The Neil Ashton Podcast

In this episode of the Neil Ashton podcast, we celebrate the life and contributions of Professor Antony Jameson, a pioneer in Computational Fluid Dynamics (CFD). The conversation explores his early influences, academic journey, and significant contributions to aerodynamics and engineering. Professor Jameson shares insights from his career in both academia and industry, highlighting pivotal moments that shaped his work in CFD and transonic flow. Prof. Jameson discusses his journey through the complexities of numerical methods for fluid flow, his transition from industry to academia, the development of influential flow codes, and the evolution of computational fluid dynamics (CFD). He reflects on the challenges of teaching, the impact of his work on the aerospace industry, and the commercialization of CFD technologies. In this conversation, he shares his journey from academia to industry, discussing the challenges and successes he faced in the field of aerodynamics and computational fluid dynamics. He reflects on the importance of innovation, the impact of industry experience on academic research, and offers valuable advice for aspiring professionals in aeronautics. The discussion also touches on the evolution of computational power and the role of machine learning in the field.
Chapters
00:00 Introduction to Computational Fluid Dynamics and Professor Jameson
05:02 Professor Jameson's Early Life and Influences
20:00 Academic Journey and Contributions to Aerodynamics
34:50 Career in Industry and Transition to Academia
48:52 Pivotal Moments in Computational Fluid Dynamics
50:19 Navigating Numerical Methods for Fluid Flow
57:02 Transitioning to Academia and Teaching Challenges
01:06:25 Developing Flow Codes FLO & SYN and Their Impact
01:12:21 The Evolution of Computational Fluid Dynamics
01:19:10 Commercialization and the Future of CFD
01:30:34 Journey to Success: From Code to Commercialization
01:37:02 Innovations in Aerodynamics: Control Theory and Design
01:43:06 The Impact of Industry Experience on Academic Research
01:51:24 The Evolution of Computational Power in Aerodynamics
02:01:29 Advice for Aspiring Aeronautics Professionals
Summary of key work:
(see http://aero-comlab.stanford.edu/jameson/publication_list.html for the publication number)
Th first work that had a strong impact on the aircraft industry was Flo22. The numerical algorithm used in Flo22 is analyzed in detail in Publication 31, Iterative solution of transonic flows.
The next work that had a worldwide impact was the JST scheme in 1981. The AIAA Paper 81-1259 (publication 67) has more than 6000 citations on Google Scholar. Prof. Jameson gave two other presentations a few months earlier which describe the numerical method in more detail. These are publications 63 and 65. More recently he gave a history of the JST scheme and its further development in publication 456, which also gives a detailed discussion of the multigrid scheme which was first described in publication 78.
The Airplane Code described in AIAA Paper 86-0103 (publication 104) was the first code that could solve the Euler equations for a complete aircraft, the culmination of 15 years of his efforts to calculate transonic flows for progressively more complex configurations and with more complete mathematical models. It was never published as a journal article. The design of algorithms for unstructured grids is comprehensively discussed in his book (publication 500).
He proposed the idea of using control theory for aerodynamic shape optimization in 1988 in publication 127, and its further development for transonic flows modeled by the RANS equations is described publications 222 and 229. Its most striking application was the aerodynamic design of the Gulfstream G650 in 2006, when he performed the calculations with Syn107 on a server in his garage.

In this episode, Neil discusses five key trends in Computational Fluid Dynamics (CFD) that are shaping the industry now and in the coming years. He emphasizes the growing importance of GPUs, the integration of AI and machine learning, the shift towards cloud computing, and the potential for mergers and acquisitions in the CFD space. Each trend is explored in detail, highlighting its implications for accuracy, efficiency, and the future of simulation technologies.
Takeaways
GPUs are becoming the primary computing platform for CFD.
AI and ML are driving advancements in CFD methodologies.
Cloud computing is essential for accessing high-performance resources.
The CFD industry is experiencing a shift towards digital certification.
Startups are emerging, focusing on innovative CFD solutions.
Mergers and acquisitions are likely to increase in the CFD market.
Higher fidelity simulations are becoming more feasible with new technologies.
The integration of AI could lead to real-time CFD capabilities.
Cost efficiency is a major driver for adopting new technologies.
The CFD landscape is evolving rapidly, with significant opportunities ahead.
Keywords
CFD, GPUs, AI, Machine Learning, Cloud Computing, Trends, Digital Certification, Mergers, Acquisitions, Simulation
Chapters
00:00 Introduction to CFD Trends
02:04 The Rise of GPUs in CFD
14:06 The Impact of AI and Machine Learning
29:39 The Shift to Cloud Computing
38:41 Digital certification: Higher-fidelity methods
43:00 Future of CFD: Mergers and Innovations

Prof. Karthik Duraisamy is a Professor at the University of Michigan, the Director of the Michigan Institute for Computational Discovery and Engineering (MICDE) and the founder of the startup Geminus.AI. In this episode, we discusses AI4Science, with a particular focus on fluid dynamics and computational fluid dynamics. Prof. Duraisamy talks about the progress and challenges of using machine learning in turbulence modeling and the potential of surrogate models (both data-driven and physics-informed neural networks). He also explores the concept of foundational models for science and the role of data and physics in AI applications. The discussion highlights the importance of using machine learning as a tool in the scientific process and the potential benefits of large language models in scientific discovery. We also discuss the need for collaboration between academia, tech companies, and startups to achieve the vision of a new platform for scientific discovery. Prof. Duraisamy predicts that in the next few years, there may be major advancements in foundation models for science however he cautions against unrealistic expectations and emphasizes the importance of understanding the limitations of AI.
Links:
Summer school tutorials https://github.com/scifm/summer-school-2024 (scroll down for links to specific tutorials)
SciFM24 recordings : https://micde.umich.edu/news-events/annual-symposia/2024-symposium/
SciFM24 Summary : https://drive.google.com/file/d/1eC2HJdpfyZZ42RaT9KakcuACEo4nqAsJ/view
Trillion parameter consortium : https://tpc.dev
Turbulence Modelling in the age of data: https://www.annualreviews.org/content/journals/10.1146/annurev-fluid-010518-040547
LinkedIn: https://www.linkedin.com/showcase/micde/
Chapters
00:00 Introduction
09:41 Turbulence Modeling and Machine Learning
21:30 Surrogate Models and Physics-Informed Neural Networks
28:42 Foundational Models for Science
35:23 The Power of Large Language Models
47:43 Tools for Foundation Models
48:39 Interfacing with Specialized Agents
53:31 The Importance of Collaboration
58:57 The Role of Agents and Solvers
01:08:26 Balancing AI and Existing Expertise
01:21:28 Predicting the Future of AI in Fluid Dynamics
01:23:18 Closing Gaps in Turbulence Modeling
01:25:42 Achieving Productivity Benefits with Existing Tools
Takeaways
-Machine learning is a valuable tool in the development of turbulence modeling and other scientific applications.
-Data-driven modeling can provide additional insights and improve the accuracy of scientific models.
-Physics-informed neural networks have potential in solving inverse problems but may not be as effective in solving complex PDEs.
-Foundational models for science can benefit from a combination of data-driven approaches and physics-based knowledge.
-Large language models have the potential to assist in scientific discovery and provide valuable insights in various scientific domains. Having a strong foundation in the domain of study is crucial before applying AI techniques.
-Collaboration between academia, tech companies, and startups is necessary to achieve the vision of a new platform for scientific discovery.
-Understanding the limitations of AI and managing expectations is important.
-AI can be a valuable tool for productivity gains and scientific assistance, but it will not replace human expertise.
Keywords
#computationalfluiddynamics , #ailearning #largelanguagemodels , #cfd , #supercomputing , #fluiddynamics

The first season of the Neil Ashton podcast comes to a close with a recap of the episodes and a glimpse into what's to come in the next season. Look out for Season 2 in September with lots more great guests and discussion on hypersonics, CFD, Formula One, cycling, space exploration and more!

In this episode of the Neil Ashton podcast, we delve into the fascinating world of cycling, focusing on the critical role of aerodynamics and the evolution of training techniques. Featuring Dr. Michael Hutchinson, a former top-level cyclist and expert in cycling aerodynamics, the conversation explores Dr. Hutch's journey from competitive cycling to becoming a prominent figure in cycling media. The discussion highlights the importance of power meters in training, the cultural landscape of cycling in the UK, and the technical innovations that have transformed the sport. In this conversation, we discuss the evolution of cycling performance, focusing on the impact of training, nutrition, and equipment. We highlight the importance of training less, the advancements in nutrition that allow cyclists to perform better, and the diverse training approaches that exist among athletes. The conversation also touches on the professionalism of cyclists, the rise of women's cycling, and the significant role of aerodynamics and equipment in enhancing performance. In this conversation, Neil and Dr Hutch discusses the intricate balance between power and aerodynamics in cycling, the evolution of rider trust in aerodynamic advice, and the significant impact of wind tunnels on performance. He explores the challenges of wind tunnel testing versus real-world validation, the role of computational fluid dynamics (CFD) in cycling aerodynamics, and the regulatory challenges that arise with advancing technology.
Dr Hutch X handle: https://x.com/Doctor_Hutch
Faster: The Obsession, Science and Luck Behind the World's Fastest Cyclists: https://www.amazon.co.uk/Faster-Obsession-Science-Fastest-Cyclists/dp/1408843757
Chapters
00:00 Introduction to the Podcast and Cycling Passion
02:57 The Intersection of Cycling and Aerodynamics
06:02 Dr. Hutch's Journey into Competitive Cycling
08:57 The Evolution of Aerodynamics in Cycling
12:13 The Role of Power Meters in Cycling Performance
15:01 Training Techniques and the Shift to Power Metrics
17:58 Transitioning from Cycling to Media and Writing
20:50 The Cultural Landscape of Cycling in the UK
24:13 Technical Innovations and Personal Experiments in Aerodynamics
27:01 The Impact of Power Meters on Training and Performance
32:51 The Power of Training Less
34:15 Evolution of Cycling Performance
38:30 Nutrition: The Game Changer
39:47 Diverse Training Approaches
42:31 The Professionalism of Cyclists
48:11 The Rise of Women's Cycling
50:33 Aerodynamics: The Key to Speed
56:06 The Impact of Equipment on Performance
01:05:08 Balancing Power and Aerodynamics in Cycling
01:07:05 The Evolution of Rider Trust in Aerodynamics
01:10:55 The Impact of Wind Tunnels on Cycling Performance
01:12:21 Challenges of Wind Tunnel Testing and Real-World Validation
01:20:26 The Role of CFD in Cycling Aerodynamics
01:25:31 Regulatory Challenges in Cycling Technology
01:31:08 The Future of Cycling: Balancing Technology and Tradition
Keywords
cycling, aerodynamics, Dr. Hutch, power meters, training techniques, cycling culture, performance metrics, cycling history, competitive cycling, cycling media, cycling, training, nutrition, performance, aerodynamics, women's cycling, professional cycling, power meter, skin suits, coaching, cycling, aerodynamics, wind tunnels, biomechanics, CFD, technology, performance, regulations, rider trust, power