This podcast focuses on explaining the fascinating ways that science and engineering change the world around us. In each episode, we talk to leading engineers from elite-level sports like cycling and Formula 1 to some of world's top academics to understand how fluid dynamics, machine learning & supercomputing are bringing in a new era of discovery. We also hear life stories, career advice and lessons they've learnt along the way that will help you to pursue a career in science and engineering.
This podcast focuses on explaining the fascinating ways that science and engineering change the world around us. In each episode, we talk to leading engineers from elite-level sports like cycling and Formula 1 to some of world's top academics to understand how fluid dynamics, machine learning & supercomputing are bringing in a new era of discovery. We also hear life stories, career advice and lessons they've learnt along the way that will help you to pursue a career in science and engineering.
In this episode, Neil Ashton discusses various conferences and workshops in the automotive, aerospace, and machine learning fields. He highlights the importance of these events for networking, education, and staying updated with industry trends. From the SAE and AIAA events to machine learning workshops, Neil provides insights into what attendees can expect and the value of participating in these gatherings.
In this episode of the Neil Ashton podcast, the host revisits key trends in Computational Fluid Dynamics (CFD) from the past year, focusing on the rise of GPUs, advancements in AI and machine learning, the shift to cloud computing, the increasing adoption of high fidelity methods, and ongoing mergers and acquisitions in the industry. Each trend is explored in depth, highlighting the implications for the future of engineering and technology
In this episode, Professor Brian Launder (Professor at the University of Manchester and Fellow of the Royal Society and Royal Academy of Engineers) shares his remarkable journey through academia, detailing his early fascination with heat transfer, his transition to MIT, and his significant contributions to turbulence modeling and computational fluid dynamics (CFD). We touch upon the key role that Professor Brian Spalding had on his career as well as work that led to the breakthrough k-epilson turbulence model as well as the pioneering work on second-moment closure model. Prof Launder highlights the key role of collaborators and ex students such as Professors Hector Iacovides, Tim Craft, Bill Jones, Kemal Hanjalić and many more. He ends with advice for early-stage researchers and reflections on more than 50 years worth of academic research.
Chapters
00:30 Introduction
05:00 Early Academic Journey
10:06 Transition to MIT and Research Focus
16:21 Return to Imperial College and Early Career
21:06 Research Projects and PhD Students
27:46 Development of the k-epilson model
33:18 CHAM and Career Changes
36:24 Move to UC Davis and New Research Directions
44:05 Challenges and Opportunities in Research
47:07 The Interview Experience
51:14 Transition to Manchester University
52:23 Research Innovations in Turbulence Modeling
57:45 The Development of the TCL Model
01:03:15 Nonlinear Eddy Viscosity Models
01:05:58 Advanced Wall Functions and Their Applications
01:10:09 Reflections on Career and Contributions
01:15:49 Legacy and Impact on Turbulence Modeling
Top Turbulence Modelling contributions (https://scholar.google.com/citations?user=Y3JbAK8AAAAJ&hl=en)
In this episode, Joris Poort, CEO and founder of Rescale, shares his personal journey on founding Rescale as well as his thoughts on the future of CAE. He discusses the challenges of introducing HPC to the cloud market, the traits that make successful founders, and the importance of perseverance and execution in entrepreneurship. Joris reflects on the early days of Rescale, the significance of early investors, and the evolving landscape of cloud computing and AI integration in engineering. The conversation highlights the complexities of transitioning to cloud solutions and the future potential of HPC in various industries. In this conversation, Joris discusses the transformative impact of AI on engineering, particularly in the context of inference, simulation, and automation. He emphasizes the importance of efficiency in engineering processes and how AI can significantly reduce the time required for complex simulations. The discussion also touches on the cultural shifts within organizations as they adapt to AI technologies, the potential for AI surrogates to revolutionize engineering practices, and the challenges of closing the sim-to-real gap. Joris offers insights for aspiring founders, encouraging them to pursue meaningful work that can drive innovation and societal progress.
Chapters
00:00 Introductions
03:30 The Genesis of Rescale: A Cloud Computing Journey
05:21 From Engineering to Entrepreneurship: The Leap of Faith
09:28 Traits of a Successful Founder: Courage and Perseverance
14:51 Tactical Steps to Startup Success: Building from the Ground Up
22:10 Milestones and Breakthroughs: The Early Days of Rescale
30:54 Navigating Challenges: The Role of Cloud Providers in HPC
35:24 The Intersection of HPC and AI Training
37:05 Cloud vs On-Premise: The Cost Debate
39:54 Complexities of HPC in Enterprises
42:27 The Slow Shift to Cloud Adoption
44:34 Optimizing Workloads with Rescale
46:50 Usability Challenges in Enterprise Software
48:32 The Rise of Neo Clouds and Competition
51:18 Speed and Efficiency in AI Training
54:34 AI's Transformative Impact on Engineering
58:54 The Future of AI Surrogates in Design
01:03:28 Agentic AI: The New Paradigm in Engineering
01:14:21 Solving Real Business Problems
01:19:26 The Impact of AI on Engineering
01:22:27 Innovation in Aerospace and Beyond
01:25:19 Cultural Change in Organizations
01:28:34 The Future of AI and Engineering
01:39:09 Advice for Aspiring Founders
Keywords
HPC, cloud computing, startup journey, Rescale, entrepreneurship, AI, technology, innovation, engineering, business, AI, engineering, inference, simulation, automation, digital twin, innovation, aerospace, machine learning, technology
In this episode of the Neil Ashton podcast, Neil discusses the impact of AI on CAE engineering, providing five essential tips for engineers to thrive in this evolving landscape. The conversation covers the importance of maintaining an open mind, continuous education, and preparing for AI physics applications. It also delves into the build vs. buy dilemma for AI solutions and the emerging concept of agentic AI, which promises to revolutionize engineering practices.
Chapters
00:00 Introduction to the Podcast and AI in Engineering
01:03 Five Tips for CAE Engineers in the Era of A1
01:24 1: Keeping an Open Mind
07:39 2: Understanding AI Physics and Its Applications
13:30 3: Preparing for AI Implementation in Engineering
18:54 4: The Build vs. Buy Dilemma in AI Solutions
22:20 5: The Future of Agentic AI in Engineering
In this conversation, Neil Ashton interviews Prof. Johannes Brandstetter, a physicist turned machine learning expert, about his journey from academia to industry, focusing on the application of machine learning in engineering and computational fluid dynamics (CFD). They discuss the Aurora project, the challenges of integrating machine learning with engineering, and the importance of data in training models. Johannes shares insights on the use of transformers in modeling, the significance of resolution independence, and the role of open-source practices in advancing the field. The conversation also touches on the challenges of founding a startup and the need for multidisciplinary collaboration in tackling complex engineering problems.
Links:
Github: https://brandstetter-johannes.github.io
Emmi AI: https://www.emmi.ai
Google scholar: https://scholar.google.com/citations?user=KiRvOHcAAAAJ&hl=de
AB-UPT transform paper: https://arxiv.org/abs/2502.09692
Chapters
00:00 Introduction to Johannes Brandstetter
07:10 The Aurora Project and Key Learnings
11:15 Machine Learning in Engineering and CFD
17:19 Challenges with Mesh Graph Networks
20:16 Transformers in Physics Modeling
31:14 Tokenization in CFD with Transformers
39:58 Challenges in High-Dimensional Meshes
41:08 Inference Time and Mesh Generation
41:36 Neural Operators and CAD Geometry
45:59 Anchor Tokens and Scaling in CFD
48:40 Data Dependency and Multi-Fidelity Models
50:32 The Role of Physics in Machine Learning
54:28 Temporal Modeling in Engineering Simulations
56:58 Learning from Temporal Dynamics
1:00:58 Stability in Rollout Predictions
1:03:48 Multidisciplinary Approaches in Engineering
1:05:18 The Startup Journey and Lessons Learned
In this episode of the Neil Ashton podcast, Professor Russell Cummings shares his extensive journey through the fields of aerodynamics, computational fluid dynamics and hypersonics. He discusses his early inspirations, his early days at University and the Hughes Aircraft Company - a key time during this life. He also talks about the cyclical nature of hypersonics research, and the challenges faced in computational fluid dynamics (CFD). Prof. Cummings emphasizes the importance of perseverance in engineering careers and the need for collaboration between experimental and computational methods. He also shares insights on the role of AI in hypersonics and offers valuable advice for aspiring engineers.
Prof. Russ Cummings graduated from California Polytechnic State University (Cal Poly) with a B.S. and M.S. in Aeronautical Engineering, before receiving his Ph.D. in Aerospace Engineering from the University of Southern California; he also received a B.A. in music from Cal Poly. He is currently Professor of Aeronautics at the U.S. Air Force Academy and Director of the Hypersonic Vehicle Simulation Institute. Prior to this he was Professor of Aerospace Engineering at Cal Poly, where he also served as department chairman for four years. He also worked at Hughes Aircraft Company, and completed a National Research Council postdoctoral research fellowship at NASA Ames Research Center, working on the computation of high angle-of-attack flowfields. He is a Fellow of the Royal Aeronautical Society and the American Institute of Aeronautics and Astronautics.
Distribution Statement A: approved for public release, PA# USAFA-DF-2025-652. The views expressed in this interview are those of the author and do not necessarily reflect the official policy or position of the United States Air Force Academy, the Air Force, the Department of Defense, or the U.S. Government.
Links
Aerodynamics for engineers: https://www.cambridge.org/us/universitypress/subjects/engineering/aerospace-engineering/aerodynamics-engineers-7th-edition?format=HB&isbn=9781009501309
RAeS Lanchester Named Lecture 2024: Frederick W. Lanchester and 'Aerodynamics' https://www.youtube.com/watch?app=desktop&v=lApNzYaZOmk&t=884s
NASA at 50 (Prof Cummings is in the picture): https://images.nasa.gov/details/ARC-1989-AC89-0276-6
Chapters
00:00 Introduction to the Podcast and Guest
04:56 Professor Russell Cummings: A Journey Through Engineering
31:14 The Evolution of Hypersonics Research
58:26 The Role of AI in Hypersonics and CFD
01:37:55 Advice for Aspiring Engineers
In this episode of the Neil Ashton podcast, Professor Mike Giles shares his extensive journey through the fields of computational fluid dynamics (CFD), computational finance and HPC. He discusses his early academic influences, his early days at Cambridge, internships at Rolls-Royce, his transition to MIT and Oxford where he made significant contributions to high-performance computing and numerical analysis. The conversation highlights his hands-on approach to research and teaching, as well as his pioneering work in Monte Carlo methods and GPU computing. This conversation explores the journey of a mathematician and engineer from MIT to Rolls-Royce and then to Oxford, highlighting the evolution of computational engineering, the development of the Hydra code, and the transition from CFD to financial applications. In this conversation, the speaker reflects on their journey through burnout, career transitions, and the evolution of their work in computational finance and numerical analysis. They discuss the challenges of managing large software projects, the shift from Hydra code development to finance, and the integration of advanced methodologies in their work. The conversation also touches on the role of high-performance computing, the impact of AI on research, and advice for the next generation of students pursuing careers in mathematics and programming.
Links:
https://people.maths.ox.ac.uk/gilesm/
Chapters
00:00 Introduction
06:25 Professor Mike Giles: A Journey Through CFD and Finance
17:30 Early Academic Influences and Career Path
29:34 Transition to MIT and Early Research
40:01 High-Performance Computing and Its Impact
41:30 Navigating Between MIT and Rolls-Royce
44:54 The Evolution of Research at MIT
48:47 Transitioning to Oxford and the Role of Rolls-Royce
51:07 The Genesis of the Hydra Code
01:00:47 The Role of Conferences in Engineering
01:10:58 The Shift from CFD to Financial Applications
01:21:30 Navigating Burnout and Career Transitions
01:24:04 Shifting Focus: From Hydrocode to Computational Finance
01:29:30 Bridging Mathematics and Finance: Methodologies and Techniques
01:35:09 The Role of High-Performance Computing in Modern Research
01:39:20 AI's Impact on Research and Future Directions
01:54:00 Advice for the Next Generation: Pursuing Passion and Skills
In this episode of the Neil Ashton podcast, the discussion revolves around foundational models in fluid dynamics, particularly in the context of computational fluid dynamics (CFD). Neil shares insights from a recent panel discussion and explores the potential of AI in predicting fluid behavior. He discusses the evolution of AI in CFD, the challenges of data availability, and the differing adoption rates between industries. The episode concludes with predictions about the future of foundational models and their impact on the engineering landscape.
Chapters
00:00 Introduction to the Podcast and Topic
01:09 Foundational Models in Fluid Dynamics
10:09 The Evolution of AI in CFD
19:52 Future Predictions and Industry Dynamics
In this episode of the Neil Ashton podcast, Neil discusses the intersection of cycling and engineering with Kurt Bergin-Taylor, head of innovation at Tudor Pro Cycling. They explore how technology and science are transforming cycling into a more competitive and innovative sport, akin to Formula One. The conversation covers various aspects of cycling, including the importance of aerodynamics, nutrition, and the holistic approach to rider performance. Kurt shares insights from his academic background and experiences in professional cycling, emphasizing the need for tailored training and the integration of technology in enhancing performance. They discuss the future of cycling innovation, emphasizing the importance of individualization in gear, collaborative relationships with partners, and the evolving mindset of young cyclists. Kurt highlights the significance of data and AI in optimizing performance and strategies in cycling, while also addressing the need for viewer engagement in the sport. Finally Kurt shares valuable advice for aspiring engineers looking to enter the cycling industry, stressing the importance of mentorship and practical experience.
Chapters
00:00 Introduction to the Podcast and Themes
04:55 Kurt Bergin-Taylor: Background and Role at Tudor Pro Cycling
10:08 The Structure and Dynamics of a Pro Cycling Team
12:59 Innovation in Cycling: Aerodynamics, Thermal Management, and Safety
19:14 Nutrition, Training, and Performance in Cycling
29:18 Future Innovations in Cycling Equipment and Systems
30:42 Understanding Individualization in Cycling Gear
34:30 Collaborative Innovation in Cycling Equipment
38:20 The Evolving Mindset of Young Cyclists
42:28 Enhancing Viewer Engagement in Cycling
46:24 The Future of Data and AI in Cycling
50:05 Advice for Aspiring Engineers in Cycling
Takeaways
- Cycling is increasingly influenced by technology and engineering.
- Tudor Pro Cycling is focused on long-term performance and innovation.
- Aerodynamics plays a crucial role in cycling performance.
- Thermal management is essential for riders in extreme conditions.
- Nutrition has dramatically improved in cycling over the last decade.
- Training methodologies must be tailored to individual riders.
- The relationship between power output and speed is complex.
- Safety innovations are critical as speeds increase in cycling.
- Understanding the whole system of rider and equipment is vital.
- Professional cyclists have different recovery capabilities compared to amateurs. Individualization in cycling gear is crucial for performance.
- Collaborative innovation with partners enhances product development.
- Young cyclists are more educated but sometimes overlook tactical aspects.
- Data-driven insights are essential for optimizing race strategies.
- Viewer engagement can be improved through real-time data sharing.
- AI and machine learning are emerging tools in cycling optimization.
- Mentorship is vital for aspiring professionals in the cycling industry.
- Practical experience and initiative can open doors in professional sports.
- Cycling offers a holistic approach to engineering and performance.
- The cycling industry is growing, providing more opportunities for engineers.
A short episode to give a brief update on what I've been doing and to say sorry for not putting out episodes recently. I've joined NVIIDA as a Distinguished CAE Architect and have been rather busy! New episodes will be coming soon! Listen to the episode to learn more.
In this episode of the Neil Ashton podcast, Neil discusses career advice for aspiring engineers, focusing on the differences between various types of companies, job roles, and the growing importance of software skills in the engineering field. The conversation highlights the pros and cons of working in large enterprises, startups, and consulting firms, as well as the diverse career paths available beyond traditional engineering roles. In this conversation, Neil discusses the evolving landscape of engineering careers, particularly focusing on the increasing relevance of software development and the tech sector. He highlights the diverse career paths available within tech, including software development, product management, and solution architecture, as well as the growing importance of AI in engineering. Neil emphasizes the opportunities for engineers to transition into tech roles and the need for a strong understanding of the tech ecosystem to navigate career decisions effectively.
Chapters
00:00 Introduction to Engineering Careers
03:01 Exploring Company Types in Engineering
06:05 Understanding Job Roles in Engineering
09:00 The Shift Towards Software in Engineering
11:52 Diverse Career Paths Beyond Traditional Engineering
14:47 The Role of Consulting in Engineering
18:03 Navigating the Job Market in Engineering
20:57 The Importance of Software Skills in Engineering
24:03 Conclusion and Future Trends in Engineering Careers
30:08 The Rise of Software Development in Engineering
31:59 The Tech Sector's Growing Relevance to Engineers
36:41 Career Paths in Tech: Software Development and Management
44:27 Understanding Product Management in Tech
48:15 The Role of Solution Architects in Tech
52:04 Consulting and Support Roles in Tech
55:54 AI's Impact on Engineering and Software Development
#careers #engineering #tech #sde #amazon #aws #google #jobs
In this episode of the Neil Ashton podcast, Professor Michael Mahoney discusses the intersection of machine learning, mathematics, and computer science. The conversation covers topics such as randomized linear algebra, foundational models for science, and the debate between physics-informed and data-driven approaches. Prof. Mahoney shares insights on the relevance of his research, the potential of using randomness in algorithms, and the evolving landscape of machine learning in scientific disciplines. He also discusses the evolution and practical applications of randomized linear algebra in machine learning, emphasizing the importance of randomness and data availability. He explores the tension between traditional scientific methods and modern machine learning approaches, highlighting the need for collaboration across disciplines. Prof Mahoney also addresses the challenges of data licensing and the commercial viability of machine learning solutions, offering insights for aspiring researchers in the field.
Prof. Mahoney website: https://www.stat.berkeley.edu/~mmahoney/
Google scholar: https://scholar.google.com/citations?user=QXyvv94AAAAJ&hl=en
Youtube version: https://youtu.be/lk4lvKQsqWU
Chapters
00:00 Introduction to the Podcast and Guest
05:51 Understanding Randomized Linear Algebra
19:09 Foundational Models for Science
32:29 Physics-Informed vs Data-Driven Approaches
38:36 The Practical Application of Randomized Linear Algebra
39:32 Creative Destruction in Linear Algebra and Machine Learning
40:32 The Role of Randomness in Scientific Machine Learning
41:56 Identifying Commonalities Across Scientific Domains
42:52 The Horizontal vs. Vertical Application of Machine Learning
44:19 The Challenge of Common Architectures in Science
46:31 Data Availability and Licensing Issues
50:04 The Future of Foundation Models in Science
54:21 The Commercial Viability of Machine Learning Solutions
58:05 Emerging Opportunities in Scientific Machine Learning
01:00:24 Navigating Academia and Industry in Machine Learning
01:11:15 Advice for Aspiring Scientific Machine Learning Researchers
Keywords
machine learning, randomized linear algebra, foundational models, physics-informed neural networks, data-driven science, computational efficiency, academic advice, numerical methods, AI in science, engineering, Randomized Linear Algebra, Machine Learning, Scientific Computing, Data Availability, Foundation Models, Academia, Industry, Research, Algorithms, Innovation
In this episode of the Neil Ashton Podcast, Dr. Prith Banerjee, CTO of Ansys, shares his extensive journey from academia to the corporate world, discussing the interplay between academia and industry, the role of startups in innovation, and the transformative potential of AI and ML in simulation. He emphasizes the importance of solving real-world problems and the need for collaboration between academia, startups, and large corporations to foster disruptive innovation. He discusses innovative business models for data sharing, the intersection of data-driven and physics-informed approaches, the role of open source in AI innovation, the potential of foundational models in computer-aided engineering (CAE), the future of quantum computing in simulation, and offers advice for aspiring innovators and entrepreneurs. He emphasizes the importance of collaboration, data governance, and the need for interdisciplinary approaches to solve complex problems in engineering and technology.
Dr. Banerjee's book - The Innovation factory: https://www.amazon.com/Innovation-Factory-Prith-Banerjee-PH/dp/B0B7LZPDZW
Youtube version of this episode: https://youtu.be/9Ic5xgJt6BQ
Chapters
00:00 Introduction to the Podcast and Guest
05:18 Dr. Prith Banerjee's Journey: From Academia to CTO
09:10 The Role of Academia, Startups, and Industry
17:22 Advice for Startups: Motivation and Market Sizing
24:04 The Impact of AI and ML on Simulation
35:07 Future of AI in Physics and Simulation
36:10 The Power of Data in AI Models
40:33 Incentivizing Data Sharing for Better Models
42:55 Physics-Driven vs Data-Driven Approaches
47:30 The Role of Open Source in AI Innovation
52:06 Foundational Models and Simulation Data
58:22 The Future of CAE and Quantum Computing
01:06:29 Advice for Aspiring Innovators
Keywords
Neil Ashton, Prith Banerjee, CAE, AI, ML, simulation, academia, startups, industry, innovation, AI, data sharing, physics-driven, open source, foundational models, quantum computing, CAE, simulation, innovation, engineering
In this episode of the Neil Ashton podcast, Peter Coen from NASA discusses the evolution and future of supersonic travel, focusing on the challenges faced by the Concorde, the technological hurdles of modern supersonic aircraft, and the innovative NASA Quesst mission (and X-59 demonstrator) that aims to provide crucial data to rewrite the aviation noise regulations. The conversation delves into the history of supersonic flight, the impact of sonic booms, and the regulatory landscape that will shape the future of aviation. In this conversation, Peter discusses the complexities of supersonic flight, focusing on the physics of shockwaves, innovative design strategies to mitigate sonic booms, and advancements in pilot visibility technology. He emphasizes the importance of human factors in aircraft design and the role of simulation in the development process. The discussion also covers the challenges of engine technology for commercial supersonic travel, the potential for hypersonic passenger travel, and the future of battery technology in aviation. Finally, Peter offers career advice for aspiring professionals in the aeronautics field.
Links
NASA Quesst mission: https://www.nasa.gov/mission/quesst/
AIAA Low-Boom Prediction Workshop: https://lbpw.larc.nasa.gov
X-59 (Lockheed Martin website): https://www.lockheedmartin.com/en-us/products/x-59-quiet-supersonic.html
Chapters
00:00 Introduction to Supersonic Travel
04:05 The History of Supersonic Flight
09:56 Challenges Faced by Concorde
16:02 Technological Challenges of Supersonic Travel
25:48 NASA's X-59 and the Quest Mission
33:45 Future of Supersonic Travel and Regulations
38:04 Understanding Shockwaves in Supersonic Flight
40:02 Design Innovations for Sonic Boom Reduction
43:16 Advancements in Pilot Visibility Technology
46:27 Human Factors in Aircraft Design
48:23 The Role of Simulation in Aircraft Development
51:42 Engine Noise and Its Impact on Supersonic Travel
54:31 The Future of Commercial Supersonic Travel
57:13 Challenges in Engine Technology for Supersonic Aircraft
01:00:17 The Intersection of Military and Supersonic Travel
01:02:09 Exploring Hypersonic Passenger Travel
01:06:39 The Future of Battery Technology in Aviation
01:09:09 Career Advice for Aspiring Aeronautics Professionals
Keywords
supersonic travel, Concorde, NASA, X-59, sonic boom, aviation technology, hypersonic flight, aerospace engineering, aircraft design, noise regulations, supersonic flight, sonic boom, aircraft design, pilot technology, simulation, engine noise, commercial aviation, hypersonic travel, battery technology, aeronautics careers, Peter Coen
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 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
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
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.
Chapters
00:00 Introduction to the Podcast and Season Two
03:38 Nikolas Tombazis: A Key Figure in Formula One
04:56 Early Influences and Passion for Engineering
08:52 The Journey Through Cambridge and PhD Studies
12:57 Entering Formula One: The Path to Benetton
18:25 The Evolution of Aerodynamics in Formula One
24:06 The Role of CFD and Wind Tunnel Technology
38:53 Balancing Technology and Entertainment in F1
44:47 The Future of AI in Formula One
54:56 Understanding Team Dynamics and Performance Variability
01:03:44 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!