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Data-driven modeling of large-eddy simulations for fuel injector design
P. J. Milan
, S. Mondal
, R. Torelli
, B. Lusch
,
R. Maulik
, G. M. Magnotti
College of Information Sciences and Technology
Institute for Computational and Data Sciences (ICDS)
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
4
Scopus citations
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Keyphrases
Large Eddy Simulation
100%
Emulator
100%
Fuel Injector
100%
Injector Design
100%
Data-driven Modeling
100%
Operating Conditions
50%
Flow Field
50%
Machine Learning Techniques
50%
Flow Dynamics
50%
Prediction Accuracy
50%
Design Parameters
50%
Flow Features
50%
Emulation Framework
50%
Regression Modeling
50%
Predictive Modeling
50%
Reconstruction Algorithm
50%
Design Exploration
50%
Property Condition
50%
Deep Neural Network
50%
Fuel Properties
50%
Flow Development
50%
Single-hole
50%
Nonlinear Dimensionality Reduction
50%
Autoencoder
50%
Turbulent multiphase Flow
50%
Diesel Injector
50%
Engineering
Design Parameter
100%
Dimensionality
100%
Machine Learning Technique
100%
Flow Dynamics
100%
Autoencoder
100%
Deep Neural Network
100%
Multiphase Flow
100%
Chemical Engineering
Learning System
100%
Deep Neural Network
100%