Announcement:

An invited panel session (PC-15) on "Data-Driven Modeling of Combustion Dynamics" will be held on Tuesday, 09 January, 2024 (1:00 - 3:00pm Eastern Time), at the AIAA SciTech Forum and Exposition. We invited four distinguishable panelists - Prof. Traian Iliescu from Virginia Tech, Prof. Ionut Farcas from UT Austin (incoming Assistant Professor at Virginia Tech), Dr. Shashank Yellapantula from the National Renewable Energy Laboratory, and Dr. Shivam Barway from the Argonne National Laboratory) to present their work and share their perspectives on the progress, challenges, and future directions of data-driven methods for challenging fluid flow problems.

Workshop Descriptions:

It is well-recognized that the popularity of data-driven modeling techniques is growing in the field of engineering. By “data-driven” we imply methods which build surrogate predictive models of physical systems (a) solely from data computed and learned from a limited number of high and/or low fidelity simulations, or (b) using some form of intrusive code modifications to the original solvers, based on low dimensional representations (such as projection-based reduced order models). We are interested in methods which enable an affordable deployment of high fidelity solvers which tend to be otherwise extremely expensive. Very encouraging applications are being demonstrated in applying such methods to linear or mildly nonlinear problems. In the realm of fluid mechanics, while there have been some successes in low speed and highly viscous flows, porting these methods to unsteady (turbulent) compressible flows introduces many new challenges. There is, however, a tremendous need for such methods in aerospace and related fields, where routine usage of high fidelity solvers is greatly desired, but limited by their cost. It remains challenging to use the current state-of-art data-driven modeling techniques (model reduction, machine learning etc.) for problems with complex fluid physics (multi-scale and multi-physics problems) to meet the requirements of many-query applications in industry (engineering design, optimization and UQ). 

Towards this goal, we are planning a series of activities beginning with an invited panel discussion at the AIAA Scitech Forum on 01/20/2021.

Workshop Objectives:

This workshop will focus on data-driven models to address unresolved challenges resulting from fluid compressibility in aerospace engineering, such as: 1. Presence of dissimilar multi-scale physics; 2. Convection-dominated nonlinear dynamics; 3. Dispersed steep gradients and highly nonlinear/stiff kinetics in combustion and hypersonic flow and 4. Non-stationary chaotic features in flow physics. The following is a list of objectives for this workshop:

• Bring together experts in both the field of computation science and engineering and motivate more research activities to address challenges in data-driven modeling of complex fluid physics

• Assess the state of progress in data-driven techniques for complex fluid physics modeling – multi-scale, multi-physics engineering problems requiring substantial computing resources (turbulence, combustion and hypersonic flow etc.)

• Identify a hierarchy of challenge problems for evaluations of different data-driven techniques using appropriate metrics for accuracy, efficiency, cost and robustness

• Guide the development of improved data-driven techniques to provide fast and accurate models for many-query applications in industry (engineering design, optimization and UQ) – determine individual limitations of current approaches

• Establish pathway to accelerate the transition of fundamental research in data-driven techniques to real industry applications. Primary focus will be aerospace applications such as: Unsteady turbulent reacting flows in rocket and gas turbine combustion chambers, hypersonic flows (inlets, combustion chambers)

Organizing Committee:

Ramakanth Munipalli                                 Air Force Research Laboratory

Cheng Huang                                               University of Kansas

Benjamin Peherstorfer                               New York University

Karen Willcox                                                University of Texas at Austin

Karthik Duraisamy                                       University of Michigan

Charbel Farhat                                              Stanford University

Jan Hesthaven                                               EPFL

Matthias Ihme                                               Stanford University

Irina Tezaur                                            Sandia National Lab

Honorary Members:

Venkateswaran Sankaran                           Air Force Research Laboratory

Fariba Fahroo                                         Air Force Office of Scientific Research

Contact Information:

Please send an email to romworkshop@gmail.com for additional information on the workshop series or to be included on the email distribution list. 

Other Resources:

• Air Force Center of Excellence on Multi-Fidelity Modeling of Rocket Combustion Dynamics: https://afcoe.engin.umich.edu/

• Scientific Machine Learning for Combustion: https://kiwi.oden.utexas.edu/research/combustion-model-reduction.php

• Pressio (open-source project aimed at enabling leading-edge projection-based reduced-order model): https://pressio.github.io/