In Situ Prediction Driven Feature Analysis in Jet Engine Simulations

2018 ◽  
Author(s):  
Soumya Dutta ◽  
Han-Wei Shen ◽  
Jen-Ping Chen
Keyword(s):  
Feature Analysis ◽  
Jet Engine ◽  
In Situ Prediction

Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning

2019 ◽  
Vol 875 ◽  
Author(s):  
Jianqing Huang ◽  
Hecong Liu ◽  
Weiwei Cai
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Proof Of Concept ◽  
Time Resolved ◽  
Computational Costs ◽  
Rapid Prediction ◽  
Computational Fluid Dynamics Cfd ◽  
In Situ Prediction ◽  
First Time

Online in situ prediction of 3-D flame evolution has been long desired and is considered to be the Holy Grail for the combustion community. Recent advances in computational power have facilitated the development of computational fluid dynamics (CFD), which can be used to predict flame behaviours. However, the most advanced CFD techniques are still incapable of realizing online in situ prediction of practical flames due to the enormous computational costs involved. In this work, we aim to combine the state-of-the-art experimental technique (that is, time-resolved volumetric tomography) with deep learning algorithms for rapid prediction of 3-D flame evolution. Proof-of-concept experiments conducted suggest that the evolution of both a laminar diffusion flame and a typical non-premixed turbulent swirl-stabilized flame can be predicted faithfully in a time scale on the order of milliseconds, which can be further reduced by simply using a few more GPUs. We believe this is the first time that online in situ prediction of 3-D flame evolution has become feasible, and we expect this method to be extremely useful, as for most application scenarios the online in situ prediction of even the large-scale flame features are already useful for an effective flame control.

Exploring the potential of NIRS technology for the in situ prediction of amygdalin content and classification by bitterness of in-shell and shelled intact almonds

2021 ◽  
Vol 294 ◽  
pp. 110406
Author(s):  
Miguel Vega-Castellote ◽  
Dolores Pérez-Marín ◽  
Irina Torres ◽  
José-Manuel Moreno-Rojas ◽  
María-Teresa Sánchez
Keyword(s):  
In Situ Prediction

Weld penetration in situ prediction from keyhole dynamic behavior under time-varying VPPAW pools via the OS-ELM model

2019 ◽  
Vol 104 (9-12) ◽  
pp. 3929-3941 ◽  
Author(s):  
Di Wu ◽  
Jieshi Chen ◽  
Hongbing Liu ◽  
Peilei Zhang ◽  
Zhishui Yu ◽  
...  
Keyword(s):  
Dynamic Behavior ◽  
Time Varying ◽  
Weld Penetration ◽  
In Situ Prediction
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