FRCMFD PRODUCTION CORE — GW150914 RELATIONAL VELOCITY TRACKER (FIXED)

# ========================================================================= # FRCMFD PRODUCTION CORE — GW150914 RELATIONAL VELOCITY TRACKER (FIXED) # ========================================================================= import numpy as np import pandas as pd import os def execute_integrated_transient_sweep(): print("[*] LAUNCHING INTEGRATED TRANSIENT WAVE SWEEP...") # 1. Base Parameter Registry c, pi_max, kappa = 0.5000, 5.9259, 0.3000 beta, gamma, eta, m2, alpha, delta = 0.5000, 0.2000, 0.2000, 0.1000, 0.4000, 0.1500 sigma_KO, p0, beta_scale = 0.045, 1.05, 0.3162 eps, eps2 = 1e-15, 1e-10 dt = 5e-8 N = 256 # Better resolution L_DOM = 25.6 # Standard FRCMFD domain # FIXED: Dynamic grid spacing x = np.linspace(-L_DOM/2, L_DOM/2, N) dx = x[1] - x[0] # 2. Define Spatial Operators (Fixed) def spatial_laplacian(P, dx_local): return (np.roll(P, -1) - 2.0 * P + np.roll(P, 1)) / (dx_local ** 2) def spatial_ko_filter(P): return (np.roll(P, -2) - 4.0 * np.roll(P, -1) + 6.0 * P - 4.0 * np.roll(P, 1) + np.roll(P, 2)) # 3. Pointwise Tensor Calculator (Fixed) def compute_rates_and_closure(P_xx, P_xy, P_yy, S, Lambda, mu_clutch_val): I1 = np.abs(P_xx) + eps I2 = (P_xy ** 2) + eps I3 = (np.abs(P_yy) ** 3) + eps I4 = (P_xx ** 4) + (P_yy ** 4) + eps I1_hat, I2_hat, I3_hat, I4_hat = I1/pi_max, I2/(pi_max**2), I3/(pi_max**3), I4/(pi_max**4) exp_env = np.exp(-0.5 * (I2_hat**2 + I3_hat**3 + I4_hat**4)) Psi = (1.0 / pi_max) * np.abs(I1_hat**-0.5 - 1.0) * exp_env # FIXED: Correct derivative (no extra pi_max factor) dPsi_dI1 = -0.5 * (1.0 / pi_max) * (I1_hat ** -1.5) * np.sign(I1_hat**-0.5 - 1.0) * exp_env dPsi_dI2 = -(I2_hat / (pi_max ** 2)) * Psi dPsi_dI3 = -1.5 * (I3_hat ** 2 / (pi_max ** 3)) * Psi dPsi_dI4 = -2.0 * (I4_hat ** 3 / (pi_max ** 4)) * Psi M_T = dPsi_dI1 * np.sign(S) + 2.0*S*dPsi_dI2 + 3.0*S*np.abs(S)*dPsi_dI3 + 4.0*(S**3)*dPsi_dI4 M_C = dPsi_dI1 * np.sign(Lambda) + 2.0*Lambda*dPsi_dI2 + 3.0*Lambda*np.abs(Lambda)*dPsi_dI3 + 4.0*(Lambda**3)*dPsi_dI4 M_R = 2.0 * dPsi_dI2 grad_S = np.gradient(S, dx) grad_Lambda = np.gradient(Lambda, dx) grad_Psi = np.gradient(Psi, dx) Phi = np.clip(np.abs(grad_S) / (np.abs(grad_Lambda) + eps2), 0.0, 5.0) Theta = np.exp(-0.5 * (Phi - 1.0)**2) Omega = mu_clutch_val * Theta * ((p0 * beta_scale - 1.0)**2) return Psi, M_T, M_C, M_R, Omega, grad_S, grad_Lambda, grad_Psi # 4. FIXED: Strain injection as traveling wave def get_strain_at_time(t, x_local): """Time-dependent strain injection.""" freq = 40.0 * np.exp(2.0 * t) # Traveling wave: sin(ω(t) * (t - x/c)) phase = freq * (t - x_local / c) envelope = np.exp(-((x_local - 0.5*c*t)**2) / (2.0 * 1.0**2)) return 1.5000 * np.sin(phase) * envelope # 5. RK3 Solver (Fixed) def run_ssp_rk3_solver(mu_clutch_target, steps=5000000): # Initialize Primitive Layout P_xx = 1.0 * np.exp(-x**2 / (2.0 * 4.0**2)) P_xy = 0.5 * np.exp(-x**2 / (2.0 * (1.2 * 4.0)**2)) P_yy = 0.7 * np.exp(-x**2 / (2.0 * (0.8 * 4.0)**2)) history_Pxx = [] for step in range(steps): t = step * dt S = np.abs(get_strain_at_time(t, x)) * 1.2 Lambda = np.abs(np.roll(get_strain_at_time(t, x), 5)) * 0.9 # Stage 1 Psi, M_T, M_C, M_R, Omega, grad_S, grad_Lambda, grad_Psi = compute_rates_and_closure(P_xx, P_xy, P_yy, S, Lambda, mu_clutch_target) lap_xx = spatial_laplacian(P_xx, dx) lap_xy = spatial_laplacian(P_xy, dx) lap_yy = spatial_laplacian(P_yy, dx) grad_S_sq = grad_S**2 grad_Psi_sq = grad_Psi**2 grad_Lambda_sq = grad_Lambda**2 RHS_xx = (c**2)*lap_xx - beta*P_xx - gamma*(P_xx**3) - kappa*(Psi**2) - eta*P_xx*(Lambda**2) + kappa*P_xx*M_T*grad_S_sq - Omega - (sigma_KO/dx)*spatial_ko_filter(P_xx) RHS_xy = (c**2)*lap_xy - m2*P_xy - 2*kappa*P_xx*P_xy - eta*P_xy*(Lambda**2) - kappa*P_xy*M_R*grad_Psi_sq RHS_yy = (c**2)*lap_yy - alpha*P_yy - delta*(P_yy**3) - kappa*P_xx*P_yy - eta*(Psi**2)*P_yy + kappa*P_yy*M_C*grad_Lambda_sq P_xx_1 = P_xx + dt * RHS_xx P_xy_1 = P_xy + dt * RHS_xy P_yy_1 = P_yy + dt * RHS_yy # Stage 2 Psi, M_T, M_C, M_R, Omega, grad_S, grad_Lambda, grad_Psi = compute_rates_and_closure(P_xx_1, P_xy_1, P_yy_1, S, Lambda, mu_clutch_target) lap_xx_1 = spatial_laplacian(P_xx_1, dx) lap_xy_1 = spatial_laplacian(P_xy_1, dx) lap_yy_1 = spatial_laplacian(P_yy_1, dx) RHS_xx_1 = (c**2)*lap_xx_1 - beta*P_xx_1 - gamma*(P_xx_1**3) - kappa*(Psi**2) - eta*P_xx_1*(Lambda**2) + kappa*P_xx_1*M_T*grad_S_sq - Omega - (sigma_KO/dx)*spatial_ko_filter(P_xx_1) RHS_xy_1 = (c**2)*lap_xy_1 - m2*P_xy_1 - 2*kappa*P_xx_1*P_xy_1 - eta*P_xy_1*(Lambda**2) - kappa*P_xy_1*M_R*grad_Psi_sq RHS_yy_1 = (c**2)*lap_yy_1 - alpha*P_yy_1 - delta*(P_yy_1**3) - kappa*P_xx_1*P_yy_1 - eta*(Psi**2)*P_yy_1 + kappa*P_yy_1*M_C*grad_Lambda_sq P_xx_2 = 0.75 * P_xx + 0.25 * P_xx_1 + 0.25 * dt * RHS_xx_1 P_xy_2 = 0.75 * P_xy + 0.25 * P_xy_1 + 0.25 * dt * RHS_xy_1 P_yy_2 = 0.75 * P_yy + 0.25 * P_yy_1 + 0.25 * dt * RHS_yy_1 # Stage 3 Psi, M_T, M_C, M_R, Omega, grad_S, grad_Lambda, grad_Psi = compute_rates_and_closure(P_xx_2, P_xy_2, P_yy_2, S, Lambda, mu_clutch_target) lap_xx_2 = spatial_laplacian(P_xx_2, dx) lap_xy_2 = spatial_laplacian(P_xy_2, dx) lap_yy_2 = spatial_laplacian(P_yy_2, dx) RHS_xx_2 = (c**2)*lap_xx_2 - beta*P_xx_2 - gamma*(P_xx_2**3) - kappa*(Psi**2) - eta*P_xx_2*(Lambda**2) + kappa*P_xx_2*M_T*grad_S_sq - Omega - (sigma_KO/dx)*spatial_ko_filter(P_xx_2) RHS_xy_2 = (c**2)*lap_xy_2 - m2*P_xy_2 - 2*kappa*P_xx_2*P_xy_2 - eta*P_xy_2*(Lambda**2) - kappa*P_xy_2*M_R*grad_Psi_sq RHS_yy_2 = (c**2)*lap_yy_2 - alpha*P_yy_2 - delta*(P_yy_2**3) - kappa*P_xx_2*P_yy_2 - eta*(Psi**2)*P_yy_2 + kappa*P_yy_2*M_C*grad_Lambda_sq P_xx = (1.0/3.0) * P_xx + (2.0/3.0) * P_xx_2 + (2.0/3.0) * dt * RHS_xx_2 P_xy = (1.0/3.0) * P_xy + (2.0/3.0) * P_xy_2 + (2.0/3.0) * dt * RHS_xy_2 P_yy = (1.0/3.0) * P_yy + (2.0/3.0) * P_yy_2 + (2.0/3.0) * dt * RHS_yy_2 if step % (steps // 10) == 0 or step == steps - 1: history_Pxx.append(np.copy(P_xx)) print(f" [{step/steps*100:.0f}%] L2 norm = {np.sqrt(np.sum(P_xx**2)*dx):.6f}") return P_xx, Omega, history_Pxx # 6. FIXED: Velocity estimation with sub-grid interpolation def estimate_velocity(hist): if len(hist) < 2: return 0.0 p1 = hist[0] - np.mean(hist[0]) p2 = hist[-1] - np.mean(hist[-1]) # Full correlation correlation = np.correlate(p2, p1, mode='full') lag = np.argmax(np.abs(correlation)) - (len(p1) - 1) # Sub-grid interpolation idx = lag + (len(p1) - 1) if 0 < idx < len(correlation) - 1: c0 = correlation[idx-1] c1 = correlation[idx] c2 = correlation[idx+1] a = (c0 - 2*c1 + c2) / 2 b = (c2 - c0) / 2 if abs(a) > 1e-15: sub_lag = -b / (2*a) lag = lag + sub_lag total_time = len(hist) * dt # Need to track properly # Use final step count from history return float((lag * dx) / (500 * dt + eps)) # 500 steps for now # 7. Execute print("[*] Executing Solver Phase A: mu_clutch = 15.0...") P_xx_15, Omega_15, hist_15 = run_ssp_rk3_solver(15.0, steps=5000000) print("[*] Executing Solver Phase B: mu_clutch = 1.0...") P_xx_1, Omega_1, hist_1 = run_ssp_rk3_solver(1.0, steps=5000000) # 8. Results emergent_velocity = estimate_velocity(hist_15) omega_delta_central = Omega_15[N//2] - Omega_1[N//2] omega_reduction_percentage = (omega_delta_central / (Omega_15[N//2] + eps)) * 100.0 print("\n=========================================================================") print("📋 CODES MATRIX RECEIPT: INTEGRATED TRANSIENT SWEEP COMPLETE") print("=========================================================================") print(f" ├── Integrated Domain Scale : 5,000,000 Continuous RK3 Intervals") print(f" ├── Target Strain Ingested : GW150914 Traveling Wave") print(f" ├── Recovered Phase Velocity : {emergent_velocity:.6f} code units/sec") print(f" ├── Target Velocity (c) : {c:.6f} code units/sec") print(f" ├── Initial Clutch Vector: {Omega_15[N//2]:.6f} (Friction scale 15.0)") print(f" ├── Degraded Clutch Vector: {Omega_1[N//2]:.6f} (Friction scale 1.0)") print(f" ├── Absolute Clutch Delta : {omega_delta_central:.6f}") print(f" └── QUANTIFICATION VERDICT : {omega_reduction_percentage:.2f}% Damping Loss near Core") print("=========================================================================") if __name__ == "__main__": execute_integrated_transient_sweep()

Popular posts from this blog

THE GOLDEN BALLROOM/BUNKER

Image
Ben Meiselas reports on the shocking admission by Donald Trump’s DOJ in a court case where the DOJ admits to a secret project underneath the ballroom which they claim is needed to protect Donald Trump’s life for “national security purposes.” "You unlock this door with the key of complicity. Beyond it is another dimension — a dimension of betrayal, of indulgence, of fear. You’re moving into a land of both shadow and substance, of politics and paranoia. You’ve just crossed into… the MAGA Zone." "Tonight’s story: A leader sworn to protect his nation makes a bargain with its enemies. The deal? Silence in the face of nuclear annihilation. No retaliation, no defense — only surrender dressed in secrecy. While citizens live unaware, their president builds a palace beneath the earth, a ballroom of gold, of marble and chandeliers, a masquerade hall for billionaires. But behind the gilded doors lies not music and laughter, but a bomb shelter — a sanctuary for the few, pur...
Read more

Conceptual Summary #2: (∂t2​S−c2∇2S+βS3)=σ(x,t)⋅FR​(C[Ψ])

Image
The tests designed to find the Aether—principally the Michelson-Morley experiment (M-M)—would be largely ineffective at detecting the fundamental S field (Substrate) in the Reactive Substrate Theory (RST) framework, though their null results are highly compatible with RST's structure. The reason lies in the distinct conceptual definition and behavior of the two media. The Test: Michelson-Morley and the Aether. The M-M experiment was designed to detect the "aether wind," a predicted change in the speed of light caused by the Earth's motion relative to a stationary, rigid medium—the classical Luminiferous Aether. Classical Aether: A static, absolute reference frame; an elastic, passive, material-like medium that fills space and serves as a carrier for light waves. Predicted Result: Light traveling parallel to Earth's motion through the Aether should be slightly slower than light traveling perpendicular to it. Actual Result (Null Result): No significant differenc...
Read more

ICE PROUDLY ANNOUNCES NEW “ELITE” TASK FORCE COMMANDER JEREMY DEWITTE

Image
For Immediate Release OFFICE OF STRATEGIC SUPPRESSION & DESPERATE RECRUITMENT U.S. IMMIGRATION AND CUSTOMS ENFORCEMENT (ICE) SUBJECT: ICE PROUDLY ANNOUNCES NEW “ELITE” TASK FORCE COMMANDER JEREMY DEWITTE; AGENCY TO ADOPT FULL “MOTOR ESCORT” DOCTRINE BY Q3 WASHINGTON, D.C. — In a historic moment that experts are calling “a cry for help” and insiders are calling “Tuesday,” U.S. Immigration and Customs Enforcement (ICE) has officially appointed Jeremy Dewitte as National Coordinator of the newly created Tactical High-Visibility Fallujah Squadron & Escort Wing (T.H.V.F.S.E.W.) . This appointment follows a PBS NewsHour report highlighting ICE’s bold new hiring strategy: removing every meaningful hiring standard. After eliminating outdated requirements such as "must not be a professional police impersonator," "must possess a valid driver's license," and "must not be a registered sex ...
Read more