Spaces:
Sleeping
Sleeping
File size: 8,975 Bytes
8a169a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | """Parameter sensitivity analysis via central finite differences."""
from dataclasses import dataclass, field
from typing import List, Dict, Optional
import numpy as np
from cryosim import predict
from cryosim.calibration.params import (
CALIBRATION_PARAMS, PARAM_NAMES, get_nominal_values, apply_overrides,
)
from cryosim.hardware.config import load_config
from cryosim.engine.fast import ICV_open
METRICS = ["mdot_kgpm", "mass_eff", "Tc_peak_K", "kWh_extend"]
_NEAR_ZERO_THRESHOLD = 0.01
@dataclass
class SensitivityResult:
"""Result of sensitivity analysis.
sensitivities: dict of {metric: {param: normalized_sensitivity}}
where normalized_sensitivity = (Δmetric/metric) / (Δparam/param)
i.e., % change in metric per % change in parameter.
rankings: dict of {metric: [(param, sensitivity), ...]} sorted by |sensitivity|
zero_sensitivity_notes: warnings about params with near-zero sensitivity
"""
param_names: List[str]
metrics: List[str]
sensitivities: Dict[str, Dict[str, float]]
rankings: Dict[str, List[tuple]]
hardware: str
Pexit: float
speed: float
zero_sensitivity_notes: List[str] = field(default_factory=list)
@dataclass
class MultiSensitivityResult:
"""Sensitivity analysis across multiple operating points."""
results: Dict[float, SensitivityResult] # Pexit -> result
combined_rankings: Dict[str, List[tuple]] # metric -> [(param, max_abs_sensitivity)]
zero_sensitivity_notes: List[str] # warnings about params with near-zero sensitivity
def run_sensitivity(
hardware: str = "old_icv",
speed: float = 0.65,
Pexit: float = 350.0,
delta_frac: float = 0.05,
Ptank: float = 7.0,
Psat: float = 2.0,
) -> SensitivityResult:
"""Run sensitivity analysis on all 7 calibratable parameters.
For each parameter, perturbs by ±delta_frac (default ±5%) and measures
the change in each metric. Returns normalized sensitivities:
(% change in metric) / (% change in param).
Args:
hardware: Base config name.
speed: Speed fraction.
Pexit: Discharge pressure [barg].
delta_frac: Fractional perturbation (0.05 = ±5%).
Ptank: Inlet tank pressure [barg].
Psat: Saturation pressure [barg].
Returns:
SensitivityResult with sensitivities and rankings.
"""
cfg = load_config(hardware)
nominal = get_nominal_values(hardware)
# Baseline prediction
baseline = predict(hardware=hardware, Pexit=Pexit, speed=speed, Ptank=Ptank, Psat=Psat)
baseline_metrics = {
"mdot_kgpm": baseline.mdot_kgpm,
"mass_eff": baseline.mass_eff,
"Tc_peak_K": baseline.Tc_peak_K,
"kWh_extend": baseline.kWh_extend,
}
sensitivities = {m: {} for m in METRICS}
for i, pname in enumerate(PARAM_NAMES):
info = CALIBRATION_PARAMS[pname]
val = nominal[i]
# Perturb ±delta_frac, clamped to bounds
delta = max(abs(val) * delta_frac, 1e-8)
val_lo = max(val - delta, info["low"])
val_hi = min(val + delta, info["high"])
actual_delta = val_hi - val_lo
if actual_delta < 1e-12:
for m in METRICS:
sensitivities[m][pname] = 0.0
continue
# Build overridden configs for low and high perturbations
params_lo = list(nominal)
params_lo[i] = val_lo
params_hi = list(nominal)
params_hi[i] = val_hi
cfg_lo = apply_overrides(cfg, params_lo)
cfg_hi = apply_overrides(cfg, params_hi)
try:
args_lo = cfg_lo.to_engine_args()
args_hi = cfg_hi.to_engine_args()
out_lo, hist_lo = ICV_open(
Pexit_barg=Pexit, speed_f=speed,
Ptank_barg=Ptank, Psat_barg=Psat, **args_lo,
)
out_hi, hist_hi = ICV_open(
Pexit_barg=Pexit, speed_f=speed,
Ptank_barg=Ptank, Psat_barg=Psat, **args_hi,
)
metrics_lo = {
"mdot_kgpm": hist_lo["mdot_kgpm"],
"mass_eff": float(out_lo[1, 0]),
"Tc_peak_K": float(np.max(hist_lo["Tc_K"])),
"kWh_extend": hist_lo["kWh_extend"],
}
metrics_hi = {
"mdot_kgpm": hist_hi["mdot_kgpm"],
"mass_eff": float(out_hi[1, 0]),
"Tc_peak_K": float(np.max(hist_hi["Tc_K"])),
"kWh_extend": hist_hi["kWh_extend"],
}
except Exception:
for m in METRICS:
sensitivities[m][pname] = 0.0
continue
# Normalized sensitivity: (Δmetric/metric_baseline) / (Δparam/param_nominal)
for m in METRICS:
dm = metrics_hi[m] - metrics_lo[m]
base_val = baseline_metrics[m]
if abs(base_val) > 1e-12 and abs(val) > 1e-12:
sensitivities[m][pname] = (dm / base_val) / (actual_delta / val)
else:
sensitivities[m][pname] = 0.0
# Build rankings (sorted by absolute sensitivity)
rankings = {}
for m in METRICS:
ranked = sorted(sensitivities[m].items(), key=lambda x: abs(x[1]), reverse=True)
rankings[m] = ranked
return SensitivityResult(
param_names=list(PARAM_NAMES),
metrics=list(METRICS),
sensitivities=sensitivities,
rankings=rankings,
hardware=hardware,
Pexit=Pexit,
speed=speed,
)
def run_multi_sensitivity(
hardware: str = "old_icv",
speed: float = 0.65,
pressures: Optional[List[float]] = None,
delta_frac: float = 0.05,
Ptank: float = 7.0,
Psat: float = 2.0,
) -> MultiSensitivityResult:
"""Run sensitivity analysis at multiple operating points.
Reveals pressure-dependent behaviour that single-point analysis misses.
For example, dcv_leakKv may show zero sensitivity at 350 bar (below the
flow cliff) but significant sensitivity at 500 bar.
Args:
hardware: Base config name.
speed: Speed fraction.
pressures: List of Pexit values to test. Default [200, 380, 500].
delta_frac: Fractional perturbation (0.05 = ±5%).
Ptank: Inlet tank pressure [barg].
Psat: Saturation pressure [barg].
Returns:
MultiSensitivityResult with per-pressure results, combined rankings,
and diagnostic notes about near-zero sensitivities.
"""
if pressures is None:
pressures = [200.0, 380.0, 500.0]
results: Dict[float, SensitivityResult] = {}
for P in pressures:
results[P] = run_sensitivity(
hardware=hardware, speed=speed, Pexit=P,
delta_frac=delta_frac, Ptank=Ptank, Psat=Psat,
)
# Combined rankings: for each param, take max |sensitivity| across pressures
combined_rankings: Dict[str, List[tuple]] = {}
for metric in METRICS:
param_max: Dict[str, float] = {}
for pname in PARAM_NAMES:
max_abs = 0.0
for P in pressures:
s = abs(results[P].sensitivities[metric].get(pname, 0.0))
if s > max_abs:
max_abs = s
param_max[pname] = max_abs
ranked = sorted(param_max.items(), key=lambda x: x[1], reverse=True)
combined_rankings[metric] = ranked
# Zero-sensitivity notes
notes: List[str] = []
for pname in PARAM_NAMES:
# Collect max |sensitivity| across all metrics and all pressures
per_pressure_max: Dict[float, float] = {}
for P in pressures:
max_across_metrics = max(
abs(results[P].sensitivities[m].get(pname, 0.0))
for m in METRICS
)
per_pressure_max[P] = max_across_metrics
all_near_zero = all(v < _NEAR_ZERO_THRESHOLD for v in per_pressure_max.values())
if all_near_zero:
notes.append(
f"{pname} shows near-zero sensitivity across all tested pressures "
f"— may not be identifiable from the data"
)
else:
# Check for pressure-dependent behavior: zero at some, significant at others
zero_pressures = [P for P, v in per_pressure_max.items() if v < _NEAR_ZERO_THRESHOLD]
sig_pressures = [P for P, v in per_pressure_max.items() if v >= _NEAR_ZERO_THRESHOLD]
if zero_pressures and sig_pressures:
zero_str = ", ".join(f"{p:.0f}" for p in zero_pressures)
sig_str = ", ".join(f"{p:.0f}" for p in sig_pressures)
notes.append(
f"{pname} has near-zero sensitivity at {zero_str} bar "
f"but significant sensitivity at {sig_str} bar "
f"— pressure-dependent"
)
return MultiSensitivityResult(
results=results,
combined_rankings=combined_rankings,
zero_sensitivity_notes=notes,
)
|