Descrizione del progetto
Apprendimento automatico consapevole della fisica per la meccanica dei fluidi
La capacità della modellazione della meccanica dei fluidi di prevedere l’evoluzione di un flusso è attivata sia da principi fisici che da approcci empirici. Da un lato, i principi fisici (ad esempio le leggi di conservazione) sono estrapolativi, vale a dire che forniscono previsioni su fenomeni che non sono stati osservati. D’altra parte, la modellazione empirica fornisce funzioni di correlazione all’interno dei dati. L’intelligenza artificiale e l’apprendimento automatico sono eccellenti nella modellazione empirica. Il progetto PhyCo, finanziato dall’UE, combinerà i principi fisici e la modellazione empirica in un approccio unificato: metodi basati sui dati vincolati dalla fisica per l’ottimizzazione multifisica. Le soluzioni di apprendimento automatico non violeranno i vincoli fisici. Il modello computazionale sarà applicato per ricostruire immagini di flusso ad alta risoluzione da dati a bassa risoluzione, ridurre al minimo le emissioni dei motori aeronautici con flussi reagenti a base di idrogeno e massimizzare la raccolta di energia a emissioni zero dalle oscillazioni della struttura del fluido.
Obiettivo
About a hundred trillion bytes of data has been created in the world while reading this sentence. Central to big data is machine learning, which is an automated way of transforming information into empirical knowledge. Machine learning techniques have been applied to some fluid mechanics problems with success, but there are still three big open questions: Do machine learning algorithms scale to engineering configurations? (Are they robust?); Can we gain physical insight into the solutions? (Are they interpretable?); Can we extrapolate knowledge to other configurations, such as multi-physics problems? (Are they generalizable?).
Fluid mechanics modelling has been historically enabled by both empirical approaches and physical principles. Machine learning models may not be interpretable and robust, but they excel at empirical modelling. On the other hand, physical principles are governed by equations that do not adaptively change, but they are interpretable and robust. This project will combine physical principles and empirical modelling into a unified approach: physics-constrained adaptive learning for multi-physics optimization of unsteady, unpredictable and uncertain flows. The learned solutions will not violate physical constraints.
The technical objectives are to combine physical principles with machine learning; design adaptive multi-physics models by on-the-fly data assimilation; optimize turbulent flows; quantify the uncertainty; and develop a code that wraps around existing simulation software and experiments. This framework will be applied to maximize energy harvesting from aeroelastic systems to produce clean energy; optimize stable aeroengines with low emissions; and reconstruct high-resolution flow fields from low-resolution experimental measurements. We will rigorously interlace chaos theory, Bayesian inference and artificial intelligence. This project will benefit industries that work with multi-physics flows and artificial intelligence companies.
Campo scientifico
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statistics
- natural sciencescomputer and information sciencescomputational sciencemultiphysics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software
Parole chiave
- adjoint equations in stability
- least square shadowing
- computational fluid dynamics
- reacting flows
- machine learning in fluid mechanics
- virtualization of flows
- gas turbines
- reduced-order models of fluids
- thermoacoustic instabilities
- aeroelastic instabilities
- combustion instabilities
- acoustic-flow interaction
- proper orthogonal decomposition
- dynamic mode decomposition
- dimensionality reduction
- flow state estimator
- uncertainty quantification in fluid dynamics
- turbule
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-STG - Starting GrantIstituzione ospitante
10129 Torino
Italia