Machine Learning Techniques for Classification of Combustion Events under Homogeneous Charge Compression Ignition (HCCI) Conditions

Shamsudheen, Fabiyan Angikath; Yalamanchi, Kiran K.; Yoo, Kwang Hee; Voice, Alexander K.; Boehman, André L.; Sarathy, Mani

doi:10.4271/2020-01-1132

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…This study shows that clustering of the data in advance can lead to an improvement in the prediction accuracy [27]. The same approach was used to classify the combustion events in a specific engine [28]. Clustering has also been used as a post-processing tool by categorizing the output data of a simulation into different groups making the data easier to analyze.…”

Section: Introductionmentioning

confidence: 98%

Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

et al. 2021

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The standards for emissions from diesel engines are becoming more stringent and accurate emission modeling is crucial in order to control the engine to meet these standards. Soot emissions are formed through a complex process and are challenging to model. A comprehensive analysis of diesel engine soot emissions modeling for control applications is presented in this paper. Physical, black-box, and gray-box models are developed for soot emissions prediction. Additionally, different feature sets based on the least absolute shrinkage and selection operator (LASSO) feature selection method and physical knowledge are examined to develop computationally efficient soot models with good precision. The physical model is a virtual engine modeled in GT-Power software that is parameterized using a portion of experimental data. Different machine learning methods, including Regression Tree (RT), Ensemble of Regression Trees (ERT), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN) are used to develop the black-box models. The gray-box models include a combination of the physical and black-box models. A total of five feature sets and eight different machine learning methods are tested. An analysis of the accuracy, training time and test time of the models is performed using the K-means clustering algorithm. It provides a systematic way for categorizing the feature sets and methods based on their performance and selecting the best method for a specific application. According to the analysis, the black-box model consisting of GPR and feature selection by LASSO shows the best performance with test R2 of 0.96. The best gray-box model consists of SVM-based method with physical insight feature set along with LASSO for feature selection with test R2 of 0.97.

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Section: Introductionmentioning

confidence: 98%

Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

et al. 2021

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“…Shamsudheen et al [21] investigated two machine learning techniques, K-nearest neighbors (KNN) and support vector machines (SVM), to classify combustion events in an HCCI engine. The study found that SVM achieved a 93.5% classification accuracy, compared to KNN's 89.2% accuracy.…”

Section: Introductionmentioning

confidence: 99%

LSTM for Modeling of Cylinder Pressure in HCCI Engines at Different Intake Temperatures via Time-Series Prediction

Sontheimer,

Singh,

Verma

et al. 2023

Machines

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Modeling engines using physics-based approaches is a traditional and widely-accepted method for predicting in-cylinder pressure and the start of combustion (SOC). However, developing such intricate models typically demands significant effort, time, and knowledge about the underlying physical processes. In contrast, machine learning techniques have demonstrated their potential for building models that are not only rapidly developed but also efficient. In this study, we employ a machine learning approach to predict the cylinder pressure of a homogeneous charge compression ignition (HCCI) engine. We utilize a long short-term memory (LSTM) based machine learning model and compare its performance against a fully connected neural network model, which has been employed in previous research. The LSTM model’s results are evaluated against experimental data, yielding a mean absolute error of 0.37 and a mean squared error of 0.20. The cylinder pressure prediction is presented as a time series, expanding upon prior work that focused on predicting pressure at discrete points in time. Our findings indicate that the LSTM method can accurately predict the cylinder pressure of HCCI engines up to 256 time steps ahead.

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Machine Learning Techniques for Classification of Combustion Events under Homogeneous Charge Compression Ignition (HCCI) Conditions

Cited by 2 publications

References 17 publications

Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

LSTM for Modeling of Cylinder Pressure in HCCI Engines at Different Intake Temperatures via Time-Series Prediction

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