arXiv subjects

Christopher J. Roy

Publications and source records attributed to Christopher J. Roy.

7 recordsLinked to original sources

arXiv2025

Data-Driven Turbulence Modeling Approach for Cold-Wall Hypersonic Boundary Layers

Wall-cooling effect in hypersonic boundary layers can significantly alter the near-wall turbulence behavior, which is not accurately modeled by traditional RANS turbulence models. To address this shortcoming, this paper presents a turbulence modeling approach for hypersonic flows with cold-wall conditions using an iterative ensemble Kalman method. Specifically, a neural-network-based turbulence model is used to provide closure mapping from mean flow quantities to Reynolds stress as well as a variable turbulent Prandtl number. Sparse observation data of velocity and temperature are used to train the turbulence model. This approach is analyzed using direct numerical simulation database for zero-pressure gradient (ZPG) boundary layer flows over a flat plate with a Mach number between 6 and 14 and wall-to-recovery temperature ratios ranging from 0.18 to 0.76. Two training cases are conducted: 1) a single training case with observation data from one flow case, 2) a joint training case where data from two flow cases are simultaneously used for training. Trained models are also tested for generalizability on the remaining flow cases in each of the training cases. The results are also analyzed for insights to inform the future work towards enhancing the generalizability of the learned turbulence model.

physics.flu-dyn↗

arXiv2025

Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state simulations

Recently, the use of neural networks to accelerate the solving of partial differential equations (PDEs) has gained significant traction in both academia and industry. However, employing neural networks as standalone surrogate models raises concerns about solution reliability, especially in precision-critical scientific tasks. This study introduces a novel "super-fidelity" method that leverages neural networks for warm-starting steady-state PDE solvers, ensuring both efficiency and accuracy. Inspired by super-resolution techniques in computer vision, this method maps low-fidelity solutions to high-fidelity targets using a vector-cloud neural network with equivariance (VCNN-e), a neural operator that preserves all necessary invariance and equivariance properties for scalar and vector predictions while seamlessly adapting to different spatial discretizations. We evaluated this approach in three scenarios: (1) a weakly nonlinear case involving low Reynolds number flows around elliptical cylinders, (2) a strongly nonlinear case with high Reynolds number flows over airfoils, and (3) a practical case with high Reynolds number flows over a wing. In all cases, the neural operator-based initialization accelerated convergence by at least two-fold compared to traditional methods, without sacrificing accuracy. The method's robustness and scalability are further demonstrated across different linear equation solvers and multi-process computing configurations. It also achieves overall time savings in scenarios with multiple simulations, even when accounting for model development time. Overall, our approach provides an effective means to accelerate steady-state PDE solutions using neural operators, maintaining high accuracy while significantly improving computational efficiency, particularly in precision-driven scientific applications.

physics.comp-ph↗

arXiv2023

CPU-GPU Heterogeneous Code Acceleration of a Finite Volume Computational Fluid Dynamics Solver

This work deals with the CPU-GPU heterogeneous code acceleration of a finite-volume CFD solver utilizing multiple CPUs and GPUs at the same time. First, a high-level description of the CFD solver called SENSEI, the discretization of SENSEI, and the CPU-GPU heterogeneous computing workflow in SENSEI leveraging MPI and OpenACC are given. Then, a performance model for CPU-GPU heterogeneous computing requiring ghost cell exchange is proposed to help estimate the performance of the heterogeneous implementation. The scaling performance of the CPU-GPU heterogeneous computing and its comparison with the pure multi-CPU/GPU performance for a supersonic inlet test case is presented to display the advantages of leveraging the computational power of both the CPU and the GPU. Using CPUs and GPUs as workers together, the performance can be improved further compared to using pure CPUs or GPUs, and the advantages can be fairly estimated by the performance model proposed in this work. Finally, conclusions are drawn to provide 1) suggestions for application users who have an interest to leverage the computational power of the CPU and GPU to accelerate their own scientific computing simulations and 2) feedback for hardware architects who have an interest to design a better CPU-GPU heterogeneous system for heterogeneous computing.

cs.DC↗

arXiv2020

Multi-GPU Performance Optimization of a CFD Code using OpenACC on Different Platforms

This paper investigates the multi-GPU performance of a 3D buoyancy driven cavity solver using MPI and OpenACC directives on different platforms. The paper shows that decomposing the total problem in different dimensions affects the strong scaling performance significantly for the GPU. Without proper performance optimizations, it is shown that 1D domain decomposition scales poorly on multiple GPUs due to the noncontiguous memory access. The performance using whatever decompositions can be benefited from a series of performance optimizations in the paper. Since the buoyancy driven cavity code is latency-bounded on the clusters examined, a series of optimizations both agnostic and tailored to the platforms are designed to reduce the latency cost and improve memory throughput between hosts and devices efficiently. First, the parallel message packing/unpacking strategy developed for noncontiguous data movement between hosts and devices improves the overall performance by about a factor of 2. Second, transferring different data based on the stencil sizes for different variables further reduces the communication overhead. These two optimizations are general enough to be beneficial to stencil computations having ghost changes on all of the clusters tested. Third, GPUDirect is used to improve the communication on clusters which have the hardware and software support for direct communication between GPUs without staging CPU's memory. Finally, overlapping the communication and computations is shown to be not efficient on multi-GPUs if only using MPI or MPI+OpenACC. Although we believe our implementation has revealed enough overlap, the actual running does not utilize the overlap well due to a lack of asynchronous progression.

cs.DC↗

arXiv2017

A Numerical Investigation of Matrix-Free Implicit Time-Stepping Methods for Large CFD Simulations

This paper is concerned with the development and testing of advanced time-stepping methods suited for the integration of time-accurate, real-world applications of computational fluid dynamics (CFD). The performance of several time discretization methods is studied numerically with regards to computational efficiency, order of accuracy, and stability, as well as the ability to treat effectively stiff problems. We consider matrix-free implementations, a popular approach for time-stepping methods applied to large CFD applications due to its adherence to scalable matrix-vector operations and a small memory footprint. We compare explicit methods with matrix-free implementations of implicit, linearly-implicit, as well as Rosenbrock-Krylov methods. We show that Rosenbrock-Krylov methods are competitive with existing techniques excelling for a number of problem types and settings.

cs.CE↗

arXiv2017

Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multi-fidelity Approach for CFD Applications

Proper quantification and propagation of uncertainties in computational simulations are of critical importance. This issue is especially challenging for CFD applications. A particular obstacle for uncertainty quantifications in CFD problems is the large model discrepancies associated with the CFD models used for uncertainty propagation. Neglecting or improperly representing the model discrepancies leads to inaccurate and distorted uncertainty distribution for the Quantities of Interest. High-fidelity models, being accurate yet expensive, can accommodate only a small ensemble of simulations and thus lead to large interpolation errors and/or sampling errors; low-fidelity models can propagate a large ensemble, but can introduce large modeling errors. In this work, we propose a multi-model strategy to account for the influences of model discrepancies in uncertainty propagation and to reduce their impact on the predictions. Specifically, we take advantage of CFD models of multiple fidelities to estimate the model discrepancies associated with the lower-fidelity model in the parameter space. A Gaussian process is adopted to construct the model discrepancy function, and a Bayesian approach is used to infer the discrepancies and corresponding uncertainties in the regions of the parameter space where the high-fidelity simulations are not performed. The proposed multi-model strategy combines information from models with different fidelities and computational costs, and is of particular relevance for CFD applications, where a hierarchy of models with a wide range of complexities exists. Several examples of relevance to CFD applications are performed to demonstrate the merits of the proposed strategy. Simulation results suggest that, by combining low- and high-fidelity models, the proposed approach produces better results than what either model can achieve individually.

physics.comp-ph↗

arXiv2015

Efficient Functional-Based Adaptation for CFD Applications

Adjoint methods have gained popularity in recent years for driving adaptation procedures which aim to reduce error in solution functionals. While adjoint methods have been proven effective for functional-based adaptation, the practical implementation of an adjoint method can be quite burdensome since code developers constantly need to ensure and maintain a dual consistent discretization as updates are made. Also, since most engineering problems consider multiple functionals, an adjoint solution must be obtained for each functional of interest which can increase the overall computational cost significantly. In this paper, an alternative to adjoints is presented which uses a sparse approximate inverse of the Jacobian of the residual to obtain approximate adjoint sensitivities for functional-based adaptation indicators. Since the approximate inverse need only be computed once, it can be recycled for any number of functionals making the new approach more efficient than a conventional adjoint method. This new method for functional-based adaptation will be tested using the quasi-1D nozzle problem, and results are presented for functionals of integrated pressure and entropy.

physics.flu-dyn↗