Researchers from the National Aeronautics and Space Administration (NASA) have teamed up with the US Department of Energys Argonne National Laboratory (ANL) to develop artificial intelligence (AI) to enhance the speed of simulations to study the behavior of air surrounding supersonic and hypersonic aircraft engines.
Fighter jets such as F-15s regularly exceed Mach 2 two times the speed of sound during the flight which is known as supersonic level. On a hypersonic flight which is Mach 5 and beyond, an aircraft flies faster than 3,000 miles per hour.
Hypersonic speeds have been made possible since the 1950s by the propulsions systems used for rockets however, engineers and scientists are working on advanced jet engine designs to make the hypersonic flight much less expensive than a rocket launch and more common such as for commercial flight, space exploration, and national defense purposes.
The newly published paper by a team of researchers from NASA and ANL details the machine learning techniques to reduce the memory and cost required to conduct computational fluid dynamics (CFD) simulations related to fuel combustion at supersonic and hypersonic speeds.
The paper was previously presented at the American Institute of Aeronautics and Astronautics SciTech Forum in January.
Before building and testing any aircraft, CFD simulations are used to determine how the various forces surrounding an aircraft in flight will interact with it. CFD consists of numerical expressions representing the behavior of fluids such as air and water.
When an aircraft breaks the sound barrier which involves traveling at speeds surpassing that of sound, it generates a shock wave which is a disturbance that makes the air around it hotter, denser, and higher in pressure causing it to behave very violently.
At hypersonic speeds, the air friction created is so strong that it could melt parts of a conventional commercial plane.
The air-breathing jet engines draw in oxygen to burn fuel as they fly so the CFD simulations have to account for major changes in the behavior of air, not only surrounding the plane but also as it moves through the engine and interacts with fuel.
While a conventional plane has fan blades to push the air along, in planes approaching Mach 3 and above speeds, their movement itself compresses the air. These aircraft designs, known as scramjets, are important to attain fuel efficiency levels that rocket propulsion cannot.
So, when it comes to CFD simulations on an aircraft capable of breaking the sound barrier, all the above factors add new levels of complexity to an already computationally intense exercise.
Because the chemistry and turbulence interactions are so complex in these engines, scientists have needed to develop advanced combustion models and CFD codes to accurately and efficiently describe the combustion physics, said Sibendu Som, a study co-author and interim center director of Argonnes Center for Advanced Propulsion and Power Research.
NASA has a hypersonic CFD code known as VULCAN-CFD which is specially meant for simulating the behavior of combustions in such a volatile environment.
This code uses something called flamelet tables where each flamelet is a small unit of a flame within the entire combustion model. This data table consists of different snapshots of burning fuel in one huge collection which takes up a large amount of computer memory to process.
Therefore, researchers at NASA and the ANL are exploring the use of AI to simplify these CFD simulations by reducing the intensive memory requirements and computational costs, to increase the pace of development of barrier-breaking aircraft.
Computational Scientists at ANL used a flamelet table generated by Argonne-developed software to train an artificial neural network that could be applied to NASAs VULCAN-CFD code. The AI used values from the flamelet table to learn shortcuts about determining the combustion behavior in supersonic engine environments.
The partnership has enhanced the capability of our in-house VULCAN-CFD tool by leveraging the research efforts of Argonne, allowing us to analyze fuel combustion characteristics at a much-reduced cost, said Robert Baurle, a research scientist at NASA Langley Research Center.
Countries across the world are racing to achieve hypersonic flight capability and an essential part of this race are simulation experiments where there is huge potential for the application of emerging tech such as AI and machine learning (ML).
Last month, according to a recent EurAsian Times report, Chinese researchers led by a top-level advisor to the Chinese military on hypersonic weapon technology, claimed a significant breakthrough in an AI system that can design new hypersonic vehicles autonomously.
Moreover, in February a Chinese space company called Space Transportation announced plans for tests beginning next year on a hypersonic plane capable of doing 7,000 miles per hour.
The company claimed that their plane could fly from Beijing to New York in an hour.
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