File size: 3,063 Bytes
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import json
import numpy as np
from modeling import preprocess
N = 96
def _coords():
yy, xx = np.meshgrid(np.linspace(0, 1, N), np.linspace(0, 1, N), indexing="ij")
return xx, yy
def _to_field(v):
a = np.asarray(v, dtype=np.float64)
if a.ndim == 0:
return np.full((N, N), float(a))
if a.shape == (N, N):
return a
yi = np.linspace(0, a.shape[0] - 1, N).round().astype(int)
xi = np.linspace(0, a.shape[1] - 1, N).round().astype(int)
return a[yi][:, xi]
def cylinder_mask(rows, cols, radius_frac=0.4):
xx, yy = _coords()
m = np.zeros((N, N))
px, py = 1.0 / cols, 1.0 / rows
r = radius_frac * min(px, py)
for i in range(rows):
cy = (i + 0.5) * py
for j in range(cols):
cx = (j + 0.5) * px
m[(xx - cx) ** 2 + (yy - cy) ** 2 <= r * r] = 1.0
return m
def rect_mask(aspect=1.0, fill=0.7):
xx, yy = _coords()
m = np.zeros((N, N))
hh = min(0.98, (fill / aspect) ** 0.5)
ww = min(0.98, aspect * hh)
x0, x1 = 0.5 - ww / 2, 0.5 + ww / 2
y0, y1 = 0.5 - hh / 2, 0.5 + hh / 2
m[(xx >= x0) & (xx <= x1) & (yy >= y0) & (yy <= y1)] = 1.0
return m
def from_fields(
mask, q_source_W_m3, k_field_W_mK, h_conv_W_m2K, T_amb_degC, domain_L_m
):
m = (_to_field(mask) > 0.5).astype(np.float64)
q = _to_field(q_source_W_m3) * m
k = _to_field(k_field_W_mK)
h = _to_field(h_conv_W_m2K)
ta = _to_field(T_amb_degC)
xx, yy = _coords()
logL = np.full((N, N), float(np.log(domain_L_m)))
field = np.stack([m, q, k, h, ta, xx, yy, logL], 0).astype(np.float32)
return preprocess(field)
def from_pack(
mask,
current_A,
soc,
R0_ohm,
k_cell_W_mK,
k_coolant_W_mK,
h_conv_W_m2K,
T_amb_degC,
domain_L_m,
beta=2.0,
):
m = (_to_field(mask) > 0.5).astype(np.float64)
hg = domain_L_m / (N - 1)
R_int = R0_ohm * (1.0 + beta * (1.0 - soc) ** 2)
P_total = current_A**2 * R_int
area = max(m.sum() * hg * hg, hg * hg)
q = m * (P_total / area)
k = np.where(m > 0, k_cell_W_mK, k_coolant_W_mK)
return from_fields(m, q, k, h_conv_W_m2K, T_amb_degC, domain_L_m)
def from_params(d):
if d.get("rows") not in (None, "") and d.get("cols") not in (None, ""):
mask = cylinder_mask(
int(float(d["rows"])),
int(float(d["cols"])),
float(d.get("radius_frac") or 0.4),
)
else:
mask = rect_mask(float(d.get("aspect") or 1.0), float(d.get("fill") or 0.7))
return from_pack(
mask,
float(d["current_A"]),
float(d["soc"]),
float(d["R0_ohm"]),
float(d["k_cell_W_mK"]),
float(d["k_coolant_W_mK"]),
float(d["h_conv_W_m2K"]),
float(d["T_amb_degC"]),
float(d["domain_L_m"]),
beta=float(d.get("beta") or 2.0),
)
def from_json(path):
with open(path) as f:
return from_params(json.load(f))
def from_csv(path):
with open(path) as f:
return from_params(next(csv.DictReader(f)))
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