Darmstadt Engine Tables

1) This data can be used to predict the number of days needed to run the RANS simulation using two different turbulence model, viz. Re-Normalized Group (RNG), K-Epsilon (k-Epsilon) and K-Omega (k-omega) turbulence models
2) This dataset will be useful for academic researchers attempting to undertake computational fluid dynamics studies in diesel engines.
3) This data is more valuable because in-cylinder air motion experimental setup is highly expensive to purchase. Hence, this data can be used as an initial and boundary conditions of CFD simulations.

1) 本数据集可用于预测采用雷诺平均纳维-斯托克斯（Reynolds-Averaged Navier-Stokes, RANS）模拟所需的运行天数，所用湍流模型分别为重整化群（Re-Normalized Group, RNG）、k-ε（K-Epsilon）以及k-ω（K-Omega）湍流模型。
2) 本数据集对于致力于开展柴油机计算流体动力学（Computational Fluid Dynamics, CFD）研究的学术研究者具有重要应用价值。
3) 该数据集的价值更为突出，原因在于缸内空气运动实验装置的采购成本极其高昂。因此，本数据集可作为CFD模拟的初始与边界条件使用。