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Uncertainty Quantification and Data Assimilation in Fluid Dynamics

→ Europe/Rome
SISSA

SISSA

Via Bonomea 265, 34136 Trieste (TS)
Gianluigi Rozza (SISSA, International School for Advanced Studies), Lorenzo Tamellini (CNR-IMATI), Marcello Meldi (Arts et Métiers ParisTech)
Description

Objectives

The workshop is designed to foster interdisciplinary dialogue and collaboration around the integration of data assimilation (DA) and uncertainty quantification (UQ) techniques in the study and control of fundamental flow configurations and complex flows of industrial interest, which is a central challenge in both academic research and industrial applications.

Flow configurations relevant for industry are inherently complex, nonlinear, and sensitive to initial and boundary conditions. Despite significant advances, the predictive power of computational fluid dynamics (CFD) in this context remains limited by uncertainties in physical models, numerical approximations, and sparse or noisy experimental data.

This workshop aims to address these challenges by showcasing how DA and UQ can be leveraged to improve the reliability, accuracy, and efficiency of CFD analysis in real-world engineering systems. A crucial goal of this meeting is to foster interactions between applied mathematicians, computational scientists, and engineers.

We welcome contributions in forms of short talks (20-30 minute) of any of these themes: 

  1. Advances in UQ and DA methodologies for CFD:  Discussing novel methodologies to increase/strengthen e.g. the ability of DA to fuse experimental measurements (e.g., particle image velocimetry, pressure sensors) with CFD to produce more accurate flow fields, and/or the ability UQ in quantifying confidence levels in simulation outputs and guiding decision-making under uncertainty.

  2. Industrial Relevance and Impact: Exploring applications in sectors such as aerospace (e.g., jet engines, airframe aerodynamics), automotive (e.g., drag reduction, combustion), energy (e.g., wind turbines, heat exchangers), and environmental engineering (e.g., pollutant dispersion, or heating, ventilation, and air conditioning systems), emphasizing how DA/UQ can support design optimization, performance monitoring, and risk assessment.

  3. Data-Driven Modeling and Reduced-Order Techniques: Discussing the coupling of machine learning and reduced-order models with DA/UQ to enable online analysis and control, especially in scenarios where full-scale simulations are expensive.

  4. Experimental Design and Sensor Placement: Presenting strategies for optimal sensor placement and experimental design to maximize the information gain from measurements, thereby improving the effectiveness of data assimilation and reducing epistemic uncertainty.

  5. Software tools enabling technological transfer from research to engineering practice in CFD.

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