This study presents a novel method for optimizing combustion kinetic mechanisms using variational inference (VI), addressing the longstanding challenge of balancing accuracy and computational efficiency. The primary focus is on the uncertainty quantification and minimization of pre-exponential factors in combustion reactions, a critical aspect of kinetic mechanism optimization. The initial phase involves identifying the most sensitive parameters through local sensitivity analysis, followed by the application of artificial neural networks (ANN) as surrogate models for each experimental condition. This approach significantly accelerates the optimization process while maintaining gradient propagation essential for variational inference. A key advancement in this research is the implementation of variational inference in place of traditional Markov chain Monte Carlo (MCMC) methods. This shift not only results in a marked improvement in the computational speed, but it also maintains a high degree of accuracy in parameter optimization. This is illustrated through a detailed comparison of posterior distributions and uncertainty constraints obtained via both MCMC and variational inference methods, employing a methanol mechanism as a test case. Moreover, the inclusion of covariance in the variational inference effectively addresses the over-constraint of parameter uncertainties observed in earlier methods, offering a more realistic depiction of parameter interdependencies. The results show complete consistency in covariance matrices inferred from both MCMC and variational inference, validating the accuracy of the new probabilistic model. In terms of computational efficiency, the variational inference method demonstrates an enhancement of more than three orders of magnitude compared to the ANN-MCMC method. This significant reduction in time cost paves the way for applying the method to larger-scale mechanisms and data sets.
原文链接
分享
引用
