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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 the shorter time to results, and better usage of existing data are two important engineers’ aspirations.  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 available to kick-start AI projects. A staggering 35,000+ citations for a particular 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.

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                                                        Optimizing a wind turbine design using the Monolith AI platform (Image courtesy:

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 develop further a design exploration study on an internal combustion engine simulation with Simcenter STAR-CCM+.

In-Cylinder Engine simulation in Simcenter STAR-CCM+

It is not uncommon for engineers who design internal combustion engines to face a relatable problem: too many design permutations to consider in too short of a time. Whether it be a physical test 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 that 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 procedure. We also focused on the evolution of results over time for the full piston cycle. Besides, the 2D planar and 3D distributions of developments 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 explore your full design space in real-time more thoroughly!

Training the Monolith AI model

Among the geometric parameters for this study, which describe the piston-cylinder bowl, three interest parameters stood out. They were selected: the port bend angle, the pre-seat height, and the valve radius. These parameters will moderate the uniformity of the secondary structures’ introduced flow and strength throughout the recirculating zones.

Therefore, when divvying up the simulation data for training and validation, we wanted to do so to challenge the AI model to make general predictions by splitting up the data in a specific way.  Specifically, we trained the AI model on only the sharp port angle cases (30° – 40°), while conducting validation testing on the less steep angle cases (40° – 50°). This helps challenge the AI model to make general predictions by training and validating flow-fields with different separation and recirculation levels.

In all circumstances, the workflow and flow of data between tools were the same:

  1. Run CFD simulations in Simcenter STAR-CCM+
  2. Extract necessary simulation data, post-process, and re-arrange (tabular)
  3. Feed prepared data to Monolith AI for AI model development
  4. Make predictions!
AI in Internal Combustion Engine CFD Parameters
                                                                                                          Geometric parameters used in training the AI model

Is the AI model accurate? Very much so

After training is completed, it’s time for validation testing to see how exact the AI model is! To accomplish this, it is as simple as restricting a portion of the data, the 40° – 50° angle port cases, from the AI model training process and then revisiting it once the model is done training. The trained model is fed the inputs from the individual validation cases and makes its own conjectures on the output responses (results). Then these predictions are judged against the CFD results, which are considered a ‘ground truth.’  The pressure, swirl, turbulent kinetic energy, and the wheel’s y component are predicted for every crank angle through the cycle.  As pictured, the Simcenter STAR-CCM+ data (white points), the machine learning predictions (red lines), and the approximation of the AI prediction uncertainty (red band) are plotted together for the case with the most different port bend angle (50°) from the training set. The outcome – a resounding success! The results are almost indistinguishable in some areas between the simulation and AI predictions.

AI in CFD Turbulent KE comparison
                                                                                         Turbulent kinetic energy comparison between Monolith AI and Simcenter STAR-CCM+

Furthermore, let’s provide a more formidable challenge for the AI model. Instead of predicting a time series evolution of single scalar values, let’s try to predict scalar values locally on each cell through the center of the domain’s distinct time instances. The AI model predictions again compare very positively to the ground truth (CFD) data! For this instant in time, both the velocity magnitude field and the turbulent energy are well predicted. The high speed jetting flow, as well as the slow-moving recirculating flow, are captured quite well!

AI in CFD Tumble vs Crank Angle Comparison
                                                                                      Tumble vs. crank angle comparison between Monolith AI and Simcenter STAR-CCM+

Simulation predictions in real-time with AI

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                                                                                    Comparison of results between Monolith AI and Simcenter STAR-CCM+

Now possibly the best part – after training is completed, predictions can be made on new cases in real-time! As someone who built up experience with CFD before AI, this is almost as good as magic no matter how many times I see it, despite knowing how it works! From the video recorded within the Monolith AI interface for my specific case, you can see that it only takes a few seconds from the time a new issue is selected until we see the AI predicted results (right) and the CFD data comparison (left). With authenticity and a bit of whimsy, I can imagine so many possibilities to augment design workflows and quicken design cycles with the wealth of data available (even from different sources).

Internal combustion engine design in Monolith AI

A world of possibilities for your future CFD simulations

This is just one example of how AI and machine learning will change the future of CFD simulations. Our recently announced partnership with Monolith AI will help accelerate design exploration through simulation, test, and AI technologies.

With an already strong foothold in data management and PLM, automation, and analytics, Monolith AI’s addition as a Simcenter partner echoes even more possibilities.

Now let’s close the blog in style – with a famous quote and a call to action. “Any sufficiently advanced technology is indistinguishable from magic.” Reach out to us today to find out how to work together to make some magic from your existing data. For more information on the partnership, click the link below.

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Disclaimer: I am the author at PLM ECOSYSTEM, focusing on developing digital-thread platforms with capabilities across CAD, CAM, CAE, PLM, ERP, and IT systems to manage the product data lifecycle and connect various industry networks. My opinions may be biased. Articles and thoughts on PLMES represent solely the author's views and not necessarily those of the company. Reviews and mentions do not imply endorsement or recommendations for purchase.

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