Courses

Course Description

Course Overview:

This course is designed to provide in-depth knowledge of Machine Learning (ML) techniques and their applications in Computational Fluid Dynamics (CFD). Over the duration of this course, students will explore the fundamentals of CFD, learn how ML is transforming this field, and work on practical implementations that combine both domains. By the end of the course, participants will be able to implement ML techniques for CFD predictions, optimizations, and simulations, creating models that enhance the efficiency and accuracy of fluid dynamics analysis.

Target Audience:

• Advanced students and researchers in fields such as aerospace, mechanical engineering, civil engineering, and applied physics.
• Professionals in industries like automotive, energy, aerospace, and computational engineering looking to integrate ML into CFD workflows.
• Individuals with a strong background in CFD and basic to intermediate knowledge of ML looking to apply these methods to fluid dynamics.

Course Structure and Modules

Module 1: Introduction to CFD and Machine Learning Integration

Overview:

This module introduces the foundations of CFD and machine learning, highlighting the areas where they intersect. The module explores the challenges in traditional CFD methods and how ML can offer solutions to these problems.

Topics:

• Introduction to CFD: Recap of the fundamental principles of fluid dynamics.
  • Governing equations: Navier-Stokes, continuity equations.
  • Numerical methods in CFD: Finite difference, finite element, and finite volume methods.
• Challenges in CFD:
  • Computational cost.
  • High-dimensional spaces.
  • Turbulence modeling.
• Introduction to Machine Learning:
  • Key concepts in ML: Supervised, unsupervised, reinforcement learning.
  • Algorithms in ML: Linear regression, decision trees, random forests, neural networks.
• ML and CFD Synergies:
  • Potential for improving predictions.
  • Optimizing simulation parameters.
  • Reducing computational costs.

Hands-on:

• Introduction to a basic CFD solver (OpenFOAM or Ansys Fluent) and how to integrate Python with these solvers.
• Introduction to Python ML libraries: Scikit-learn, TensorFlow, and PyTorch.

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Module 2: Data-Driven Approaches in CFD

Overview:

This module will cover how data-driven approaches can enhance traditional CFD simulations, including dimensionality reduction and data assimilation techniques.

Topics:

• Generating Datasets for CFD:
  • Methods for generating high-quality CFD datasets.
  • Using simulations to train ML models.
• Dimensionality Reduction Techniques:
  • Principal Component Analysis (PCA).
  • Autoencoders.
  • Manifold learning.
• Data Assimilation in CFD:
  • Kalman filters.
  • Ensemble Kalman filters.
  • Bayesian optimization.

Hands-on:

• Dimensionality Reduction: Use autoencoders or PCA to reduce the complexity of a CFD dataset.
• Dataset Preparation: Generate datasets from simulation results and learn how to preprocess them for ML algorithms.

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Module 3: Supervised Learning for CFD Predictions

Overview:

Learn how to apply supervised learning techniques, such as regression and classification, to predict the results of CFD simulations. This module will also cover turbulence modeling using supervised ML.

Topics:

• Regression in CFD:
  • Linear regression.
  • Polynomial regression.
  • Support Vector Regression (SVR).
• Neural Networks for CFD:
  • Fully connected neural networks.
  • Convolutional Neural Networks (CNNs) for CFD.
• Turbulence Modeling with ML:
  • Predicting turbulence closure models using neural networks.
  • Hybrid RANS-ML models.

Hands-on:

• Build a regression model to predict velocity or pressure distributions.
• Train a neural network to replace a standard turbulence model in a CFD simulation.

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Module 4: Unsupervised Learning for CFD Analysis

Overview:

Explore how unsupervised learning techniques can be applied in CFD to identify patterns, cluster data, and reduce dimensionality.

Topics:

• Clustering in CFD:
  • K-means clustering.
  • Hierarchical clustering.
• Anomaly Detection:
  • Detecting simulation errors or irregularities.
• Reduced Order Models (ROMs):
  • Proper Orthogonal Decomposition (POD).
  • Dynamic Mode Decomposition (DMD).

Hands-on:

• Use K-means clustering to identify flow patterns in a CFD dataset.
• Build a reduced order model using POD and compare it to a full-scale simulation.

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Module 5: Neural Networks in Fluid Dynamics

Overview:

This module provides a deeper dive into the application of neural networks for fluid dynamics. You will learn how deep learning methods can enhance simulation capabilities.

Topics:

• Deep Learning Models for CFD:
  • Recurrent Neural Networks (RNNs).
  • Long Short-Term Memory (LSTM) networks for unsteady flows.
• Physics-Informed Neural Networks (PINNs):
  • Incorporating physical laws into neural networks.
  • Solving Navier-Stokes equations using PINNs.
• Convolutional Neural Networks (CNNs) for Spatial Data:
  • Using CNNs to capture spatial dependencies in CFD datasets.

Hands-on:

• Build an LSTM model to predict unsteady flow patterns.
• Use a PINN to solve a simplified fluid dynamics problem (2D flow over a cylinder).

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Module 6: Surrogate Modeling for CFD

Overview:

This module focuses on creating surrogate models that approximate the behavior of complex CFD simulations using ML techniques, reducing the need for repeated high-fidelity simulations.

Topics:

• Introduction to Surrogate Models:
  • Kriging.
  • Radial Basis Functions (RBF).
  • Gaussian Process Regression (GPR).
• Applications of Surrogate Models:
  • Parameter optimization.
  • Uncertainty quantification.
• Active Learning for Surrogate Models:
  • Using ML to refine surrogate models based on new data.

Hands-on:

• Build a surrogate model for a CFD problem (e.g., airfoil optimization) using Gaussian Process Regression.

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Module 7: Reinforcement Learning in Fluid Dynamics

Overview:

Learn how reinforcement learning (RL) is being applied in fluid dynamics to optimize control systems, boundary conditions, and more.

Topics:

• Introduction to RL:
  • Key concepts: states, actions, rewards.
  • Q-learning, deep Q-networks (DQN).
• CFD Control Problems:
  • Boundary condition control.
  • Optimizing fluid flows using RL.
• Multi-Agent RL in Fluid Dynamics:
  • Swarm optimization and agent-based models in fluid simulations.

Hands-on:

• Implement a reinforcement learning agent to optimize drag reduction for a simplified flow control problem.

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Module 8: AI-Accelerated CFD Solvers

Overview:

Explore the latest developments in AI-accelerated solvers that use ML to speed up CFD computations.

Topics:

• Hybrid ML-CFD Solvers:
  • Leveraging ML to accelerate traditional solvers.
• AI-Based Meshing:
  • ML-driven mesh refinement.
• AI Acceleration for Turbulence Models:
  • ML-based approaches to speeding up RANS and LES simulations.

Hands-on:

• Use an AI-accelerated solver to speed up CFD simulations while maintaining accuracy.

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Module 9: Case Studies and Real-World Applications

Overview:

This module focuses on real-world applications of ML in CFD, showcasing how ML-based techniques are being implemented in industries like automotive, aerospace, and civil engineering.

Topics:

• CFD in Automotive Engineering:
  • Aerodynamics optimization using ML-based surrogate models.
• CFD in Aerospace Engineering:
  • Improving wing design with AI-accelerated solvers.
• Environmental and Civil Engineering:
  • Modeling water flow, pollution dispersal, and weather forecasting using ML-CFD techniques.

Hands-on:

• Work on a capstone project where students apply ML techniques to solve a complex CFD problem in their domain of interest.

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Module 10: Final Project and Assessment

Overview:

In this final module, students will demonstrate their mastery of ML and CFD by completing a final project that integrates the concepts learned throughout the course.

Final Project:

• Choose a complex CFD problem (from aerospace, automotive, energy, etc.).
• Develop an ML model to optimize, predict, or accelerate the CFD simulation.
• Present the results, showing how ML improved the workflow.

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Course Materials

• Python Notebooks for hands-on coding exercises.
• Simulation Data for training and testing ML models.
• Access to CFD Software such as OpenFOAM, ANSYS Fluent, or COMSOL.
• ML Libraries: TensorFlow, PyTorch, Scikit-learn.

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Assessment and Certification

• Quizzes after each module.
• Hands-on exercises graded on implementation and results.
• Final project evaluation based on innovation, application of ML, and presentation of results.
• Certification awarded upon successful completion of the course.

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Conclusion:

This course will equip engineers and researchers with cutting-edge skills to leverage the power of machine learning in enhancing computational fluid dynamics workflows. By combining theoretical knowledge with practical implementation, students will be ready to apply these techniques in real-world scenarios, boosting the efficiency, speed, and accuracy of CFD simulations.