A Neural Network-Based Model for Hydrogen-Air Combustion

被引：0
作者：
Kosyanchuk, Vasily [1 ]
机构：
[1] Lomonosov Moscow State Univ, Inst Mech, Lab Nanomech, Michurinskyi Ave 1, Moscow 119192, Russia
来源：
INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS | 2025年
关键词：
Artificial neural networks (ANN); neural networks; machine learning; combustion; hydrogen; chemical kinetics; PHYSICS-BASED APPROACH; DYNAMIC ADAPTIVE CHEMISTRY; FUEL COMBUSTION; KINETIC-MODELS; PDF SIMULATION; TABULATION; FRAMEWORK; IMPLEMENTATION; METHODOLOGY; MECHANISMS;
D O I：
10.1142/S0219876224500920
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Chemistry evaluation is a bottleneck to computational fluid dynamics (CFD) simulations of many real-life problems such as propulsion system design, engine diagnostics, and atmospheric modeling. In this work, we study approach for accelerating chemical kinetics calculations using artificial neural networks (ANNs) on the example of combustion of a hydrogen-air mixture. This work carries out a detailed exploratory study of the optimal design of a fully connected neural network, including the number of network parameters, number of layers as well as used activation function. Part of the work is also dedicated to investigation and optimization of network training process itself. Comparison with the results of other works, bringing some unification to the widely disparate reported results, is also performed. Links to the used datasets and the resulting neural network are provided.
引用
收藏
页数：28
相关论文
共 75 条
  • [1] Aldakheel F., Elsayed E. S., Zohdi T. I., Wriggers P., Efficient multiscale modeling of heterogeneous materials using deep neural networks, Comput. Mech, 72, pp. 155-171, (2023)
  • [2] Alqahtani S., Echekki T., A data-based hybrid model for complex fuel chemistry acceleration at high temperatures, Combust. Flame, 223, pp. 142-152, (2021)
  • [3] An J., He G., Luo K., Qin F., Liu B., Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/ carbon monoxide/kerosene combustion, Int. J. Hydrog. Energy, 45, pp. 29594-29605, (2020)
  • [4] Anitescu C., Atroshchenko E., Alajlan N., Rabczuk T., Artificial neural network methods for the solution of second order boundary value problems, Comput., Mater. Contin, 59, pp. 345-359, (2019)
  • [5] Blasco J., Fueyo N., Dopazo C., Ballester J., Modelling the temporal evolution of a reduced combustion chemical system with an artificial neural network, Combust. Flame, 113, pp. 38-52, (1998)
  • [6] Boustani N., Emrouznejad A., Gholami R., Despic O., Ioannou A., Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks, Ann. Oper. Res, 339, pp. 613-630, (2024)
  • [7] Buchheit K., Owoyele O., Jordan T., Van Essendelft D., The stabilized explicit variable-load solver with machine learning acceleration for the rapid solution of stiff chemical kinetics, (2019)
  • [8] Chatzopoulos A., Rigopoulos S., A chemistry tabulation approach via rate-controlled constrained equilibrium (RCCE) and artificial neural networks (ANNs), with application to turbulent non-premixed CH4/H2/N2 flames, Proc. Combust. Inst, 34, pp. 1465-1473, (2013)
  • [9] Chen X., Mehl C., Faney T., Di Meglio F., Clustering-enhanced deep learning method for computation of full detailed thermochemical states via solver-based adaptive sampling, Energy Fuels, 37, pp. 14222-14239, (2023)
  • [10] Chen X., Liang C., Huang D., Real E., Wang K., Pham H., Dong X., Luong T., Hsieh C.-J., Lu Y., Et al., Symbolic discovery of optimization algorithms, (2024)
12345678