Simcenter STAR-CCM+ & Monolith Bring Machine Learning To CFD Simulations
Artificial Intelligence (AI) For CFD Simulations
Artificial Intelligence (AI) is not just a buzzword anymore. AI is already transforming technology in every industry. For engineers, product designers and computational fluid dynamics (CFD) simulation specialists, an important question is: how can AI benefit me as an engineer designing systems and hardware?
We know shorter time to results and better usage of existing data are two important aspirations for engineers. Alongside our efforts to accomplish this within the simulation process, we see large dividends from investment in AI technology specifically. There are many useful ways to integrate AI into a CFD space to augment productivity. In this blog, we discuss one such example in detail: using simulation data to train a machine learning (ML) model to make real-time predictions possible throughout the design space. But first, a bit about how AI has permeated into engineering simulation space from hype to proven value.
Monolith AI + Simcenter STAR-CCM+ => Design Freedom
GPUs have advanced from hundreds of parallel (CUDA) cores to thousands in the last 5 years. This is integral for quick calculation times in AI modeling. 100,000+ publications on AI topics globally cited for 2019. Countless platforms are available to kick-start AI projects. A staggering 35,000+ citations for a certain popular machine learning module in Python (Scikit-learn). It is truly an exciting time for CFD engineers, as we can take advantage of these incredible advancements in AI without having graduate degrees in AI ourselves.
We are past the point of questioning if AI can be useful in our simulation communities. Instead, we are now asking (among other questions) which problems are most suitable for AI to help us with. To answer this, Monolith AI, the leading platform for design and engineering, and Simcenter STAR-CCM+ have joined forces to bring the next level of design freedom to engineers.
AI + CFD for an Internal Combustion Engine (ICE) Design
Let’s consider a proof of concept we recently completed with Monolith AI and Simcenter STAR-CCM+. We used AI to further what is possible in a design exploration study on an internal combustion engine simulation with Simcenter STAR-CCM+.
It is not uncommon for engineers who design internal combustion engines to be faced with a relatable problem: too many design permutations to consider in too short of a time. Whether it be physical tests, and/or a variety of simulation tools, there are too many variables and different data sources to consider. To effectively sift through all possibilities and arrive at the most superior design is challenging.
Our solution is to create an AI model which can take important geometric parameter values as input from a user. The model will then provide accurate predictions of the performance associated with that design in real-time.
Furthermore, we didn’t just task ourselves with predicting a single scalar for a design. We also focused on the evolution of results over time for the full piston cycle. In addition, the 2D planar and 3D distributions of results throughout the domain were also considered. All it takes is a few hours spent planning both your simulation and AI modeling space up front, and at your fingertips, you will have the ability to more thoroughly explore your full design space in real time!