Industrial Heat Sink


This tutorial uses Modulus to conduct a thermal simulation of NVIDIA’s NVSwitch heatsink. You will learn:

  1. How to use hFTB algorithm to solve conjugate heat transfer problems

  2. How to build a gPC based Surrogate via Transfer Learning


This tutorial assumes you have completed tutorial Moving Time Window: Taylor Green Vortex Decay as well as the tutorial Conjugate Heat Transfer on conjugate heat transfer.

This tutorial solves the conjugate heat transfer problem of NVIDIA’s NVSwitch heat sink as shown in Fig. 157. Similar to the previous FPGA problem, the heat sink is placed in a channel with inlet velocity similar to its operating conditions. This case differs from the FPGA one, because you will be using the real heat properties for atmospheric air and copper as the heat sink material. Unlike Heat Transfer with High Thermal Conductivity, a hFTB algorithm will be used to handle the large conductivity differences.


Fig. 157 NVSwitch heat sink geometry

Using real heat properties causes an issue on the interface between the solid and fluid because the conductivity is around 4 orders of magnitude different (Air: 0.0261 \(W/m.K\) and Copper: 385 \(W/m.K\)). To remedy this, Modulus has a static conjugate heat transfer approached referred to as heat transfer coefficient forward temperature backward or hFTB 1. This method works by iteratively solving for the heat transfer in the fluid and solid where they are one way coupled. Using the hFTB method, assign Robin boundary conditions on the solid interface and Dirichlet boundaries for the fluid. The simulation starts by giving an initial guess for the solid temperature and uses a hyper parameter \(h\) for the Robin boundary conditions. A description of the algorithm is shown in Fig. 158. A more complete description can be found here 1.


Fig. 158 hFTB algorithm

The case setup for this problem is similar to the FPGA and three fin examples (covered in tutorials Parameterized 3D Heat Sink and FPGA Heat Sink with Laminar Flow) however, this section shows construction of multiple train domains to implement the hFTB method.


The python script for this problem can be found at examples/limerock/limerock_hFTB.

Defining Domain

This case setup skips over several sections of the code and only focuses on the portions related to the hFTB algorithm. You should be familiar with how to set up the flow simulation from previous tutorials. Geometry construction is not discussed in detail as well and all relevant information can be found in examples/limerock/limerock_hFTB/ The code description begins by defining the parameters of the simulation and importing all needed modules.


from limerock_geometry import LimeRock # make limerock limerock = LimeRock() ############# # Real Params ############# # fluid params fluid_viscosity = 1.84e-05 # kg/m-s fluid_density = 1.1614 # kg/m3 fluid_specific_heat = 1005 # J/(kg K) fluid_conductivity = 0.0261 # W/(m K) # copper params copper_density = 8930 # kg/m3 copper_specific_heat = 385 # J/(kg K) copper_conductivity = 385 # W/(m K) # boundary params inlet_velocity = 5.7 # m/s inlet_temp = 0 # K # source source_term = 2127.71 # K/m source_origin = (-0.061667, -0.15833, limerock.geo_bounds_lower[2]) source_dim = (0.1285, 0.31667, 0) ################ # Non dim params ################ length_scale = 0.0575 # m velocity_scale = 5.7 # m/s time_scale = length_scale / velocity_scale # s density_scale = 1.1614 # kg/m3 mass_scale = density_scale * length_scale ** 3 # kg pressure_scale = mass_scale / (length_scale * time_scale ** 2) # kg / (m s**2) temp_scale = 273.15 # K watt_scale = (mass_scale * length_scale ** 2) / (time_scale ** 3) # kg m**2 / s**3 joule_scale = (mass_scale * length_scale ** 2) / (time_scale ** 2) # kg * m**2 / s**2 ############################## # Nondimensionalization Params ############################## # fluid params nd_fluid_viscosity = fluid_viscosity / ( length_scale ** 2 / time_scale ) # need to divide by density to get previous viscosity nd_fluid_density = fluid_density / density_scale nd_fluid_specific_heat = fluid_specific_heat / (joule_scale / (mass_scale * temp_scale)) nd_fluid_conductivity = fluid_conductivity / (watt_scale / (length_scale * temp_scale)) nd_fluid_diffusivity = nd_fluid_conductivity / ( nd_fluid_specific_heat * nd_fluid_density ) # copper params nd_copper_density = copper_density / (mass_scale / length_scale ** 3) nd_copper_specific_heat = copper_specific_heat / ( joule_scale / (mass_scale * temp_scale) ) nd_copper_conductivity = copper_conductivity / ( watt_scale / (length_scale * temp_scale) ) nd_copper_diffusivity = nd_copper_conductivity / ( nd_copper_specific_heat * nd_copper_density ) # boundary params nd_inlet_velocity = inlet_velocity / velocity_scale nd_volumetric_flow = limerock.inlet_area * nd_inlet_velocity nd_inlet_temp = inlet_temp / temp_scale nd_source_term = source_term / (temp_scale / length_scale)


We nondimensionalize all parameters so that the scales for velocity, temperature, and pressure are roughly in the range 0-1. Such nondimensionalization trains the Neural network more efficiently.

Sequence Solver

Now setup the solver. Similar to the moving time window implementation in Tutorial Moving Time Window: Taylor Green Vortex Decay, construct a separate neural network that stores the thermal solution from the previous cycles fluid solution. We suggest that this problem is either run on \(8\) GPUs or gradient aggregation frequency is set to \(8\). Details on running with multi-GPUs and multi-nodes can be found in tutorial Performance and the details on using gradient aggregation can be found in tutorial Modulus Configuration.

Next, set up a train domain to only solve for the temperature in the fluid given a Dirichlet boundary condition on the solid. This will be the first stage of the hFTB method. After getting this initial solution for the temperature in the fluid solve for the main loop of the hFTB algorithm. Now you will solve for both the fluid and solid in a one way coupled manner. The Robin boundary conditions for the solid are coming from the previous iteration of the fluid solution.


Sometimes for visualization purposes it is beneficial to visualize the results on a mesh. Here, this is done using the VTKUniformGrid method. Note that the SDF was used as a mask function to filter out the temperature evaluations outside the solid.


Multi-GPU training is currently not supported for this problem.


import torch from import DataLoader, Dataset from torch import Tensor import copy import numpy as np from sympy import Symbol, Eq, tanh, Or, And from omegaconf import DictConfig, OmegaConf import hydra from hydra.utils import to_absolute_path from typing import Dict import modulus from modulus.hydra import to_absolute_path, instantiate_arch, ModulusConfig from import csv_to_dict from modulus.solver import SequentialSolver from modulus.domain import Domain from modulus.geometry.primitives_3d import Box, Channel, Plane from modulus.models.fourier_net import FourierNetArch from modulus.models.arch import Arch from modulus.domain.constraint import ( PointwiseBoundaryConstraint, PointwiseInteriorConstraint, ) from modulus.domain.monitor import PointwiseMonitor from modulus.domain.inferencer import PointVTKInferencer from import ( VTKUniformGrid, ) from modulus.key import Key from modulus.node import Node from modulus.eq.pdes.basic import NormalDotVec, GradNormal from modulus.eq.pdes.advection_diffusion import AdvectionDiffusion from modulus.distributed.manager import DistributedManager from limerock_properties import * from flux_diffusion import ( FluxDiffusion, FluxIntegrateDiffusion, FluxGradNormal, FluxRobin, Dirichlet, ) class hFTBArch(Arch): def __init__( self, arch: Arch, ) -> None: output_keys = arch.output_keys + [ Key( + "_prev_step") for x in arch.output_keys ] super().__init__( input_keys=arch.input_keys, output_keys=output_keys, periodicity=arch.periodicity, ) # set networks for current and prev time window self.arch_prev_step = arch self.arch = copy.deepcopy(arch) for param, param_prev_step in zip( self.arch.parameters(), self.arch_prev_step.parameters() ): param_prev_step.requires_grad = False def forward(self, in_vars: Dict[str, Tensor]) -> Dict[str, Tensor]: y_prev_step = self.arch_prev_step.forward(in_vars) y = self.arch.forward(in_vars) for (key, b) in y_prev_step.items(): y[key + "_prev_step"] = b return y def move_network(self): for param, param_prev_step in zip( self.arch.parameters(), self.arch_prev_step.parameters() ): = param.detach().clone().data param_prev_step.requires_grad = False @modulus.main(config_path="conf", config_name="conf_thermal") def run(cfg: ModulusConfig) -> None: if DistributedManager().distributed: print("Multi-GPU currently not supported for this example. Exiting.") return # make list of nodes to unroll graph on ad = AdvectionDiffusion( T="theta_f", rho=nd_fluid_density, D=nd_fluid_diffusivity, dim=3, time=False ) dif = FluxDiffusion(D=nd_copper_diffusivity) flow_grad_norm = GradNormal("theta_f", dim=3, time=False) solid_grad_norm = FluxGradNormal() integrate_flux_dif = FluxIntegrateDiffusion() robin_flux = FluxRobin( theta_f_conductivity=nd_fluid_conductivity, theta_s_conductivity=nd_copper_conductivity, h=500.0, ) dirichlet = Dirichlet(lhs="theta_f", rhs="theta_s") flow_net = FourierNetArch( input_keys=[Key("x"), Key("y"), Key("z")], output_keys=[Key("u"), Key("v"), Key("w"), Key("p")], ) f_net = FourierNetArch( input_keys=[Key("x"), Key("y"), Key("z")], output_keys=[Key("theta_f")] ) thermal_f_net = hFTBArch(f_net) thermal_s_net = FourierNetArch( input_keys=[Key("x"), Key("y"), Key("z")], output_keys=[Key("theta_s")] ) flux_s_net = FourierNetArch( input_keys=[Key("x"), Key("y"), Key("z")], output_keys=[ Key("flux_theta_s_x"), Key("flux_theta_s_y"), Key("flux_theta_s_z"), ], ) thermal_nodes = ( ad.make_nodes(detach_names=["u", "v", "w"]) + dif.make_nodes() + flow_grad_norm.make_nodes() + solid_grad_norm.make_nodes() + integrate_flux_dif.make_nodes( detach_names=["flux_theta_s_x", "flux_theta_s_y", "flux_theta_s_z"] ) + robin_flux.make_nodes( detach_names=[ "theta_f_prev_step", "theta_f_prev_step__x", "theta_f_prev_step__y", "theta_f_prev_step__z", ] ) + dirichlet.make_nodes(detach_names=["theta_s"]) + [flow_net.make_node(name="flow_network", optimize=False)] + [ thermal_f_net.make_node( name="thermal_fluid_network", optimize=True ) ] + [ thermal_s_net.make_node( name="thermal_solid_network", optimize=True ) ] + [flux_s_net.make_node(name="flux_solid_network", optimize=True)] ) # make domain for first cycle of hFTB cycle_1_domain = Domain("cycle_1") # add constraints to solver x, y, z = Symbol("x"), Symbol("y"), Symbol("z") import time as time tic = time.time() # inlet inlet = PointwiseBoundaryConstraint( nodes=thermal_nodes, geometry=limerock.inlet, outvar={"theta_f": nd_inlet_temp}, batch_size=cfg.batch_size.inlet, batch_per_epoch=50, lambda_weighting={"theta_f": 1000.0}, ) cycle_1_domain.add_constraint(inlet, "inlet") # outlet outlet = PointwiseBoundaryConstraint( nodes=thermal_nodes, geometry=limerock.outlet, outvar={"normal_gradient_theta_f": 0}, batch_size=cfg.batch_size.outlet, lambda_weighting={"normal_gradient_theta_f": 1.0}, ) cycle_1_domain.add_constraint(outlet, "outlet") # channel walls insulating walls = PointwiseBoundaryConstraint( nodes=thermal_nodes, geometry=limerock.geo, outvar={"normal_gradient_theta_f": 0}, batch_size=cfg.batch_size.no_slip, criteria=Or( Or( Eq(y, limerock.geo_bounds_lower[1]), Eq(z, limerock.geo_bounds_lower[2]) ), Or( Eq(y, limerock.geo_bounds_upper[1]), Eq(z, limerock.geo_bounds_upper[2]) ), ), lambda_weighting={"normal_gradient_theta_f": 1.0}, ) cycle_1_domain.add_constraint(walls, name="ChannelWalls") # flow interior low res away from heat sink lr_interior_f = PointwiseInteriorConstraint( nodes=thermal_nodes, geometry=limerock.geo, outvar={"advection_diffusion_theta_f": 0}, batch_size=cfg.batch_size.lr_interior_f, criteria=Or( (x < limerock.heat_sink_bounds[0]), (x > limerock.heat_sink_bounds[1]) ), lambda_weighting={"advection_diffusion_theta_f": 1000.0}, ) cycle_1_domain.add_constraint(lr_interior_f, "lr_interior_f") # flow interiror high res near heat sink hr_interior_f = PointwiseInteriorConstraint( nodes=thermal_nodes, geometry=limerock.geo, outvar={"advection_diffusion_theta_f": 0}, batch_size=cfg.batch_size.hr_interior_f, lambda_weighting={"advection_diffusion_theta_f": 1000.0}, criteria=And( (x > limerock.heat_sink_bounds[0]), (x < limerock.heat_sink_bounds[1]) ), ) cycle_1_domain.add_constraint(hr_interior_f, "hr_interior_f") # fluid solid interface interface = PointwiseBoundaryConstraint( nodes=thermal_nodes, geometry=limerock.geo_solid, outvar={"theta_f": 0.05}, batch_size=cfg.batch_size.interface, criteria=z > limerock.geo_bounds_lower[2], lambda_weighting={"theta_f": 100.0}, ) cycle_1_domain.add_constraint(interface, "interface") # add inferencer data vtk_obj = VTKUniformGrid( bounds=[limerock.geo_bounds[x], limerock.geo_bounds[y], limerock.geo_bounds[z]], npoints=[256, 128, 256], export_map={"u": ["u", "v", "w"], "p": ["p"], "theta_f": ["theta_f"]}, ) def mask_fn(x, y, z): sdf = limerock.geo.sdf({"x": x, "y": y, "z": z}, {}) return sdf["sdf"] < 0 grid_inferencer = PointVTKInferencer( vtk_obj=vtk_obj, nodes=thermal_nodes, input_vtk_map={"x": "x", "y": "y", "z": "z"}, output_names=["u", "v", "w", "p", "theta_f"], mask_fn=mask_fn, mask_value=np.nan, requires_grad=False, batch_size=100000, ) cycle_1_domain.add_inferencer(grid_inferencer, "grid_inferencer") # make domain for all other cycles cycle_n_domain = Domain("cycle_n") # inlet cycle_n_domain.add_constraint(inlet, "inlet") # outlet cycle_n_domain.add_constraint(outlet, "outlet") # channel walls insulating cycle_n_domain.add_constraint(walls, name="ChannelWalls") # flow interior low res away from heat sink cycle_n_domain.add_constraint(lr_interior_f, "lr_interior_f") # flow interiror high res near heat sink cycle_n_domain.add_constraint(hr_interior_f, "hr_interior_f") # diffusion dictionaries diff_outvar = { "diffusion_theta_s": 0, "compatibility_theta_s_x_y": 0, "compatibility_theta_s_x_z": 0, "compatibility_theta_s_y_z": 0, "integrate_diffusion_theta_s_x": 0, "integrate_diffusion_theta_s_y": 0, "integrate_diffusion_theta_s_z": 0, } diff_lambda = { "diffusion_theta_s": 1000000.0, "compatibility_theta_s_x_y": 1.0, "compatibility_theta_s_x_z": 1.0, "compatibility_theta_s_y_z": 1.0, "integrate_diffusion_theta_s_x": 1.0, "integrate_diffusion_theta_s_y": 1.0, "integrate_diffusion_theta_s_z": 1.0, } # solid interior interior_s = PointwiseInteriorConstraint( nodes=thermal_nodes, geometry=limerock.geo_solid, outvar=diff_outvar, batch_size=cfg.batch_size.interior_s, lambda_weighting=diff_lambda, ) cycle_n_domain.add_constraint(interior_s, "interior_s") # limerock base sharpen_tanh = 60.0 source_func_xl = (tanh(sharpen_tanh * (x - source_origin[0])) + 1.0) / 2.0 source_func_xh = ( tanh(sharpen_tanh * ((source_origin[0] + source_dim[0]) - x)) + 1.0 ) / 2.0 source_func_yl = (tanh(sharpen_tanh * (y - source_origin[1])) + 1.0) / 2.0 source_func_yh = ( tanh(sharpen_tanh * ((source_origin[1] + source_dim[1]) - y)) + 1.0 ) / 2.0 gradient_normal = ( nd_source_term * source_func_xl * source_func_xh * source_func_yl * source_func_yh ) base = PointwiseBoundaryConstraint( nodes=thermal_nodes, geometry=limerock.geo_solid, outvar={"normal_gradient_flux_theta_s": gradient_normal}, batch_size=cfg.batch_size.base, criteria=Eq(z, limerock.geo_bounds_lower[2]), lambda_weighting={"normal_gradient_flux_theta_s": 10.0}, ) cycle_n_domain.add_constraint(base, "base") # fluid solid interface interface = PointwiseBoundaryConstraint( nodes=thermal_nodes, geometry=limerock.geo_solid, outvar={"dirichlet_theta_s_theta_f": 0, "robin_theta_s": 0}, batch_size=cfg.batch_size.interface, criteria=z > limerock.geo_bounds_lower[2], lambda_weighting={"dirichlet_theta_s_theta_f": 100.0, "robin_theta_s": 1.0}, ) cycle_n_domain.add_constraint(interface, "interface") # add fluid inferencer data cycle_n_domain.add_inferencer(grid_inferencer, "grid_inferencer") # add solid inferencer data vtk_obj = VTKUniformGrid( bounds=[ limerock.geo_hr_bounds[x], limerock.geo_hr_bounds[y], limerock.geo_hr_bounds[z], ], npoints=[128, 128, 512], export_map={"theta_s": ["theta_s"]}, ) def mask_fn(x, y, z): sdf = limerock.geo.sdf({"x": x, "y": y, "z": z}, {}) return sdf["sdf"] > 0 grid_inferencer = PointVTKInferencer( vtk_obj=vtk_obj, nodes=thermal_nodes, input_vtk_map={"x": "x", "y": "y", "z": "z"}, output_names=["theta_s"], mask_fn=mask_fn, mask_value=np.nan, requires_grad=False, batch_size=100000, ) cycle_n_domain.add_inferencer(grid_inferencer, "grid_inferencer_solid") # peak temperature monitor invar_temp = limerock.geo_solid.sample_boundary( 10000, criteria=Eq(z, limerock.geo_bounds_lower[2]) ) peak_temp_monitor = PointwiseMonitor( invar_temp, output_names=["theta_s"], metrics={"peak_temp": lambda var: torch.max(var["theta_s"])}, nodes=thermal_nodes, ) cycle_n_domain.add_monitor(peak_temp_monitor) # make solver slv = SequentialSolver( cfg, [(1, cycle_1_domain), (20, cycle_n_domain)], custom_update_operation=thermal_f_net.move_network, ) # start solver slv.solve() if __name__ == "__main__": run()

To confirm the accuracy of the model, the results are compared for pressure drop and peak temperature with the OpenFOAM and a commercial solver results, and the results are reported in Table 14. The results show good accuracy achieved by the hFTB method. Table 15 demonstrates the impact of mesh refinement on the solution of the commercial solver where with increasing mesh density and mesh quality, the commercial solver results show convergence towards the Modulus results. A visualization of the heat sink temperature profile is shown in Fig. 159.

Table 14 A comparison for the solver and Modulus results for NVSwitch pressure drop and peak temperature.



Commercial Solver


Pressure Drop \((Pa)\)




Peak Temperature \((^{\circ} C)\)




Table 15 Commercial solver mesh refinement results for NVSwitch pressure drop and peak temperature.

Number of elements

Pressure drop (Pa)

Peak temperature \((^{\circ} C)\)

Commercial solver


% diff

Commercial solver


% diff

22.4 M







24.7 M







26.9 M







30.0 M




32.0 M





Fig. 159 NVSwitch Solid Temperature

Previously, Chapter Parameterized 3D Heat Sink showed that by parameterizing the input of the neural network, you can solve for multiple design parameters in a single run and use that parameterized network for design optimization. This section introduces another approach for parameterization and design optimization, which is based on constructing a surrogate using the solution obtained from a limited number of non-parameterized neural network models. Compared to the parameterized network approach that is limited to the CSG module, this approach can be used for parameterization of both constructive solid and STL geometries, and additionally, can offer improved accuracy specially for cases with a high-dimensional parameter space and also in cases where some or all of the design parameters are discrete. However, this approach requires training of multiple neural networks and may require multi-node resources.

This section focuses on surrogates based on the generalized Polynomial Chaos (gPC) expansions. The gPC is an efficient tool for uncertainty quantification using limited data, and in introduced in Section Generalized Polynomial Chaos. It starts off by generating the required number of realizations form the parameter space using a low discrepancy sequence such as Halton or Sobol. Next, for each realization, a separate neural network model is trained. Note that these trainings are independent from each other and therefore, this training step is embarrassingly parallel and can be done on multiple GPUs or nodes. Finally, a gPC surrogate is trained that maps the parameter space to the quantities of interest (e.g., pressure drop and peak temperature in the heat sink design optimization problem).

In order to reduce the computational cost of this approach associated with training of multiple models, transfer learning is used, that is, once a model is fully trained for a single realization, it is used for initialization of the other models, and this can significantly reduce the total time to convergence. Transfer learning has been previously introduced in Chapter STL Geometry: Blood Flow in Intracranial Aneurysm.

Here, to illustrate the gPC surrogate modeling accelerated via transfer learning, consider the NVIDIA’s NVSwitch heat sink introduced above. We introduce four geometry parameters related to fin cut angles, as shown in Fig. 160. We then construct a pressure drop surrogate. Similarly, one can also construct a surrogate for the peak temperature and use these two surrogates for design optimization of this heat sink.


Fig. 160 NVSwitch heat sink geometry parameterization. Each parameter ranges between 0 and \(\pi/6\).

The scripts for this example are available at examples/limerock/limerock_transfer_learning. Following Section Generalized Polynomial Chaos, one can generate 30 geometry realizations according to a Halton sequence by running, as follows


# import libraries import numpy as np import chaospy # define parameter ranges fin_front_top_cut_angle_ranges = (0.0, np.pi / 6.0) fin_front_bottom_cut_angle_ranges = (0.0, np.pi / 6.0) fin_back_top_cut_angle_ranges = (0.0, np.pi / 6.0) fin_back_bottom_cut_angle_ranges = (0.0, np.pi / 6.0) # generate samples samples = chaospy.generate_samples( order=30, domain=np.array( [ fin_front_top_cut_angle_ranges, fin_front_bottom_cut_angle_ranges, fin_back_top_cut_angle_ranges, fin_back_bottom_cut_angle_ranges, ] ).T, rule="halton", ) samples = samples.T np.random.shuffle(samples) np.savetxt("samples.txt", samples)

Then train a separate flow network for each of these realizations using transfer learning. To do this, update the configs for network checkpoint, learning rate and decay rate, and the maximum training iterations in conf/ Also change the sample_id variable in, and then run This is repeated until all of the geometry realizations are covered. These flow models are initialized using the trained network for the base geometry (as shown in Fig. 157), and are trained for a fraction of the total training iterations for the base geometry, with a smaller learning rate and a faster learning rate decay, as specified in conf/config.yaml. This is because you only need to fine-tune these models as opposed to training them from the scratch. Please note that, before you launch the transfer learning runs, a flow network for the base geometry needs to be fully trained.

Fig. 161 shows the front and back pressure results for different runs. It is evident that the pressure has converged faster in the transfer learning runs compared to the base geometry full run, and that transfer learning has reduced the total time to convergence by a factor of 5.


Fig. 161 NVSwitch front and back pressure convergence results for different geometries using transfer learning.

Finally, randomly divide the pressure drop data obtained from these models into training and test sets, and construct a gPC surrogate, as follows:


# import libraries import numpy as np import csv import chaospy # load data samples = np.loadtxt("samples.txt") num_samples = len(samples) # read monitored values y_vec = [] for i in range(num_samples): front_pressure_dir = ( "./outputs/limerock_flow/tl_" + str(i) + "/monitors/front_pressure.csv" ) back_pressure_dir = ( "./outputs/limerock_flow/tl_" + str(i) + "/monitors/back_pressure.csv" ) with open(front_pressure_dir, "r", encoding="utf-8", errors="ignore") as scraped: front_pressure = float(scraped.readlines()[-1].split(",")[1]) with open(back_pressure_dir, "r", encoding="utf-8", errors="ignore") as scraped: back_pressure = float(scraped.readlines()[-1].split(",")[1]) pressure_drop = front_pressure - back_pressure y_vec.append(pressure_drop) y_vec = np.array(y_vec) # Split data into training and validation val_portion = 0.15 val_idx = np.random.choice( np.arange(num_samples, dtype=int), int(val_portion * num_samples), replace=False ) val_x, val_y = samples[val_idx], y_vec[val_idx] train_x, train_y = np.delete(samples, val_idx, axis=0).T, np.delete( y_vec, val_idx ).reshape(-1, 1) # Construct the PCE distribution = chaospy.J( chaospy.Uniform(0.0, np.pi / 6), chaospy.Uniform(0.0, np.pi / 6), chaospy.Uniform(0.0, np.pi / 6), chaospy.Uniform(0.0, np.pi / 6), ) expansion = chaospy.generate_expansion(2, distribution) poly = chaospy.fit_regression(expansion, train_x, train_y) # PCE closed form print("__________") print("PCE closd form:") print(poly) print("__________") # Validation print("PCE evaluatins:") for i in range(len(val_x)): pred = poly(val_x[i, 0], val_x[i, 1], val_x[i, 2], val_x[i, 3])[0] print("Sample:", val_x[i]) print("True val:", val_y[i]) print("Predicted val:", pred) print("Relative error (%):", abs(pred - val_y[i]) / val_y[i] * 100) print("__________")

The code for constructing this surrogate is available at Fig. 162 shows the gPC surrogate performance on the test set. The relative errors are below 1%, showing the good accuracy of the constructed gPC pressure drop surrogate.


Fig. 162 The gPC pressure drop surrogate accuracy tested on four geometries



Sebastian Scholl, Bart Janssens, and Tom Verstraete. Stability of static conjugate heat transfer coupling approaches using robin interface conditions. Computers & Fluids, 172, 06 2018.

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