831 lines
32 KiB
Python
831 lines
32 KiB
Python
from __future__ import annotations
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from collections.abc import Callable
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from dataclasses import dataclass, replace
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from math import floor, isfinite
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from typing import Literal
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from app.simulation.core.base import DynamicComponent
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from app.simulation.core.metadata import ResultVariableMetadata
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from app.simulation.solvers.algebraic import PressureFlowSolver
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from app.simulation.solvers.mechanical import (
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MechanicalConstraintGroup,
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MechanicalStateReducer,
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)
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from app.simulation.solvers.pneumatic_storage import (
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IdealPneumaticStorageReducer,
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ideal_storage_group_is_reducible,
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)
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from app.simulation.solvers.pneumatic_volume import PneumaticVolumeResolver
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from app.simulation.solvers.solver import ODESolution, SolveIVPConfig, integrate_ode
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from app.simulation.solvers.signal import SignalResolver
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from app.simulation.solvers.stream import StreamResolver
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from app.simulation.systems.network import Endpoint, SimulationNetwork
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SimulationProgressCallback = Callable[[float, str], None]
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SimulationCancellationCheck = Callable[[], bool]
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SimulationRunStatus = Literal["completed", "cancelled", "failed"]
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@dataclass(frozen=True)
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class SimulationPreparationIssue:
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code: str
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message: str
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def as_dict(self) -> dict[str, str]:
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return {"code": self.code, "message": self.message}
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class SimulationPreparationError(ValueError):
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def __init__(self, issues: tuple[SimulationPreparationIssue, ...]) -> None:
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super().__init__("The compiled model is not ready for simulation.")
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self.issues = issues
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class ThermofluidClosureError(RuntimeError):
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"""Raised when stream enthalpy and pressure-flow do not reach one fixed point."""
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class SimulationSampleTimeError(ValueError):
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"""Stable failure contract for an unsafe or unrepresentable sample grid."""
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def __init__(self, code: str, message: str) -> None:
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super().__init__(message)
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self.code = code
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@dataclass(frozen=True)
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class GenericSimulationResult:
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success: bool
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status: SimulationRunStatus
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message: str
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simulated_until: float
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requested_stop_time: float
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variables: tuple[ResultVariableMetadata, ...]
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series: dict[str, list[float]]
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final: dict[str, float]
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diagnostics: dict[str, object]
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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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"status": self.status,
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"partial": self.status != "completed",
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"message": self.message,
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"simulatedUntil": self.simulated_until,
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"requestedStopTime": self.requested_stop_time,
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"variables": [variable.as_dict() for variable in self.variables],
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"series": self.series,
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"final": self.final,
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"diagnostics": self.diagnostics,
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}
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class _UnionFind:
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def __init__(self, items: set[Endpoint]) -> None:
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self.parent = {item: item for item in items}
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def find(self, item: Endpoint) -> Endpoint:
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parent = self.parent[item]
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if parent != item:
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self.parent[item] = self.find(parent)
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return self.parent[item]
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def union(self, first: Endpoint, second: Endpoint) -> None:
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first_root = self.find(first)
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second_root = self.find(second)
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if first_root != second_root:
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self.parent[second_root] = first_root
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def _equation_port(component_name: str, variable: str) -> Endpoint | None:
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parts = variable.rsplit(".", 2)
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if len(parts) != 3:
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return None
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prefix, port_name, variable_name = parts
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if prefix != component_name or variable_name != "p":
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return None
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return Endpoint(component_name, port_name)
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def simulation_preparation_issues(
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network: SimulationNetwork,
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) -> tuple[SimulationPreparationIssue, ...]:
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issues: list[SimulationPreparationIssue] = []
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physical_endpoints = {
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Endpoint(component.name, port_name)
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for component in network.components.values()
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for port_name in component.required_connection_ports
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}
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connected_endpoints = {
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endpoint
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for connection in network.connections
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if connection.kind == "physical"
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for endpoint in connection.endpoints
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}
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for endpoint in sorted(physical_endpoints - connected_endpoints, key=str):
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issues.append(
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SimulationPreparationIssue(
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"PORT_UNCONNECTED",
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f"Physical port {endpoint} must be connected before simulation.",
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)
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)
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structure = network.pressure_flow_structure_dict()
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if not structure["isSquare"]:
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issues.append(
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SimulationPreparationIssue(
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"PRESSURE_FLOW_SYSTEM_NOT_SQUARE",
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"Pressure-flow equation count does not match the unknown count: "
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f"{structure['equationCount']} equations for {structure['unknownCount']} unknowns.",
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)
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)
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dynamic_names = {
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component.name
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for component in network.components.values()
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if isinstance(component, DynamicComponent)
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}
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if not dynamic_names:
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issues.append(
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SimulationPreparationIssue(
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"DYNAMIC_STATE_MISSING",
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"Each simulated network requires at least one storage component.",
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)
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)
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physical_component_names = {
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component.name
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for component in network.components.values()
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if any(
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definition.kind == "physical"
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for definition in component.active_port_definitions
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)
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}
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adjacency = {name: set() for name in physical_component_names}
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for connection in network.connections:
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if connection.kind != "physical":
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continue
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first, second = connection.endpoints
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adjacency[first.component].add(second.component)
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adjacency[second.component].add(first.component)
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remaining = set(adjacency)
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while remaining:
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start = remaining.pop()
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group = {start}
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stack = [start]
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while stack:
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current = stack.pop()
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for neighbour in adjacency[current] - group:
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group.add(neighbour)
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remaining.discard(neighbour)
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stack.append(neighbour)
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if not (group & dynamic_names):
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issues.append(
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SimulationPreparationIssue(
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"ALGEBRAIC_ISLAND_HAS_NO_STORAGE",
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"A connected physical network has no pressure/enthalpy storage anchor: "
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+ ", ".join(sorted(group))
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+ ".",
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)
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)
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if physical_endpoints:
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effort_groups = _UnionFind(physical_endpoints)
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for connection in network.connections:
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if connection.kind == "physical":
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effort_groups.union(*connection.endpoints)
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storage_ports: dict[Endpoint, str] = {}
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for component in network.components.values():
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for equation in component.pressure_flow_equation_residuals():
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pressure_ports = [
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endpoint
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for variable in equation.variables
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if (endpoint := _equation_port(component.name, variable)) is not None
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]
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if equation.relation == "equal" and len(pressure_ports) == 2:
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effort_groups.union(pressure_ports[0], pressure_ports[1])
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if equation.relation == "state":
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for endpoint in pressure_ports:
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storage_ports[endpoint] = component.name
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storages_by_group: dict[Endpoint, dict[Endpoint, str]] = {}
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for endpoint, component_name in storage_ports.items():
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storages_by_group.setdefault(effort_groups.find(endpoint), {})[
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endpoint
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] = component_name
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for storage_endpoints in storages_by_group.values():
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storage_names = set(storage_endpoints.values())
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if len(storage_names) > 1:
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if ideal_storage_group_is_reducible(
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network,
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storage_endpoints,
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):
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continue
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issues.append(
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SimulationPreparationIssue(
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"IDEAL_STORAGE_COUPLING_UNSUPPORTED",
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"Storage components are connected without a resistance: "
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+ ", ".join(sorted(storage_names))
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+ ". Insert an orifice or pipe between them.",
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)
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)
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return tuple(issues)
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def simulation_sample_times(
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config: SolveIVPConfig,
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step: float,
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*,
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max_points: int = 10001,
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) -> list[float]:
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if max_points < 2:
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raise SimulationSampleTimeError(
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"SIMULATION_SAMPLE_LIMIT_INVALID",
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"Simulation sample limit must allow at least two points.",
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)
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t_start = float(config.t_start)
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t_stop = float(config.t_stop)
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if not isfinite(t_start) or not isfinite(t_stop):
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raise SimulationSampleTimeError(
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"SIMULATION_VALUE_NOT_FINITE",
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"Simulation start and stop times must be finite.",
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)
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if step <= 0.0 or not isfinite(step):
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raise SimulationSampleTimeError(
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"SIMULATION_SAMPLE_STEP_INVALID",
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"Simulation sample step must be finite and greater than zero.",
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)
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duration = t_stop - t_start
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if not isfinite(duration):
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raise SimulationSampleTimeError(
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"SIMULATION_TIME_SPAN_NOT_FINITE",
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"Simulation time span must be finite.",
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)
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if duration <= 0.0:
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raise SimulationSampleTimeError(
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"SIMULATION_TIME_RANGE_INVALID",
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"Simulation stop time must be greater than start time.",
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)
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# Bound the grid before dividing by a potentially tiny step or allocating
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# the result list. This avoids both float-to-int overflow and an OOM-sized
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# ``range``/list when input comes from an external System XML document.
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maximum_interval_count = max_points - 1
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if step < duration / maximum_interval_count:
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raise SimulationSampleTimeError(
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"SIMULATION_SAMPLE_COUNT_EXCEEDED",
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f"Simulation sample count exceeds the limit of {max_points}; "
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"increase sampleStep.",
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)
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ratio = duration / step
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if not isfinite(ratio):
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raise SimulationSampleTimeError(
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"SIMULATION_SAMPLE_COUNT_EXCEEDED",
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f"Simulation sample count exceeds the limit of {max_points}; "
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"increase sampleStep.",
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)
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interval_count = int(floor(ratio))
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last_regular_time = t_start + interval_count * step
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append_stop = last_regular_time < t_stop
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requested_point_count = interval_count + 1 + int(append_stop)
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if requested_point_count > max_points:
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raise SimulationSampleTimeError(
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"SIMULATION_SAMPLE_COUNT_EXCEEDED",
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f"Simulation requests {requested_point_count} samples; "
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f"the limit is {max_points}.",
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)
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times = [t_start]
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for index in range(1, interval_count + 1):
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candidate = t_start + index * step
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if not isfinite(candidate):
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raise SimulationSampleTimeError(
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"SIMULATION_SAMPLE_TIME_UNREPRESENTABLE",
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"Simulation sampleStep cannot be represented over the requested "
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"absolute time range.",
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)
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if candidate >= t_stop:
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candidate = t_stop
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if candidate <= times[-1]:
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raise SimulationSampleTimeError(
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"SIMULATION_SAMPLE_TIME_UNREPRESENTABLE",
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"Simulation sampleStep is too small to advance floating-point "
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"time over the requested absolute time range.",
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)
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times.append(candidate)
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if candidate == t_stop:
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break
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if times[-1] < t_stop:
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times.append(t_stop)
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if len(times) < 2 or any(
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current >= following
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for current, following in zip(times, times[1:])
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):
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raise SimulationSampleTimeError(
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"SIMULATION_SAMPLE_TIME_UNREPRESENTABLE",
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"Simulation sample times must contain at least two strictly "
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"increasing values.",
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)
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return times
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class GenericFluidSystem:
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"""Topology-driven, semi-explicit fluid simulation for registered components."""
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def __init__(self, network: SimulationNetwork) -> None:
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issues = simulation_preparation_issues(network)
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if issues:
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raise SimulationPreparationError(issues)
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self.network = network
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self.dynamic_components = network.dynamic_components()
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self.mechanical_state_reducer = MechanicalStateReducer(
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network,
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self.dynamic_components,
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)
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self.pneumatic_storage_reducer = IdealPneumaticStorageReducer(
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network,
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self.mechanical_state_reducer,
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)
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self.pressure_flow_solver = PressureFlowSolver(network)
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self.pneumatic_volume_resolver = PneumaticVolumeResolver(network)
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self.signal_resolver = SignalResolver(network)
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self.stream_resolver = StreamResolver(network)
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self.algebraic_solve_count = 0
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self.max_algebraic_residual = 0.0
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self.max_algebraic_evaluations = 0
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self.max_stream_iterations = 0
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self.max_thermofluid_iterations = 0
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self.signal_propagation_count = 0
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self.pneumatic_volume_propagation_count = 0
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self._jacobian_sparsity = None
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def initial_state_vector(self) -> list[float]:
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return self.pneumatic_storage_reducer.synchronize_state_vector(
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self.mechanical_state_reducer.initial_state_vector(),
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validate=True,
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)
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def apply_state_vector(self, values: list[float]) -> None:
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self.mechanical_state_reducer.apply_state_vector(
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self.pneumatic_storage_reducer.synchronize_state_vector(values)
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)
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def _build_jacobian_sparsity(self):
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"""Build a conservative state dependency graph for implicit solvers.
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Two state entries are coupled when a physical path connects them without
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crossing a third storage state. This over-approximates the local
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pressure-flow/mechanical closure while preserving branch sparsity.
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"""
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from scipy.sparse import lil_matrix
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entries = self.mechanical_state_reducer.state_entries
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entry_components: list[set[str]] = []
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entry_sizes: list[int] = []
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for entry in entries:
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if isinstance(entry, MechanicalConstraintGroup):
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entry_components.append(
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{component.name for component in entry.components}
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)
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entry_sizes.append(2)
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else:
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entry_components.append({entry.name})
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entry_sizes.append(entry.state_size)
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owner_by_component = {
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component_name: entry_index
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for entry_index, component_names in enumerate(entry_components)
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for component_name in component_names
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}
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adjacency = {name: set() for name in self.network.components}
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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, second = connection.endpoints
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adjacency[first.component].add(second.component)
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adjacency[second.component].add(first.component)
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dependencies: list[set[int]] = []
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for entry_index, component_names in enumerate(entry_components):
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visited = set(component_names)
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pending = list(component_names)
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found = {entry_index}
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while pending:
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current = pending.pop()
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for neighbour in adjacency[current] - visited:
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visited.add(neighbour)
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neighbour_entry = owner_by_component.get(neighbour)
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if (
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neighbour_entry is not None
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and neighbour_entry != entry_index
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):
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found.add(neighbour_entry)
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else:
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pending.append(neighbour)
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dependencies.append(found)
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offsets = [0]
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for state_size in entry_sizes:
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offsets.append(offsets[-1] + state_size)
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sparsity = lil_matrix(
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(offsets[-1], offsets[-1]),
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dtype=bool,
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)
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for row_entry, column_entries in enumerate(dependencies):
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for column_entry in column_entries:
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sparsity[
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offsets[row_entry] : offsets[row_entry + 1],
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offsets[column_entry] : offsets[column_entry + 1],
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] = True
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return sparsity.tocsr()
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|
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def jacobian_sparsity(self):
|
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if self._jacobian_sparsity is None:
|
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self._jacobian_sparsity = self._build_jacobian_sparsity()
|
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return self._jacobian_sparsity
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|
|
def jacobian_sparsity_diagnostics(self) -> dict[str, float | int]:
|
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from scipy.optimize._numdiff import group_columns
|
|
|
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sparsity = self.jacobian_sparsity()
|
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group_count = int(group_columns(sparsity).max(initial=-1)) + 1
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state_count = int(sparsity.shape[0])
|
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return {
|
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"nonzeroCount": int(sparsity.nnz),
|
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"density": (
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float(sparsity.nnz) / float(state_count * state_count)
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if state_count
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else 0.0
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),
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"colorGroupCount": group_count,
|
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}
|
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|
|
def _close_current_state(self, time: float) -> dict[str, dict[str, float]]:
|
|
signal = self.signal_resolver.solve(time)
|
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self.signal_propagation_count += signal.propagated
|
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self.pressure_flow_solver.propagate_equal_efforts(("x", "v"))
|
|
pneumatic_volume = self.pneumatic_volume_resolver.solve()
|
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self.pneumatic_volume_propagation_count += pneumatic_volume.propagated
|
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for component in self.dynamic_components:
|
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component.refresh_thermodynamic_ports()
|
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algebraic = self.pressure_flow_solver.solve(
|
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effort_variables=("p",),
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)
|
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pressure_flow_solve_count = 1
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|
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# Some constitutive flow laws recover their upstream temperature from
|
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# connected stream enthalpy, while junction stream mixing itself depends
|
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# on the resulting mass flows. A single stream -> pressure-flow refresh
|
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# leaves that two-way coupling to the next RHS call, making the ODE RHS
|
|
# depend on evaluation history and corrupting finite-difference
|
|
# Jacobians. Close both layers to one fixed point inside this call.
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physical_ports = tuple(
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port
|
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for component in self.network.components.values()
|
|
for definition in component.active_port_definitions
|
|
if definition.kind == "physical"
|
|
for port in (component.get_port(definition.name),)
|
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)
|
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connected_h: dict[str, dict[str, float]] = {}
|
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max_coupling_iterations = 25
|
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flow_relative_tolerance = 1.0e-12
|
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for coupling_iteration in range(1, max_coupling_iterations + 1):
|
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previous_flows = tuple(port.m_flow for port in physical_ports)
|
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stream, connected_h = self.stream_resolver.solve()
|
|
temperature_reference_h = (
|
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self.stream_resolver.connected_temperature_reference_enthalpies()
|
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)
|
|
for component in self.dynamic_components:
|
|
component.update_stream_outflows(connected_h[component.name])
|
|
component.update_flow_temperature_references(
|
|
temperature_reference_h[component.name]
|
|
)
|
|
algebraic = self.pressure_flow_solver.solve(
|
|
effort_variables=(
|
|
("p",) if pressure_flow_solve_count == 0 else ()
|
|
),
|
|
)
|
|
pressure_flow_solve_count += 1
|
|
current_flows = tuple(port.m_flow for port in physical_ports)
|
|
flow_scale = max(
|
|
[abs(value) for value in (*previous_flows, *current_flows)] + [1.0]
|
|
)
|
|
max_flow_delta = max(
|
|
(
|
|
abs(current - previous)
|
|
for previous, current in zip(previous_flows, current_flows)
|
|
),
|
|
default=0.0,
|
|
)
|
|
if max_flow_delta <= flow_relative_tolerance * flow_scale:
|
|
break
|
|
else:
|
|
raise ThermofluidClosureError(
|
|
"Stream enthalpy and pressure-flow coupling did not converge "
|
|
f"after {max_coupling_iterations} iterations."
|
|
)
|
|
self.max_thermofluid_iterations = max(
|
|
self.max_thermofluid_iterations,
|
|
coupling_iteration,
|
|
)
|
|
self.mechanical_state_reducer.update_constraint_accelerations()
|
|
self.algebraic_solve_count += pressure_flow_solve_count
|
|
self.max_algebraic_residual = max(
|
|
self.max_algebraic_residual,
|
|
algebraic.max_scaled_residual,
|
|
)
|
|
self.max_algebraic_evaluations = max(
|
|
self.max_algebraic_evaluations,
|
|
algebraic.evaluations,
|
|
)
|
|
self.max_stream_iterations = max(
|
|
self.max_stream_iterations,
|
|
stream.iterations,
|
|
)
|
|
return connected_h
|
|
|
|
def consistent_initial_state_vector(self, time: float = 0.0) -> list[float]:
|
|
state = self.initial_state_vector()
|
|
self.apply_state_vector(state)
|
|
self._close_current_state(time)
|
|
return state
|
|
|
|
def rhs(self, _time: float, state_vector: list[float]) -> list[float]:
|
|
self.apply_state_vector(state_vector)
|
|
connected_h = self._close_current_state(_time)
|
|
return self.pneumatic_storage_reducer.coupled_derivatives(
|
|
self.mechanical_state_reducer.state_derivatives(connected_h)
|
|
)
|
|
|
|
def _append_current_state(self, series: dict[str, list[float]]) -> None:
|
|
for component in self.network.components.values():
|
|
for relative_key, value in component.result_values().items():
|
|
series.setdefault(
|
|
f"{component.name}.{relative_key}", []
|
|
).append(value)
|
|
|
|
def simulate(
|
|
self,
|
|
config: SolveIVPConfig,
|
|
*,
|
|
sample_step: float,
|
|
progress_callback: SimulationProgressCallback | None = None,
|
|
cancel_check: SimulationCancellationCheck | None = None,
|
|
) -> GenericSimulationResult:
|
|
last_reported_progress = -1.0
|
|
last_reported_phase = ""
|
|
|
|
def report_progress(
|
|
progress: float,
|
|
phase: str,
|
|
*,
|
|
force: bool = False,
|
|
) -> None:
|
|
nonlocal last_reported_phase, last_reported_progress
|
|
if progress_callback is None:
|
|
return
|
|
bounded_progress = min(1.0, max(0.0, progress))
|
|
if (
|
|
force
|
|
or phase != last_reported_phase
|
|
or bounded_progress - last_reported_progress >= 0.0025
|
|
):
|
|
last_reported_phase = phase
|
|
last_reported_progress = max(
|
|
last_reported_progress,
|
|
bounded_progress,
|
|
)
|
|
progress_callback(last_reported_progress, phase)
|
|
|
|
report_progress(0.0, "initializing", force=True)
|
|
integration_config = config
|
|
if isinstance(config.atol, (int, float)):
|
|
integration_config = replace(
|
|
config,
|
|
atol=self.mechanical_state_reducer.absolute_tolerances(
|
|
float(config.atol)
|
|
),
|
|
)
|
|
t_eval = simulation_sample_times(config, sample_step)
|
|
signal_event_times = self.signal_resolver.event_times(
|
|
config.t_start,
|
|
config.t_stop,
|
|
)
|
|
initial_state = self.consistent_initial_state_vector(config.t_start)
|
|
report_progress(0.0, "integrating", force=True)
|
|
duration = config.t_stop - config.t_start
|
|
furthest_solver_time = config.t_start
|
|
|
|
def report_solver_time(time: float) -> None:
|
|
nonlocal furthest_solver_time
|
|
furthest_solver_time = max(furthest_solver_time, float(time))
|
|
time_fraction = (
|
|
(furthest_solver_time - config.t_start) / duration
|
|
if duration > 0.0
|
|
else 1.0
|
|
)
|
|
report_progress(time_fraction, "integrating")
|
|
|
|
def monitored_rhs(time: float, state_vector: list[float]) -> list[float]:
|
|
if cancel_check is None:
|
|
report_solver_time(time)
|
|
return self.rhs(time, state_vector)
|
|
|
|
solution = integrate_ode(
|
|
rhs=monitored_rhs,
|
|
initial_state=initial_state,
|
|
config=integration_config,
|
|
t_eval=t_eval,
|
|
cancel_check=cancel_check,
|
|
accepted_step_callback=(
|
|
report_solver_time if cancel_check is not None else None
|
|
),
|
|
breakpoints=signal_event_times,
|
|
state_transition_handler=(
|
|
self.mechanical_state_reducer.state_transition
|
|
if self.mechanical_state_reducer.has_state_events
|
|
else None
|
|
),
|
|
jac_sparsity=(
|
|
self.jacobian_sparsity()
|
|
if integration_config.method in {"BDF", "Radau"}
|
|
else None
|
|
),
|
|
)
|
|
if isinstance(solution, ODESolution):
|
|
run_status: SimulationRunStatus = solution.status
|
|
integration_error = solution.error
|
|
else:
|
|
run_status = "completed" if bool(solution.success) else "failed"
|
|
integration_error = None
|
|
result_message = str(solution.message)
|
|
postprocess_progress = (
|
|
1.0
|
|
if run_status == "completed"
|
|
else max(0.0, last_reported_progress)
|
|
)
|
|
report_progress(postprocess_progress, "postprocessing", force=True)
|
|
times = [float(value) for value in solution.t]
|
|
if isinstance(solution, ODESolution):
|
|
solver_segment_diagnostics = [
|
|
segment.as_dict() for segment in solution.solver_segments
|
|
]
|
|
else:
|
|
solver_segment_diagnostics = [
|
|
{
|
|
"startTime": float(config.t_start),
|
|
"requestedStopTime": float(config.t_stop),
|
|
"simulatedUntil": times[-1] if times else float(config.t_start),
|
|
"nfev": int(getattr(solution, "nfev", 0)),
|
|
"njev": int(getattr(solution, "njev", 0)),
|
|
"nlu": int(getattr(solution, "nlu", 0)),
|
|
"acceptedStepCount": 0,
|
|
"solverStartCount": 1,
|
|
"stateTransitionCount": 0,
|
|
"recoverableRetryCount": 0,
|
|
}
|
|
]
|
|
solver_total_keys = (
|
|
"nfev",
|
|
"njev",
|
|
"nlu",
|
|
"acceptedStepCount",
|
|
"solverStartCount",
|
|
"stateTransitionCount",
|
|
"recoverableRetryCount",
|
|
)
|
|
solver_totals = {
|
|
key: sum(int(segment[key]) for segment in solver_segment_diagnostics)
|
|
for key in solver_total_keys
|
|
}
|
|
jacobian_diagnostics = (
|
|
self.jacobian_sparsity_diagnostics()
|
|
if integration_config.method in {"BDF", "Radau"}
|
|
else None
|
|
)
|
|
if jacobian_diagnostics is not None:
|
|
color_group_count = int(jacobian_diagnostics["colorGroupCount"])
|
|
for segment in solver_segment_diagnostics:
|
|
segment["finiteDifferenceRhsEstimate"] = (
|
|
int(segment["njev"]) * color_group_count
|
|
)
|
|
solver_totals["finiteDifferenceRhsEstimate"] = sum(
|
|
int(segment["finiteDifferenceRhsEstimate"])
|
|
for segment in solver_segment_diagnostics
|
|
)
|
|
|
|
series: dict[str, list[float]] = {"time": []}
|
|
postprocessing_error: Exception | None = None
|
|
self.mechanical_state_reducer.reset_constraint_modes()
|
|
for time_index in range(len(times)):
|
|
if (
|
|
run_status == "completed"
|
|
and cancel_check is not None
|
|
and cancel_check()
|
|
):
|
|
run_status = "cancelled"
|
|
result_message = "Simulation was stopped while preparing partial results."
|
|
break
|
|
state = [
|
|
float(solution.y[state_index][time_index])
|
|
for state_index in range(len(solution.y))
|
|
]
|
|
try:
|
|
self.apply_state_vector(state)
|
|
self._close_current_state(times[time_index])
|
|
self._append_current_state(series)
|
|
series["time"].append(times[time_index])
|
|
except Exception as exc:
|
|
run_status = "failed"
|
|
result_message = str(exc)
|
|
postprocessing_error = exc
|
|
break
|
|
if len(series["time"]) < 2:
|
|
if postprocessing_error is not None:
|
|
raise postprocessing_error
|
|
if integration_error is not None:
|
|
raise integration_error
|
|
|
|
final = {
|
|
key: values[-1]
|
|
for key, values in series.items()
|
|
if key != "time" and values
|
|
}
|
|
diagnostics = {
|
|
"integration": {
|
|
"method": integration_config.method,
|
|
"jacobianSparsity": jacobian_diagnostics,
|
|
"segmentCount": len(solver_segment_diagnostics),
|
|
"segments": solver_segment_diagnostics,
|
|
"totals": solver_totals,
|
|
},
|
|
"pressureFlow": {
|
|
"solveCount": self.algebraic_solve_count,
|
|
"maxScaledResidual": self.max_algebraic_residual,
|
|
"maxEvaluationsPerSolve": self.max_algebraic_evaluations,
|
|
"last": (
|
|
self.pressure_flow_solver.last_diagnostics.as_dict()
|
|
if self.pressure_flow_solver.last_diagnostics is not None
|
|
else None
|
|
),
|
|
},
|
|
"stream": {
|
|
"maxIterationsPerSolve": self.max_stream_iterations,
|
|
"maxThermofluidIterations": self.max_thermofluid_iterations,
|
|
"last": (
|
|
self.stream_resolver.last_diagnostics.as_dict()
|
|
if self.stream_resolver.last_diagnostics is not None
|
|
else None
|
|
),
|
|
},
|
|
"signal": {
|
|
"propagations": self.signal_propagation_count,
|
|
"eventTimes": list(signal_event_times),
|
|
"last": (
|
|
self.signal_resolver.last_diagnostics.as_dict()
|
|
if self.signal_resolver.last_diagnostics is not None
|
|
else None
|
|
),
|
|
},
|
|
"pneumaticVolume": {
|
|
"propagations": self.pneumatic_volume_propagation_count,
|
|
"last": (
|
|
self.pneumatic_volume_resolver.last_diagnostics.as_dict()
|
|
if self.pneumatic_volume_resolver.last_diagnostics is not None
|
|
else None
|
|
),
|
|
},
|
|
"stateCount": len(initial_state),
|
|
"sampleCount": len(series["time"]),
|
|
}
|
|
variables = tuple(
|
|
variable
|
|
for variable in self.network.result_variable_metadata()
|
|
if variable.key in series
|
|
)
|
|
report_progress(
|
|
1.0 if run_status == "completed" else max(0.0, last_reported_progress),
|
|
"complete" if run_status == "completed" else run_status,
|
|
force=True,
|
|
)
|
|
return GenericSimulationResult(
|
|
success=run_status == "completed" and bool(solution.success),
|
|
status=run_status,
|
|
message=result_message,
|
|
simulated_until=(
|
|
float(series["time"][-1])
|
|
if series["time"]
|
|
else float(config.t_start)
|
|
),
|
|
requested_stop_time=float(config.t_stop),
|
|
variables=variables,
|
|
series=series,
|
|
final=final,
|
|
diagnostics=diagnostics,
|
|
)
|