A general CFD framework for fault-resilient simulations based on multi-resolution information fusion

Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em

doi:10.1016/j.jcp.2017.06.044

Cited by 12 publications

(10 citation statements)

References 23 publications

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“…Compared with CFD, the ANFIS prediction model had higher calculation accuracy under the verification by root-mean-square error (RMSE). Lee et al [18] proposed a CFD-based multi-resolution simulation framework to resolve multiscale problems. The approximation theory and domain decomposition were combined with statistical techniques, such as the co-kriging method, to predict the result.…”

Section: Intelligence In Fsi Simulationmentioning

confidence: 99%

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction

Zou

et al. 2022

Micromachines

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Based on expert system theory and fluid–structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design for additive manufacturing (DfAM). By changing design parameters, a series of flow channel models can be obtained. According to the design characteristics, the system can intelligently allocate suitable computational models to compute the flow field of a specific model. The pressure-based normal stress is abstracted from the results and transmitted to the solid region by the fluid–structure (FS) interface to analyze the strength of the structure. The design space is obtained by investigating the simulation results with the metamodeling method, which is further applied for pursuing design objectives under constraints. Finally, the improved design is derived by gradient-based optimization. This system can improve the accuracy of the FSI simulation and the efficiency of the optimization process. The design optimization of a flow channel in a simplified hydraulic manifold is applied as the case study to validate the feasibility of the proposed system.

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Section: Intelligence In Fsi Simulationmentioning

confidence: 99%

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction

Zou

et al. 2022

Micromachines

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“…Ongoing research is exploring a variety of other 2D interpolation schemes that may provide the patch‐edge values. In the scenario of very large‐scale computations of complex physics each patch may be allocated to each compute core (Lee et al 32 discussed fault tolerance with patches in exascale computation). Then the nearest‐neighbor linear interpolation is simple, gives acceptable basic accuracy (errors

𝒪 (D^{2})

, Section 2.3) and has the advantage of minimizing interpatch/core communication.…”

Section: Stagger Patches Of Staggered Microcode In 2d Spacementioning

confidence: 99%

Large‐scale simulation of shallow water waves via computation only on small staggered patches

Bunder

Divahar

Kevrekidis

et al. 2020

Numerical Methods in Fluids

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A multiscale computational scheme is developed to use given small microscale simulations of complicated physical wave processes to empower macroscale system‐level predictions. By coupling small patches of simulations over unsimulated space, large savings in computational time are realizable. Here, we generalize the patch scheme to the case of wave systems on staggered grids in two‐dimensional (2D) space. Classic macroscale interpolation provides a generic coupling between patches that achieves consistency between the emergent macroscale simulation and the underlying microscale dynamics. Spectral analysis indicates that the resultant scheme empowers feasible computation of large macroscale simulations of wave systems even with complicated underlying physics. As an example of the scheme's application, we use it to simulate some simple scenarios of a given turbulent shallow water model.

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“…Later, the Cokriging method was extended to utilizing correlation between the same QoI from models with different fidelities [5][6][7][8]. This GP-based multi-fidelity method is very useful in scientific computing, because low-fidelity models, e.g., coarse-grained molecular dynamics [9,10], Reynoldsaverage Navier-Stokes equations [11,12], numerical simulations on coarse grids, are often used with high-fidelity models, e.g., molecular dynamics, full Navier-Stokes equations, numerical simulations on fine grids [13], in optimization, uncertainty quantification (UQ), control [14], variable-fidelity quantum mechanical calculations of bandgaps of solids [15], etc. In these tasks, the multi-fidelity method leverages low-fidelity models for speedup, while uses a high-fidelity model to establish accuracy and/or convergence guarantees.…”

Section: Introductionmentioning

confidence: 99%

Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression

Deng¹

2020

CICP

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A general CFD framework for fault-resilient simulations based on multi-resolution information fusion

Cited by 12 publications

References 23 publications

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction

Large‐scale simulation of shallow water waves via computation only on small staggered patches

Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression

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