Extraction of Atomization Process from High-Fidelity Simulations

Recent advances in computational efficiency and numerical methods allow researchers to simulate atomizing systems robustly and accurately. These simulations have proven crucial in furthering atomization research, which is vital in numerous industrial and environmental applications. Significant limitations still exist in this field, however. Numerically simulating multiple phases is computationally expensive and requires high-performance computing. Most researchers and engineers do not have access to supercomputers and require low-fidelity atomization models. The accuracy of these simplified models relies largely on the quality of data extracted from atomization simulations. However, resultant data sets from high-fidelity simulations are often tens to hundreds of terabytes, severely limiting researchers’ ability to parse them for relevant data. To address this problem, we developed a methodology that can be applied to numerical simulations of atomizing systems to identify and extract data from breakup and coalescence events vital to the atomization process. The data is orders of magnitude smaller but can provide statistics on the local conditions which lead to liquid breakup. This information can be used to better inform low-fidelity atomization models and elucidate the underlying physics of atomization.