Once the AI models are trained, they can be run as part of the CFD CAE software in a form of an add-on. As the current version of the CFD Suite (March 2021) is data-driven, the prediction is organized alongside these steps:
1. First, the initial part (i.e., 3–10%) of the simulation is done with a CFD solver
2. Then the CFD Suite takes over the results and its AI model(s) predict the results, accelerating time to results
3. Note that CFD Suite can work with 3D data, producing 100% compatible results with the client’s existing CFD software tools. Therefore, results predicted by CFD Suite can be taken for further processing and analysis right away and no further formatting is necessary.
CFD Suite AI models are produced per solver and can accurately predict results for a family of simulations.
Once the AI models have been trained, the users can freely modify input parameters and still get accurate and fast results. Breaking down the inferencing (prediction) part into steps leads to the following structure of the prediction process:
AI models can work in 3 modes
· ACCURATE — focused on the accuracy of the predictions,
· MEDIUM — offering a trade-off between accuracy and acceleration
· FAST — maximizing reduction of time to results.
Currently, steady-state CFD simulations are supported with transient being on the roadmap.
In the example presented above (FAST mode), the process requires 3.2% of a conventional CFD simulation done by a CFD solver. Afterward, CFD Suite takes over the results, does the data formatting for AI prediction purposes, predicts the outcome of the simulation, and re-calculates the data back to the configured formats. Overall, the acceleration is about getting the results at least 28 times faster for the FAST mode.
On the inside, CFD Suite is based on the reduced ResNet network organized as residual blocks. This is combined with byteLAKE’s proprietary AI models.