OpenFoam along with Python and use of Artificial Intelligence
What you will learn
Very Basics of the CFD and the Open Foam Software
Real Examples of the use of Open Foam for CFD
Different Types of CFD Software and there use
Types of fields where CFD can be used
Add-On Information:
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- Master the OpenFoam Ecosystem: Navigate OpenFoam’s intricate directory structure, command-line utilities, and core libraries, gaining fluency in setting up, executing, and monitoring complex CFD simulations from scratch.
- Advanced Meshing Strategies: Delve into sophisticated meshing techniques using OpenFoam’s native utilities like
blockMeshandsnappyHexMesh, mastering boundary layer resolution, feature capturing, and creating high-quality computational grids for challenging geometries. - Solver Customization & Optimization: Gain hands-on experience in selecting, configuring, and optimizing various OpenFoam solvers for diverse flow regimes, including incompressible, compressible, transient, and steady-state problems, understanding the underlying numerical schemes.
- Python for Workflow Automation: Leverage Python scripting to streamline your CFD workflow, automating OpenFoam case setup, parameter variation, post-processing, and generating custom reports, significantly boosting efficiency and reproducibility.
- Intelligent Data Analysis with Python: Employ Python’s powerful libraries for advanced data analysis and visualization of OpenFoam simulation results, extracting meaningful insights and creating publication-quality plots far beyond standard post-processors.
- Introduction to AI-Driven CFD: Explore the cutting-edge integration of Artificial Intelligence and Machine Learning techniques to enhance CFD, covering topics like data-driven turbulence modeling, surrogate model generation for rapid design exploration, and predictive analytics in fluid dynamics.
- Reduced-Order Modeling (ROM): Understand the principles and applications of Reduced-Order Models, learning how AI can be used to drastically reduce computational cost while maintaining accuracy for specific CFD problems, enabling faster iterations in design optimization.
- Turbulence Modeling Deep Dive: Acquire a comprehensive understanding of various turbulence models (RANS, LES, DNS principles) and their practical application within OpenFoam, knowing when and how to select the appropriate model for engineering accuracy.
- Multiphase Flow Simulations: Tackle complex multiphase phenomena, including free surface flows, bubbly flows, and particle-laden flows, learning to set up and analyze these intricate scenarios within the OpenFoam framework.
- Heat Transfer & Conjugate Heat Transfer: Model heat transfer mechanisms comprehensively, including convection, conduction, and radiation, extending to Conjugate Heat Transfer (CHT) problems where fluid and solid heat transfer interact, crucial for thermal management applications.
- Parallel Computing for Performance: Learn to effectively parallelize OpenFoam simulations across multiple processors, optimizing computational resources to handle large-scale, high-fidelity CFD problems efficiently.
- Validation, Verification & Best Practices: Develop a rigorous approach to validating and verifying CFD results against experimental data or analytical solutions, fostering a critical mindset for robust and reliable simulation outcomes.
- PROS:
- Open-Source Mastery: Gain expert-level proficiency in OpenFoam, a powerful, free, and highly customizable open-source CFD package, making you a valuable asset without proprietary software licensing costs.
- Future-Proof Skills: Acquire a unique blend of CFD, Python programming, and foundational AI/ML skills, positioning you at the forefront of modern engineering simulation and data-driven design.
- Real-World Problem Solving: Develop the ability to tackle complex, industry-relevant fluid dynamics and thermal challenges, translating theoretical knowledge into practical, actionable solutions.
- Community & Customization: Understand how to leverage OpenFoam’s vast community support and its flexible architecture for advanced customization, enabling you to adapt to virtually any CFD scenario.
- CONS:
- Steep Learning Curve: While comprehensive, the course covers advanced topics and multiple software/paradigms, requiring significant dedication and independent practice to fully master the breadth of concepts and tools.
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