AI for CFD: byteLAKE’s approach (part3) | by Marcin Rojek | Jul, 2020

Welcome to part 3 of my AI for CFD blog post series. Previously I explained how the team at byteLAKE took their first steps into the world of CFD somewhat 10 years ago. I summarized how we tried different approaches as we progressed and delivered projects focused on both GPU and FPGA adaptation work. While moving to heterogeneous computing seemed like the right and the only choice at that time to speed up the calculations, we always knew something was missing in the equation. Nevertheless, we listened to the voice of our clients and partners and eventually decided to start building a cross-platform solution with the option for extra benefits offered by heterogeneous architectures rather than build a solution that is tied to any particular hardware. Also, having completed several research projects, we saw a great and at the same time uncharted potential for applying AI (Artificial Intelligence) in one more area, this time HPC (High-Performance Computing) industrial simulations.

Based on all of these experiences we always asked the following question within the byteLAKE team: could AI enable tremendous acceleration of calculations across the CFD industry? Well, it seems that AI has a lot to offer for CFD. Follow this blog post series to stay up to date with byteLAKE’s CFD Suite development work.

byteLAKE’s AI for CFD, CFD Suite is a cross-platform solution and is not tied to any particular hardware. It can work on CPU-only as well as heterogeneous architectures though. We designed in a way that our partners can use CFD Suite in their existing environments and so that no adjustments are needed.

So what is byteLAKE’s CFD Suite? It’s a collection of innovative AI Models for CFD (Computational Fluid Dynamics). We believe we are on our way to offer, through AI, the level of calculations acceleration that is beyond what’s achievable with hardware upgrades or algorithms adaptation to hardware accelerators alone. Needless to say, that the latter approach comes at a huge cost which we aim to eliminate.

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Our strategy is to replace the numerical solvers with equivalent AI models or enable interaction between AI and solvers for much faster analysis and reduced cost of trial & error experiments. In other words, the CFD Suite will make it possible to run many more experiments and better explore the design space before decisions are made. All within radically lower budgets related to the time and resources needed. We are working on solver specific AI models to ensure high accuracy of predictions. Also, byteLAKE’s CFD Suite will become a foundation for bringing the Digital Twin concept to the world of CFD simulations. The models will be generalized so that they can work across various use cases or geometries. We have already reached a milestone where the same model can be successfully used when simulation input parameters change.

byteLAKE’s CFD Suite is a collection of innovative AI Models for CFD (Computational Fluid Dynamics). Ultra-fast results, radically lower TCO. Explore new possibilities. One model even if simulation parameters change.

One can say that byteLAKE’s CFD Suite is a collection of plug & play modules or add-ons for your existing CFD software or a collection of tools that can work in parallel to your existing toolchain or simulation workflow. There is no need to make any adjustments to your existing infrastructure. It can work on CPU-only architectures, CPU+GPU, FPGAs being on the roadmap as well, on your laptop/PC, on-premises data center, will be available through our Cloud Computing partners and what have you. In other words, when byteLAKE says you will eventually get an AI model that is trained to handle your simulation(s), you will get a tool that:

• takes input data which is the same as you now provide to your existing CFD tools
• output generated will also be compatible with what you are getting through your existing toolchain, meaning can be directly used for visualization purposes in your tools.

Bottom line is that byteLAKE’s CFD Suite has been designed as a cross-platform solution to address your CFD acceleration needs. Full compatibility with existing environments makes the learning curve as short as possible.

AI Models within byteLAKE’s CFD Suite work as plug & play modules or tools, fully compatible with your existing toolchains or infrastructure (no hardware-specific requirements). Their ultimate purpose is to generate CFD simulation results much faster than their counterpart CFD solvers.

How does the CFD Suite work? Although it is still a work-in-process with the first product being scheduled for launch in November 2020, let me tell a few words about the underlying architecture as of July 2020. High level view is as illustrated below.

You typically create a line-up of input data describing the phenomena, objects, and related characteristics like i.e. velocities. Such data is then transferred into CFD solvers which produce results sequentially, each iteration or a time step becoming an intermediate result of your simulation. All such intermediate results wrap up into what’s called the simulation result. Then such a result is becoming a foundation for further analysis (and calculations) to answer the designer’s questions, dispel doubts and establish optimal configurations.

Process-wise this is unchanged in the case of AI. Only the results (both simulation result, intermediate results if needed, and further calculations) can be produced much faster. It must be noted that AI is just a tool here, used to accelerate the calculations. Thus, there is again no need for any adjustments on your side and it really does not matter if your organization is ready for AI or not. It does not matter whether you have already considered AI within your roadmaps or strategies either. AI is a hidden layer within byteLAKE’s CFD Suite, designed to work seamlessly. As mentioned, its only purpose is to accelerate the calculations and not generate any requirements in terms of organizational changes, infrastructure upgrades, etc.

On the inside, you will find byteLAKE’s proprietary combination of various CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network), the latter mostly being LSTM (Long Short-Term Memory) networks. For time being we are mostly simulating the results for 2D and 3D meshes of sizes respectively from 32×32(x32) to 128×128(x128). The algorithm we are starting with is the general advection algorithm (which is MPDATA in our case; used i.e. in weather simulations as part of the COSMO Model). So far (July 2020) we have successfully predicted the next 8, 16, and 32 steps. Regarding the accuracy, example results are as follows: