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‎examples/AAA/AAA_pulsatile_transfer.py‎

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"""3-D Unsteady Pulsatile Flow through an Axisymmetric AAA — Time-Marching TL.
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Run directly or via the CLI:
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python examples/AAA/AAA_pulsatile_transfer.py
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python examples/AAA/AAA_pulsatile_transfer.py myconfig.yaml
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python -m underPINN run examples/AAA/AAA_pulsatile_transfer.yaml
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Same time-marching transfer-learning framework as ``pipe_flow_pulsatile_transfer``
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but in the **AAA (bulge)** geometry, Newtonian rheology. The inlet imposes a
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pulsatile parabolic profile
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u_inlet(r, t) = (V_max + V_amp · sin(2π t / T_period)) · (1 − r²/R_vessel²)
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with no-slip on the curved wall and p = 0 at the outlet. The steady
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Poiseuille profile is used as the initial condition at t = 0. Overlapping
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windows are supported via ``time_marching.stride < dT``.
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Network: (x, y, z, t) → (u, v, w, p)
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"""
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from __future__ import annotations
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import os
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os.environ.setdefault("XLA_PYTHON_CLIENT_PREALLOCATE", "false")
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from underPINN.config.loader import cfg_get
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from underPINN.pde.navier_stokes_3d import UnsteadyNS3DPDE
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from underPINN.geometry.aaa import BulgeGeometry
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from underPINN.utils.pulsatile_time_march import (
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cosine_squared_inlet_factory,
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make_model_from_cfg,
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parabolic_steady_uvw_factory,
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run_pulsatile_time_march,
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)
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def run_AAA_pulsatile_transfer(cfg) -> dict:
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ph = cfg.physics
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Re = float(cfg_get(ph, "Re", default=40.0))
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R_vessel = float(cfg_get(ph, "R_vessel", default=0.5))
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R_AAA = float(cfg_get(ph, "R_AAA", default=1.0))
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L = float(cfg_get(ph, "L", default=7.0))
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x_lo = float(cfg_get(ph, "x_lo", default=-3.5))
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x0 = float(cfg_get(ph, "x0", default=-2.0))
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L_AAA = float(cfg_get(ph, "L_AAA", default=1.5))
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V_max = float(cfg_get(ph, "V_max", default=2.0))
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V_amp = float(cfg_get(ph, "V_amp", default=1.0))
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T_period = float(cfg_get(ph, "T_period", default=1.0))
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x_hi = x_lo + L
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x_mid = 0.5 * (x_lo + x_hi)
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print(f"AAA pulsatile (3-D unsteady): Re={Re}, R_vessel={R_vessel}, "
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f"R_AAA={R_AAA}, x∈[{x_lo}, {x_hi}], x0={x0}, L_AAA={L_AAA}")
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print(f" Inlet peak(t) = {V_max} + {V_amp}·sin(2π t/{T_period}) "
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f"(parabolic profile)")
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geom = BulgeGeometry(R_vessel=R_vessel, R_AAA=R_AAA, L=L,
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x_lo=x_lo, x0=x0, L_AAA=L_AAA)
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model, _ = make_model_from_cfg(cfg)
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pde = UnsteadyNS3DPDE(model, Re=Re)
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return run_pulsatile_time_march(
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cfg,
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problem_spec=dict(
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problem="AAA_pulsatile_transfer",
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label="AAA pulsatile flow (Newtonian)",
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geom=geom, pde=pde,
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inlet_target_fn=cosine_squared_inlet_factory(
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R_vessel, V_max, V_amp, T_period),
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steady_uvw_fn=parabolic_steady_uvw_factory(R_vessel, V_max),
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physics_dict={"Re": Re, "R_vessel": R_vessel, "R_AAA": R_AAA,
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"L": L, "x_lo": x_lo, "x0": x0, "L_AAA": L_AAA,
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"V_max": V_max, "V_amp": V_amp, "T_period": T_period},
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plot_extent=(x_lo, x_hi, x_mid),
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),
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out_dir_default="outputs/AAA_pulsatile_transfer",
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)
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if __name__ == "__main__":
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import sys
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import pathlib
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_HERE = pathlib.Path(__file__).parent
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cfg_path = str(
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pathlib.Path(sys.argv[1]) if len(sys.argv) > 1
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else _HERE / "AAA_pulsatile_transfer.yaml"
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)
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from underPINN.config.loader import load_config
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run_AAA_pulsatile_transfer(load_config(cfg_path))

‎examples/AAA/AAA_pulsatile_transfer.yaml‎

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problem: AAA_pulsatile_transfer
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# Run via:
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# python examples/AAA/AAA_pulsatile_transfer.py
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# python -m underPINN run examples/AAA/AAA_pulsatile_transfer.yaml
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#
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# Same domain and Reynolds number as the steady AAA case
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# (examples/AAA/config.yaml). Pulsatile inlet:
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# u_inlet(r,t) = (V_max + V_amp·sin(2πt/T_period))·(1 − r²/R_vessel²)
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# Long-time integration via time-marching transfer learning, optionally with
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# OVERLAPPING windows (set stride < dT).
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network:
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type : gated_mlp # 'mlp' or 'gated_mlp'
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layers: [4, 128, 128, 128, 128, 4] # (x, y, z, t) → (u, v, w, p)
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physics:
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Re : 40.0
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R_vessel : 0.5 # straight-vessel radius
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R_AAA : 1.0 # peak bulge radius
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L : 7.0 # total domain length
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x_lo : -3.5 # inlet x
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x0 : -2.0 # bulge centre
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L_AAA : 1.5 # axial bulge width
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V_max : 2.0 # mean centreline peak
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V_amp : 1.0 # pulsation amplitude
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T_period : 1.0 # pulse period
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# ── Time-marching transfer learning ──────────────────────────────────────────
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time_marching:
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T_total : 4.0 # 4 pulse cycles
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dT : 0.5 # window length
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stride : 0.5 # set < dT for overlapping windows
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n_first_epochs : 12000
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n_warm_epochs : 4000
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n_cold_epochs : 12000
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compare_no_transfer: true
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restart : true
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first_lr : 1.0e-3
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warm_lr : 5.0e-4
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cold_lr : 1.0e-3
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lr_alpha : 0.01
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# ── Collocation data (per window) ────────────────────────────────────────────
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data:
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n_interior: 30000
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n_wall : 6000
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n_inlet : 1200
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n_outlet : 1200
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n_ic : 2500
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batch_r : 2048
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batch_bc : 512
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loss:
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w_pde : 1.0
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w_wall : 100.0
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w_inlet : 50.0
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w_outlet: 20.0
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w_ic : 100.0
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output:
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dir: outputs/AAA_pulsatile_transfer

‎examples/AAA_rheology/AAA_rheology_pulsatile.py‎

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"""3-D Unsteady Pulsatile Flow through an Axisymmetric AAA with Carreau Blood.
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Time-marching transfer learning combining the AAA bulge geometry with the
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Carreau (shear-thinning blood) constitutive law:
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μ*(γ̇*) = 1 + (β − 1)[1 + (Cu γ̇*)²]^((n−1)/2)
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blood: β = 16, n = 0.3568.
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Domain and Reynolds number are identical to the steady AAA-rheology case
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(``examples/AAA_rheology/config.yaml``). Inlet imposes the developed Carreau
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radial profile modulated in time:
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u_inlet(r, t) = (V_max + V_amp · sin(2π t / T_period)) · u*_carr(r/R_vessel)
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Network: (x, y, z, t) → (u, v, w, p)
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"""
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from __future__ import annotations
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import os
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os.environ.setdefault("XLA_PYTHON_CLIENT_PREALLOCATE", "false")
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from underPINN.config.loader import cfg_get
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from underPINN.pde.carreau_ns_3d import UnsteadyCarreauNS3DPDE
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from underPINN.geometry.aaa import BulgeGeometry
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from underPINN.utils.pulsatile_time_march import (
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carreau_inlet_factory,
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carreau_steady_uvw_factory,
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make_model_from_cfg,
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run_pulsatile_time_march,
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)
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def run_AAA_rheology_pulsatile(cfg) -> dict:
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ph = cfg.physics
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Re = float(cfg_get(ph, "Re", default=40.0))
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R_vessel = float(cfg_get(ph, "R_vessel", default=0.5))
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R_AAA = float(cfg_get(ph, "R_AAA", default=1.0))
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L = float(cfg_get(ph, "L", default=7.0))
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x_lo = float(cfg_get(ph, "x_lo", default=-3.5))
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x0 = float(cfg_get(ph, "x0", default=-2.0))
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L_AAA = float(cfg_get(ph, "L_AAA", default=1.5))
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V_max = float(cfg_get(ph, "V_max", default=2.0))
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V_amp = float(cfg_get(ph, "V_amp", default=1.0))
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T_period = float(cfg_get(ph, "T_period", default=1.0))
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beta = float(cfg_get(ph, "beta", default=16.0))
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Cu = float(cfg_get(ph, "Cu", default=10.0))
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n = float(cfg_get(ph, "n", default=0.3568))
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x_hi = x_lo + L
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x_mid = 0.5 * (x_lo + x_hi)
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print(f"Carreau pulsatile AAA (3-D unsteady): Re={Re}, R_vessel={R_vessel}, "
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f"R_AAA={R_AAA}, x∈[{x_lo}, {x_hi}], x0={x0}, L_AAA={L_AAA}")
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print(f" Carreau: β={beta}, Cu={Cu}, n={n}")
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print(f" Inlet peak(t) = {V_max} + {V_amp}·sin(2π t/{T_period}) "
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f"(Carreau-developed profile)")
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geom = BulgeGeometry(R_vessel=R_vessel, R_AAA=R_AAA, L=L,
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x_lo=x_lo, x0=x0, L_AAA=L_AAA)
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model, _ = make_model_from_cfg(cfg)
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pde = UnsteadyCarreauNS3DPDE(model, Re=Re, beta=beta, Cu=Cu, n=n)
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return run_pulsatile_time_march(
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cfg,
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problem_spec=dict(
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problem="AAA_rheology_pulsatile",
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label="Carreau pulsatile AAA",
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geom=geom, pde=pde,
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inlet_target_fn=carreau_inlet_factory(
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R_vessel, V_max, V_amp, T_period, beta, Cu, n),
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steady_uvw_fn=carreau_steady_uvw_factory(
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R_vessel, V_max, beta, Cu, n),
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physics_dict={"Re": Re, "R_vessel": R_vessel, "R_AAA": R_AAA,
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"L": L, "x_lo": x_lo, "x0": x0, "L_AAA": L_AAA,
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"V_max": V_max, "V_amp": V_amp, "T_period": T_period,
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"beta": beta, "Cu": Cu, "n": n},
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plot_extent=(x_lo, x_hi, x_mid),
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),
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out_dir_default="outputs/AAA_rheology_pulsatile",
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)
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if __name__ == "__main__":
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import sys
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import pathlib
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_HERE = pathlib.Path(__file__).parent
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cfg_path = str(
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pathlib.Path(sys.argv[1]) if len(sys.argv) > 1
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else _HERE / "AAA_rheology_pulsatile.yaml"
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)
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from underPINN.config.loader import load_config
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run_AAA_rheology_pulsatile(load_config(cfg_path))

‎examples/AAA_rheology/AAA_rheology_pulsatile.yaml‎

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problem: AAA_rheology_pulsatile
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# Run via:
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# python examples/AAA_rheology/AAA_rheology_pulsatile.py
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# python -m underPINN run examples/AAA_rheology/AAA_rheology_pulsatile.yaml
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#
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# Same domain/Re as the steady AAA-rheology case
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# (examples/AAA_rheology/config.yaml). Carreau blood rheology in the AAA bulge,
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# pulsatile inlet.
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network:
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type : gated_mlp
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layers: [4, 128, 128, 128, 128, 128, 4] # (x, y, z, t) → (u, v, w, p)
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physics:
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Re : 40.0
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R_vessel : 0.5
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R_AAA : 1.0
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L : 7.0
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x_lo : -3.5
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x0 : -2.0
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L_AAA : 1.5
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V_max : 2.0
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V_amp : 1.0
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T_period : 1.0
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# Carreau rheology — blood values (Nagargoje et al. 2021, Table II):
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# λ = 3.131 s, n = 0.3568, μ0/μ∞ = 16
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# Diameter-based non-dim: D = 2·R_vessel = 1.0, U_mean = V_max/2 = 1.0
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# Re = ρ U_mean D / μ∞ = 40, Cu = λ U_mean / D = 3.131
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beta : 16.0
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Cu : 3.131 # = λ U_mean / D
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n : 0.3568
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time_marching:
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T_total : 4.0
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dT : 0.5
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stride : 0.5
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n_first_epochs : 12000
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n_warm_epochs : 4000
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n_cold_epochs : 12000
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compare_no_transfer: true
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restart : true
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first_lr : 1.0e-3
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warm_lr : 5.0e-4
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cold_lr : 1.0e-3
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lr_alpha : 0.01
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data:
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n_interior: 40000
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n_wall : 8000
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n_inlet : 1500
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n_outlet : 1500
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n_ic : 3000
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batch_r : 2048
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batch_bc : 512
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loss:
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w_pde : 1.0
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w_wall : 100.0
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w_inlet : 50.0
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w_outlet: 20.0
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w_ic : 100.0
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output:
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dir: outputs/AAA_rheology_pulsatile

‎examples/AAA_rheology/config.yaml‎

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x0 : -2.0 # axial centre of the bulge
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L_AAA : 1.5 # full axial width of the bulge region
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V_max : 2.0 # inlet centreline velocity (matches Newtonian V_max)
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# Carreau rheology (blood values from the paper)
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# Carreau rheology (Nagargoje, Mishra & Gupta 2021, Table II — blood):
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# λ = 3.131 s, n = 0.3568, μ0/μ∞ = 16
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# Non-dim groups in this sim are diameter/mean-velocity based:
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# D = 2·R_vessel = 1.0, U_mean = V_max/2 = 1.0 (parabolic)
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# Re = ρ U_mean D / μ∞ = 40
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# Cu = λ U_mean / D = 3.131
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beta : 16.0 # viscosity ratio μ0/μ∞ (1 = Newtonian)
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Cu : 10.0 # Carreau number (shear-thinning strength)
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Cu : 3.131 # = λ·U_mean/D
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n : 0.3568 # power-law index
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