Theoretical and Experimental Analysis of Dynamic Characteristics for a Valve Train System

Hu, Bo; Li, Yunzhe; Yin, Lairong

doi:10.3390/s21196328

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Research on valve trains has been actively conducted in recent years. Hu et al developed a flexible dynamic model of valve trains by considering the gyroscopic effects and valve gaps, which was achieved through theoretical-experimental analysis of the dynamic properties of valve trains [8]. Similarly, Hu et al constructed a flexible dynamic model of the valve train system by considering the multi-axis flexibility of the shaft and linkage, and the gyroscopic effect of the camshaft and rotor [9].…”

Section: Related Workmentioning

confidence: 99%

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Dynamic Characteristics Prediction Model for Diesel Engine Valve Train Design Parameters Based on Deep Learning

2023

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This paper presents a comprehensive study on the utilization of machine learning and deep learning techniques to predict the dynamic characteristics of design parameters, exemplified by a diesel engine valve train. The research aims to address the challenging and time-consuming analysis required to optimize the performance and durability of valve train components, which are influenced by numerous factors. To this end, dynamic analyses data have been collected for diesel engine specifications and used to construct a regression prediction model using a gradient boosting regressor tree (GBRT), a deep neural network (DNN), a one-dimensional convolution neural network (1D-CNN), and long short-term memory (LSTM). The prediction model was utilized to estimate the force and valve seating velocity values of the valve train system. The dynamic characteristics of the case were evaluated by comparing the actual and predicted values. The results showed that the GBRT model had an R2 value of 0.90 for the valve train force and 0.97 for the valve seating velocity, while the 1D-CNN model had an R2 value of 0.89 for the valve train force and 0.98 for the valve seating velocity. The results of this study have important implications for advancing the design and development of efficient and reliable diesel engines.

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Section: Related Workmentioning

confidence: 99%

“…Among the various boosting algorithms, gradient boosting is a highly popular and widely used method that aims to improve the model's prediction accuracy by building on the predictions made by previous models [14]. [8,9] Engine valve train Flexible Dynamic Model…”

Section: Predictive Model 31 Gradient Boosting Regressor Tree (Gbrt)mentioning

confidence: 99%

Dynamic Characteristics Prediction Model for Diesel Engine Valve Train Design Parameters Based on Deep Learning

2023

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“…Proc. 2023, 45, 46 2 of 4 appropriate stiffness of the desmodromic actuation mechanism is essential to achieving accurate valve timing and reliable operation under various engine conditions [5].…”

Section: Fe Model and Meshingmentioning

confidence: 99%

“…This precise valve control allows for optimized valve timings, improved volumetric efficiency, and better combustion characteristics, contributing to enhanced power output, fuel efficiency, and reduced emissions. Ensuring the appropriate stiffness of the desmodromic actuation mechanism is essential to achieving accurate valve timing and reliable operation under various engine conditions [5].…”

Section: Introductionmentioning

confidence: 99%