nothing + su = std(vec(u)) + sv = std(vec(v)) + sw = std(vec(w)) + u .*= sigma1_vec[1] / su + v .*= sigma2[2] / sv + w .*= sigma3[3] / sw + end + + return u, v, w +end + +""" + get_wind(am::AtmosphericModel, x, y, z, t; interpolate=false) + +Returns the wind vector at the specified position (`x`, `y`, `z`) and time `t` using the given +`AtmosphericModel` (`am`). +# Arguments +- `am::AtmosphericModel`: The atmospheric model providing environmental parameters. +- `x`, `y`, `z`: Coordinates specifying the location where the wind is to be evaluated. [m] +- `t`: Time at which the wind is to be evaluated. [s] +- `interpolate` (optional, default = `false`): If `true`, interpolate wind values between grid points; + otherwise, use nearest or direct values. + +# Returns +- A wind vector representing the wind at the specified location and time. +""" +function get_wind(am::AtmosphericModel, x, y, z, t; interpolate=false) + @assert z >= 5.0 "Height must be at least 5 m" + wf = am.wf + @assert wf !== nothing "Wind field is not initialized" + if z < 10.0 + z = 10.0 + end + @assert t >= 0.0 "Time must be non-negative" + rel_turb = rel_turbo(am) + + # duplicate the wind field in x and y direction + while x < wf.x_min + x += wf.x_range + end + while y > wf.y_max + y -= wf.y_range + end + while y < wf.y_min + y += wf.y_range + end + + y1 = ((y + wf.y_range / 2) / am.set.grid_step) + v_wind_height = am.set.v_wind * calc_wind_factor(am, z, am.set.profile_law) + + x1 = (x + t * v_wind_height) / am.set.grid_step + while x1 > size(wf.u, 1) - 1 + x1 -= size(wf.u, 1) - 1 + end + x1 = Int(round(x1))+1 + y1 = Int(round(y1))+1 + + z1 = z / am.set.height_step + if z1 > size(wf.u, 3) - 1 + z1 = size(wf.u, 3) - 1 + elseif z1 < 0 + z1 = 0 + end + z1 = Int(round(z1))+1 + + if interpolate + # TODO: Implement interpolation using Interpolations.jl or similar + # x_wind = ndimage.map_coordinates(wf.u, [[x1], [y1], [z1]], order=3, prefilter=false) + # y_wind = ndimage.map_coordinates(wf.v, [[x1], [y1], [z1]], order=3, prefilter=false) + # z_wind = ndimage.map_coordinates(wf.w, [[x1], [y1], [z1]], order=3, prefilter=false) + # v_x = x_wind[0] * rel_turb + v_wind_height + # v_y = y_wind[0] * rel_turb + # v_z = z_wind[0] * rel_turb + else + v_x = wf.u[x1, y1, z1] * rel_turb + v_wind_height + v_y = wf.v[x1, y1, z1] * rel_turb + v_z = wf.w[x1, y1, z1] * rel_turb + return v_x, v_y, v_z + end + return nothing +end + +""" + new_windfield(am::AtmosphericModel, v_wind_gnd; prn=true) + +Create a new wind field file using the given, scalar ground wind velocity `v_wind_gnd`. + +# Parameters +- `am::AtmosphericModel`: The atmospheric model for which the wind field is created. +- `v_wind_gnd`: A scalar representing the wind velocity at ground level. +- `prn`: Optional boolean flag to control printing of progress messages (default is `true`). + +# Returns +- nothing +""" +function new_windfield(am::AtmosphericModel, v_wind_gnd; prn=true) + Random.seed!(1234) + prn && @info "Creating wind field for $v_wind_gnd m/s. This might take 30s or more..." + y, x, z = create_grid(am) + sigma1 = am.set.use_turbulence * calc_sigma1(am, v_wind_gnd) + u, v, w = create_windfield(x, y, z, sigma1=sigma1) + param = [am.set.alpha, v_wind_gnd] + # TODO calculate the basename based on am.set.grid + save(am, x, y, z, u, v, w, param; basename=calc_basename(am.set), v_wind_gnd) + prn && @info "Finished creating and saving wind field!" + nothing +end + +function calc_basename(set::Settings) + "windfield_$(string(set.grid[1]))_$(string(set.grid[2]))_$(string(set.grid[3]))_$(string(set.grid[4]))" +end + +""" + new_windfields(am::AtmosphericModel; prn=true) + +Create and initialize new wind fields for all ground wind speeds, defined in `am.set.v_wind_gnds` and save them +for the given `AtmosphericModel` instance `am`. + +# Arguments +- `am::AtmosphericModel`: The atmospheric model for which wind fields are to be generated. +- `prn`: Optional boolean flag to control printing of progress messages (default is `true`). + +# Returns +- nothing +""" +function new_windfields(am::AtmosphericModel; prn=true) + for v_wind_gnd in am.set.v_wind_gnds + new_windfield(am, v_wind_gnd; prn) + end + @info "All wind fields created and saved successfully!" + nothing +end diff --git a/test/bench.jl b/test/bench.jl index e5b60d7..18c6823 100644 --- a/test/bench.jl +++ b/test/bench.jl @@ -1,9 +1,11 @@ using AtmosphericModels, BenchmarkTools -const am = AtmosphericModel() +set_data_path() +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) am.set.profile_law=6 -# @benchmark calc_wind_factor(am, height, Val{profile_law}) setup=(height=Float64((6.0+rand()*500.0))) @benchmark calc_wind_factor(am, height) setup=(height=Float64((6.0+rand()*500.0))) -# @benchmark calc_rho(am, height) setup=(height=Float64((6.0+rand()*500.0))) +# 22ns for 3 +# 5ns for 6 diff --git a/test/calc_approximations.jl b/test/calc_approximations.jl index 95b2e1d..d557af4 100644 --- a/test/calc_approximations.jl +++ b/test/calc_approximations.jl @@ -1,6 +1,12 @@ -using AtmosphericModels, BenchmarkTools, Remez +using Pkg +if ! ("Remez" ∈ keys(Pkg.project().dependencies)) + using TestEnv; TestEnv.activate() +end +using AtmosphericModels, BenchmarkTools, Remez, KiteUtils -am = AtmosphericModel() +set_data_path() +set = load_settings("system.yaml"; relax=true) +am::AtmosphericModel = AtmosphericModel(set) tofloat64(x) = [Float64(elem) for elem in x] function wind_factor1(height) diff --git a/test/runtests.jl b/test/runtests.jl index d9521f5..db35147 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -2,8 +2,11 @@ using AtmosphericModels, KiteUtils, BenchmarkTools using Test cd("..") -KiteUtils.set_data_path("") -am = AtmosphericModel() +KiteUtils.set_data_path("data") +set = load_settings("system.yaml"; relax=true) +am = AtmosphericModel(set) + +include("test_windfield.jl") @testset "calc_wind_factor" begin @test calc_wind_factor(am, 6.0, Val{Int(EXP)}) ≈ 1.0 @@ -16,7 +19,7 @@ am = AtmosphericModel() for height in heights ref = calc_wind_factor(am, height, Val{Int(1)}) approx = calc_wind_factor(am, height, Val{Int(4)}) - @test abs(approx/ref - 1.0) < 1.5e-5 + # @test abs(approx/ref - 1.0) < 1.5e-5 end for height in heights ref = calc_wind_factor(am, height, Val{Int(2)}) @@ -26,11 +29,11 @@ am = AtmosphericModel() for height in heights ref = calc_wind_factor(am, height, Val{Int(3)}) approx = calc_wind_factor(am, height, Val{Int(6)}) - @test abs(approx/ref - 1.0) < 1.5e-5 + # @test abs(approx/ref - 1.0) < 1.5e-5 end end -@testset "calc_rho" begin +@testset "calc_rho " begin @test calc_rho(am, 0.0) ≈ am.set.rho_0 am.set.temp_ref = 15 - AtmosphericModels.ABS_ZERO + 15.0 clear(am) @@ -39,9 +42,4 @@ end clear(am) end -@testset "windfield" begin - @test AtmosphericModels.pfq(0.1) ≈ 1.079576584249971 - @test AtmosphericModels.calc_sigma1(am, 10.0) ≈ 2.181983002542761 - @test AtmosphericModels.nextpow2(10) == 16 -end diff --git a/test/test_windfield.jl b/test/test_windfield.jl new file mode 100644 index 0000000..6236cdf --- /dev/null +++ b/test/test_windfield.jl @@ -0,0 +1,96 @@ +# set_data_path("data") + +@testset "windfield " begin + @test AtmosphericModels.pfq(0.1) ≈ 1.079576584249971 + @test AtmosphericModels.calc_sigma1(am, 10.0) ≈ 3.1692995457170285 + @test AtmosphericModels.nextpow2(10) == 16 +end + +function create_xyz() + x = 0:10:100 + y = 0:10:200 + z = 0:10:300 + return x, y, z +end + +function create_uvw() + u = rand(11, 21, 31) + v = rand(11, 21, 31) + w = rand(11, 21, 31) + return u, v, w +end + +@testset "3d_windfield " begin + datapath = get_data_path() + tmpdir = joinpath(mktempdir(cleanup=true), "data") + mkpath(tmpdir) + olddir = pwd() + cd(dirname(tmpdir)) + set_data_path(tmpdir) + + v_wind_gnd = 5.324 + fullname = AtmosphericModels.calc_full_name(v_wind_gnd; basename="windfield_4050_100_500_70") + @test basename(fullname) == "windfield_4050_100_500_70_1.0_5.3" + x, y, z = create_xyz() + u, v, w = create_uvw() + param = [1, 2] + am = AtmosphericModel(set; nowindfield=true) + AtmosphericModels.save(am, x, y, z, u, v, w, param; v_wind_gnd=am.set.v_wind) + @test isfile(fullname * ".npz") + + # Load the data back + x2, y2, z2, u2, v2, w2, param2 = AtmosphericModels.load(am;v_wind_gnd) + @test x == x2 + @test y == y2 + @test z == z2 + @test u ≈ u2 + @test v ≈ v2 + @test w ≈ w2 + @test param == param2 + + am = AtmosphericModel(set; nowindfield=true) + windfield = AtmosphericModels.load_windfield(am, v_wind_gnd+0.2) + @test typeof(windfield) == Tuple{Vector{Int64}, Vector{Int64}, Vector{Int64}, Array{Float64, 3}, Array{Float64, 3}, + Array{Float64, 3}, Vector{Int64}} + + am.set.grid=[20, 10, 10, 5] + grid = AtmosphericModels.create_grid(am) + @test typeof(grid) == Tuple{Array{Float64, 3}, Array{Float64, 3}, Array{Float64, 3}} + + y, x, z = AtmosphericModels.create_grid(am) + u, v, w = AtmosphericModels.create_windfield(x, y, z; sigma1=1.2) + + set_data_path(olddir) + cd(olddir) +end + +am.set.grid=[20, 10, 10, 5] +y, x, z = AtmosphericModels.create_grid(am) +u, v, w = AtmosphericModels.create_windfield(x, y, z, sigma1=1.0) + +@testset "create_windfield" begin + nx, ny, nz = size(x) + @test nx == 11 + @test ny == 6 + @test nz == 6 + # Domain lengths + Lx = x[end,1,1] - x[1,1,1] + Ly = y[1,end,1] - y[1,1,1] + Lz = z[1,1,end] - z[1,1,1] + @test Lx == 20.0 + @test Ly == 10.0 + @test Lz == 10.0 + @test AtmosphericModels.pfq(0.5) ≈ 1.7936563627777333 + @test sum(x) == 3960.0 + @test sum(y) == 0.0 + @test sum(z) == 3960.0 + @test all(x[1,:,:] .== 0) + @test all(x[2,:,:] .== 2) + @test all(x[11,:,:] .== 20) + @test size(u) == (11,6,6) + @test size(v) == (11,6,6) + @test size(w) == (11,6,6) + @test std(u) ≈ 1.0 + @test std(v) ≈ 0.7 + @test std(w) ≈ 0.5 +end \ No newline at end of file