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Keywords: machine learningClose
Proc. ASME. ICEF2022, ASME 2022 ICE Forward Conference, V001T02A004, October 16–19, 2022
Paper No: ICEF2022-89295
... Abstract Nearly four hundred different samples of jet and diesel fuels were used to train and test Machine Learning (ML) models for Derived Cetane Number (DCN – ASTM D6890) prediction using eight of the fuels’ physical properties as model inputs. Linear Regression (LR), Artificial Neural...
Proc. ASME. ICEF2022, ASME 2022 ICE Forward Conference, V001T04A003, October 16–19, 2022
Paper No: ICEF2022-88851
... internal combustion engines fast Fourier transform principal component analysis machine learning neural network Proceedings of the ASME 2022 ICE Forward Conference ICEF2022 October 16-19, 2022, Indianapolis, Indiana ICEF2022-88851 MACHINE LEARNING-BASED FAULT DETECTION AND DIAGNOSIS OF INTERNAL...
Proc. ASME. ICEF2022, ASME 2022 ICE Forward Conference, V001T04A006, October 16–19, 2022
Paper No: ICEF2022-91169
... neural network machine learning (ML) models which were trained on different test-train data splits of test-cell recorded steady-state medium-duty (MD) diesel engine data. The output data was used to develop engine actuator maps by utilizing a genetic algorithm (GA). The genetic algorithm contains...
Proc. ASME. ICEF2021, ASME 2021 Internal Combustion Engine Division Fall Technical Conference, V001T03A005, October 13–15, 2021
Paper No: ICEF2021-67888
... simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field...
Jihad Badra, Fethi Khaled, Meng Tang, Yuanjiang Pei, Janardhan Kodavasal, Pinaki Pal, Opeoluwa Owoyele, Carsten Fuetterer, Mattia Brenner, Aamir Farooq
Proc. ASME. ICEF2019, ASME 2019 Internal Combustion Engine Division Fall Technical Conference, V001T06A007, October 20–23, 2019
Paper No: ICEF2019-7238
... 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 Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines...