AI and Deep Learning CFD Model for Flow Prediction

AI and Deep Learning CFD Model for Flow Prediction

In a series of publications Prof. Thi-Thu-Huong Le, Hoyeun Kang, and colleagues from Pusan National University (PNU) in Korea, have successfully developed a new deep learning (DL) CFD methodology for flow prediction using FEATool Multiphysics with AI and machine learning.

Although traditional Computational Fluid Dynamics (CFD) solvers are becoming increasingly common tool in aerospace, automotive, and other engineering fields, while effective, face limitations, primarily related to high computational cost. Especially when high spatial and temporal resolution is needed for accurate simulation, traditional CFD solvers become computationally expensive, and can sometimes require days or even weeks to perform accurate analysis.


Classic CFD solver compared to AI deep learning CFD solution

Deep learning have potential to offer an alternative approach that may significantly reduce computational cost, creating an AI CFD model on an accurate fluid dynamics dataset. The autors propose such a new CFD model, named CFDformer, which combines a Vision Transformer (ViT) and a U-shaped Convolutional Neural Network (U-Net) in an encoder-decoder architecture to predict fluid flow on 2D geometries.

A DL-CFD model agent can offer a number of advantages compared to traditional CFD simulations

  • Act as Surrogate Models - Deep learning models, trained on data from CFD simulations, can act as surrogate models, effectively approximating the solutions to the Navier-Stokes equations. This approach bypasses the need for computationally intensive iterative calculations.

  • Speed Up Simulations - Deep learning models can significantly reduce simulation times. For example, CFDformer, a hybrid model combining a Vision Transformer and a U-Net, was shown to decrease analysis time by up to 99.94% compared to standard CFD solvers.

  • Complex Flow Scenarios - Deep learning models can handle complex flow scenarios with relatively high accuracy, and are particularly well-suited for modeling flows around obstacles, such as found in aerodynamics applications.

  • Adapt to New Conditions - Some deep learning models can generalize well to new, unseen conditions not encountered during training. This allows them to make reasonable predictions for conditions within a specific range, even without explicit training data for those conditions.

  • Enhancing Feature Extraction - Deep learning models can be designed to extract both local and global features from input data, improving the accuracy of flow approximations. For instance, CFDformer uses convolutional layers to capture local spatial features and a Vision Transformer to analyze global flow features.

The FEATool Multiphysics toolbox was used to generate datasets of 2D incompressible and laminar flows around various obstacles, including cylinders, triangles, rectangles, and pentagons. In particular, as FEATool supports multiple CFD solvers, such as OpenFOAM, using multiple solvers is ideal way to generate accurate and trustworthy, benchmark and validation CFD studies and datasets. These datasets served as the ground truth for training and evaluating deep learning models.


Using FEATool Multiphysics for AI CFD data collection

FEATool’s easy to use GUI, and integration with a MATLAB programming and scripting API allowed for easy manipulation and analysis of the simulation data, streamlining the process of preparing the data for deep learning. Once FEATool completed the simulations, the researchers extracted the velocity and pressure data at each grid point. This data was then preprocessed and formatted as input for training deep learning models.

See the linked references for more detailed information about this research.

References