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A few flavors of data-driven reduced-order models for fluid flows

Event Type: 
Date and Time: 
Friday, January 28, 2022 - 16:15
Location: 
Building 300, Room 300
Event Sponsor: 
Parviz Moin, Director of Center for Turbulence Research
Speaker(s): 
Dr. Charlélie Laurent

This presentation will detail some of my recent works related to the formulation and computation of data-driven reduced-order models for fluid flows.

In the first part, the concept of Flame Transfer Function (FTF) is introduced. This low-order model, based on a simple Fourier transform, is one of the most common in thermoacoustics, where it is used to represent the dynamical response of a flame to acoustic perturbations. The challenge of the present work lies in the configuration for which a FTF is computed: it consists of a doubly-transcritical LO2-LCH4 coaxial jet-flame, typical of those found in future reusable liquid-rocket engines. The FTF is computed from data simulated with the “real-gas'' version of the unstructured Large Eddy Simulation (LES) solver AVBP (CERFACS, France). Physical interpretations of the FTF are provided, with a particular emphasis on the contributions to the heat-release fluctuations; the effect of heat losses at the injector are also briefly discussed.

In a second independent part, a Deep Neural Network for the identification and reduction of high-dimensional systems is presented. This type of auto-encoder architecture has become ubiquitous in Scientific Machine Learning, due to its applicability to any video-like data obtained either from experiments or simulations. It presents however a major shortcoming, characterized by a strong time-step bias, which prevents accurate model prediction at arbitrary instants. A method is therefore introduced to remediate this point. It relies on the inversion of a Linear Multistep Method, to convert a discrete-time model into a continuous-time model. The approach is illustrated on simple, synthetic data.

Bio: 
Dr. Charlélie Laurent is a postdoctoral fellow at the Center for Turbulence Research at Stanford University, working in the PSAAP-III program. He received his PhD in June 2020 from the Institut National Polytechnique de Toulouse (France), under the supervision of Thierry Poinsot. His PhD thesis focuses on the combination of high-fidelity simulations and reduced-order models to predict combustion instabilities in liquid rocket-engines and gas turbines. Following his PhD graduation, he transitioned to the field of Scientific Machine Learning, with two successive postdoctoral positions at ISAE-Supaero (Toulouse, France), and Johannes Gutenberg Universität (Mainz, Germany). His most recent interest is centered on the use of Deep Neural Networks for system identification and reduction, including some applications to fluid flows.