Firefly Optimization and Mathematical Modeling of a Vehicle Crash Test Based on Single-Mass

Klausen, Andreas; Tørdal, Sondre Sanden; Karimi, Hamid Reza; Robbersmyr, Kjell G.; Jecmenica, Mladen; Melteig, Ole

doi:10.1155/2014/150319

Cited by 16 publications

(6 citation statements)

References 19 publications

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“…Different forms are configured and used in analyzing the crash response. For instance, the basic lumped mass model consists of masses and linear springs [1], LPM including beam elements, plastic hinges, nonlinear contact spring elements, and rigid solids [2], and models using nonlinear spring and damper [3,4]. However, considering the mathematical complexity of a crash event representation, it was concluded that the determination of the force elements in the mathematical model has a remarkable influence on response prediction [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Optimization Design on Functionally Graded Cem for Trains Based on LPM Model with Calibrated Parameters

Chen

2020

Shock and Vibration

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Lumped parameter modeling (LPM) combined with optimization techniques is an efficient approach for parametric configuration design of energy absorption to improve crashworthiness performance during train collision. This work proposed a simplified model by introducing a bar element to consider the influence of the carbody in a collision process. The optimization method is applied to calibrate the equivalent parameters of the bar element. Bar elements with calibrated parameters are adopted in establishing a one-dimensional (1D) model for the train crash. Subsequently, a novel crash energy management (CEM) mode with functionally graded energy (FGE) configuration is introduced to the train crash model for improving crashworthiness performance. The influence of parameters in graded function on interfacial force and peak acceleration is investigated and optimal design parameters are obtained by Nondominated Sorting Genetic Algorithm (NSGA-II). It is concluded that considering the behavior of the carbody can improve the accuracy of LPM in predicting the longitudinal response, and the gradient CEM is a potential energy configuration mode to improve the crashworthiness of the train in a collision.

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Section: Introductionmentioning

confidence: 99%

Optimization Design on Functionally Graded Cem for Trains Based on LPM Model with Calibrated Parameters

Chen

2020

Shock and Vibration

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“…In [7], the authors proposed an approach to control the seat belt restraint system force during a frontal crash to reduce thoracic injury. Klausen et al [8,9] introduced a firefly optimization method to estimate parameters of vehicle crash test based on a single spring-mass-damper model. Ofochebe et al in [10], studied the performance of vehicle front structure using a 5-DOFs lumped mass-spring model composed of body, engine, the cross-member, the suspension and the bumper masses.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Vehicle Crashworthiness Parameters Using Piecewise Lumped Parameters and Finite Element Models

Munyazikwiye¹,

Vysochinskiy²,

Khadyko³

et al. 2018

Preprint

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Estimating the vehicle crashworthiness parameters experimentally is expensive and time consuming. For these reasons different modelling approaches are utilized to predict the vehicle behaviour and reduce the need for full-scale crash testing. The earlier numerical methods used for vehicle crashworthiness analysis were based on the use of lumped parameters models (LPM), a combination of masses and nonlinear springs interconnected in various configurations. Nowadays, the explicit nonlinear finite element analysis (FEA) is probably the most widely recognized modelling technique. Although informative, finite element models (FEM) of vehicle crash are expensive both in terms of man-hours put into assembling the model and related computational costs. A simpler analytical tool for early analysis of vehicle crashworthiness could greatly assist the modelling and save time. In this paper a simple piecewise LPM composed of a mass-spring-damper system, is used to estimate the vehicle crashworthiness parameters, focusing on the dynamic crush and the acceleration severity index (ASI). The model is first calibrated against a full-scale crash test and a FEM, post-processed with the LS-DYNA software, at an impact velocity of 56 km/h. The genetic algorithm is used to calibrate the model by estimating the piecewise lumped parameters (stiffness and damping of the front structure of the vehicle). After calibration, the LPM is applied to a range of velocities (40, 48, 64 and 72 km/h). The predictions for crashworthiness parameters from the LPM were compared with the predictions from the FEA and the results are much similar. It is shown that the LPM can assist in crash analysis, since LPM has some predictive capabilities and requires less computation time in comparison with the explicit nonlinear FEA.

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“…), simulate the system. Pawlus et al [7][8][9] fit the real car displacement to the model's responses by applying an LPM, and in Klausen et al, 5 various types of LPM together with a firefly optimization are applied.…”

Section: Introductionmentioning

confidence: 99%

“…There are usually two categories of vehicle crash analysis: finite element method (FEM) models and lumped parameter models (LPM). 5 The analysis by FEM is generally done by making models on CAD software. This method provides a very detailed analysis and gives the material responses during the crash.…”

Section: Introductionmentioning

confidence: 99%

Mathematical model of a vehicle crash: A case study

Varanis

Mereles

2016

International Journal of Mechanical Engineering Education

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Case studies are very useful when it comes to students' education. They show to them the issues that are faced by the mechanical engineers and what the students should do to resolve those issues during their professional career. In that sense, this paper presents a case study that can be presented in undergraduate and graduate courses where a vehicle crash collision is modeled by a spring-mass-damper system. The spring of the system will be considered to have a nonlinear force-deflection characteristic. The model proposed in this paper allows one to obtain the parameters of the system, and then compare them with the ones obtained experimentally to test the suitability of the model with the vehicle crash. The responses of the system will be obtained by numerical approximation by using the Runge-Kutta algorithm.

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Firefly Optimization and Mathematical Modeling of a Vehicle Crash Test Based on Single-Mass

Cited by 16 publications

References 19 publications

Optimization Design on Functionally Graded Cem for Trains Based on LPM Model with Calibrated Parameters

Optimization Design on Functionally Graded Cem for Trains Based on LPM Model with Calibrated Parameters

Prediction of Vehicle Crashworthiness Parameters Using Piecewise Lumped Parameters and Finite Element Models

Mathematical model of a vehicle crash: A case study

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