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.Chapters00:00 Introduction to Computational Fluid Dynamics and Professor Jameson05:02 Professor Jameson's Early Life and Influences20:00 Academic Journey and Contributions to Aerodynamics34:50 Career in Industry and Transition to Academia48:52 Pivotal Moments in Computational Fluid Dynamics50:19 Navigating Numerical Methods for Fluid Flow57:02 Transitioning to Academia and Teaching Challenges01:06:25 Developing Flow Codes FLO & SYN and Their Impact01:12:21 The Evolution of Computational Fluid Dynamics01:19:10 Commercialization and the Future of CFD01:30:34 Journey to Success: From Code to Commercialization01:37:02 Innovations in Aerodynamics: Control Theory and Design01:43:06 The Impact of Industry Experience on Academic Research01:51:24 The Evolution of Computational Power in Aerodynamics02:01:29 Advice for Aspiring Aeronautics ProfessionalsSummary 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 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 Chapters00:00 Introduction to the Podcast and Cycling Passion02:57 The Intersection of Cycling and Aerodynamics06:02 Dr. Hutch's Journey into Competitive Cycling08:57 The Evolution of Aerodynamics in Cycling12:13 The Role of Power Meters in Cycling Performance15:01 Training Techniques and the Shift to Power Metrics17:58 Transitioning from Cycling to Media and Writing20:50 The Cultural Landscape of Cycling in the UK24:13 Technical Innovations and Personal Experiments in Aerodynamics27:01 The Impact of Power Meters on Training and Performance32:51 The Power of Training Less34:15 Evolution of Cycling Performance38:30 Nutrition: The Game Changer39:47 Diverse Training Approaches42:31 The Professionalism of Cyclists48:11 The Rise of Women's Cycling50:33 Aerodynamics: The Key to Speed56:06 The Impact of Equipment on Performance01:05:08 Balancing Power and Aerodynamics in Cycling01:07:05 The Evolution of Rider Trust in Aerodynamics01:10:55 The Impact of Wind Tunnels on Cycling Performance01:12:21 Challenges of Wind Tunnel Testing and Real-World Validation01:20:26 The Role of CFD in Cycling Aerodynamics01:25:31 Regulatory Challenges in Cycling Technology01:31:08 The Future of Cycling: Balancing Technology and TraditionKeywordscycling, 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

Discover the exciting future of Computational Fluid Dynamics with a focus on emerging trends. Learn how GPUs are becoming the go-to platform, while AI and machine learning are transforming CFD methodologies. Cloud computing is reshaping how resources are accessed, promoting digital certification. Startups are stepping up with innovative solutions, and mergers and acquisitions are on the rise in this dynamic industry. The rapid evolution is poised to enhance simulation accuracy and efficiency, opening up new opportunities!

In this episode of the Neil Ashton podcast, Nikolas Tombazis discusses his journey into engineering and Formula One, starting from his passion for mathematics, physics, and design. He shares how his childhood dream of designing Formula One cars led him to pursue engineering. Tombazis also talks about his experience at Cambridge University and the freedom he enjoyed during his university years. He then delves into his decision to pursue a PhD in experimental aerodynamics and the valuable skills he gained from his research. Tombazis reflects on the challenges and responsibilities of being a chief aerodynamicist in Formula One, as well as the evolving role of CFD in the industry. The conversation explores the advancements in wind tunnel technology and computational fluid dynamics (CFD) in Formula One. It discusses the role of CFD as a design tool and the potential for it to become the predominant tool in the future. The conversation also touches on the balance between the technical aspects of the sport and the entertainment value for fans. The importance of teamwork, leadership, and culture in Formula One teams is highlighted, as well as the challenges of maintaining success and avoiding complacency. The conversation concludes with advice for aspiring Formula One professionals, emphasizing the value of a broad skill set and the potential for Formula One as a stepping stone to other industries.Chapters00:00 Introduction to the Podcast and Season Two03:38 Nikolas Tombazis: A Key Figure in Formula One04:56 Early Influences and Passion for Engineering08:52 The Journey Through Cambridge and PhD Studies12:57 Entering Formula One: The Path to Benetton18:25 The Evolution of Aerodynamics in Formula One24:06 The Role of CFD and Wind Tunnel Technology38:53 Balancing Technology and Entertainment in F144:47 The Future of AI in Formula One54:56 Understanding Team Dynamics and Performance Variability01:03:44 Advice for Aspiring Engineers in Formula One

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!

Anima Anandkumar, a Bren Professor at Caltech and a leading scientist in AI and ML, shares her journey from academia to industry roles at Nvidia and AWS. They discuss the groundbreaking role of AI in enhancing weather forecasting and complex phenomena modeling, including applications in fluid dynamics and nuclear fusion. Anandkumar emphasizes the need for collaboration between AI experts and domain specialists. She also highlights the revolutionary potential of neural operators in improving simulation accuracy, bridging the gap between traditional methods and AI advancements.

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/viewTrillion parameter consortium  : https://tpc.devTurbulence Modelling in the age of data: https://www.annualreviews.org/content/journals/10.1146/annurev-fluid-010518-040547LinkedIn:  https://www.linkedin.com/showcase/micde/Chapters00:00 Introduction09:41 Turbulence Modeling and Machine Learning21:30 Surrogate Models and Physics-Informed Neural Networks28:42 Foundational Models for Science35:23 The Power of Large Language Models47:43 Tools for Foundation Models48:39 Interfacing with Specialized Agents53:31 The Importance of Collaboration58:57 The Role of Agents and Solvers01:08:26 Balancing AI and Existing Expertise01:21:28 Predicting the Future of AI in Fluid Dynamics01:23:18 Closing Gaps in Turbulence Modeling01:25:42 Achieving Productivity Benefits with Existing ToolsTakeaways-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

In this episode, Neil interviews Professor Max Welling, one of the foremost experts in Machine Learning about AI4Science: the use of machine learning and AI to solve challenges in various scientific disciplines. They discuss and debate between data-driven and physics-driven approaches, the potential for foundational models, the importance of open sourcing models and data, the challenges of data sharing in science, and the ethical considerations of releasing powerful models. The conversation covers the role of academia, industry, and startups in driving innovation, with a focus on the field of AI. Professor Welling discusses the advantages and limitations of each sector and shares his experience in academia, big tech companies, and startups. The conversation then shifts to Professor Wellings  new company; CuspAI, which focuses on material discovery for carbon capture using metal organic frameworks and machine learning. Prof. Welling provides insights into the potential applications of this technology and the importance of addressing sustainability challenges. The conversation concludes with a discussion on career advice and the future of AI for science.Links CuspAI : https://www.cusp.ai University website: https://staff.fnwi.uva.nl/m.welling/Google scholar: https://scholar.google.com/citations?user=8200InoAAAAJ&hl=enAI4Science NeurIPS 2023 workshop: https://neurips.cc/virtual/2023/workshop/66548 AI4Science NeurIPS 2022 workshop: https://nips.cc/virtual/2022/workshop/50019Aurora paper: https://arxiv.org/abs/2405.13063 Chapters00:00 Introduction to the Neil Ashton Podcast00:39 Guest Introduction: Professor Max Welling11:12 Data-Driven vs. Physics-Driven Approaches in Machine Learning for Science17:00 Foundational models for science23:08 Discussion around Open-Sourcing Models and Data29:26 Ethical Considerations in Releasing Powerful Models for Public Use33:14 Collaboration and Shared Resources in Addressing Global Challenges34:07 The Role of Academia, Industry, and Startups43:27 Material Discovery for Carbon Capture52:02 Career Advice for Early-stage Researchers01:01:07 The Future of AI for Science and SustainabilityKeywordsAI for science, machine learning, data-driven approaches, physics-driven approaches, foundational models, open sourcing, data sharing, ethical considerations, blockchain technology, academia, industry, startups, AI, material discovery, carbon capture, metal organic frameworks, machine learning, sustainability, career advice, future of AI for science

This episode sets the scene for upcoming discussions on AI4Science with world renowned experts on machine learning. The focus is on using machine learning to solve scientific problems, such as computational fluid dynamics, weather modeling, material design, and drug discovery. The episode introduces the concept of machine learning and its potential to accelerate simulations and predictions. The episode also discusses the differences between machine learning for scientific problems and large language models, and the ongoing debate on incorporating physics into machine learning models.Chapters00:30 Introduction: AI for Science and Machine Learning02:29 The Importance of Computational Fluid Dynamics04:53 The Limitations of Physical Testing and Simulation05:53 Accelerating Simulations and Predictions with Machine Learning09:51 Data-Driven vs Physics-Informed Approaches in Machine Learning13:10 The Future of Machine Learning in Science: Foundational Models

In this episode of the Neil Ashton podcast, Neil interviews Dr. Chris Rumsey,  Research Scientist at NASA Langley Research Center.  Chris is one of the main CFD experts at NASA Langley is globally reconised as a leader in CFD, particularly for aeronautical applications. The conversation focuses on computational fluid dynamics (CFD) and turbulence modeling. They discuss Chris's career, his role in public dissemination of CFD methods, and his involvement in the Turbulence Modeling website. They also explore the High Lift Prediction Workshop and the role of machine learning in CFD and turbulence modeling. The conversation provides insights into working at NASA and the challenges and advancements in CFD and turbulence modeling. In this conversation, Neil and Chris Rumsey discuss the progress and challenges in solving the problem of high-lift aerodynamics in aircraft design. They explore the concept of certification by analysis and the role of computational fluid dynamics (CFD) in reducing the need for expensive wind tunnel and flight tests. They also delve into the use of machine learning in CFD and the challenges of reproducibility. The conversation then shifts to conferences, with Neil and Chris sharing their experiences and favorite events. They conclude by discussing career advice for aspiring aerospace professionals and the unique aspects of working at NASA.00:00 Introduction to the Neil Ashton podcast01:09 Focus on Computational Fluid Dynamics and Turbulence Modeling06:51 Chris Rumsey's Journey to NASA09:13 From Art to Aeronautical Engineering13:08 Transitioning to Turbulence Modeling15:34 The Origins of the Turbulence Modeling Website20:40 Verification and Validation in Turbulence Modeling24:34 The Role of Machine Learning in Turbulence Modeling26:00 Advancements in High Lift Prediction27:28 Challenges in High Lift Prediction28:25 Thoughts on Working at NASA29:42 Certification by Analysis: Reducing the Cost of Aircraft Certification31:09 The Role of Machine Learning in CFD and Certification by Analysis34:03 The Value of Conferences in Networking and Specialized Learning40:30 Career Advice for Aspiring Aerospace Professionals48:45 Curating and Documenting Knowledge in the Aerospace Community