33 citations · 91 across the 5 of their papers we have counts for
5 papers
Level-set based pre-processing algorithm for particle-based methods
Yongchuan Yu, Yujie Zhu, Chi Zhang +2
Obtaining high quality particle distribution representing clean geometry in pre-processing is essential for the simulation accuracy of the particle-based methods. In this paper, se…
Physics-driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks
Hao Ma, Yuxuan Zhang, Nils Thuerey +2
Recently, physics-driven deep learning methods have shown particular promise for the prediction of physical fields, especially to reduce the dependency on large amounts of pre-comp…
Heat transfer augmentation by recombination reactions in turbulent reacting boundary layers at elevated pressures
Nikolaos Perakis, Oskar Haidn, Matthias Ihme
A study of a reacting boundary layer flow with heat transfer at conditions typical for configurations at elevated pressures has been performed using a set of direct numerical simul…
Generative Adversarial Networks with Physical Evaluators for Spray Simulation of Pintle Injector
Hao Ma, Botao Zhang, Chi Zhang +1
Due to the adjustable geometry, pintle injectors are specially suitable for the liquid rocket engines which require a widely throttleable range. While applying the conventional com…
A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks
Hao Ma, Xiangyu Hu, Yuxuan Zhang +2
With several advantages and as an alternative to predict physics field, machine learning methods can be classified into two distinct types: data-driven relying on training data and…