feat(examples): added FiPy example

examples/fipy/readme.md

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+# Example Diffusion
+Simple GridFire case with FiPy used for spatial diffusion
+
+To run this you must have matplotlib, gridfire, and fipy installed in your python enviroment

examples/fipy/run.py

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+import numpy as np
+import fipy as fp
+import gridfire as gf
+import matplotlib.pyplot as plt
+from gridfire.type import NetIn
+import fourdst
+
+def main():
+    Y_solar = [7.0262E-01, 9.7479E-06, 6.8955E-02, 2.5000E-04, 7.8554E-05, 6.0144E-04, 8.1031E-05, 2.1513E-05]
+    S = ["H-1", "He-3", "He-4", "C-12", "N-14", "O-16", "Ne-20", "Mg-24"]
+    base_comp = fourdst.composition.Composition(S, Y_solar)
+
+    stellar_policy = gf.policy.MainSequencePolicy(base_comp)
+    construct = stellar_policy.construct()
+
+    point_solver = gf.solver.PointSolver(construct.engine)
+    grid_solver = gf.solver.GridSolver(construct.engine, point_solver)
+    solver_ctx = gf.solver.GridSolverContext(construct.scratch_blob)
+
+    solver_ctx.zone_completion_logging = False
+
+    species_list = construct.engine.getNetworkSpecies(construct.scratch_blob)
+
+    plot_species = ["H-1", "He-4", "C-12", "O-16", "N-14", "Mg-24"]
+    nx = 100
+    dx = 1.0
+    mesh = fp.Grid1D(nx=nx, dx=dx)
+    center_idx = nx // 2
+
+    T9 = fp.CellVariable(name="Temperature (T9)", mesh=mesh, value=0.015)
+
+    x = mesh.cellCenters[0]
+    T9.setValue(0.015 + 0.005 * fp.numerix.exp(-((x - (nx*dx/2))**2) / 50.0))
+
+    species_vars = {}
+    for i, sp in enumerate(species_list):
+        sp_name = sp.name()
+        val = base_comp.getMolarAbundance(sp) if base_comp.contains(sp) else 0.0
+        species_vars[sp_name] = fp.CellVariable(name=sp_name, mesh=mesh, value=val)
+    D_T = 1e4
+    D_Y = 1e2
+
+    eq_T = fp.TransientTerm() == fp.DiffusionTerm(coeff=D_T)
+    eqs_Y = {sp_name: fp.TransientTerm() == fp.DiffusionTerm(coeff=D_Y) for sp_name in species_vars}
+    dt = 0.1
+    t_final = 3e14
+    t = 0.0
+    rho_const = 160.0
+    cp_const = 2.0e8
+
+    plt.ion()
+    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 12))
+
+    line_T, = ax1.plot(mesh.cellCenters[0], T9.value, label="T9 (Billion K)", color='red')
+    ax1.set_ylabel("Temperature (T9)")
+    ax1.legend()
+
+    lines_Y = {}
+    colors = plt.cm.tab10(np.linspace(0, 1, len(plot_species)))
+    for sp_name, color in zip(plot_species, colors):
+        if sp_name in species_vars:
+            lines_Y[sp_name], = ax2.plot(mesh.cellCenters[0], species_vars[sp_name].value, label=sp_name, color=color)
+    ax2.set_ylabel("Molar Abundance")
+    ax2.set_xlabel("Zone Index")
+    ax2.set_yscale('log')
+    ax2.set_ylim(1e-12, 1.5)
+    ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+    time_history = []
+    history_vars = {sp: [] for sp in plot_species}
+    lines_time = {}
+
+    for sp_name, color in zip(plot_species, colors):
+        lines_time[sp_name], = ax3.plot([], [], label=f"Total {sp_name}", color=color, linewidth=2)
+
+    ax3.set_xlabel("Time (s)")
+    ax3.set_ylabel("Total Moles (mol/cm$^2$)")
+    ax3.set_xscale('log')
+    ax3.set_yscale('log')
+    ax3.set_xlim(1e-1, t_final)
+    ax3.set_ylim(1e-5, 1e5)
+    ax3.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+    plt.tight_layout()
+
+    while t < t_final:
+        for sp_name, sp_var in species_vars.items():
+            eqs_Y[sp_name].solve(var=sp_var, dt=dt)
+
+        net_ins = []
+        T9_array = T9.value
+
+        for i in range(nx):
+            net_in = NetIn()
+            net_in.temperature = max(1e6, T9_array[i] * 1e9) # Convert T9 back to Kelvin
+            net_in.density = rho_const
+            net_in.tMax = dt
+
+            local_Y = [max(0.0, species_vars[sp.name()].value[i]) for sp in species_list]
+
+            net_in.composition = fourdst.composition.Composition(
+                [sp.name() for sp in species_list], local_Y
+            )
+            net_ins.append(net_in)
+
+        results = grid_solver.evaluate(solver_ctx, net_ins)
+
+        for i in range(nx):
+            for sp in species_list:
+                sp_name = sp.name()
+                if results[i].composition.contains(sp):
+                    species_vars[sp_name].value[i] = results[i].composition.getMolarAbundance(sp)
+        time_history.append(max(t, 1e-5))
+
+        for sp_name in plot_species:
+            if sp_name in species_vars:
+                tot_moles = np.sum(species_vars[sp_name].value * rho_const * dx)
+                history_vars[sp_name].append(tot_moles)
+
+        line_T.set_ydata(T9_array)
+        for sp_name in plot_species:
+            if sp_name in species_vars:
+                lines_Y[sp_name].set_ydata(species_vars[sp_name].value)
+                lines_time[sp_name].set_data(time_history, history_vars[sp_name])
+
+        ax1.set_ylim(min(T9_array)*0.95, max(T9_array)*1.05)
+
+        valid_mins = []
+        valid_maxs = []
+        for sp_name in plot_species:
+            if len(history_vars[sp_name]) > 0:
+                arr = np.array(history_vars[sp_name])
+                pos_arr = arr[arr > 0]
+                if len(pos_arr) > 0:
+                    valid_mins.append(np.min(pos_arr))
+                valid_maxs.append(np.max(arr))
+
+        if valid_mins and valid_maxs:
+            ax3.set_ylim(min(valid_mins) * 0.5, max(valid_maxs) * 2.0)
+
+        if t > 1e-1:
+            ax3.set_xlim(1e-1, max(t * 1.5, 1.0))
+
+        fig.canvas.draw()
+        fig.canvas.flush_events()
+
+        t += dt
+        print(f"Time: {t:.2e}s | Center Temp: {T9.value[center_idx]*1e9:.2e} K | dt: {dt:.2e}s")
+        dt = min(dt * 2, 5e12)
+
+    plt.ioff()
+    plt.show()
+
+if __name__ == "__main__":
+    main()