Computational Fluid Dynamics on AWS

Computational Fluid Dynamics on AWS

First Published March 10, 2020 Updated July 27, 2021

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Notices

Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided "as is" without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers.

? 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Contents

Introduction ..........................................................................................................................1 Why CFD on AWS?.............................................................................................................3 Getting started with AWS ....................................................................................................4 CFD approaches on AWS...................................................................................................6

Architectures..................................................................................................................... 6 Software............................................................................................................................ 9 Cluster lifecycle ..............................................................................................................12 CFD case scalability.......................................................................................................13 Optimizing HPC components ............................................................................................19 Compute .........................................................................................................................19 Network........................................................................................................................... 20

This paper has been archived Storage ...........................................................................................................................21

Visualization ...................................................................................................................25

Costs ........F..o...r...t..h...e....l..a..t..e...s..t...t..e...c..h...n...i.c..a...l...c..o...n...t..e..n...t..,...r..e...f.e...r...t..o....t..h...e....A...W.....S..............26 Conclusion .......................W.....h..i..t..e..p...a...p...e...r.s....&.....G...u...i..d..e...s....p...a..g...e...:...................................29 Contributors .........h...t..t.p...s...:./../..a...w....s....a...m.....a..z..o...n......c..o...m..../..w....h...i..t.e...p...a...p...e..r..s..........................29

Further reading ..................................................................................................................29 Document revisions...........................................................................................................30

Abstract

The scalable nature and variable demand of computational fluid dynamics (CFD) workloads makes them well suited for a cloud computing environment. This whitepaper describes best practices for running CFD workloads on Amazon Web Services (AWS). Use this document to learn more about AWS services, and the related quick start tools that simplify getting started with running CFD cases on AWS.

This paper has been archived

For the latest technical content, refer to the AWS Whitepapers & Guides page:



Amazon Web Services

Computational Fluid Dynamics on AWS

Introduction

Fluid dynamics is the study of the motion of fluids, usually in the presence of an object. Typical fluid flows of interest to engineers and scientist include flow in pipes, through engines, and around objects, such as buildings, automobiles, and airplanes. Computational fluid dynamics (CFD) is the study of these flows using a numerical approach. CFD involves the solution of conservation equations (mass, momentum, energy, and others) in a finite domain.

Many CFD tools are currently available, including specialized and "in-house" tools. This variety is the result of the broad domain of physical problems solved with CFD. There is not a universal code for all applications, although there are packages that offer great capabilities. Broad CFD capabilities are available in commercial packages, such as ANSYS Fluent, Siemens Simcenter STAR-CCM+, Metacomp Technologies CFD++, and open-source packages, such as OpenFOAM and SU2.

A typical CFD simulation involves the following four steps.

? Define the geometry -- In some cases, this step is simple, such as modeling

flow in a duct. ITn hothiserpcaaspese, trhihs satespbinevoelvnesacromchplievx ecodmponents and

moving parts, such as modeling a gas turbine engine. For many cases, the

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Processing Unit (GWPUh).itOeftpena,ptheergse&omGeturyidisepsropviadegdeb:y a designer, but the

CFD be a

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This can of system

memory (RAM) depending on the complexity of the geometry.

? Generate the mesh or grid -- Mesh generation is a critical step because computational accuracy is dependent on the size, cell location, and skewness of the cells. In Figure 1, a hybrid mesh is shown on a slice through an aircraft wing. Mesh generation can be iterative with the solution, where fixes to the mesh are driven by an understanding of flow features and gradients in the solution. Meshing is frequently an interactive process and its elliptical nature generally requires a substantial amount of memory. Like geometry definition, generating a single mesh can take hours, days, weeks, and sometimes months. Many mesh generation codes are still limited to a single node, so general-purpose Amazon Elastic Compute Cloud (Amazon EC2) instances, such as the M family, or memory-optimized instances, such as the R family, are often used.

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