【摘要】 燃爆单元宽度λ是衡量可燃气体燃爆风险的一项重要参数,通常认为其与特征化学反应区宽度δ的比值是无量纲活化能和无量纲温度的函数。在以上述两个无量纲量为自变量、以λ/δ的对数为因变量对实验数据进行回归的基础上,更进一步引入无量纲压力作为第3个自变量进行回归。另外,针对传统参数回归方法的不足,采用基于机器学习的支持向量回归方法进行数据拟合。比较回归结果发现,与采用二变量模型及参数回归方法相比,采用三变量模型及支持向量回归方法的计算结果与实验数据的拟合度更好,并能更为准确地预测不同初始条件下可燃爆气体的特征单元宽度。更多还原

【Abstract】 Detonation cell width λ is an important parameter in quantifying explosion risk of a flammable gas mixture. Generally,the ratio between λ and characteristic chemical reaction zone width δ is considered as a function of a dimensionless activation energy and a dimensionless temperature. As a step further than the regression for experimental data with these two dimensionless parameters as independent variables and with the logarithm of λ/δ as the dependent variable,a dimensionless pressure was introduced as the 3rd independent variable in regression. Meanwhile,in consideration of the disadvantages of classical parametric regression methods,support vector regression method based on machine learning was applied to fit the data. The comparison among the regression results shows that,compared with the 2-variable mod-el and parametric regression method,the 3-varaible model with support vector regression method can offer better fitness to experimental data as well as higher accuracy in predicting detonation cell width for flammable gas mixtures with different initial conditions.更多还原