AEM Seminar: Data Science Tools for Studying Laser-Induced Ignition in a Rocket Combustor
Abstract:
Laser-induced ignition is envisioned as a lightweight and
effective technology for second-stage boosters and low-orbit maneuvers.
One of the critical design challenges is to ensure reliability while
minimizing fuel waste and overpressure. The presence of variability in
the propellant mixture at the time of the laser firing, the imprecision
present in the laser targeting and other potential differences between
the design scenarios and the real-world operations make the process
highly stochastic. In a large project at Stanford, we have developed
high-fidelity simulation tools to faithfully represent the high-speed
turbulent propellant dynamics, the laser energy deposition and the
combustion dynamics of reactive mixture. The computations, together with
a companion experimental campaign form the basis of several
data-science activities. We will summarize how we have used the datasets
to: (1) build data-driven surrogates to study ignition reliability, (2)
perform validation in latent spaces to compare 100s of experimental and
computational realizations, (3) carry out uncertainty quantification
and attribution studies using multi-fidelity ensembles, (4) develop
machine learning tools to enhance experimental diagnostics.
Bio:
Gianluca Iaccarino is the Robert Bosch Chair and Professor of the
Mechanical Engineering Department at Stanford University. He received
his PhD in Italy from the Politecnico di Bari (Italy) in 2005, and
joined the faculty at Stanford in 2007. Since 2014, he has been the
Director of the PSAAP Center at Stanford, funded by the US Department of
Energy focused on multiphysics simulations, uncertainty quantification,
data science and exascale computing. He received the US ACM Thomas
Hughes Medal, the Presidential Early Career Award for Scientists and
Engineers (PECASE) award and is a Fellow of APS and Senior Fellow of
AIAA.