Scope of Artificial Intelligence for Automation and Optimization in CFD Modeling

Artificial Intelligence (AI) for CFD Modeling

  • Artificial Intelligence (AI) has the potential to significantly impact Computational Fluid Dynamics (CFD) modeling in various ways. CFD involves the simulation of fluid flow and heat transfer using numerical methods, and AI can enhance this process in several aspects.
  • The application of Artificial Intelligence (AI) in Computational Fluid Dynamics (CFD) modeling has the potential to significantly impact and enhance various aspects of fluid flow simulations.
  • Here are several areas where AI can play a crucial role in the scope of CFD modeling:
Comparison of CFD, experiment and AI tools
Comparison of CFD, experiment, and AI tools

Machine Learning Algorithms:

  • Machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning, are applied to learn patterns and relationships within CFD data.
  • These algorithms can be used for tasks such as predicting flow behavior, optimizing designs, and improving simulation accuracy.

Optimization and Design Exploration:

  • AI algorithms, particularly optimization techniques and machine learning models, can be employed to optimize designs for better aerodynamic performance.
  • This includes the development of more efficient shapes for aircraft, automobiles, and other fluid-dependent systems.
Combustion stability Techniques in Industrial Burners
Combustion Stability Techniques in Industrial Burners

Reducing Modeling Efforts

  • AI can help in reducing the computational resources required for CFD simulations.
  • Machine learning models, such as surrogate models or neural networks
  • It can be trained to approximate CFD simulations, allowing for faster evaluations and reduced computational expenses.

Mesh Generation and Adaptation:

  • AI techniques can be applied to automate mesh generation and adaptation processes.
  • This includes the development of adaptive meshes that adjust based on the simulation progress or the use of generative models to create high-quality meshes.
AI models and CFD for meshing
AI models and CFD for meshing

Physics-Informed Machine Learning:

  • Combining physics-based models with machine-learning approaches can lead to more accurate predictions.
  • Physics-informed machine learning techniques incorporate known physical principles into the learning process,
  • improving the reliability of predictions.

Real-Time Flow Control and Prediction:

  • AI can be used for real-time flow control, where machine learning models adapt parameters to optimize flow behavior.
  • Additionally, AI algorithms can predict flow patterns and identify critical points in complex systems.

Data-Driven Approaches:

  • Utilizing large datasets from experiments or simulations, machine learning models can be trained to learn patterns and behaviors in fluid flow.
  • This data-driven approach can enhance the accuracy of simulations and provide insights into complex flow phenomena.

Surrogate Modeling:

  • Surrogate models, often based on machine learning, are trained to approximate complex CFD simulations.
  • These surrogate models can provide fast and computationally inexpensive predictions, making them suitable for use in optimization, sensitivity analysis, and uncertainty quantification.
AI Neural Network for turbulence modeling
AI Neural Network for turbulence modeling

Neural Networks and Deep Learning:

  • Neural networks, especially deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can be used for tasks such as feature extraction, pattern recognition, and even direct simulation of fluid flow.
  • Deep learning models have shown promise in capturing complex relationships within CFD data.

Automation of Simulation Processes:

  • AI can be used to automate various aspects of CFD simulations, such as mesh generation, parameter tuning, and post-processing.
  • This automation reduces the manual effort required in setting up and running simulations, making the process more efficient.

Uncertainty Quantification:

  • AI, especially Bayesian methods, can be applied for uncertainty quantification in CFD simulations.
  • This involves assessing and quantifying uncertainties in input parameters and simulation results.

Complex Flow Modeling 

  • Integration of AI can help in handling multi-physics simulations more efficiently.
  • This involves coupling fluid dynamics with other physical phenomena, such as heat transfer, structural mechanics, and chemical reactions.

Improve Modeling Approach:

  • AI tools can assist engineers in the modeling process, providing real-time feedback, suggesting improvements,
  • AI can facilitate a more interactive and efficient simulation workflow.

Key Application of AI for CFD Modeling

  • The application of Artificial Intelligence (AI) in Computational Fluid Dynamics (CFD) modeling encompasses various aspects of the simulation process.
  • These applications highlight the diverse ways in which AI is contributing to the advancement of CFD modeling, offering opportunities to enhance simulation accuracy, efficiency, and insight into complex fluid flow phenomena.
  • As AI technologies continue to evolve, their integration with CFD is likely to lead to further innovations in fluid dynamics simulations.
  • Here are specific applications where AI is commonly employed in CFD:

Geometry Optimization:

  • AI algorithms can be used to optimize the geometry of objects subjected to fluid flow. The mistakes in geometry making can be reduced as presented in the post.
  • This involves generating and refining shapes that enhance aerodynamic or hydrodynamic performance.
  • Genetic algorithms, neural networks, or other optimization techniques can be applied for this purpose.
Complex Geometry for CFD Modeling
Complex Geometry for CFD Modeling Using AI

Mesh Generation and Adaptation:

  • AI can automate mesh generation processes, selecting appropriate mesh sizes and adapting them dynamically during simulation.
  • This reduces the manual effort required in setting up simulations and ensures better accuracy in capturing fluid behavior.
CFD modeling of human lungs
CFD modeling of human lungs

Simulation of Complex

  • AI techniques can improve multiphysics or turbulent flow modeling, a critical aspect of CFD simulations.
  • CFD Input parameters can customized over the traditional inputs to avoid numerical issues like divergence and convergence issues.
  • Machine learning models can be trained to better predict and simulate turbulent flows
  • providing more accurate and efficient solutions compared to traditional turbulence models.

Automation of Modeling Processes

  • Using scripting languages we can use AI tools to run multiple simulations
  • CFD inputs  can be given using scripting files in Pythons and output can be generated in the report forms
  • Scripting files in pythons helps in the automation of the CFD modeling process with less effort as presented in the post.
Script for ANSYS FLUENT and OpenFOAM in python cover
Script for ANSYS FLUENT and OpenFOAM in Python cover

Conclusion

  • The scope of AI in CFD modeling is continually expanding as researchers and engineers explore new ways to leverage machine learning and other AI techniques to improve the accuracy, efficiency, and usability of fluid flow simulations.
  • It’s important to note that while AI offers promising opportunities for advancing CFD modeling, challenges such as interpretability, reliability, and the need for large labeled datasets still exist.
  • Integrating AI into CFD workflows requires a multidisciplinary approach, involving expertise in both fluid dynamics and machine learning.
  • The field is evolving rapidly, and ongoing research continues to explore new ways to leverage AI for CFD applications.

References

  1. B.Liu, M.G.,X.Sun, H. Du, Application of AI for CFD Modeling, Front.  Comp. Intel.System (2022)
  2. L.Li, C. F. Lange, Y. Ma, AI aided CFD analysis regime validation for  feature-based cyclic CFD  process, Taylor and Francis (2018)