摘要
由于一些经验性的假设,高超声速热化学非平衡流动模拟中广泛使用的双温度模型包含很大的不确定性.为了克服传统双温度模型的缺陷,本文基于修正Marrone-Treanor model的方法建立了修正的Macheret-Fridman model.一些典型的算例被用来验证上述修正模型和广泛采用的双温度模型的精度.此外,本文重点分析和讨论了修正模型预测精度提高的原因.研究结果表明,基于Marrone-Treanor model的修正方法可以推广至传统的双温度模型中,并显著提高其预测精度.此外,精确模拟高超声速热化学非平衡流动需要考虑三个方面的因素:离解速率、振动-离解耦合作用以及非Boltzmann效应.非Boltzmann效应降低了离解速率和振动导致的离解能变化.对比而言,离解速率的影响大于非Boltzmann效应导致的离解能的变化.为了提高传统双温度模型的预测精度,未来工作可以主要集中在提高离解速率的精度上面.
Due to the empirical assumptions,the widely-used two-temperature models for hypersonic nonequilibrium flow include considerable uncertainties.To overcome the limitations and shortcomings of two-temperature models,the modified Macheret-Fridman model is developed based on the correction method of the modified Marrone-Treanor model.Some typical test cases are employed to assess the accuracy of the modified and widely-used two-temperature models.Furthermore,the reason for improving the accuracy of modified two-temperature models is analyzed and discussed.This work indicates that the correction method based on the modified Marrone-Treanor model is easily applied and extended to the other widely-used two-temperature models,significantly improving their accuracy.In addition,modeling highly nonequilibrium dissociating flows requires considering three critical respects,i.e.,the dissociation rates,the vibration-dissociation coupling effect,and the non-Boltzmann effect.The non-Boltzmann effect reduces the dissociation rates and vibrational energy per dissociation.Comparatively,the dissociation rates have more influence than changing the value of the non-Boltzmann factor for vibrational energy loss per dissociation.Future work can focus on enhancing the accuracy of the dissociation rates to improve the accuracy of widely-used two-temperature models.
作者
王小永
洪启臻
胡远
孙泉华
Xiaoyong Wang;Qizhen Hong;Yuan Hu;Quanhua Sun(State Key Laboratory of High Temperature Gas Dynamics,Institute of Mechanics,Chinese Academy of Sciences,Beijing 100190,China;School of Engineering Science,University of Chinese Academy of Sciences,Beijing 100049,China)
基金
supported by the National Key Research and Development Program of China (Grant No.2019YFB1704204)
the National Natural Science Foundation of China (Grant No.12002348).