Car Crash Simulations Research Articles

Knowledge discovery assistants for crash simulations with graph algorithms and energy absorption features

We propose the representation of data from finite element car crash simulations in a graph database to empower analysis approaches. The industrial perspective of this work is to narrow the gap between the uptake of modern machine learning methods and the current computer-aided engineering-based vehicle development workflow. The main goals for the graph representation are to achieve searchability and to enable pattern and trend investigations in the product development history. In this context, we introduce features for car crash simulations to enrich the graph and to provide a summary overview of the development stages. These features are based on the energy output of the finite element solver and, for example, enable filtering of the input data by identifying essential components of the vehicle. Additionally, based on these features, we propose fingerprints for simulation studies that assist in summarizing the exploration of the design space and facilitate cross-platform as well as load-case comparisons. Furthermore, we combine the graph representation with energy features and use a weighted heterogeneous graph visualization to identify outliers and cluster simulations according to their similarities. We present results on data from the real-life development stages of an automotive company.

Development of impact attenuator analysis tools in crash scenario using Euler method and finite element analysis

The problem considered in this work is the development of simulation method for simulating car crash which utilizes simple car—impact attenuator model developed in MATLAB. Usually, car crash simulation is done using full finite element simulation which could take hours or days depending on the model size. The purpose of proposed method is to achieve quick results on the car crash simulation. Past works which utilizes simple car—impact attenuator model to simulate car crash use continuous time model and the impact attenuator parameter is obtained from the experimental results. Different from the related works, this work uses discrete time model, and the impact attenuator parameter is obtained from finite element simulation. Therefore, the proposed simulation method is not only achieving quick simulation results but also minimizing the cost and time in obtaining the impact attenuator parameter. The proposed method is suitable for parametric study of impact attenuator.

Lightweight Design of Patch Plate on Car-body B-pillar based on Side Impact Safety

The B-pillar of automobile needs to meet the requirements of vehicle strength and rigidity, and also consider the fuel economy of vehicle. Therefore, the design and development of B-pillar is a difficult point in the field of car body design and manufacturing. Based on the side impact regulations, the safety model ling and simulation analysis of the B-pillar of the vehicle was carried out to obtain the change law of the intrusion amount and the intrusion speed of the five key points in the whole process. According to the analysis results of side impact of B-pillar, a scheme to reduce the material thickness of B-pillar body and increase patch plate for lightweight design was proposed, and a comparative analysis of the safety of side impact was made. In view of the problem that the intrusion of B-pillar of a real vehicle model did not conform to the regulations, the design scheme of adding patch plate was proposed to improve the safety of side impact. According to the actual collision results, the simulation model was modified, and the design scheme was simulated and optimized. The reliability of the design scheme was verified by the real vehicle collision analysis. The results show that: in the side collision of B-pillar, the intrusion of D2 position measurement point is the largest, the intrusion velocity of D3 position measurement point is the largest, and the intrusion amount and intrusion speed of D5 position measurement point are the smallest. Patch plates are added to the inner side of adjacent area of D2 position measurement point. The welding point is welded with B-pillar structure, and other areas of B-pillar keep the same structure, so as to realize lightweight and effective improvement of safety. Under the condition of maintaining the original material and thickness of B-pillar, two patches with thickness of 2 mm and material of B340LA are added in the middle of B-pillar to improve the structural strength. The defect area is set at the wrinkle position of the original B-pillar to guide the deformation mode of the B-pillar. The relative deviation between simulation calculation and test intrusion is less than 20 %, and the car crash simulation model with improved B-pillar structure is more accurate. For this type of car, the optimization and improvement effect of B-pillar structure is ideal, which improves the passenger safety protection ability in side impact.

An ontology-based knowledge management approach supporting simulation-aided design for car crash simulation in the development phase

In the automotive industry, the design process is both costly and time-consuming. This research focuses on improving the design process by mainly reducing time while producing more robust vehicles. Vehicle development is based on simulation; thus, the design process is referred to as simulation-aided design. Engineering design is highly collaborative and knowledge intensive. Therefore, knowledge management plays a crucial role in today's global economy and is essential for the competitiveness of companies. However, current research on engineering knowledge management focuses on either the codification or the personalisation approaches of knowledge management. Thus, this paper addresses an integrated and collaborative approach. This paper aims to develop an ontology-based knowledge management approach to support simulation-aided design, specifically car crash simulation. The knowledge management support system is designed to ensure the capture and retrieval of engineering knowledge and to enable collaboration between different stakeholders. An evaluation of the models and technologies used is also undertaken, based on use case scenarios.

An adaptive shell element for explicit dynamic analysis of failure in laminated composites Part 1: Adaptive kinematics and numerical implementation

To introduce more fibre-reinforced polymers in cars, the automotive industry is strongly dependent on efficient modelling tools to predict the correct energy absorption in crash simulations. In this context, an adaptive modelling technique shows great potential. However, as the critical energy absorption in a crash occurs over a very short period of time, and since the deformation behaviour is very complex, car crash simulations are usually performed using explicit dynamic finite element solvers. Therefore, any practical adaptive technique must be adapted to an explicit setting in a software available to the automotive companies. In this paper, we propose an adaptive method for explicit finite element analysis and describe its implementation in the commercial finite element solver LS-DYNA. The method allows for both so-called weak discontinuities (discontinuities in strain), which are crucial for accurate stress and intralaminar damage predictions, and strong discontinuities (discontinuities in displacements), needed for a proper representation of growing delamination cracks. In particular, we detail the implementation of the proposed method into LS-DYNA and also how we propose to remedy the non-physical oscillations arising from the implementation of the adaptive scheme in a explicit dynamic setting. The paper is concluded with numerical examples where we demonstrate the potential for the adaptive approach and also perform a detailed study on its accuracy and stability.

TOWARDS A FRAMEWORK FOR INTEGRATED AND COLLABORATIVE KNOWLEDGE MANAGEMENT FOR ENGINEERING DESIGN – A CASE STUDY

AbstractIn automotive industry, the design process is costly and time-consuming. Car safety is a crucial factor in the development of a vehicle, which is why crash simulation is an essential step in the design process. To improve car crash simulation analysis, it is necessary to reduce the time required and support the resolution of encountered design issues. We propose a knowledge management approach to support car crash simulation analysis and ensure the collaboration of different stakeholders. In a knowledge-intensive context, we used an ontology-based approach to formalise and capture knowledge.

Fitting CAD data to scanned data with large deformation☆

Abstract Recently, largely deformed simulation models, such as car crash simulations, have been in high demand. To evaluate such models, it is necessary to use actual deformation results to compare and validate them. When measuring actual deformed objects, an X-ray CT is useful because it is non-destructive. However, matching undeformed CAD data and scanned deformation data is difficult. We propose a system for users to set control points or control lines on feature points and predict deformation using affine transformation with the moving least squares method. In the proposed method, undesirable distortions are reduced by evaluating scaling using singular value index and introducing offset control points. The deformed CAD data are obtained by matching predicted CAD data and a polygon mesh generated by deformed CT data. In addition, the surface elements of the generated deformed CAD data are evaluated for CAE.

Empirical Study of Car Crash Simulation Analysis within the Development Phase

AbstractCar crash simulation analysis is an important phase within the vehicle development. It intends to analyse the crashworthiness of the vehicle model and examine the level of passive security. However, this activity is not trivial because of the considerable collaboration within the project, the large amount of analysed and exchanged data and a high exigency. Consequently, a solution to assist, ease and reduce the time of the process is desired.To study the current practices followed in the car crash simulation analysis an empirical study has been conducted. This study has been applied within the simulation analysis team, in the development phase, within an automotive company. This paper describes a qualitative analysis of the industrial context and diagnoses the dysfunctions in the current practices. This paper also highlights the current challenges encountered in the car crash simulation analysis.

A Geometrical Method for Low-Dimensional Representations of Simulations

We propose a new data analysis approach for the efficient post-processing of bundles of finite element data from numerical simulations. The approach is based on the mathematical principles of symmetry. We consider the case where simulations of an industrial product are contained in the space of surface meshes embedded in R^3. Furthermore, we assume that distance preserving transformations exist, albeit unknown, which map simulation to simulation. In this setting, a discrete Laplace-Beltrami operator can be constructed on the mesh, which is invariant to isometric transformations and therefore valid for all simulations. The eigenfunctions of such an operator are used as a common basis for all (isometric) simulations. One can use the projection coefficients instead of the full simulations for further analysis. To extend the idea of invariance, we employ a discrete Fokker-Planck operator, that in the continuous limit converges to an operator invariant to a nonlinear transformation, and use its eigendecomposition accordingly. The data analysis approach is applied to time-dependent datasets from numerical car crash simulations. One observes that only a few spectral coefficients are necessary to describe the data variability, and low dimensional structures are obtained. The eigenvectors are seen to recover different independent variation modes such as translation, rotation, or global and local deformations. An effective analysis of the data from bundles of numerical simulations is made possible, in particular an analysis for many simulations in time.

A parametric and non-intrusive reduced order model of car crash simulation

Industrials have an intensive use of numerical simulations in order to avoid physical testing and to speed up the design stages of their products. The numerical testing is indeed quicker to set-up, less expensive, and supplies a lot of information about the system under study. Moreover, it can be much closer to the physical tests as the computation power increases. Despite the rise of this power, time consuming simulations remain challenging to be used in design process, especially in an optimization study. Crash simulations belong to this category. These rapid dynamic computations are used by RENAULT during the sizing of the vehicle structure in order to ensure that it meets specifications set up to reach safety criteria in case of accidents. They are completed using finite element software such as VPS (Virtual Performance Solver) developed by ESI group that will be used in this study. For car manufacturers, the goal of the optimization study is to minimize the mass of the vehicle (and thus its consumption) by modifying the thicknesses of some parts (from 20 to 100 variables). Industrials such as RENAULT currently perform optimization studies based on numerical design of experiments. The number of computations required by this technique is from 3 to 10 times the number of variables. This is too much in order to be intensively used in a design process.In order to decrease the time-to-market and to explore alternative technical solutions, we explore the potential of using a parametrized reduced order model in the optimization studies. The parametrized reduced order model gives an estimation of the high-fidelity result for a new set of parameters without using the solver, by analysing the existing results of previous computations with various sets of parameters. The developed reduced order model is called ReCUR. It is partly based on a CUR approach embedded in a regression analysis. The regression statistical model uses the data of a few calculations made with the solver. Other tools such as clustering and linear programming are used to get the regression analysis more efficient.It is hoped to drastically reduce the number of required simulations of a standard optimization study. In this paper, the construction of the reduced order model will be presented. Then, the relevancy of using the reduced order model into a design process will be exhibited through the treatment of two industrial test-cases. Some improvements of the method as well as several potential uses will then be outlined. The applications will highlight the promising power of the method to shorten design process using optimization and long-run simulations.

Erratum to: Identification of nonlinear behavior with clustering techniques in car crash simulations for better model reduction

Erratum to: Identification of nonlinear behavior with clustering techniques in car crash simulations for better model reduction

Identification of nonlinear behavior with clustering techniques in car crash simulations for better model reduction

Car crash simulations need a lot of computation time. Model reduction can be applied in order to gain time-savings. Due to the highly nonlinear nature of a crash, an automatic separation in parts behaving linearly and nonlinearly is valuable for the subsequent model reduction. We analyze existing preprocessing and clustering methods like k-means and spectral clustering for their suitability in identifying nonlinear behavior. Based on these results, we improve existing and develop new algorithms which are especially suited for crash simulations. They are objectively rated with measures and compared with engineering experience. In future work, this analysis can be used to choose appropriate model reduction techniques for specific parts of a car. A crossmember of a 2001 Ford Taurus finite element model serves as an industrial-sized example. Since a non-intrusive black box approach is assumed, only heuristic approaches are possible. We show that our methods are superior in terms of simplicity, quality and speed. They also free the user from arbitrarily setting parameters in clustering algorithms. Though we improved existing methods by an order of magnitude, preparing them for the use with a full car model, they still remain heuristic approaches that need to supervised by experienced engineers.

A sparse grid based method for generative dimensionality reduction of high-dimensional data

Generative dimensionality reduction methods play an important role in machine learning applications because they construct an explicit mapping from a low-dimensional space to the high-dimensional data space. We discuss a general framework to describe generative dimensionality reduction methods, where the main focus lies on a regularized principal manifold learning variant. Since most generative dimensionality reduction algorithms exploit the representer theorem for reproducing kernel Hilbert spaces, their computational costs grow at least quadratically in the number n of data. Instead, we introduce a grid-based discretization approach which automatically scales just linearly in n. To circumvent the curse of dimensionality of full tensor product grids, we use the concept of sparse grids. Furthermore, in real-world applications, some embedding directions are usually more important than others and it is reasonable to refine the underlying discretization space only in these directions. To this end, we employ a dimension-adaptive algorithm which is based on the ANOVA (analysis of variance) decomposition of a function. In particular, the reconstruction error is used to measure the quality of an embedding. As an application, the study of large simulation data from an engineering application in the automotive industry (car crash simulation) is performed.

Dependence Modelling with Regular Vine Copula Models: A Case-Study for Car Crash Simulation Data

SummaryThe analysis of car crash output parameters such as firewall intrusion points assist the overall engineering process. Such data are nowadays collected from many numerical simulations and it is not possible for the engineer to analyse this growing amount of data by hand. Therefore, data mining and statistical methods are needed. Here, we propose to use the flexible class of regular vine (R-vine) copulas for modelling the dependence between such output variables. R-vine copulas are multivariate copulas constructed hierarchically from bivariate copulas as building blocks. We introduce the concept of such constructions and their graphical tree representation. Applied to simulated frontal crash data of a Ford Taurus such graphs help us to illustrate the dependence structure among different firewall intrusion locations. The big advantage of R-vines compared with standard approaches such as the multivariate normal distribution or the multivariate Gaussian copula is the ability to model asymmetries and dependence in the tails. Our application demonstrates the strong potential of R-vines in the engineering context and opens further application areas.

Model reduction and clustering techniques for crash simulations

AbstractModel reduction in car crash simulations is a fairly new research field. In this paper, a possible workflow is presented: Since nonlinear behavior can occur, parts with linear and nonlinear behavior need to be separated with clustering methods such as k‐means or spectral clustering. For the latter, a nonlinear reduction technique such as POD‐DEIM needs to be applied. A longitudinal chassis beam of a 2001 Ford Taurus is used to examine the different clustering methods. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Material characterization of liver parenchyma using specimen-specific finite element models

The liver is one of the most frequently injured abdominal organs during motor vehicle crashes. Realistic car crash simulations require incorporating strain-rate dependent mechanical properties of soft tissue in finite element (FE) material models. This study presents a total of 30 tension tests performed on fresh bovine liver parenchyma at various loading rates in order to characterize the biomechanical and failure properties of liver parenchyma. Each specimen, cut in a standard dog-bone shape, was tested until failure at one of three loading rates (0.01s−1, 0.1s−1, 1s−1) using a tensile testing setup. Load and acceleration recorded from each specimen grip were employed to calculate the time history of force at specimen ends. The shapes of all specimens were reconstructed from laser scans recorded prior to each test and then used to develop specimen-specific FE models. A first-order Ogden material model and the time histories of specimen end displacement were assigned to each specimen FE model. The failure Green-Lagrangian strain showed averages around 50% and no significant dependence on loading rates, but the failure 2nd Piola—Kirchhoff stress showed rate-dependence with average values ranging from 33kPa to 94kPa. The FE models with material model parameters identified using a simulation-based optimization replicated well the time history of load recorded during the test. The FE simulations with model parameters identified using an analytical approach or based on the displacement of optical markers showed a significantly stiffer response and lower failure stress/strain than the FE specimen-specific models. This study provides novel biomechanical and failure data which can be easily implemented in FE models and used to assess injury risk in automobile collisions.

Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods

Nowadays, product development in the car industry heavily relies on numerical simulations. For example, it is used to explore the influence of design parameters on the weight, costs or functional properties of new car models. Car engineers spend a considerable amount of their time analyzing these influences by inspecting the arising simulations one at a time. Here, we propose using methods from machine learning to semi-automatically analyze the arising finite element data and thereby significantly assist in the overall engineering process.We combine clustering and nonlinear dimensionality reduction to show that the method is able to automatically detect parameter dependent structure instabilities or reveal bifurcations in the time-dependent behavior of beams. In particular we study recent nonlinear and sparse grid approaches, respectively. Our examples demonstrate the strong potential of our approach for reducing the data analysis effort in the engineering process, and emphasize the need for nonlinear methods for such tasks.

Head Motions While Riding Roller Coasters

The risk of traumatic brain injury (TBI) while riding roller coasters has received substantial attention. Case reports of TBI around the time of riding roller coasters have led many medical professionals to assert that the high gravitational forces (G-forces) induced by roller coasters pose a significant TBI risk. Head injury research, however, has shown that G-forces alone cannot predict TBI. Established head injury criterions and procedures were employed to compare the potential of TBI between daily activities and roller coaster riding. Three-dimensional head motions were measured during 3 different roller coaster rides, a pillow fight, and car crash simulations. Data was analyzed and compared with published data, using similar analyses of head motions. An 8.05 m/s car crash lead to the largest head injury criterion measure of 28.1 and head impact power of 3.41, over 6 times larger than the roller coaster rides of 4.1 and 0.36. Notably, the linear and rotational components of head acceleration during roller coaster rides were milder than those induced by many common activities. As such, there appears to be an extremely low risk of TBI due to the head motions induced by roller coaster rides.

THE FLEXIBLE GABOR-WAVELET TRANSFORM FOR CAR CRASH SIGNAL ANALYSIS

This paper is concerned with the spectral decomposition and the adaptive analysis of data coming from car crash simulations. The mathematical ingredient of the proposed signal processing technique is the flexible Gabor-wavelet transform or the α-transform that reliably detects both high and low frequency components of such complicated short-time signals. We go from the functional treatment of this wavelet-type transform to its numerical implementation and we show how it can be used as an improved tool for spectral investigations compared to the short-time Fourier transform or the classical wavelet transform.

Mesh Variables Mapping Algorithm Used in Car Crash Simulation and Its Verification

In car crash simulation,the effects of thickness change,hardening and residual stress occurring during the forming process of sheet metal part on the car crash simulation results should be considered.The mesh model transferred directly from the forming equipment is usually not suitable to simulate the car crash due to obvious deformation of mesh,large number and small size of elements.An algorithm to transfer the element variables such as stress,strain and thickness distribution from the sheet metal forming mesh system to car crash forming simulation mesh system is proposed.A calculation program is also developed according to the proposed algorithm.The effectiveness of the algorithm is verified by comparison of one car body B pillar crash result with two different meshes.The algorithm can decrease the remeshing of model and avoid the variables mapping after remeshing.It provides a convenient method to incorporate forming effect in car crash model.