完善 XML 通用仿真与结果查看
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from __future__ import annotations
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from dataclasses import dataclass
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from math import sqrt
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from PythonModels.core.network import SimulationNetwork
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from PythonModels.core.ports import PortState, VariableRole
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class AlgebraicSolveError(RuntimeError):
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def __init__(self, message: str, diagnostics: "AlgebraicSolveDiagnostics") -> None:
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super().__init__(message)
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self.diagnostics = diagnostics
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@dataclass(frozen=True)
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class AlgebraicUnknown:
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component: str
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port: str
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variable: str
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role: VariableRole
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state: PortState
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@property
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def id(self) -> str:
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return f"{self.component}.{self.port}.{self.variable}"
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def read(self) -> float:
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return float(getattr(self.state, self.variable))
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def write(self, value: float) -> None:
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setattr(self.state, self.variable, float(value))
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@dataclass(frozen=True)
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class AlgebraicSolveDiagnostics:
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success: bool
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message: str
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evaluations: int
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pressure_scale: float
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flow_scale: float
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max_scaled_residual: float
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max_raw_residual: float
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def as_dict(self) -> dict[str, object]:
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return {
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"success": self.success,
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"message": self.message,
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"evaluations": self.evaluations,
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"pressureScale": self.pressure_scale,
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"flowScale": self.flow_scale,
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"maxScaledResidual": self.max_scaled_residual,
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"maxRawResidual": self.max_raw_residual,
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}
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class PressureFlowSolver:
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"""Solve the acausal pressure-flow subsystem for a compiled network."""
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def __init__(
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self,
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network: SimulationNetwork,
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*,
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residual_tolerance: float = 1e-7,
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max_evaluations: int = 500,
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) -> None:
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self.network = network
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self.residual_tolerance = residual_tolerance
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self.max_evaluations = max_evaluations
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self.unknowns = self._build_unknowns()
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self.last_diagnostics: AlgebraicSolveDiagnostics | None = None
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def _build_unknowns(self) -> tuple[AlgebraicUnknown, ...]:
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unknowns: list[AlgebraicUnknown] = []
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for component in self.network.components.values():
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for definition in component.port_definitions:
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if definition.kind != "physical":
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continue
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state = component.get_port(definition.name)
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for variable in definition.variables:
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if variable.role not in {"effort", "flow"}:
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continue
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unknowns.append(
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AlgebraicUnknown(
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component=component.name,
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port=definition.name,
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variable=variable.name,
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role=variable.role,
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state=state,
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)
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)
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return tuple(unknowns)
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def _seed_equal_pressures(self) -> None:
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for _ in range(max(2, len(self.network.connections))):
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changed = False
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for connection in self.network.connections:
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if connection.kind != "physical":
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continue
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first = self.network.components[
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connection.endpoint_a.component
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].get_port(connection.endpoint_a.port)
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second = self.network.components[
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connection.endpoint_b.component
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].get_port(connection.endpoint_b.port)
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if first.p > 0.0 and second.p <= 0.0:
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second.p = first.p
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changed = True
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elif second.p > 0.0 and first.p <= 0.0:
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first.p = second.p
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changed = True
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for component in self.network.components.values():
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equal_pressure_equations = [
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equation
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for equation in component.pressure_flow_equation_residuals()
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if equation.relation == "equal" and equation.role == "effort"
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]
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for equation in equal_pressure_equations:
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states = []
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for variable in equation.variables:
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_, port_name, variable_name = variable.rsplit(".", 2)
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if variable_name == "p":
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states.append(component.get_port(port_name))
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if len(states) != 2:
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continue
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first, second = states
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if first.p > 0.0 and second.p <= 0.0:
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second.p = first.p
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changed = True
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elif second.p > 0.0 and first.p <= 0.0:
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first.p = second.p
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changed = True
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if not changed:
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break
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def _scales(self) -> tuple[float, float]:
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pressure_scale = max(
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[
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abs(unknown.read())
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for unknown in self.unknowns
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if unknown.role == "effort" and unknown.read() > 0.0
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]
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+ [1e5]
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)
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estimated_flows = [
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abs(float(getattr(component, "K_eff"))) * sqrt(pressure_scale)
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for component in self.network.components.values()
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if hasattr(component, "K_eff")
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]
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flow_scale = max(
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estimated_flows
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+ [
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abs(unknown.read())
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for unknown in self.unknowns
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if unknown.role == "flow"
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]
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+ [1e-3]
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)
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return pressure_scale, flow_scale
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def solve(self) -> AlgebraicSolveDiagnostics:
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try:
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import numpy as np
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from scipy.optimize import least_squares
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except ImportError as exc:
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raise RuntimeError(
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"Topology-driven simulation requires SciPy; install requirements.txt."
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) from exc
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self._seed_equal_pressures()
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pressure_scale, flow_scale = self._scales()
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positive_pressures = [
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unknown.read()
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for unknown in self.unknowns
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if unknown.role == "effort" and unknown.read() > 0.0
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]
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fallback_pressure = (
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sum(positive_pressures) / len(positive_pressures)
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if positive_pressures
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else pressure_scale
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)
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def variable_scale(unknown: AlgebraicUnknown) -> float:
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return pressure_scale if unknown.role == "effort" else flow_scale
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x0 = np.asarray(
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[
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(
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unknown.read()
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if unknown.role != "effort" or unknown.read() > 0.0
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else fallback_pressure
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)
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/ variable_scale(unknown)
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for unknown in self.unknowns
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],
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dtype=float,
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)
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lower = np.asarray(
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[
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1.0 / pressure_scale if unknown.role == "effort" else -np.inf
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for unknown in self.unknowns
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]
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)
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upper = np.full(len(self.unknowns), np.inf)
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def assign(values) -> None:
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for unknown, value in zip(self.unknowns, values):
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unknown.write(float(value) * variable_scale(unknown))
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def scaled_residuals(values):
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assign(values)
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equations = self.network.pressure_flow_equation_residuals()
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return np.asarray(
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[
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equation.value
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/ (pressure_scale if equation.role == "effort" else flow_scale)
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for equation in equations
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],
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dtype=float,
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)
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result = least_squares(
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scaled_residuals,
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x0,
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bounds=(lower, upper),
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x_scale="jac",
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ftol=1e-10,
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xtol=1e-10,
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gtol=1e-10,
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max_nfev=self.max_evaluations,
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)
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assign(result.x)
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equations = self.network.pressure_flow_equation_residuals()
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scaled = [
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abs(
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equation.value
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/ (pressure_scale if equation.role == "effort" else flow_scale)
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)
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for equation in equations
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]
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success = bool(result.success) and max(scaled, default=0.0) <= self.residual_tolerance
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diagnostics = AlgebraicSolveDiagnostics(
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success=success,
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message=str(result.message),
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evaluations=int(result.nfev),
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pressure_scale=pressure_scale,
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flow_scale=flow_scale,
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max_scaled_residual=max(scaled, default=0.0),
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max_raw_residual=max((abs(item.value) for item in equations), default=0.0),
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)
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self.last_diagnostics = diagnostics
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if not success:
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raise AlgebraicSolveError(
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"Pressure-flow equations did not converge to the requested tolerance.",
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diagnostics,
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)
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return diagnostics
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