Scientific machine learning paves way for rapid rocket engine design
Scientific
machine learning paves way for rapid rocket engine design
Austin TX (SPX) Apr 17, 2020
Austin TX (SPX) Apr 17, 2020
"It's not rocket
science" may be a tired cliche, but that doesn't mean designing rockets is
any less complicated. Time, cost and safety prohibit testing the stability of a
test rocket using a physical build "trial and error" approach. But
even computational simulations are extremely time consuming. A single analysis
of an entire SpaceX Merlin rocket engine, for example, could take weeks, even
months, for a supercomputer to provide satisfactory predictions.
One group of researchers
at The University of Texas at Austin is developing new "scientific machine
learning" methods to address this challenge. Scientific machine learning
is a relatively new field that blends scientific computing with machine
learning.
Through a combination of
physics modeling and data-driven learning, it becomes possible to create
reduced-order models - simulations that can run in a fraction of the time,
making them particularly useful in the design setting.
The goal of the work,
led by Karen Willcox at the Oden Institute for Computational Engineering and
Sciences, is to provide rocket engine designers with a fast way to assess
rocket engine performance in a variety of operating conditions.
"Rocket engineers
tend to explore different designs on a computer before building and
testing," Willcox said. "Physical build and test is not only
time-consuming and expensive, it can also be dangerous."
But the stability of a
rocket's engine, which must be able to withstand a variety of unforeseen
variables during any flight, is a critical design target engineers must be
confident they have met before any rocket can get off the ground.
The cost and time it
takes to characterize the stability of a rocket engine comes down to the sheer
complexity of the problem. A multitude of variables affect engine stability,
not to mention the speed at which things can change during a rocket's journey.
The research by Willcox
is outlined in a recent paper co-authored by Willcox and published online by
AIAA Journal. It is part of a Center of Excellence on Multi-Fidelity Modeling
of Rocket Combustion Dynamics funded by the Air Force Office of Scientific
Research and Air Force Research Laboratory.
"The reduced-order
models being developed by the Willcox group at UT Austin's Oden Institute will
play an essential role in putting rapid design capabilities into the hands of
our rocket engine designers," said Ramakanth Munipalli, senior aerospace
research engineer in the Combustion Devices Branch at Air Force Rocket Research
Lab.
"In some important
cases, these reduced-order models are the only means by which one can simulate
a large propulsion system. This is highly desirable in today's environment
where designers are heavily constrained by cost and schedule."
The new methods have
been applied to a combustion code used by the Air Force known as General
Equation and Mesh Solver (GEMS). Willcox's group received "snapshots"
generated by running the GEMS code for a particular scenario that modeled a
single injector of a rocket engine combustor.
These snapshots
represent the instantaneous fields of pressure, velocity, temperature and
chemical content in the combustor, and they serve as the training data from
which Willcox and her group derive the reduced-order models.
Generating that training
data in GEMS takes about 200 hours of computer processing time. Once trained,
the reduced-order models can run the same simulation in seconds. "The
reduced-order models can now be run in place of GEMS to issue rapid
predictions," Willcox said.
But these models do more
than just repeat the training simulation.
They also can simulate
into the future, predicting the physical response of the combustor for
operating conditions that were not part of the training data.
Although not perfect,
the models do an excellent job of predicting overall dynamics. They are
particularly effective at capturing the phase and amplitude of the pressure
signals, key elements for making accurate engine stability predictions.
"These
reduced-order models are surrogates of the expensive high-fidelity model we
rely upon now," Willcox said. "They provide answers good enough to
guide engineers' design decisions, but in a fraction of the time."
How does it work?
Deriving reduced-order models from training data is similar in spirit to
conventional machine learning. However, there are some key differences.
Understanding the physics affecting the stability of a rocket engine is
crucial. And these physics must then be embedded into the reduced-order models
during the training process.
"Off-the-shelf
machine learning approaches will fall short for challenging problems in
engineering and science such as this multiscale, multiphysics rocket engine
combustion application," Willcox said.
"The physics are
just too complicated and the cost of generating training data is just too high.
Scientific machine learning offers greater potential because it allows learning
from data through the lens of a physics-based model. This is essential if we
are to provide robust and reliable results."
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