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In this work, an artificial neural network (ANN) aided vapor–liquid equilibrium (VLE) model is developed and coupled with a fully compressible computational fluid dynamics (CFD) solver to simulate the transcritical processes occurring in high-pressure liquid-fueled propulsion systems. The ANN is trained in Python using TensorFlow, optimized for inference using Open Neural Network Exchange Runtime, and coupled with a C++ based CFD solver. This plug-and-play model/methodology can be used to convert any multi-component CFD solver to simulate transcritical processes using only open-source packages, without the need of in-house VLE model development. The solver is then used to study high-pressure transcritical shock-droplet interaction in both two- and four-component systems and a turbulent temporal mixing layer (TML), where both qualitative and quantitative agreement (maximum relative error less than 5%) is shown with respect to results based on both direct evaluation and the state-of-the-art in situ adaptive tabulation (ISAT) method. The ANN method showed a 6 times speed-up over the direct evaluation and a 2.2-time speed-up over the ISAT method for the two-component shock-droplet interaction case. The ANN method is faster than the ISAT method by 12 times for the four-component shock-droplet interaction. A 7 times speed-up is observed for the TML case for the ANN method compared to the ISAT method while achieving a data compression factor of 2881. The ANN method also shows intrinsic load balancing, unlike traditional VLE solvers. A strong parallel scalability of this ANN method with the number of processors was observed for all the three test cases. Code repository for 0D VLE solvers, and C++ ANN interface—https://github.com/UMN-CRFEL/ANN_VLE.git.
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Artificial Neural Network based Vapor-Liquid Equilibrium Modeling for Simulation of Transcritical Multiphase Flows
The requirement of high power outputs and high efficiencies of combustion engines such as rocket engines, diesel engines, and gas turbines has resulted in the incremented of the system pressure close to the thermodynamically critical point. This increase in pressure often leads to the fluids becoming either transcritical or supercritical in state. This has led to increased interest in both the multi-component phase change phenomena as well as their chemical reactions. In this work, an artificial neural network (ANN) aided VLE model is coupled with a fully compressible computational fluid dynamics (CFD) solver to simulate the transcritical processes occurring in high-pressure liquid-fueled propulsion systems. The ANN is trained on Python using the TensorFlow library, optimized for inference (i.e., prediction) using ONNX Run-time (a cross-platform inference and training machine-learning accelerator), and coupled with a C++ based fully compressible CFD solver. This plug-and-play model/methodology can be used to convert any fully compressible and conservative CFD solver to simulate transcritical processes using only open-source packages, without the need of in-house VLE-based CFD development. The solver is then used to study high-pressure shock-droplet interaction in both two- and four-component systems where qualitative and quantitative agreement is shown with results based on both direct evaluation and the state-of-the-art in-situ adaptive tabulation (ISAT) method. The ANN model is faster than the direct evaluation method and the ISAT model by 4 times for the four-component shock-droplet interaction. The ANN model also shows implicit load balancing as long as the MPI decomposition is performed uniformly amongst the number of cores chosen, as the inference time for ANN predict does not change with the change in thermodynamic state, unlike traditional VLE solvers. Regarding the parallel scalability of this model, good strong scaling characteristics with number of processors is also observed.
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- Award ID(s):
- 2023932
- PAR ID:
- 10562074
- Publisher / Repository:
- American Institute of Aeronautics and Astronautics
- Date Published:
- ISBN:
- 978-1-62410-711-5
- Page Range / eLocation ID:
- AIAA 2024-1638
- Format(s):
- Medium: X
- Location:
- Orlando, FL
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.2514/6.2024-1638
