generalizzazione sorgente FDM a posizione arbitraria x_src

Sostituisce la BC Neumann ghost-cell a x=0 con BC Robin su entrambi
i bordi. Q(t) viene iniettato come termine sorgente puntuale al nodo
più vicino a X_SRC, dopo le BCs per non essere sovrascritto.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-05-13 22:02:22 +02:00
parent 378a3b1676
commit 15510a06d1
+36 -52
View File
@@ -1,12 +1,12 @@
"""
FTCS (Forward-Time Centered-Space) explicit finite difference solver
for the 1D heat equation:
for the 1D heat equation with a movable point heat source:
dT/dt = alpha * d²T/dx²
dT/dt = alpha * d²T/dx² + Q(t) * alpha / (k * dx) * delta(x - x_src)
Boundary conditions:
- x=0 (Neumann): heat flux step Q(t) applied via ghost cell
- x=L (Robin): convective boundary condition
Boundary conditions (both ends Robin / convective):
- x=0 (Robin): convective exchange with ambient
- x=L (Robin): convective exchange with ambient
Returns T_matrix of shape (NX, NT).
"""
@@ -15,7 +15,6 @@ import sys
import os
import numpy as np
# Allow importing config from the project root
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import config
@@ -26,76 +25,61 @@ def solve():
Returns
-------
T_matrix : np.ndarray, shape (NX, NT)
Temperature at each spatial node (row) and time step (column).
T_matrix[i, n] = T(x_i, t_n).
x_vals : np.ndarray, shape (NX,)
Spatial node positions from 0 to L.
t_vals : np.ndarray, shape (NT,)
Time values from 0 to T_END.
x_vals : np.ndarray, shape (NX,)
t_vals : np.ndarray, shape (NT,)
"""
# -----------------------------------------------------------------
# Parameters from config
# -----------------------------------------------------------------
alpha = config.ALPHA
k = config.K
L = config.L
T0 = config.T0
Q_val = config.Q_VAL
alpha = config.ALPHA
k = config.K
L = config.L
T0 = config.T0
Q_val = config.Q_VAL
t_step = config.T_STEP
h = config.H_CONV
T_amb = config.T_AMB
T_end = config.T_END
NX = config.NX
NT = config.NT
h = config.H_CONV
T_amb = config.T_AMB
T_end = config.T_END
NX = config.NX
NT = config.NT
x_src = config.X_SRC
# -----------------------------------------------------------------
# Grid
# -----------------------------------------------------------------
x_vals = np.linspace(0.0, L, NX)
t_vals = np.linspace(0.0, T_end, NT)
dx = x_vals[1] - x_vals[0]
dt = t_vals[1] - t_vals[0]
# -----------------------------------------------------------------
# Stability check (CFL for explicit heat equation: r <= 0.5)
# -----------------------------------------------------------------
r = alpha * dt / dx**2
if dt > dx**2 / (2.0 * alpha):
if r > 0.5:
print(
f"[FDM WARNING] Stability condition violated: "
f"dt={dt:.6g} > dx²/(2*alpha)={dx**2/(2.0*alpha):.6g} (r={r:.4f} > 0.5). "
f"r={r:.4f} > 0.5 (dt={dt:.6g}, dx={dx:.6g}). "
"Solution may diverge."
)
# -----------------------------------------------------------------
# Allocate output matrix and set initial condition
# -----------------------------------------------------------------
T_matrix = np.zeros((NX, NT), dtype=np.float64)
T_matrix[:, 0] = T0 # uniform IC
i_src = int(np.argmin(np.abs(x_vals - x_src)))
robin_coeff = dx * h / k
T_matrix = np.zeros((NX, NT), dtype=np.float64)
T_matrix[:, 0] = T0
# Working array for the current time level
T_cur = np.full(NX, T0, dtype=np.float64)
# -----------------------------------------------------------------
# Time integration
# -----------------------------------------------------------------
for n in range(NT - 1):
t_now = t_vals[n]
# --- Neumann BC at x=0: ghost cell ---
# Q(t) = Q_val if t >= t_step else 0
Q = Q_val if t_now >= t_step else 0.0
T_ghost = T_cur[1] + 2.0 * dx * Q / k # ghost node T[-1]
# --- Interior FTCS update (indices 1 .. NX-2) ---
T_new = T_cur.copy()
# Interior FTCS (nodi 1 .. NX-2)
T_new[1:-1] = T_cur[1:-1] + r * (T_cur[2:] - 2.0 * T_cur[1:-1] + T_cur[:-2])
# --- Apply Neumann BC at i=0 using ghost cell ---
T_new[0] = T_cur[0] + r * (T_cur[1] - 2.0 * T_cur[0] + T_ghost)
# Robin BC a x=0
T_new[0] = (T_cur[1] + robin_coeff * T_amb) / (1.0 + robin_coeff)
# --- Apply Robin BC at x=L (explicit) ---
T_new[-1] = (T_cur[-2] + dx * h / k * T_amb) / (1.0 + dx * h / k)
# Robin BC a x=L
T_new[-1] = (T_cur[-2] + robin_coeff * T_amb) / (1.0 + robin_coeff)
# Sorgente puntuale al nodo i_src — applicata dopo le BCs per non essere sovrascritta
Q_now = Q_val if t_now >= t_step else 0.0
T_new[i_src] += Q_now * alpha * dt / (k * dx)
T_cur = T_new
T_matrix[:, n + 1] = T_cur