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Researchers are developing a faster modeling technique for rocket engine designers to test performance in different conditions.
"It's not rocket science" may be a tired cliché, but that doesn't mean designing rockets is any less complicated by Science Daily.
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| Scientific Machine Learning Paves Way for Rapid Rocket Engine Design Photo: University of Texas at Austin - UT News |
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...
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.
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Journal Reference:
1. Renee Swischuk, Boris Kramer, Cheng Huang, Karen Willcox. Learning Physics-Based Reduced-Order Models for a Single-Injector Combustion Process. AIAA Journal, 2020; 1 DOI: 10.2514/1.J058943
