The Kelvin-Helmholtz (KH) Rayleigh-Taylor (RT) breakup model has been widely utilized in the simulations of diesel spray, whereas the calibrations of the five constants in the KH-RT model highly depend on the operator's experience. To overcome the shortcoming, advanced data analysis techniques were introduced into this study by employing genetic algorithm (GA) and global sensitivity analysis for the simulations using the Reynolds-averaged Navier-Stokes (RANS) turbulence model. First, a genetic algorithm was used to optimize the five constants for the representative cases of diesel spray under different ambient temperatures and pressures. Based on the optimal solutions obtained from the GA optimizations and global sensitivity analysis, the dominant parameters affecting the predicted liquid penetrations of diesel spray, including C_RT and C_b, are identified. By fitting these optimal solutions, two correlations for C_RT and C_b are derived as: C_RT = 3.2 × ρ_amb^-0.32Т_amb^-0.2 and C_b = 4.24 × ln(ρ_amb) + 4.74. The change of the breakup length constant (C_b) for the introduction of the RT mechanism with the ambient density (ρ_amb) and the variation of the optimal child droplet size constant of the RT mechanism (C_RT) with the ambient temperature and pressure (T_amb and ρ_amb) can be understood from the instability of the RT mechanism and the derivation of the RT breakup model, respectively. Extensive validations indicate that the derived correlations are suitable for diesel spray under wide ranges of ambient pressure, ambient temperature, injection pressure, and nozzle diameter for various experimental data sources in the literature.

中文翻译：

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基于先进数据分析技术的宽条件下柴油喷雾 KELVIN-HELMHOLTZ RAYLEIGH-TAYLOR (KH-RT) 分解模型中的常数校准

Kelvin-Helmholtz (KH) Rayleigh-Taylor (RT) 破碎模型已广泛用于柴油喷雾的模拟，而 KH-RT 模型中五个常数的校准高度依赖于操作者的经验。为了克服这一缺点，本研究引入了先进的数据分析技术，采用遗传算法（GA）和全局灵敏度分析，使用雷诺平均纳维-斯托克斯（RANS）湍流模型进行模拟。首先，使用遗传算法优化柴油喷雾在不同环境温度和压力下的代表性情况的五个常数。基于从 GA 优化和全局灵敏度分析中获得的最优解，影响柴油喷雾预测液体渗透的主要参数，包括 C_RT和 C _b被识别。通过拟合这些最优解，C _RT和 C _b的两个相关性导出为：C _RT = 3.2 × ρ _amb ^-0.32 Т _amb ^-0.2和 C _b = 4.24 × ln(ρ _amb ) + 4.74。引入 RT 机制的破裂长度常数 (C _{b ) 随环境密度 (ρ amb} ) 的变化以及 RT 机制的最佳子液滴尺寸常数 (C _RT ) 随环境温度和压力的变化（T _amb和 ρ _amb) 可以分别从 RT 机制的不稳定性和 RT 解体模型的推导来理解。广泛的验证表明，对于文献中的各种实验数据源，导出的相关性适用于大范围的环境压力、环境温度、喷射压力和喷嘴直径下的柴油喷雾。