Youtor
🌐 Grid Dynamics Torus Reactor Simulation with Perlin Noise and ReactionDiffusion
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=j62FZexe0FE
This video showcases an older simulation of a torus reactor using the torus library in Python. The code employs advanced mathematical techniques, including Perlin noise and reaction-diffusion processes, to generate a complex system. (when I was still working in Euclidean grids) • The variables used in this simulation are: • shape: the size of the grid (200x200) • interval: the time step for the simulation (0.1 milliseconds) • Da, Db, Dc: diffusion rates • feed, k: feed and kill rates • The reaction-diffusion process is defined by the following equations: • A_t = Da * A_xx + F_A - D_A * ∇^2 A • B_t = Db * B_xx + F_B - D_B * ∇^2 B • C_t = Dc * C_xx + F_C - D_C * ∇^2 C • where A, B, and C are the concentrations of the three species, F_A, F_B, and F_C are the source terms, D_A, D_B, and D_C are the diffusion coefficients, and ∇^2 is the Laplace operator. • The Perlin noise function used to initialize the grid is defined as: • generate_perlin_noise_2d(shape, res) • This function generates a 2D Perlin noise field with shape (shape[0], shape[1]) and resolution (res[0], res[1]). • The simulation is updated using the following rules: • • torus_A = torus_A + Da * diffused_A - reaction_AB + feed * (1 - torus_A) • torus_B = torus_B + Db * diffused_B - reaction_BC - k * torus_B • torus_C = torus_C + Dc * diffused_C - reaction_CA + k * (1 - torus_C) • where reaction_AB, reaction_BC, and reaction_CA are the reaction terms. • • Tags: • • #gridynamics • #torusreactor • #perlinnoise • #reactiondiffusion • #chaostheory • #complexsystems • #mathematicalmodeling • #scientificsimulation • #python programming
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