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Title: MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles

Abstract

Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraints, and objective functions increases, the cost and duration for optimal calibration become prohibitively large. In order to reduce the number of dynamometer tests required for calibrating modern engines, a large-scale simulation-driven machine learning approach is presented in this work. A parallel, fast, robust, physics-based reduced-order engine simulator is used to obtain performance and emission characteristics of engines over a wide range of control parameters under various transient driving conditions (drive cycles). We scale the simulation up to 3,906 nodes of the Theta supercomputer at the Argonne Leadership Computing Facility to generate data required to train a machine learning model. The trained model is then used to predict various engine parameters of interest, and the results are compared with those predicted by the engine simulator. Our results show that a deepneural-network-based surrogate model achieves high accuracy: Pearson product-moment correlationmore » values larger than 0.99 and mean absolute percentage error within 1.07% for various engine parameters such as exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once trained, the deep-neural-network-based surrogate model is fast for inference: it requires about 16 mu s for predicting the engine performance and emissions for a single design configuration compared with about 0.5 s per configuration with the engine simulator. Moreover, we demonstrate that transfer learning and retraining can be leveraged to incrementally retrain the surrogate model to cope with new configurations that fall outside the training data space.« less

Authors:
;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1574769
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Journal Volume: 11501; Conference: 2019 ISC High Performance, 06/16/19 - 06/20/19, Frankfurt, DE
Country of Publication:
United States
Language:
English
Subject:
deep learning; deep neural networks; machine learning; surrogate modeling; transient driving cycle modeling

Citation Formats

Aithal, Shashi M., and Balaprakash, Prasanna. MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. United States: N. p., 2019. Web. doi:10.1007/978-3-030-20656-7_10.
Aithal, Shashi M., & Balaprakash, Prasanna. MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. United States. doi:10.1007/978-3-030-20656-7_10.
Aithal, Shashi M., and Balaprakash, Prasanna. Tue . "MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles". United States. doi:10.1007/978-3-030-20656-7_10.
@article{osti_1574769,
title = {MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles},
author = {Aithal, Shashi M. and Balaprakash, Prasanna},
abstractNote = {Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraints, and objective functions increases, the cost and duration for optimal calibration become prohibitively large. In order to reduce the number of dynamometer tests required for calibrating modern engines, a large-scale simulation-driven machine learning approach is presented in this work. A parallel, fast, robust, physics-based reduced-order engine simulator is used to obtain performance and emission characteristics of engines over a wide range of control parameters under various transient driving conditions (drive cycles). We scale the simulation up to 3,906 nodes of the Theta supercomputer at the Argonne Leadership Computing Facility to generate data required to train a machine learning model. The trained model is then used to predict various engine parameters of interest, and the results are compared with those predicted by the engine simulator. Our results show that a deepneural-network-based surrogate model achieves high accuracy: Pearson product-moment correlation values larger than 0.99 and mean absolute percentage error within 1.07% for various engine parameters such as exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once trained, the deep-neural-network-based surrogate model is fast for inference: it requires about 16 mu s for predicting the engine performance and emissions for a single design configuration compared with about 0.5 s per configuration with the engine simulator. Moreover, we demonstrate that transfer learning and retraining can be leveraged to incrementally retrain the surrogate model to cope with new configurations that fall outside the training data space.},
doi = {10.1007/978-3-030-20656-7_10},
journal = {},
issn = {0302--9743},
number = ,
volume = 11501,
place = {United States},
year = {2019},
month = {1}
}

Conference:
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