Jun 28,2024

014 - Part IV The Application of Artificial Intelligence in Fluid Dynamics

 

The Application of Artificial Intelligence in Fluid Dynamics

 

Course Number: 014

 

Fee: Free

 

Length: 4 Hours

 

Credits: This course qualifies for 4 PDH (unstructured) and 4 CEU (structured) technical learning credits issued by most US State Engineering Regulatory Organizations and all Canadian Provincial and Territorial Engineering Regulatory Organizations)

 

Online Self-Directed Format: Primarily Text, with Illustrations and Video

 

Course Description (Syllabus):

 

Welcome to the course "The Application of Artificial Intelligence in Fluid Dynamics" This course is designed to provide a comprehensive understanding of the intersection between artificial intelligence (AI) and fluid dynamics. 

 

The course begins with an introduction to the principles of fluid dynamics, including the Navier-Stokes equations and the continuity equation, which describe the motion and conservation of fluids. We will also provide an overview of AI, its definition, branches, and historical context, and explain how it has evolved over time. The course will then explore the intersection of AI and fluid dynamics, discussing the reasons for integrating AI with fluid dynamics and the potential benefits and challenges of doing so.

 

The course will cover various AI techniques, including machine learning, supervised learning, regression models, classification models, unsupervised learning, clustering, dimensionality reduction, and reinforcement learning. We will explain the principles of neural networks, including the basics, architecture, layers, neurons, activation functions, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). We will also discuss the applications of these techniques in fluid dynamics, including predictive modeling, predicting flow patterns, turbulence modeling, optimization, shape optimization of aerodynamic structures, real-time control of fluid systems, data-driven simulations, and enhancements to computational fluid dynamics (CFD) simulations.

 

The course will also cover the tools and software used in the application of AI in fluid dynamics, including popular AI frameworks such as TensorFlow and PyTorch, as well as specialized datasets and software for fluid dynamics such as ANSYS and OpenFOAM. We will discuss emerging trends in the field, including explainable AI in fluid dynamics and quantum computing applications. By the end of this course, you will have a comprehensive understanding of the application of AI in fluid dynamics, its potential benefits, challenges, and future directions.

 

This course will be updated as new AI technology becomes available for Fluid Dynamics. Once enrolled in this course you can use this as an ongoing resource for sources of the latest specialized AI software applications in Fluid Dynamics.

 

Learning Outcomes: By this course you will learn:

 

1. Introduction:

 

Overview

Definitions

Key Principles 

       Benefits 

 

2. AI Models in Fluid Dynamics:

 

Machine Learning Models – Classification, Regression, Clustering, Reduction, Decision Trees, Vector Support Machines

Deep Learning Models – Architecture, Convolutional Neural Networks, Flow Visualization, Recurrent Neural Networks

 

3. Applications of AI in Fluid Dynamics:

 

Predictive Modeling

Turbulence Modeling

Optimized Solutions

Predicting Flow Patterns

Shape Optimization

Real Time Control of Fluid Systems

CFD Simulations

Data Driven Simulations

Hybrid Simulations

Real World Case Studies

 

4. Tools for Applying AI in Fluid Dynamics:

 

Training an AI Model

Specialized Software for Appling AI in Fluid Dynamics

Frameworks for Training an AI Model

Special Datasets for Fluid Dynamics

Tools for Gathering and Processing Datasets

 

5. Future Directions and Trends in AI Driven Fluid Dynamics:

 

Emerging Trends

Explainable AI – XAI Frameworks Definitions and Software Applications

Quantum Computing Applications

Community Collaboration

 

Who Should Take This Course:

 

Engineer students and engineers less familiar with the use of AI in Fluid Dynamics and engineers who wish to update their knowledge of this rapidly evolving technology in this field of engineering.

 

Course Instructions:

 

ENROLLMENT: You must be registered and logged into the Engineer eLearning Centre website to access this online, self-directed course.

 

Once you are logged into your Engineer eLearning account click “Browse Courses” to find this course. Click Enroll and confirm you wish to enroll.

 

Once you have confirmed your enrollment you will be returned to your account “My Courses”. Click Enter Course in course profile box. You can enter the course at any time and as many times as you wish. There is no time limit as to when you must complete this course. To access the course learning content click the module headings in the side menu.

 

Credit Options

 

After completing this course you have the option of either receiving eMETA PDH or CEU credit(s) or both. eMETA credits qualify for Unstructured and Structured CEU technical learning credits required by most US State Engineering Regulatory Organizations and all Canadian Provincial and Territorial Engineering Regulatory Organizations.

 

To receive your eMETA CEU credit you will be required to take an automated quiz.

 

Instructions on how to receive your credit(s) and download your PDF Certificate(s) are provided in the side menu of this course. Your credits will also be registered in your Engineer eLearning Centre account. You can download a Transcript of all credits earned in the Engineer eLearning Centre by clicking the Transcript tab in your account.

 

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