同步仿真框架并接入AMESim气动组件
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"""Flow junction components."""
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from __future__ import annotations
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from collections.abc import Mapping
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from app.simulation.core.base import AlgebraicComponent
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from app.simulation.core.catalog import ComponentDisplaySpec, PortDisplaySpec
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from app.simulation.core.equations import EquationResidual
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from app.simulation.core.medium import IdealGasMedium
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from app.simulation.core.ports import PortDefinition
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class Tee(AlgebraicComponent):
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"""Python port of ModelicaModels.Mytee."""
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MODEL_TYPE = "tee"
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MODEL_VERSION = "1.0.0"
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PORTS = (
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PortDefinition.pneumatic("port_in", nominal_role="bidirectional"),
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PortDefinition.pneumatic("port_out1", nominal_role="bidirectional"),
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PortDefinition.pneumatic("port_out2", nominal_role="bidirectional"),
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)
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PARAMETERS = ()
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RESULT_VARIABLES = ()
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DISPLAY = ComponentDisplaySpec(
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label="三通",
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library_id="experimental",
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category_id="junctions",
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symbol="tee",
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ports=(
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PortDisplaySpec("port_in", "left", order=10),
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PortDisplaySpec("port_out1", "right", order=20),
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PortDisplaySpec("port_out2", "right", order=30),
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),
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order=50,
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)
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def __init__(self, name: str) -> None:
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super().__init__(name=name)
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self.set_parameter_values({})
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self.port_in = self.register_declared_port("port_in")
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self.port_out1 = self.register_declared_port("port_out1")
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self.port_out2 = self.register_declared_port("port_out2")
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@classmethod
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def create(
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cls,
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*,
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name: str,
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medium: IdealGasMedium,
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parameters: Mapping[str, float],
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) -> Tee:
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return cls(name=name)
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def pressure_flow_equation_residuals(self) -> tuple[EquationResidual, ...]:
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return (
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EquationResidual(
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id=f"{self.name}:common_pressure_out1",
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owner="component",
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owner_id=self.name,
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relation="equal",
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variables=(f"{self.name}.port_in.p", f"{self.name}.port_out1.p"),
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role="effort",
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value=self.port_in.p - self.port_out1.p,
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),
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EquationResidual(
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id=f"{self.name}:common_pressure_out2",
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owner="component",
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owner_id=self.name,
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relation="equal",
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variables=(f"{self.name}.port_in.p", f"{self.name}.port_out2.p"),
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role="effort",
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value=self.port_in.p - self.port_out2.p,
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),
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EquationResidual(
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id=f"{self.name}:mass_flow_balance",
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owner="component",
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owner_id=self.name,
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relation="sumToZero",
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variables=(
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f"{self.name}.port_in.m_flow",
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f"{self.name}.port_out1.m_flow",
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f"{self.name}.port_out2.m_flow",
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),
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role="flow",
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value=(
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self.port_in.m_flow
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+ self.port_out1.m_flow
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+ self.port_out2.m_flow
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),
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),
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)
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def update_stream_outflows(self, connected_h: Mapping[str, float]) -> None:
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incoming = [
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(port.m_flow, connected_h[name])
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for name, port in self.ports.items()
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if port.m_flow > 1e-12
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]
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total_flow = sum(m_flow for m_flow, _ in incoming)
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if total_flow > 1e-12:
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mixed_h = sum(
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m_flow * enthalpy for m_flow, enthalpy in incoming
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) / total_flow
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else:
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values = list(connected_h.values())
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mixed_h = sum(values) / len(values) if values else 0.0
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for port in self.ports.values():
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port.h_outflow = mixed_h
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def mixed_inlet_enthalpy(
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self,
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branch1_m_flow: float,
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branch1_h: float,
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branch2_m_flow: float,
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branch2_h: float,
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fallback_h: float = 0.0,
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) -> float:
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positive_1 = max(branch1_m_flow, 0.0)
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positive_2 = max(branch2_m_flow, 0.0)
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total = positive_1 + positive_2
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if total <= 1e-9:
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return fallback_h
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return (positive_1 * branch1_h + positive_2 * branch2_h) / total
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def inlet_stream_enthalpy(
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self,
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branch1_m_flow: float,
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branch1_h: float,
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branch2_m_flow: float,
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branch2_h: float,
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fallback_h: float,
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) -> float:
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"""Approximate `inStream(port_in.h_outflow)` for the current tee topology."""
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return self.mixed_inlet_enthalpy(
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branch1_m_flow,
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branch1_h,
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branch2_m_flow,
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branch2_h,
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fallback_h=fallback_h,
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)
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def branch_actual_stream_enthalpy(
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self,
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branch_m_flow: float,
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branch_h: float,
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inlet_h: float,
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) -> float:
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"""Approximate `actualStream(branch.h_outflow)` for a tee branch port."""
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return inlet_h if branch_m_flow > 0.0 else branch_h
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@staticmethod
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def _solve_linear_2x2(
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a11: float,
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a12: float,
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a21: float,
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a22: float,
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b1: float,
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b2: float,
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) -> tuple[float, float] | None:
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determinant = a11 * a22 - a12 * a21
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if abs(determinant) <= 1e-12:
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return None
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x1 = (b1 * a22 - b2 * a12) / determinant
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x2 = (a11 * b2 - a21 * b1) / determinant
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return x1, x2
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def solve_branch_outlet_flows_from_energy_balance(
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self,
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*,
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ratio_branch1: float,
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ratio_branch2: float,
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inlet_h_branch1: float,
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inlet_h_branch2: float,
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branch1_h: float,
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branch2_h: float,
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inlet_h: float,
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q_in_branch1: float,
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q_in_branch2: float,
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tolerance: float = 1e-12,
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) -> tuple[float, float]:
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"""Solve branch outlet flows for the current three-port downstream tee use-case."""
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rhs_branch1 = q_in_branch1 * inlet_h_branch1
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rhs_branch2 = q_in_branch2 * inlet_h_branch2
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def solve_both_forward() -> tuple[float, float] | None:
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return self._solve_linear_2x2(
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(1.0 + ratio_branch1) * branch1_h,
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ratio_branch1 * branch2_h,
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ratio_branch2 * branch1_h,
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(1.0 + ratio_branch2) * branch2_h,
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rhs_branch1,
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rhs_branch2,
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)
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def solve_one_reverse(
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*,
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branch1_reverse: bool,
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) -> tuple[float, float] | None:
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if branch1_reverse:
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return self._solve_linear_2x2(
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inlet_h * (1.0 + ratio_branch1),
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ratio_branch1 * inlet_h,
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ratio_branch2 * inlet_h,
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branch2_h + ratio_branch2 * inlet_h,
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rhs_branch1,
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rhs_branch2,
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)
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return self._solve_linear_2x2(
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branch1_h + ratio_branch1 * inlet_h,
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ratio_branch1 * inlet_h,
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ratio_branch2 * inlet_h,
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inlet_h * (1.0 + ratio_branch2),
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rhs_branch1,
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rhs_branch2,
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)
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def solve_both_reverse() -> tuple[float, float] | None:
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return self._solve_linear_2x2(
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inlet_h * (1.0 + ratio_branch1),
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ratio_branch1 * inlet_h,
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ratio_branch2 * inlet_h,
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inlet_h * (1.0 + ratio_branch2),
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rhs_branch1,
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rhs_branch2,
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)
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candidate_solvers = (
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(
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solve_both_forward,
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lambda q1, q2: q1 >= -tolerance and q2 >= -tolerance,
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),
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(
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lambda: solve_one_reverse(branch1_reverse=True),
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lambda q1, q2: q1 < -tolerance and q2 >= -tolerance and q1 + q2 > tolerance,
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),
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(
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lambda: solve_one_reverse(branch1_reverse=True),
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lambda q1, q2: q1 < -tolerance and q2 >= -tolerance and q1 + q2 <= tolerance,
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),
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(
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lambda: solve_one_reverse(branch1_reverse=False),
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lambda q1, q2: q2 < -tolerance and q1 >= -tolerance and q1 + q2 > tolerance,
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),
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(
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lambda: solve_one_reverse(branch1_reverse=False),
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lambda q1, q2: q2 < -tolerance and q1 >= -tolerance and q1 + q2 <= tolerance,
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),
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(
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solve_both_reverse,
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lambda q1, q2: q1 < -tolerance and q2 < -tolerance,
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),
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)
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for solver, predicate in candidate_solvers:
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candidate = solver()
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if candidate is None:
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continue
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q_out_branch1, q_out_branch2 = candidate
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if predicate(q_out_branch1, q_out_branch2):
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return q_out_branch1, q_out_branch2
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return solve_both_forward() or (0.0, 0.0)
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