On the assessment of the macro-element methodology for full vehicle crashworthiness analysis

Γεωργίου, Γεωργία; Zeguer, Tayeb

doi:10.1080/13588265.2017.1328723

Cited by 15 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial Neural Networks. Artificial neural network (ANN) is a nonlinear processing system formed by the interrelation of a large number of processing units [6]. Artificial neural network is a data model established by simulating the neurons of animals.…”

Section: Neural Network Control Theorymentioning

confidence: 99%

“…e emergence of finite element method provides an effective tool for solving the problem [19]. e basic idea of the finite element method is to discretize a continuous solution region into a finite set of elements that are connected to each other in a certain way [6]. at is to say, a finite element set is used to replace the contiguous solution region with the same geometric shape or approximately the same geometry.…”

Section: Basic Idea Of Finite Elementmentioning

confidence: 99%

See 1 more Smart Citation

Finite Element Structure Analysis of Automobile Suspension Control Arm Based on Neural Network Control

2021

Security and Communication Networks

View full text Add to dashboard Cite

The control arm is an important transmission and guidance device in the Macpherson suspension system, which has an important impact on the ride comfort, operation stability, and safety of the vehicle, so it is necessary to study the structural performance of the control arm. In this paper, based on neural network control model, finite element analysis, and fatigue analysis theory, the strength, stiffness, and dynamic and fatigue performance of the control arm are studied and analyzed. Taking the ground contact force of the tire as the input condition, the static analysis of the front suspension is carried out, and the boundary condition of the load of the control arm is extracted. The finite element strength of the control arm is calculated in the OptiStruct solver under the conditions of uneven road, braking, and turning. At the same time, the longitudinal stiffness and lateral stiffness of the control arm are analyzed. The simulation results show that the control arm has better strength and stiffness performance.

show abstract

Section: Neural Network Control Theorymentioning

confidence: 99%

Section: Basic Idea Of Finite Elementmentioning

confidence: 99%

Finite Element Structure Analysis of Automobile Suspension Control Arm Based on Neural Network Control

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Several benchmark tests conducted by Takada and Abramowicz (2003) showed that MEM needs an incomparably shorter pre-processing time comparing to FEM (i.e., the calculation effort is much lower), and the resulting results present a high accuracy. The most recent assessment of MEM application in the crashworthiness analysis was conducted by Georgiou and Zeguer (2018). Their work investigates advantages and weak points of simplified crash phenomena modeling using MEM.…”

Section: Macro Element Methods and Visual Crash Studiomentioning

confidence: 99%

“…The modeling results using the VCS program of some typical problems have been validated in the past in many papers mentioned already in the chapters 3.1 and 3.2 (Takada and Abramowicz (2003); Georgiou and Zeguer (2018); Kim and Wierzbicki 2004). The validation of crashworthiness results for a thin-walled beam was presented, for example, in the previous work of authors, i.e., Pyrz and Krzywoblocki (2017).…”

Section: Compliance With Ethical Standardsmentioning

confidence: 99%

Crashworthiness optimization of front rail structure using macro element method and evolutionary algorithm

Pyrz

Krzywoblocki

2019

Struct Multidisc Optim

View full text Add to dashboard Cite

In this paper, the research on crashworthiness optimization of thin-walled structures is presented. The model of "S"-shaped frame subjected to complex crush load is analyzed. The objective of the study is to determine the optimal dimensions of the space frame cross section so as to achieve the maximal energy absorption of the structure and to fulfill the design requirements related to the crushing force value and the geometry relationship. The Visual Crash Studio (VCS) software, based on the macro element methodology, is used to simulate the response of a thin-walled beam during the impact and to determine crashworthy parameters. The results present a very good correlation with the finite element calculations but can be obtained in a much shorter time. The VCS software was coupled to evolutionary algorithm developed to determine the best solution. The optimization procedures revealed the necessity of a new design criterion related to maximal bending moments in the structure. This formulation enabled to obtain very good results after short processing on a PC computer. The proposed approach may be successfully used at early stages of the crashworthiness analysis.

show abstract

“…They extended their study to the optimization of the front rail dimensions (2019). Georgiou and Zeguer (2018) carried out crash analysis of the whole vehicle and individual sub-assemblies separately using this method.…”

Section: Introductionmentioning

confidence: 99%

Optimal crashworthiness design of vehicle S-frame using macro-element method and evolutionary algorithm

Pyrz

Krzywoblocki

Wolska

2022

Struct Multidisc Optim

View full text Add to dashboard Cite

The paper presents the crashworthiness optimization of a thin-walled frame applied as energy-absorbing element in a car structure. Crushing parameters of S-frame are modeled using the macro-element methodology. This method is implemented in the Visual Crash Studio software that enables very fast simulation of structural behavior during the impact. The objective is to determine the optimal dimensions of the frame cross-section to achieve the maximal energy absorption. Moreover, the selection of the best angle between the frame segments is investigated. In the formulation of the optimization problem, constraints related to the progressive collapse of deformation zones, required by the macro-element modeling, have been introduced. An Evolutionary Algorithm was applied to search the best solution. A real-life example of thin-walled S-frame is investigated in numerical examples. An attempt to find the solution by solving a sequence of simpler problems with reduced number of design variables is investigated. This approach is compared to the best result obtained for the problem including all design variables at the same time. This study illustrates the potential of the optimization in early-design stages of the vehicle development process and prepares perspectives for the optimization of complex energy-absorbing systems.

show abstract

On the assessment of the macro-element methodology for full vehicle crashworthiness analysis

Cited by 15 publications

References 19 publications

Finite Element Structure Analysis of Automobile Suspension Control Arm Based on Neural Network Control

Finite Element Structure Analysis of Automobile Suspension Control Arm Based on Neural Network Control

Crashworthiness optimization of front rail structure using macro element method and evolutionary algorithm

Optimal crashworthiness design of vehicle S-frame using macro-element method and evolutionary algorithm

Contact Info

Product

Resources

About