Resolved Analytics surveyed CFD users and found that the overwhelming majority are using fewer than 257 processors, impacting parallel programming capacity:
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The survey finishes with the conclusion that hardware and software costs continue to limit the parallelization of CFD.įigure 1. At each numerical iteration, the sub-grids communicate boundary information with the adjacent sub-grids and the CFD solution advances toward convergence. In CFD, parallel execution refers to dividing the domain or grid into sub-grids and assigning a processing unit to each sub-grid. One of the statistics shown is the commonly-used levels of parallelism by CFD users today. Just recently, an NVIDIA partner, Resolved Analytics, published a survey on CFD users and tools. It is no longer uncommon for a single simulation to require thousands of CPU core hours to provide a result, and a single design product can require 10,000 to 1,000,000 simulations or more. As organizations are starting to incorporate CFD earlier on in their design cycles, while simultaneously growing the complexity of their models, both in terms of model size and representative physics, to increase the fidelity of the simulations – the industry has reached a tipping point. The complexity in the model quickly grows when additional physics are considered when making design choices, such as studying automotive aeroacoustics which has an influence on customer perception, passenger safety and comfort, or studying road vehicle platooning.Īll the tools used for modeling different flow situations take a staggering amount of compute processing power. Depending on the intended purpose of the simulation, users get to pick if they want to run a steady or a transient simulation using the traditional Navier-Stokes formulation for fluid flow or use alternative frameworks like the lattice Boltzmann method.Įven within the realm of Navier-Stokes solutions, one has a variety of turbulence models and methodologies, such as what scales are resolved and what are modeled, to choose from for the simulations. The other reason for the growth of CFD tools is the fact that capturing all the relevant physics for every type of use case within a single tool is time-consuming to build and validate.įor instance, in the use case of vehicle aerodynamics, a digital wind tunnel can be used to study and evaluate the flow over the geometry and to evaluate the drag produced by the designed surface which has direct implications on vehicle performance. The growing need for accuracy in simulations to help minimize testing led to the incorporation of multi-physics capabilities into CFD tools, such as the inclusion of heat transfer, mass transfer, chemical reactions, particulate flows, and more.
This ubiquity is part of why there are so many CFD tools, commercial, and open-source software available today.
However CFD isn’t only an analysis tool, it is now used to make design improvements without having to resort to time-consuming and expensive physical testing for every design/operation point that is being evaluated. The past decade saw a wider adoption of CFD as a critical tool for engineers and equipment designers to study or predict the behavior of their designs. Evolution of engineering applications for CFD This fact alone is enough proof to show the new age of CFD has arrived. The end of 2021 and beginning of 2022 saw the two largest commercial computational fluid dynamics (CFD) tool vendors, Ansys and Siemens, both launch versions of their flagship CFD tools with support for GPU acceleration. Now is such a critical and transitional moment for the largest single segment of industrial high-performance computing (HPC).
When a technology reaches the required level of maturity, adoption transitions from those considered visionaries to early majority adopters.
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