Best Practices for Running Computational Fluid Dynamics (CFD) Workloads on AWS

By Amazon Web Services

July 8, 2020

The scalable nature and variable demand of CFD workloads makes them well-suited for a cloud computing environment. Many of the AWS instance types, such as the compute family instance types, are designed to include support for this type of workload. AWS has network options that support extreme scalability and short turn-around time as necessary.

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 through computer simulation and modeling. CFD involves the solution of conservation equations (mass, momentum, energy, and others) in a finite domain.

A typical CFD simulation involves the following four steps.

  1. Define the Geometry

In some cases, this step is simple, such as modeling flow in a duct. In other cases, this step involves complex components and moving parts, such as modeling a gas turbine engine. For many cases, the geometry creation is extremely time-consuming. The geometry step is graphics intensive and requires a capable graphics workstation, preferably with a Graphics Processing Unit (GPU). Often, the geometry is provided by a designer, but the CFD engineer must “clean” the geometry for input into the flow solver.

  1. 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. 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.

  1. Solve for the solution

For some solutions, tens of thousands of cores (processors) run over weeks to achieve a solution. Conversely, some jobs may run in just minutes when scaled out appropriately. There are choices for model equations depending on the desired solution fidelity. The Navier-Stokes equations form the basis of the solved equations for most fluid calculations. The addition of chemical reactions, liquid and gas multi-phase flows, and many other physical properties create increasing complexity. For example, increasing the fidelity of turbulence modeling is achieved with various approximate equation sets such as Reynolds Average Navier-Stokes (RANS), Large Eddy Simulation (LES), Delayed Detached Eddy Simulation (DDES), and Direct Numerical Simulation (DNS). These models typically do not change the fundamental computational characteristics or scaling of the simulation.

  1. Post process through visualization

Examples of this step include the creation of images, video, and post processing of flow fields, such as creating total forces and moments. Similar to the geometry and mesh steps, this can be graphics and memory intensive CFD workloads that typically scale well on the cloud. Most codes rely on domain decomposition to distribute portions of the calculation to the compute nodes. A case can be run highly parallel to receive results in minutes, or large numbers of cases can run simultaneously as efficiently and cheaply as possible to allow the timely completion of all cases.

Why CFD on AWS?

AWS is a great place to run CFD cases. CFD workloads are typically MPI-based, tightly coupled workloads relying on a large number of cores across many nodes. Many of the AWS instance types, such as the compute family instance types, are designed to include support for this type of workload. AWS has network options that support extreme scalability and short turn-around time as necessary. Small CFD cases can be run on a single node, with a large number of cores, and do not require the use of multiple instances.

 

CFD workloads typically scale well on the cloud. Most codes rely on domain decomposition to distribute portions of the calculation to the compute nodes. A case can be run highly parallel to receive results in minutes, or large numbers of cases can run simultaneously as efficiently and cheaply as possible to allow the timely completion of all cases.

The cloud offers a quick way to deploy and turn around CFD w

orkloads at any scale without the need to own your own infrastructure. You can run jobs that once were in the realm of national labs or large industry. In just an hour or two, you can deploy CFD software, upload input files, launch compute nodes, and complete jobs on a large number of cores. When your job completes, results can be visualized and downloaded, and then all resources can be terminated – allowing you to only pay for what you use. If preferred, your results can be securely archived in cloud storage. Due to cloud scalability, you have the option to run multiple cases simultaneously with a dedicated cluster for each case.

The cloud accommodates the variable demand of CFD. Often, there is a need to run a large number of cases as quickly as possible. Situations can require a sudden burst of tens, to hundreds, to thousands of calculations immediately, and then perhaps noruns until the next cycle. The need to run a large number of cases could be for a preliminary design review, or perhaps a sweep of cases for the creation of a solution database. On the cloud, the cost is the same to run many jobs simultaneously, in parallel, as it is to run them serially, so you can get your data more quickly and at no extra cost. The cost savings in engineering time is an often forgotten part of cost analysis. Running in parallel can be an ideal solution for design optimization. On AWS, you can launch the cases you need when your case is ready, without waiting in a queue for available cluster resources.

Cloud computing is a strong choice for other CFD steps. You can easily launch a GPU instance, a high-memory instance, or a cluster of high-memory instances, to handle the geometry, meshing, and post-processing. With remote visualization software available to handle the display, you can manage the GPU instance running your post-processing visualization from any screen (laptop, desktop, web browser) as though you were working on a large workstation.

Read the full white paper to learn about best practices for running computational fluid dynamics (CFD) workloads on AWS and quick start tools.

Get started with running your CAE/CFD workloads now – fill the form and get a $100 AWS credit!

 

 

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