RANS uncertainty, data-driven turbulence modeling and AI for Science are the focus of this conversation with Professor Paola Cinnella, Professor of Fluid Mechanics at Sorbonne University and Director of SCAI. Neil and Paola discuss high-order methods, dense gases, Bayesian uncertainty, AirfRANS, surrogate modeling, scientific publishing and education in the AI era.
Full episode, corrected transcript and resources:
https://neilashton.co.uk/podcasts/s4-e4-prof-paola-cinnella-on-ai-for-science-and-fluid-mechanics/
Topics
Fluid mechanics, CFD and high-order schemes
Dense gases, real-gas effects and expansion shockwaves
Uncertainty quantification and Bayesian methods
RANS turbulence-model uncertainty
AirfRANS and CFD datasets for machine learning
Turbulence modeling vs. surrogate modeling
Scientific publishing and ML-for-CFD standards
SCAI and AI for Science
Education, ChatGPT and centaur scientists
Papers
Quantification of model uncertainty in RANS simulations: A review — Heng Xiao, Paola Cinnella
https://doi.org/10.1016/j.paerosci.2018.10.001
Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression — Martin Schmelzer, Richard P. Dwight, Paola Cinnella
https://doi.org/10.1007/s10494-019-00089-x
Bayesian estimates of parameter variability in the k-epsilon turbulence model — W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijl
https://doi.org/10.1016/j.jcp.2013.10.027
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions
https://arxiv.org/abs/2212.07564
Data-driven turbulence modeling — Paola Cinnella
https://arxiv.org/abs/2404.09074
Direct numerical simulations of supersonic turbulent channel flows of dense gases — Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelt
https://doi.org/10.1017/jfm.2017.237
Links
Paola Cinnella named Director of SCAI
https://scai.sorbonne-universite.fr/news/paola-cinnella-new-director
SCAI
https://scai.sorbonne-universite.fr/
Paola Cinnella — HAL publications
https://cv.hal.science/paola-cinnella
Paola Cinnella — Google Scholar
https://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJ
ERCOFTAC SIG 54 — Machine Learning for Fluid Dynamics
https://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/
Chapters
00:00 Podcast intro
00:39 Introducing Prof. Paola Cinnella
03:28 Conversation begins
03:56 How Paola Found Fluid Mechanics
07:09 Moving from Italy to France
08:37 High-Order Schemes and Compressible Flows
09:30 Building an Academic Career
12:06 Dense Gases and Uncertainty Quantification
15:16 Expansion Shockwaves and Real-Gas Effects
19:17 Returning to Paris and Academic Mobility
24:52 Academia, Passion and Persistence
27:51 Bayesian Methods and Turbulence Uncertainty
30:47 Learning Statistics Across Disciplines
33:07 LearnFluidS, AirfRANS and CFD Datasets
36:33 Skepticism and Physics in ML Turbulence Modeling
40:41 Could ML Lead to a Universal Turbulence Model?
42:59 Turbulence Models, Surrogate Models and RANS
45:03 Why LES Alone Cannot Solve Optimization
47:15 Multi-Fidelity Modeling
49:08 What Computers & Fluids Looks for in ML-for-CFD Papers
54:05 CFD Metrics vs. Machine-Learning Metrics
57:13 Overselling, Publication Pressure and Quality
01:02:22 SCAI and AI for Science
01:06:07 Cross-Disciplinary AI for Science
01:09:26 Education in the AI Era
01:12:44 Critical Thinking and AI Outputs
01:18:15 AI as a Companion, Not a Replacement
01:21:42 AlphaFold and the Future of Discovery
01:23:43 Training Centaur Scientists
01:25:11 Closing Thoughts
