Nav: Home

Scientific machine learning paves way for rapid rocket engine design

April 16, 2020

"It's not rocket science" may be a tired cliché, 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."
-end-


University of Texas at Austin

Related Learning Articles:

When learning on your own is not enough
We make decisions based on not only our own learning experience, but also learning from others.
Learning more about particle collisions with machine learning
A team of Argonne scientists has devised a machine learning algorithm that calculates, with low computational time, how the ATLAS detector in the Large Hadron Collider would respond to the ten times more data expected with a planned upgrade in 2027.
Getting kids moving, and learning
Children are set to move more, improve their skills, and come up with their own creative tennis games with the launch of HomeCourtTennis, a new initiative to assist teachers and coaches with keeping kids active while at home.
How expectations influence learning
During learning, the brain is a prediction engine that continually makes theories about our environment and accurately registers whether an assumption is true or not.
Technology in higher education: learning with it instead of from it
Technology has shifted the way that professors teach students in higher education.
Learning is optimized when we fail 15% of the time
If you're always scoring 100%, you're probably not learning anything new.
School spending cuts triggered by great recession linked to sizable learning losses for learning losses for students in hardest hit areas
Substantial school spending cuts triggered by the Great Recession were associated with sizable losses in academic achievement for students living in counties most affected by the economic downturn, according to a new study published today in AERA Open, a peer-reviewed journal of the American Educational Research Association.
Lessons in learning
A new Harvard study shows that, though students felt like they learned more from traditional lectures, they actually learned more when taking part in active learning classrooms.
Learning to look
A team led by JGI scientists has overhauled the perception of inovirus diversity.
Sleep readies synapses for learning
Synapses in the hippocampus are larger and stronger after sleep deprivation, according to new research in mice published in JNeurosci.
More Learning News and Learning Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Listen Again: The Power Of Spaces
How do spaces shape the human experience? In what ways do our rooms, homes, and buildings give us meaning and purpose? This hour, TED speakers explore the power of the spaces we make and inhabit. Guests include architect Michael Murphy, musician David Byrne, artist Es Devlin, and architect Siamak Hariri.
Now Playing: Science for the People

#576 Science Communication in Creative Places
When you think of science communication, you might think of TED talks or museum talks or video talks, or... people giving lectures. It's a lot of people talking. But there's more to sci comm than that. This week host Bethany Brookshire talks to three people who have looked at science communication in places you might not expect it. We'll speak with Mauna Dasari, a graduate student at Notre Dame, about making mammals into a March Madness match. We'll talk with Sarah Garner, director of the Pathologists Assistant Program at Tulane University School of Medicine, who takes pathology instruction out of...
Now Playing: Radiolab

What If?
There's plenty of speculation about what Donald Trump might do in the wake of the election. Would he dispute the results if he loses? Would he simply refuse to leave office, or even try to use the military to maintain control? Last summer, Rosa Brooks got together a team of experts and political operatives from both sides of the aisle to ask a slightly different question. Rather than arguing about whether he'd do those things, they dug into what exactly would happen if he did. Part war game part choose your own adventure, Rosa's Transition Integrity Project doesn't give us any predictions, and it isn't a referendum on Trump. Instead, it's a deeply illuminating stress test on our laws, our institutions, and on the commitment to democracy written into the constitution. This episode was reported by Bethel Habte, with help from Tracie Hunte, and produced by Bethel Habte. Jeremy Bloom provided original music. Support Radiolab by becoming a member today at Radiolab.org/donate.     You can read The Transition Integrity Project's report here.