Construction of end-to-end machine learning surrogate model and its application in detonation driving problem

BAI Jingsong; LIU Yang; CHEN Han; ZHONG Min

doi:10.11883/bzycj-2024-0099

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BAI Jingsong, LIU Yang, CHEN Han, ZHONG Min. Construction of end-to-end machine learning surrogate model and its application in detonation driving problem[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0099

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BAI Jingsong, LIU Yang, CHEN Han, ZHONG Min. Construction of end-to-end machine learning surrogate model and its application in detonation driving problem[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0099

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Construction of end-to-end machine learning surrogate model and its application in detonation driving problem

doi: 10.11883/bzycj-2024-0099

National Key Laboratory of Shock Wave and Detonation Physics, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang 621999, Sichuan, China

Received Date: 2024-04-10
Rev Recd Date: 2024-08-15

Available Online: 2024-08-16

Abstract

Abstract

Artificial intelligence/machine learning methods can discover hidden physical patterns in data. By constructing an end-to-end surrogate model between state parameters and dynamic results, many complex engineering problems such as strong coupling, nonlinearity, and multiphysics can be efficiently solved. In the field of highly nonlinear explosion and shock dynamics, a classic detonation driving problem was chosen as the research object. Using numerical simulation results as training data for machine learning surrogate models, and combining forward simulation and reverse design organically. Based on deep neural network technology, an end-to-end surrogate model was constructed between feature position velocity profiles, material dynamic deformation, and engineering factors. And the calculation accuracy of the surrogate model was provided, verifying the ability to invert engineering factors from velocity profiles. The research results indicate that the end-to-end surrogate model has high predictive ability, with relative errors of less than 1% in both velocity profile prediction and engineering factor estimation. It can be applied to the rapid design, high-precision prediction, and agile iteration of highly nonlinear explosion and impact dynamics problems.
- computational explosion mechanics,
- detonation drive,
- artificial intelligence,
- machine learning,
- end-to-end surrogate model,
- deep neural network

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References(25)

References

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