Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion

2022 ◽  
Vol 236 ◽  
pp. 111814
Author(s):  
Kevin M. Gitushi ◽  
Rishikesh Ranade ◽  
Tarek Echekki
2004 ◽  
Author(s):  
Raghurama Reddy ◽  
Roberto Gomez ◽  
Junwoo Lim ◽  
Yang Wang ◽  
Sergiu Sanielevici

2012 ◽  
Author(s):  
Hong G Im ◽  
Arnaud Trouve ◽  
Christopher J Rutland ◽  
Jacqueline H Chen

2009 ◽  
Author(s):  
Hong G. Im ◽  
Arnaud Trouve ◽  
Christopher J. Rutland ◽  
Jacqueline H. Chen

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Liu ◽  
Majid Allahyari ◽  
Jorge S. Salinas ◽  
Nadim Zgheib ◽  
S. Balachandar

AbstractHigh-fidelity simulations of coughs and sneezes that serve as virtual experiments are presented, and they offer an unprecedented opportunity to peer into the chaotic evolution of the resulting airborne droplet clouds. While larger droplets quickly fall-out of the cloud, smaller droplets evaporate rapidly. The non-volatiles remain airborne as droplet nuclei for a long time to be transported over long distances. The substantial variation observed between the different realizations has important social distancing implications, since probabilistic outlier-events do occur and may need to be taken into account when assessing the risk of contagion. Contrary to common expectations, we observe dry ambient conditions to increase by more than four times the number of airborne potentially virus-laden nuclei, as a result of reduced droplet fall-out through rapid evaporation. The simulation results are used to validate and calibrate a comprehensive multiphase theory, which is then used to predict the spread of airborne nuclei under a wide variety of ambient conditions.


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