Crashworthiness optimisation of a composite energy-absorbing structure for subway vehicles based on hybrid particle swarm optimisation

Xie, Suchao; Li, Haihong; Yang, Chengxing; Yao, Shuguang

doi:10.1007/s00158-018-2022-3

Cited by 41 publications

(7 citation statements)

References 52 publications

(45 reference statements)

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“…e calculation results were in good agreement with the calculation results of the three-dimensional collision dynamics model of the train [21]. Xu and Xie et al conducted equivalent scale one-dimensional and three-dimensional collision tests of train and energy-absorbing structure [22][23][24][25][26][27], which verified the accuracy of one-dimensional and three-dimensional train collision dynamics models and the effectiveness of the numerical simulation of the energyabsorbing structure in terms of speed, acceleration, and interface force.…”

Section: Introductionsupporting

confidence: 66%

Research on Calculation Method for Maximum Mean Acceleration in Longitudinal Train Collision

Zhang

Wang

Zhu

et al. 2021

Shock and Vibration

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A large number of numerical simulations are required to design an energy absorption scheme for train crashworthiness, leading to low design efficiency in the early stage. Based on train collision dynamics theory and the finite element method, a dynamic finite element model of longitudinal train collision is established. According to the model, we studied the acceleration time-history characteristics during the train collision process, obtained the mean-peak ratio coefficient, and determined the calculation formula for the maximum mean acceleration of a longitudinal train collision. Through characteristic analysis of the vehicle acceleration, interface force, and other parameters during a longitudinal train collision, the calculation method of the mean acceleration was improved. The analysis shows that the maximum mean acceleration depends on two stages in the collision process: (1) the coupler action of the head vehicle: the mean-peak ratio coefficient of the head vehicle is 0.7 in this stage, and the mean-peak ratio coefficient of other vehicles is 0.43; (2) the coupler of the collision interface is cut off, and the energy absorption devices of the head vehicle or intermediate vehicle absorb energy; the mean-peak ratio coefficient of the vehicle is 0.93 in this stage. On this basis, a mathematical function is established describing the mean acceleration of the vehicle and the average crushing force of the coupler collapse tube and the energy-absorbing device. The calculation formula is obtained for the maximum mean acceleration of the longitudinal train collision, and the results are compared with the mean acceleration obtained by numerical simulation. The Kruskal–Wallis ANOVA multisample independent nonparametric test was conducted to verify the reliability of the calculation results in the 95% confidence interval. The calculation formula can be used to calculate the maximum mean acceleration in the energy allocation stage of train crashworthiness design to effectively improve the efficiency of train collision energy allocation.

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Section: Introductionsupporting

confidence: 66%

Research on Calculation Method for Maximum Mean Acceleration in Longitudinal Train Collision

Zhang

Wang

Zhu

et al. 2021

Shock and Vibration

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“…A crashworthy structure should have excellent capacity of absorbing sufficient energy, and the recorded load–displacement curve shall be considered and the crashworthiness index shall be calculated 14,27 . Peak crushing force (PCF) refers to the maximum crushing force generated during the crushing process of thin‐walled structures; average impact force ( F mean ) is the mean load value during quasi‐static loading; total energy absorption (EA) is the sum of energy absorbed by the structure during dynamic loading; specific energy absorption (SEA) refers to the energy absorption capacity of the energy absorbing structure under the unit mass; and crushing force efficiency (CFE) represents the fluctuation of load during crushing.…”

Section: Resultsmentioning

confidence: 99%

“…Qin et al 15 optimized the crashworthiness design of Al/CFRP hybrid gradient structures under different loading angles through mapping methods. Xie et al 27 proposed a hybrid particle swarm optimization (HPSO) to optimize the mathematical model of composite energy absorbing structure, and obtained the optimal configuration of the structure, proving that this algorithm has good applicability in crashworthiness optimization.…”

Section: Introductionmentioning

confidence: 99%

Crashworthiness analysis and multi‐objective optimization of Al/CFRP hybrid tube with initial damage under transverse impact

Cai,

Ma,

Wang

et al. 2023

Polymer Composites

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Damage can cause uncontrollable changes during the service of a structure, leading to a reduction in mechanical properties and ultimately to structural failure. This paper presents an experimental and numerical study of the crashworthiness of thin‐walled Al/CFRP hybrid tubes with pre‐existing transverse compression damage under transverse impact loading. Experiments shows that initial damage has a more limited effect on the energy absorption capacity of hybrid tubes under impact loading, but has a greater effect on their deformation pattern and the evolution of damage. After impact, cracks on the surface of the damaged tube propagate into multiple, discontinuous cracks with a total length similar to the length of the tube. Conversely, only short cracks are generated on the intact tube. Subsequent finite element simulations demonstrate the validity and accuracy of a coupled multi‐loads model and explore the effect of different structural parameters (winding angle and number of CFRP layers, and thickness of Al tube) on the crashworthiness of the hybrid tubes. Finally, a multi‐objective snake optimizer algorithm (MOSO) was used to obtain an optimal hybrid tube structure for multiple loading conditions in order to minimize the effect of initial damage on the crashworthiness of the hybrid tube. In comparison to the simulation outcomes for the original structure, the peak crushing force (PCF) decreased by 28.46%, whereas the specific energy absorption (SEA) increased by 44.59%.Highlight The effect of pre‐existing damage on the crashworthiness and deformation pattern of hybrid tube structures is investigated by means of experimental and numerical simulations. A multi‐step finite element model was developed using the restart method to realize the crashworthiness analysis of the hybrid tube under multiple load coupling conditions. The prediction accuracy of four surrogate models for the crashworthiness of hybrid tubes is compared and analyzed. The multi‐objective snake optimizer algorithm (MOSO) is proposed to obtain the optimum hybrid tube structure for multiple loading conditions.

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“…Because of its ease of implementation and fast searching speed, the PSO algorithm has been widely used in various engineering applications [54,[56][57][58] including several studies that utilized PSO in honeycomb sandwich composite investigations [41,[59][60][61][62][63]. However, it is surprising that these studies did not consider all the honeycomb core's physical parameters [core thickness (D), cell wall angle (φ), cell wall thickness (t) and the cell wall length (a)] in their optimization problems, considering the impact that each of these parameters has on the performance of honeycomb cores.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) Approach for Behaviour Prediction and Structural Optimization of Lightweight Sandwich Composite Heliostats

2021

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The necessity to diminish the heliostats’ cost so that central tower concentrating solar power (CSP) systems can stride to the forefront to become the technology of choice for generating renewable electricity is obliging the industry to consider innovative designs, leading to new materials being implemented into the development of heliostats. Honeycomb sandwich composites offer a lightweight but stiff structure that appear to be an ideal substitute for existing heliostat mirrors and their steel supporting trusses, avoiding large drive units and reducing energy consumption. However, realizing a honeycomb sandwich composite as a heliostat, among a multitude of possible combinations can be tailored from, that delivers the best trade-off between the panel’s weight reduction (broadly equates to cost) and structural integrity is cumbersome and challenging due to the complex nonlinear material behaviour, along with the large number of design variables and performance constraints. We herein offer a simulation–optimization model for behaviour prediction and structural optimization of lightweight honeycomb sandwich composite heliostats utilizing artificial neural network (ANN) technique and particle swarm optimization (PSO) algorithm. Considering various honeycomb core configurations and several loading conditions, a thorough investigation was carried out to optimally choose the training algorithm, number of neurons in the hidden layer, activation function in a network and the suitable swarm size that delivers the best performance for convergence and processing time. Carried out for three case scenarios, each with different design requirements, the results showed that the proposed integrated ANN-PSO approach provides a useful, flexible and time-efficient tool for heliostat designers to predict and optimize the structural performance of honeycomb sandwich composite-based heliostats as per desired requirements. Knowing that heliostats in the field are not all subjected to the same wind conditions, this method offers flexibility to tailor heliostats independently, allowing them to be made lighter depending on the local wind speed in the field. This could lead to reductions in the size of drive units used to track the heliostat, and the foundations required to support these structures. Such reductions would deliver real cost savings, which are currently an impediment to the wider spread use of CSP systems.

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Crashworthiness optimisation of a composite energy-absorbing structure for subway vehicles based on hybrid particle swarm optimisation

Abstract: Crashworthiness optimisation of a composite energy-absorbing structure for subway vehicles based on hybrid particle swarm optimisation. Structural and Multidisciplinary Optimization.

Cited by 41 publications

References 52 publications

Research on Calculation Method for Maximum Mean Acceleration in Longitudinal Train Collision

Research on Calculation Method for Maximum Mean Acceleration in Longitudinal Train Collision

Crashworthiness analysis and multi‐objective optimization of Al/CFRP hybrid tube with initial damage under transverse impact

Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) Approach for Behaviour Prediction and Structural Optimization of Lightweight Sandwich Composite Heliostats

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