New Insights into Vehicle Structural Strength and Dynamics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 4853

Special Issue Editors


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Guest Editor
School of Mechanical Engineering, Shandong University, Jinan 250061, China
Interests: vehicle structural strength; multidisciplinary optimization; MCDM
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Interests: bionic structure design; traffic and vehicle crash safety; simulation; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vehicle structural strength and dynamics is a field of applied research, including areas such as materials science, structure design, physics, and of course, dynamics. The research subject involves road-, rail- and other ground-based vehicle systems and their components. As vehicles become more light weight, intelligent, and comfortable, the industry is placing increasing demands on the structural strength and dynamics of these vehicles. The growth of computational capacities has led to further research in this field and more industrial applications. Many advanced technologies have been applied to the field of vehicle structural strength and dynamics, such as topology optimization, advanced control theory, numerical simulation, data-driven ones, evolutionary computation, and deep learning.

This Special Issue on “New Insights into Vehicle Structural Strength and Dynamics” aims to incorporate the latest research progress relating to vehicle structural strength and dynamics analysis, and advanced materials, structures, and design methods. Topics include, but are not limited to, the following:

  • Novel computer-aided modelling and simulation for the validation of vehicle strength and dynamics;
  • Rapid prototyping, analytical and numerical simulation technologies of materials with novel mechanical properties;
  • Advanced control systems for improving vehicle dynamics;
  • Performance analysis techniques for different key indicators of vehicle structures;
  • Multidisciplinary optimization methods of advanced vehicle structures;
  • Other related research topics.

Dr. Honghao Zhang
Prof. Dr. Yong Peng
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vehicle structural strength
  • vehicle system dynamics
  • modelling
  • advanced material
  • control system
  • performance analysis techniques
  • multidisciplinary optimization method

Published Papers (3 papers)

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Research

23 pages, 9287 KiB  
Article
Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
by Honghao Zhang, Dongtao Yu, Tao Li, Lingyu Wang, Zhongwei Huang and Yong Zhang
Appl. Sci. 2023, 13(17), 9750; https://doi.org/10.3390/app13179750 - 29 Aug 2023
Cited by 1 | Viewed by 892
Abstract
Thin-walled structures, when used for high-speed railways, can effectively mitigate the irreversible destruction when a malfunction occurs. Nested thin-walled tubes, as energy-absorbing structures, possess excellent specific energy absorption (SEA) and crushing force efficiency (CFE). This paper conducts multi-objective optimization [...] Read more.
Thin-walled structures, when used for high-speed railways, can effectively mitigate the irreversible destruction when a malfunction occurs. Nested thin-walled tubes, as energy-absorbing structures, possess excellent specific energy absorption (SEA) and crushing force efficiency (CFE). This paper conducts multi-objective optimization by focusing on a square corrugation nested structure with a double octagon inner wall, namely SCOD, to ameliorate the crashworthiness of the nested structure. The finite element model of the SCOD is constructed and validated by test data. A set of experimental design points with good spatial distribution are obtained using the optimal Latin hypercube (LHC) method. The polynomial response surface (PRS) method was applied to establish the fitting relationship between design variables and optimization objectives, and validation is accomplished. The DCNSGA-III algorithm is employed for optimization, resulting in a Pareto alternative solution set with good population diversity and convergence. In addition, to observe the optimized performance, a set of optimal solutions considering a single objective value is derived, and a comprehensive optimal solution is obtained by applying the minimum distance selection method (TMDSM). Finally, the proposed optimized system is analyzed and validated. According to the alternative reference solutions, the initial peak force (IPCF) reduces by 53.75% and CFE increases by 8.7%. This paper provides some reference for the optimization design in practical engineering. Full article
(This article belongs to the Special Issue New Insights into Vehicle Structural Strength and Dynamics)
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16 pages, 6944 KiB  
Article
State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise
by Haiming Xu, Tianjian Yu, Chunyang Chen and Xun Wu
Appl. Sci. 2023, 13(11), 6494; https://doi.org/10.3390/app13116494 - 26 May 2023
Cited by 3 | Viewed by 1406
Abstract
The accurate prediction of the state of charge (SOC) of Ni-Cd batteries is critical for developing battery management systems for high-speed trains. To address the challenges of the large floating charge voltage of Ni-Cd batteries and the vulnerability of a battery’s SOC to [...] Read more.
The accurate prediction of the state of charge (SOC) of Ni-Cd batteries is critical for developing battery management systems for high-speed trains. To address the challenges of the large floating charge voltage of Ni-Cd batteries and the vulnerability of a battery’s SOC to environmental factors such as temperature, this paper proposes an adaptive adjustment mechanism-based particle swarm optimization (APSO) generalized regression neural network (GRNN) model. The proposed model introduces the concept of the particle aggregation degree to quantify the convergence of the particle swarm optimization (PSO) algorithm. Furthermore, the speed weight of the particle swarm is adaptively adjusted using a comprehensive loss function to optimize the parameters of the GRNN model. To validate the proposed method, simulation experiments are conducted under test conditions using Ni-Cd batteries, and the prediction accuracies of various algorithms are compared. The experimental results demonstrate that the APSO-GRNN model significantly reduces the model’s prediction error. In addition, under the influence of different temperatures and noises, this method demonstrates strong robustness and high practical application value by accurately predicting the SOC, even with limited data samples. Full article
(This article belongs to the Special Issue New Insights into Vehicle Structural Strength and Dynamics)
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26 pages, 14050 KiB  
Article
Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
by Wei Zhou, Aliyu Kasimu, Yitong Wu, Mingzan Tang, Xifeng Liang and Chen Jiang
Appl. Sci. 2023, 13(9), 5375; https://doi.org/10.3390/app13095375 - 25 Apr 2023
Viewed by 1984
Abstract
Wind measurement in confined spaces is a challenge due to the influence of the dimensions of anemometers in intrusive flow-field measurements where the anemometer probes directly contact and influence the near-probe flow field. In this work, a new wind speed detection methodology is [...] Read more.
Wind measurement in confined spaces is a challenge due to the influence of the dimensions of anemometers in intrusive flow-field measurements where the anemometer probes directly contact and influence the near-probe flow field. In this work, a new wind speed detection methodology is proposed based on wind-induced motion of a stick via vision-based recognition. The target’s displacement in pixel coordinates is mapped to its angular displacement in world coordinates to derive wind speed and direction information by applying the calibration coefficients. Simulation experiments were carried out to validate the model, the error of which was within an angular displacement of 4.0° and 3.0° for wind speed and direction detections, respectively. When applied to the measurement of wind speed in the inner equipment cabin of a stationary high-speed train, the error was within ±1.1 m/s in terms of average RMSE. Thus, the proposed method provides an accurate and economic option for monitoring 2D wind in a confined space. Full article
(This article belongs to the Special Issue New Insights into Vehicle Structural Strength and Dynamics)
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