The previously developed analytical/numerical model for predicting heat transfer and component diffusion in composite multi-component droplets is adjusted for use in practical engineering applications related to the analysis of droplet heating and evaporation, and the onset of puffing and micro-explosions in those droplets. This adjustment allowed us to gain new insights into the previously developed models of these processes. The focus of the analysis is on kerosene/water droplets. It is demonstrated that the number of terms in the series in the analytical solution to the heat transfer equation can be reduced to just one or two to ensure that the maximal error of the model prediction does not exceed 1\%, unless we are interested in the processes at the very start of heating. At the same time, the minimal number of terms in the series in the analytical solution to the component diffusion equation should be at least seven to ensure that the errors of the prediction of the numerical code do not exceed 3\%. It is shown that, to ensure that the analytical/numerical code predicts physically consistent results, the maximal absolute error of calculation of the eigenvalues based on the bisection method cannot exceed 10^-7. It is shown that using these limiting values for each of these input parameters leads to about 50%-75% reduction in the CPU time required to obtain results close to those which were obtained using the non-optimised version of the numerical code. The overall reduction in CPU time can be as up to about 95%. The predictions of the adjusted analytical/numerical code are validated against in-house experimental data and data available in the literature.

中文翻译：

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一种组合分析/数值方法，用于对复合多组分燃料/水滴中导致膨胀和微爆炸的过程进行建模

先前开发的用于预测复合多组分液滴中的传热和组分扩散的分析/数值模型经过调整，可用于与液滴加热和蒸发以及这些液滴中膨胀和微爆炸的分析相关的实际工程应用。这一调整使我们能够对先前开发的这些过程模型获得新的见解。分析的重点是煤油/水滴。事实证明，传热方程解析解中的级数可以减少到一到两项，以确保模型预测的最大误差不超过 1\%，除非我们感兴趣加热开始时的过程。同时，分量扩散方程解析解的级数最少应为7项，以保证数值代码的预测误差不超过3%。结果表明，为保证解析/数值代码预测的物理一致结果，基于二分法计算特征值的最大绝对误差不能超过10^-7。结果表明，对这些输入参数中的每一个使用这些限制值会导致获得与使用数字代码的非优化版本获得的结果接近的结果所需的 CPU 时间减少约 50%-75%。CPU 时间的总体减少量最高可达 95% 左右。调整后的分析/数值代码的预测根据内部实验数据和文献中可用的数据进行验证。