Remove SymbolicAWEModel code from the package

Project.toml

@@ -37,6 +37,7 @@ StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
+SymbolicAWEModels = "9c9a347c-5289-41db-a9b9-25ccc76c3360"
 SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 Timers = "21f18d07-b854-4dab-86f0-c15a3821819a"
@@ -71,7 +72,6 @@ KiteUtils = "0.10.15"
 LaTeXStrings = "1.4.0"
 LinearAlgebra = "1.10, 1.11"
 LinearSolve = "~2.39.0, 3"
-ModelingToolkit = "~9.78.0"
 NLSolversBase = "~7.8.3"
 NLsolve = "4.5"
 NonlinearSolve = "4.8.0"
@@ -93,11 +93,12 @@ StaticArrays = "1.9.7"
 Statistics = "1"
 StatsBase = "0.34"
 Sundials = "4.24"
+SymbolicAWEModels = "0.1.2"
 SymbolicIndexingInterface = "0.3"
 SymbolicUtils = "3.25"
 Test = "1"
 Timers = "0.1.5"
-VortexStepMethod = "1.2.6"
+VortexStepMethod = "2.0.0"
 WinchModels = "0.3.6"
 julia = "1.10, 1.11"
 

data/settings_ram.yaml

@@ -3,14 +3,14 @@ system:
                                    #   use / as path delimiter, even on Windows 
     time_lapse:   1.0              # relative replay speed
     sim_time:   100.0              # simulation time             [sim only]
-    segments:       6              # number of tether segments
+    segments:       3              # 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
 
 initial:
-    l_tethers: [50.0, 50.0, 50.0]  # initial tether length       [m]
+    l_tethers: [50.0, 50.4, 50.4]  # initial tether length       [m]
     elevation: 70.8                # initial elevation angle   [deg]
     v_reel_outs: [0.0, 0.0, 0.0]   # initial reel out speed    [m/s]
 
@@ -69,7 +69,7 @@ environment:
     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, 4=F
+    profile_law: 3           # 1=EXP, 2=LOG, 3=EXPLOG, 4=FAST_EXP, 5=FAST_LOG, 6=FAST_EXPLOG
     # the following parameters are for calculating the turbulent wind field using the Mann model
     use_turbulence: 0.0      # turbulence intensity relative to Cabau, NL
     v_wind_gnds: [3.483, 5.324, 8.163] # wind speeds at ref height for calculating the turbulent wind field [m/s]

examples/lin_ram_model.jl

@@ -1,93 +1,62 @@
 # Copyright (c) 2025 Bart van de Lint
 # SPDX-License-Identifier: MPL-2.0
 
-#=
-This example demonstrates linearized model accuracy by comparing:
-1. Nonlinear SymbolicAWEModel model simulation 
-2. Linearized state-space model simulation
-
-Both models start from the same operating point and are subjected
-to identical steering inputs. The resulting state trajectories are
-plotted together to visualize how well the linearized model
-approximates the nonlinear dynamics.
-=#
+# This example demonstrates how to:
+# - Load a system configuration from a YAML file,
+# - Initialize a SymbolicAWEModel with specified winch torques,
+# - Stabilize the system at its operating point,
+# - Linearize the system at that point,
+# - And plot the Bode plots of the resulting linearized system.
 
 using Timers
 tic()
 @info "Loading packages "
 
 PLOT = true
-if PLOT
-    using Pkg
-    if ! ("LaTeXStrings" ∈ keys(Pkg.project().dependencies))
-        using TestEnv; TestEnv.activate()
-    end
-    using ControlPlots, LaTeXStrings, ControlSystemsBase
+using Pkg
+if ! ("LaTeXStrings" ∈ keys(Pkg.project().dependencies))
+    using TestEnv; TestEnv.activate()
 end
+using ControlPlots, LaTeXStrings, ControlSystemsBase
 
-using KiteModels, LinearAlgebra, Statistics, OrdinaryDiffEqCore 
+using SymbolicAWEModels, KiteUtils, LinearAlgebra, Statistics
 using ModelingToolkit
 using ModelingToolkit: t_nounits
 toc()
 
-# TODO: use sparse autodiff
-
-# Simulation parameters
-dt = 0.001
-total_time = 1.0  # Increased from 0.1s to 1.0s for better dynamics observation
-vsm_interval = 3
-steps = Int(round(total_time / dt))
-
-# Steering parameters
-steering_freq = 1/2  # Hz - full left-right cycle frequency
-steering_magnitude = 5.0      # Magnitude of steering input [Nm]
-
 # Initialize model
-set = load_settings("system_ram.yaml")
-set.segments = 3
+set = Settings("system_ram.yaml")
 set_values = [-50.0, 0.0, 0.0]  # Set values of the torques of the three winches. [Nm]
-set.quasi_static = false
-set.physical_model = "simple_ram"
 
-@info "Creating SymbolicAWEModel model..."
+@info "Creating SymbolicAWEModel..."
 s = SymbolicAWEModel(set)
-s.set.abs_tol = 1e-2
-s.set.rel_tol = 1e-2
 toc()
 
 # Define outputs for linearization - heading
-lin_outputs = @variables heading(t_nounits)[1]
+@variables begin
+    heading(t_nounits)[1]
+    angle_of_attack(t_nounits)[1]
+    tether_len(t_nounits)[1:3]
+    winch_force(t_nounits)[1:3]
+end
+lin_outputs = [heading[1], angle_of_attack[1], tether_len[1], winch_force[1]]
+@info "Linear outputs: $lin_outputs"
 
 # Initialize at elevation with linearization outputs
-s.sys_struct.winches[2].tether_length += 0.2
-s.sys_struct.winches[3].tether_length += 0.2
-KiteModels.init!(s; 
-    remake=false,
-    reload=true,
-    lin_outputs  # Specify which outputs to track in linear model
-)
+SymbolicAWEModels.init!(s; lin_outputs)
 sys = s.sys
 
-@show rad2deg(s.integrator[sys.elevation[1]])
-
-
 @info "System initialized at:"
 toc()
 
 # --- Stabilize system at operating point ---
 @info "Stabilizing system at operating point..."
-s.integrator.ps[sys.stabilize] = true
-stabilization_steps = Int(10 ÷ dt)
-for i in 1:stabilization_steps
-    next_step!(s; dt, vsm_interval=0.05÷dt)
-end
-s.integrator.ps[sys.stabilize] = false
+SymbolicAWEModels.find_steady_state!(s)
 
 # --- Linearize at operating point ---
 @info "Linearizing system at operating point..."
-@time (; A, B, C, D) = KiteModels.linearize(s)
-@time (; A, B, C, D) = KiteModels.linearize(s)
-@show norm(A)
+(; A, B, C, D) = SymbolicAWEModels.linearize!(s)
+@time (; A, B, C, D) = SymbolicAWEModels.linearize!(s)
 @info "System linearized with matrix dimensions:" A=size(A) B=size(B) C=size(C) D=size(D)
 
 sys = ss(A,B,C,D)

examples/ram_air_kite.jl

@@ -5,13 +5,13 @@ using Timers
 tic()
 @info "Loading packages "
 
-PLOT = true
+PLOT = false
 using Pkg
 if ! ("LaTeXStrings" ∈ keys(Pkg.project().dependencies))
     using TestEnv; TestEnv.activate()
 end
 using ControlPlots, LaTeXStrings
-using KiteModels, LinearAlgebra, Statistics
+using SymbolicAWEModels, KiteUtils, LinearAlgebra, Statistics
 
 if ! @isdefined SIMPLE
     SIMPLE = false
@@ -30,40 +30,31 @@ steering_freq = 1/2  # Hz - full left-right cycle frequency
 steering_magnitude = 10.0      # Magnitude of steering input [Nm]
 
 # Initialize model
-set = load_settings("system_ram.yaml")
-set.segments = 3
+set = Settings("system_ram.yaml")
 set_values = [-50, 0.0, 0.0]  # Set values of the torques of the three winches. [Nm]
 set.quasi_static = false
 set.physical_model = SIMPLE ? "simple_ram" : "ram"
 
-@info "Creating wing, aero, vsm_solver, sys_struct and symbolic_awe_model:"
+@info "Creating SymbolicAWEModel:"
 sam = SymbolicAWEModel(set)
-sam.set.abs_tol = 1e-2
-sam.set.rel_tol = 1e-2
 toc()
 
-# init_Q_b_w, R_b_w = KiteModelsam.initial_orient(sam.set)
-# init_kite_pos = init!(sam.sys_struct, sam.set, R_b_w, init_Q_b_w)
-# plot(sam.sys_struct, 0.0; zoom=false, front=false)
-
 # Initialize at elevation
-set.l_tethers[2] += 0.2
-set.l_tethers[3] += 0.2
-init!(sam; remake=false, reload=false)
+SymbolicAWEModels.init!(sam; remake=false, reload=false)
 sys = sam.sys
 
 @info "System initialized at:"
 toc()
 
 # Stabilize system
-find_steady_state!(sam)
+SymbolicAWEModels.find_steady_state!(sam)
 
 logger = Logger(length(sam.sys_struct.points), steps)
 sys_state = SysState(sam)
 t = 0.0
 runtime = 0.0
 integ_runtime = 0.0
-bias = set.quasi_static ? 0.45 : 0.40
+bias = set.quasi_static ? 0.45 : 0.35
 t0 = sam.integrator.t
 
 try
@@ -73,14 +64,13 @@ try
         PLOT && plot(sam, t; zoom=false, front=false)
 
         # Calculate steering inputs based on cosine wave
-        steering = steering_magnitude * cos(2π * steering_freq * t + bias)-0.001*t*t
+        steering = steering_magnitude * cos(2π * steering_freq * t + bias)
         set_values = -sam.set.drum_radius .* sam.integrator[sys.winch_force]
         _vsm_interval = 1
         if t > 1.0
             set_values .+= [0.0, steering, -steering]  # Opposite steering for left/right
             _vsm_interval = vsm_interval
         end
-
         # Step simulation
         steptime = @elapsed next_step!(sam; set_values, dt, vsm_interval=vsm_interval)
         t_new = sam.integrator.t
@@ -149,3 +139,5 @@ display(p)
 @info "Performance:" times_realtime=(total_time/2)/runtime integrator_times_realtime=(total_time/2)/integ_runtime
 
 # 55x realtime (PLOT=false, CPU: Intel i9-9980HK (16) @ 5.000GHz)
+# 40-65x realtime (PLOT=false, CPU: Intel i9-9980HK (16) @ 5.000GHz) - commit 6620ed5d0a38e96930615aad9a66e4cd666955f2
+# 40x realtime (PLOT=false, CPU: Intel i9-9980HK (16) @ 5.000GHz) - commit 88a78894038d3cbd50fbff83dfbe5c26266b0637