ASDL 연구팀, Computer Physics Communications 학술지 논문 게재
ASDL 연구팀에서 Computer Physics Communications 학술지에 투고한 논문이 2023년 9월자로 게재되었습니다. 축하합니다!
Title: IDEA: Artificial neural network models for 11-species air properties at thermochemical equilibrium
Author: Hojun You, Juhyun Kim and Chongam Kim
Journal: Computer Physics Communications
DOI: https://doi.org/10.1016/j.cpc.2023.108788
Abstract:
Accurate prediction of high-temperature air properties is essential in many aerodynamic applications under hypersonic flight conditions. Various curve-fit models using piecewise polynomial fittings have been commonly adopted to approximate equilibrium air properties at high temperatures. Several shortcomings including low accuracy, lack of diversity, and discontinuity at curve-fit boundary still remain, causing numerical troubles in computational procedures. To address the issues, IDEA, an open-source C++ library that enables fast and accurate computations of the equilibrium air properties and their first and second derivatives, is newly developed based on the artificial neural network (ANN). IDEA, which stands for the Infinitely Differentiable Equilibrium Air, predicts thermodynamic and transport properties of 11-species thermochemical equilibrium air at the temperature range up to 25,000 K and density range from 1e-7 to 1e+3 amagats. The training data is constructed from the kinetic molecular theory using the equilibrium constant method with the rigid-rotor, harmonic-oscillator model. As the name suggests, IDEA's models are infinitely differentiable in the application range; thus, they have enhanced convergence in computational fluid dynamics (CFD) when using gradient-based methods. Using a newly developed training process based on the Levenberg–Marquardt algorithm with weighted mean squared error loss, IDEA provides more accurate and diverse property models with much fewer parameters than previous piecewise polynomial fitting models. In addition, the proposed training method offers easy extensions to various property models with different species data. IDEA provides C interfaces that can be used for programs in various computer languages, such as C/C++, Fortran, Python, and MATLAB. IDEA's modeling routines are thread-safe, so they can be safely used for parallel programs without performance loss. The accuracy and enhanced convergence of IDEA is demonstrated via several high-speed flow computations
Title: IDEA: Artificial neural network models for 11-species air properties at thermochemical equilibrium
Author: Hojun You, Juhyun Kim and Chongam Kim
Journal: Computer Physics Communications
DOI: https://doi.org/10.1016/j.cpc.2023.108788
Abstract:
Accurate prediction of high-temperature air properties is essential in many aerodynamic applications under hypersonic flight conditions. Various curve-fit models using piecewise polynomial fittings have been commonly adopted to approximate equilibrium air properties at high temperatures. Several shortcomings including low accuracy, lack of diversity, and discontinuity at curve-fit boundary still remain, causing numerical troubles in computational procedures. To address the issues, IDEA, an open-source C++ library that enables fast and accurate computations of the equilibrium air properties and their first and second derivatives, is newly developed based on the artificial neural network (ANN). IDEA, which stands for the Infinitely Differentiable Equilibrium Air, predicts thermodynamic and transport properties of 11-species thermochemical equilibrium air at the temperature range up to 25,000 K and density range from 1e-7 to 1e+3 amagats. The training data is constructed from the kinetic molecular theory using the equilibrium constant method with the rigid-rotor, harmonic-oscillator model. As the name suggests, IDEA's models are infinitely differentiable in the application range; thus, they have enhanced convergence in computational fluid dynamics (CFD) when using gradient-based methods. Using a newly developed training process based on the Levenberg–Marquardt algorithm with weighted mean squared error loss, IDEA provides more accurate and diverse property models with much fewer parameters than previous piecewise polynomial fitting models. In addition, the proposed training method offers easy extensions to various property models with different species data. IDEA provides C interfaces that can be used for programs in various computer languages, such as C/C++, Fortran, Python, and MATLAB. IDEA's modeling routines are thread-safe, so they can be safely used for parallel programs without performance loss. The accuracy and enhanced convergence of IDEA is demonstrated via several high-speed flow computations
