含能金属颗粒释能特性模拟及预测方法研究进展OA北大核心CSTPCD

Energetic metal materials such as aluminum and iron powder,serving as unconventional fuels,were of high energy density and carbon-free nature,which have attracted more and more attention in the fields of solid propulsion and renewable energy utilization.Some advancements in prediction and simulation methods for aluminum and iron and other metal particles were reviewed,specifically including quantum chemical calculation methods and molecular dynamics simulations at the microscopic level,as well as single-particle combustion models and particle cloud combustion models at the macroscopic level.Furthermore,the advantages and disadvantages of simulation methods at various scales as well as the existing issues and challenges were summarized.Finally,the simulation and prediction methods of energy release characteristics of energetic metal particles were prospected.On the one hand,the multi-scale coupling simulation of energy release process of metal particles can be realized by machine learning,simplifying macro problems and other methods to improve the calculation efficiency and reduce the calculation costs;on the other hand,data-driven or mechanism-data-driven modeling can be constructed to better deal with the possible nonlinear and uncertain problem.