Prediction of the performance and exhaust emissions of ethanol-diesel engine using different neural network

Mao, Gongping; Zhang, Cheng; Shi, Kaikai; Wang, Ping

doi:10.1080/15567036.2019.1656307

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…For linear static problems, we can usually solve them with global grid conditions and obtain acceptable results in terms of both computation time and accuracy. For dealing with dynamic or nonlinear problems, the solutions to these problems are very sensitive to mesh quality (Mao et al, 2019). With poor-quality meshes, simulations can give inaccurate results.…”

Section: Grid Techniquementioning

confidence: 99%

Research on Numerical Simulation of Pcci Engine: A Review

Minh Tuan,

Dao Nam

2023

J. tech. innov.

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The global environment is being affected by the use of fossil fuels to provide energy for internal combustion engines (ICE) due to a large amount of CO2 emission. Specific toxic substances are NOx, and CO, soot, particulate matter that exists in the environment at high densities while having a global impact. The automotive industry of countries worldwide is constantly changing and evolving to discover new solutions that reduce fuel use while developing cleaner and more advanced combustion methods, which as a low-temperature combustion engine (LTC). This study uses computational fluid dynamics (CFD) to understand partial pre-mixed compression combustion (PPCI), with HCCI and RCCI being advanced low-temperature combustion concepts. Lowering the average combustion temperature is an advantage of PCCI; it optimizes CO and NOx emission rates while maintaining high thermal efficiency. Investigate the intake pattern, including flame length, mixing pulse, and intake air volume, to determine the importance of that factor for oxidation using the LES. Studies have been performed using RANS and LES models to stimulate mixing and combustion.

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Section: Grid Techniquementioning

confidence: 99%

Research on Numerical Simulation of Pcci Engine: A Review

Minh Tuan,

Dao Nam

2023

J. tech. innov.

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“…For now, several studies about the use of machine learning are very useful in developing the automotive industry [1], [2], and this fact shows that the development of the automotive industry does not always depend on the field of mechanical engineering i.e. fuel and engine performance [3][4][5], but also on the field of machine learning of computer vision.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

Marianingsih¹,

Widodo²,

Pieter³

et al. 2022

JMEST

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The mechanized ability to specify the way surface type is a piece of key enlightenment for autonomous transportation machine navigation like wheelchairs and smart cars. In the present work, the extracted features from the object are getting based on structure and surface evidence using Gray Level Co-occurrence Matrix (GLCM). Furthermore, K-Nearest Neighbor (K-NN) Classifier was built to classify the road surface image into three classes, asphalt, gravel, and pavement. A comparison of KNN and Naïve Bayes (NB) was used in present study. We have constructed a road image dataset of 450 samples from real-world road images in the asphalt, gravel, and pavement. Experiment result that the classification accuracy using the K-NN classifier is 78%, which is better as compared to Naïve Bayes classifier which has a classification accuracy of 72%. The paving class has the smallest accuracy in both classifier methods. The two classifiers have nearly the same computing time, 3.459 seconds for the KNN Classifier and 3.464 seconds for the Naive Bayes Classifier.

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“…Considering the Radial Basis Function (RBF) neural network with the preponderance of strong nonlinear fitting ability, simple structure,.etc. For example, Mao et al [16] used back-propagation(BP), Elman network, RBF and generalized regression neural network (GRNN) to predict the performance of the target engine, according to the results of the four network assessments, RBF with the best prediction effect. The RBF is very precise and practical method to perform the prediction and model nonlinear phenomena of engine performance.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive-Tunable-Based Hybrid RBF Network for EGTM Prediction

Liu¹,

Zeng

Tian³

et al. 2021

IEEE Access

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Aero-engine exhaust gas temperature margin (EGTM) is one of the main indexes of engine replacement; however, the application of existing methods in EGTM forecasting is restricted because of the limited prediction accuracy and many non-linearities. In this study, an adaptive-tunable-based hybrid radial basis function (RBF) network is proposed to improve the prediction accuracy of aero-engine EGTM. Firstly, a hybrid RBF network consisting of a RBF network and a linear regression model is built as a fundamental EGTM predictive algorithm. Secondly, to increase the network's adaptation capabilities, the structural parameters of the proposed network are adaptively modulated by Brownian motion modeling and particle filter without physics-based models. Finally, multiple sets of EGTM data from a certain type aero-engines in an airline company is selected for engine removal time prediction. Experiment results demonstrate that the proposed adaptive-tunable-based hybrid RBF network with a high prediction accuracy, and can reflect the characteristics of EGTM well and truly, which can capture the dynamic nature of EGTM in time during the forecasting process.

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Prediction of the performance and exhaust emissions of ethanol-diesel engine using different neural network

Cited by 9 publications

References 24 publications

Research on Numerical Simulation of Pcci Engine: A Review

Research on Numerical Simulation of Pcci Engine: A Review

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

An Adaptive-Tunable-Based Hybrid RBF Network for EGTM Prediction

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