Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Data detailed on Machine Learning have been presented. According to news originatingfrom Stanford, California, by New sRx correspondents, research stated, "Predictive simulations of two-phaseflows are highly sought after because of their widespread applications in propulsion, energy, agriculture,and medicine. One crucial goal for many of these simulation s is the accurate and efficient prediction ofthe size distribution and number d ensity of atomized drops."Financial support for this research came from Advanced Simulation and Computing (ASC) programof the US Department of Energy's National Nuclear Security Adminis tration (NNSA) via the PSAAP-IIICenter at Stanford University.Our news journalists obtained a quote from the research from Stanford University , "The multi-scalenature of these flows makes it practically impossible to capt ure all scales within a single simulation. Inparticular, the breakup processes producing the smallest drops through secondary breakup often necessitateresolut ions far below the Kolmogorov scale. Consequently, models must be employed for s econdarybreakup. Existing physics- based and stochastic breakup models are not universal and fail to account forthe local and instantaneous flow field and dro p geometry. We present a physics-informed machine learningmodel for predicting the statistics of daughter drops generated during the breakup of under-resolved drops.By training on high-fidelity simulations, the model can predict breakup o utcomes from severely underresolvedinput fields. This is made possible by a ca reful choice of quantities of interest and by takinginspiration from the discre te nature of breakup events to encode the temporal evolution via a mixtureof si gmoid functions. We showcase proof-of-concept results from the canonical setting s of 3D Taylor-Green vortex flows and homogeneous isotropic turbulence. Compared to results generated by low-resolutionsimulations (i.e., without a model) and baseline state-of-the-art models, our approach achieves superioraccuracy in pre dicting drop size distribution and critical quantities of interest, such as surf ace area."
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