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 or even months for a supercomputer to provide satisfactory predictions.

Scientific machine learning methods were developed to address this challenge. Scientific machine learning 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 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 reduced-order models will play an essential role in putting rapid design capabilities into the hands of rocket engine designers. In some cases, these 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). “Snapshots” were 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 serve as the training data from which the researchers derived 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 models also can simulate into the future, predicting the physical response of the combustor for operating conditions that were not part of the training data. The models are particularly effective at capturing the phase and amplitude of the pressure signals — key elements for making accurate engine stability predictions.

Deriving reduced-order models from training data is similar 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.

For more information, contact John Holden at This email address is being protected from spambots. You need JavaScript enabled to view it.; 512-529-6013.


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This article first appeared in the July, 2020 issue of Tech Briefs Magazine.

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