scholarly journals Smart Proxy Modeling Application of Artificial Intelligence & Machine Learning in Computational Fluid Dynamics

2020 ◽  
Author(s):  
A. Aboaba ◽  
Y. Martinez ◽  
S. Mohaghegh ◽  
M. Shahnam ◽  
C. Guenther ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Proxy Modeling

Computational Fluid Dynamics Based Mixing Prediction for Tilt Pad Journal Bearing TEHD Modeling—Part II: Implementation With Machine Learning

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Jongin Yang ◽  
Alan Palazzolo
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Three Dimensional ◽  
Leading Edge ◽  
Cfd Modeling ◽  
Inlet Temperature ◽  
Training Set ◽  
Simplified Approach ◽  
Flow Mixing

Abstract Reynolds based thermo-elasto-hydrodynamic (TEHD) simulations of tilting pad journal bearings (TPJBs) generally provide accurate results; however, the uncertainty of the pad’s leading edge thermal boundary conditions causes uncertainty of the results. The highly complex thermal-flow mixing action between pads (BPs) results from the oil supply nozzle jets and geometric features. The conventional Reynolds approach employs mixing coefficients (MCs), estimated from experience, to approximate a uniform inlet temperature for each pad. Part I utilized complex computational fluid dynamics (CFD) flow modeling to illustrate that temperature distributions at the pad inlets may deviate strongly from being uniform. The present work retains the uniform MC model but obtains the MC from detailed three-dimensional CFD modeling and machine learning, which could be extended to the radially and axially varying MC case. The steps for implementing an artificial neural network (ANN) approach for MC regression are provided as follows: (1) utilize a design of experiment step for obtaining an adaptable training set, (2) conduct CFD simulations on the BP to obtain the outputs of the training set, (3) apply an ANN learning process by Levenverg–Mardquart backpropagation with the Bayesian regularization, and (4) couple the ANN MC results with conventional TEHD Reynolds models. An approximate log fitting method provides a simplified approach for MC regression. The effectiveness of the Reynolds TEHD TPJB model with ANN regression-based MC distributions is confirmed by comparison with CFD based TEHD TPJB model results. The method obtains an accuracy nearly the same as the complete CFD model, but with the computational economy of a Reynolds approach.

scholarly journals Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Jihad A. Badra ◽  
Fethi Khaled ◽  
Meng Tang ◽  
Yuanjiang Pei ◽  
Janardhan Kodavasal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Internal Combustion Engines ◽  
Optimization Problems ◽  
Combustion Engine ◽  
Methodological Approach ◽  
System Optimization ◽  
Operating Conditions ◽  
Attractive Alternative

Abstract Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

Machine Learning Computational Fluid Dynamics

2021 ◽  
Author(s):  
Ali Usman ◽  
Muhammad Rafiq ◽  
Muhammad Saeed ◽  
Ali Nauman ◽  
Andreas Almqvist ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics
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