five

Reduction of Detailed Chemical Mechanisms Using Reaction Class-Based Global Sensitivity and Path Sensitivity Analyses

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
https://figshare.com/articles/dataset/Reduction_of_Detailed_Chemical_Mechanisms_Using_Reaction_Class-Based_Global_Sensitivity_and_Path_Sensitivity_Analyses/9773621
下载链接
链接失效反馈
官方服务:
资源简介:
Reduced chemical mechanisms with a small size and good performance are very important for the simulation of advanced combustion engines. In the present study, a new reduction method of detailed chemical mechanisms was proposed using reaction class-based global sensitivity and path analyses. During the reduction process, the influence of the species and reactions was determined according to the contribution of their corresponding reaction classes to the prediction uncertainties by calculating the nominal sensitivity index and the path sensitivity coefficient of each reaction class from the detailed mechanism. Furthermore, the dependence of the prediction target on the operating temperature, pressure, and equivalence ratio was studied. After establishing the initial reduced mechanism, the refinement of the rate coefficients in the fuel-specific submechanism was conducted to improve the nominal predicted value of the reduced mechanism covering broad temperature conditions. Based on the proposed method, a reduced n-heptane mechanism with 89 species and 276 reactions is obtained from a detailed one comprising 645 species and 2827 reactions. By comparing the calculated value of the reduction targets from the reduced mechanism and the detailed mechanism over broad operating conditions, the reliability of the reduced mechanism was examined. Good agreements for the predicted data between the reduced and detailed mechanisms indicate the advantages of the present reduction method. Compared to the other methods, the reduced mechanism built using the present method was capable of better reproducing the prediction performance of the detailed mechanism with a more compact size.
创建时间:
2019-08-22
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作