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

Guennec, Yves Le; Brunet, Jean Patrick Leopold; Daim, Fatima; Chau, Ming; Tourbier, Yves

doi:10.1016/j.cma.2018.03.005

Cited by 35 publications

(18 citation statements)

References 38 publications

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“…In such context, the use of special-purpose RODM is justified, featuring intermediate characteristics of calculation time and accuracy in respect to analytical methods and FEM/MBS. 49…”

Section: Methodsmentioning

confidence: 99%

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Performance improvement of advanced driver assistance systems yields two major benefits: increasingly rapid progress towards autonomous driving and a simultaneous advance in vehicle safety. Integration of multiple advanced driver assistance systems leads to the so-called automated driving system, which can intervene jointly on braking and steering to avert impending crashes. Nevertheless, obstacles such as stationary vehicles and buildings can interpose between the opponent vehicles and the working field of advanced driver assistance systems’ sensors, potentially resulting in an inevitable collision state. Currently available devices cannot properly handle an inevitable collision state, because its occurrence is not subject to evaluations by the system. In the present work, criteria for intervention on braking and steering are introduced, based on the vehicle occupants’ injury risk. The system must monitor the surrounding and act on the degrees of freedom adapting to the evolution of the scenario, following an adaptive logic. The model-in-the-loop, software-in-the-loop and hardware-in-the-loop for such adaptive intervention are first introduced. To highlight the potential benefits offered by the adaptive advanced driver assistance systems, simulation software has been developed. The adaptive logic has been tested in correspondence of three inevitable collision state conditions between two motor vehicles: at each instant, the adaptive logic attitude of creating impact configurations associated with minimum injury risk is ultimately demonstrated.

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“…In such context, the use of special-purpose RODM is justified, featuring intermediate characteristics of calculation time and accuracy in respect to analytical methods and FEM/MBS. 49…”

Section: Methodsmentioning

confidence: 99%

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…We can observe that the real intrusion of nb 659 quite different from the ROM prediction of Table 2. Such differences are due to the number of computations used in the ROM (2) and the severe difference between the two behaviours in crash of those points. The accuracy of the ROM may be increased by adding new features to the X matrix, among which materials, spotwelds, or part geometry.…”

Section: Resultsmentioning

confidence: 99%

“…The ReCUR method stands for Regression-CUR [1,2], a regression technique based on a CUR approximation, used to extract specific rows and columns of the matrix and to compute a regression with those extractions as features (Fig. 2).…”

Section: The Recur Methodsmentioning

confidence: 99%

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Toward new methods for optimization study in automotive industry including recent reduction techniques

Gstalter

Assou

Tourbier

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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In the last years, the automotive engineering industry has been deeply influenced by the use of «machine learning» techniques for new design and innovation purposes. However, some specific engineering aspects like numerical optimization study still require the development of suitable high-performance machine learning approaches involving parametrized Finite Elements (FE) structural dynamics simulation data. Weight reduction on a car body is a crucial matter that improves the environmental impact and the cost of the product. The actual optimization process at Renault SA uses numerical Design of Experiments (DOE) to find the right thicknesses and materials for each part of the vehicle that guarantees a reduced weight while keeping a good behavior of the car body, identified by criteria or sensors on the body (maximum displacements, upper bounds of instantaneous acceleration …). The usual DOE methodology generally uses between 3 and 10 times the numbers of parameters of the study (which means, for a 30-parameters study, at least 90 simulations, with typically 10 h per run on a 140-core computer). During the last 2 years, Renault's teams strived to develop a disruptive methodology to conduct optimization study. By 'disruptive' , we mean to find a methodology that cuts the cost of computational effort by several orders of magnitude. It is acknowledged that standard DoEs need a number of simulations which is at least proportional to the dimension of the parameter space, leading generally to hundreds of fine simulations for real applications. Comparatively, a disruptive method should require about 10 fine evaluations only. This can be achieved by means of a combination of massive data knowledge extraction of FE crash simulation results and the help of parallel high-performance computing (HPC). For instance, in the recent study presented by Assou et al. (A car crash reduced order model with random forest. In: 4th International workshop on reduced basis, POD and PGD Model Reduction Techniques-MORTech 2017. 2017), it took 10 runs to find a solution of a 34-parameter problem that fulfils the specifications. In order to improve this method, we must extract more knowledge from the simulation results (correlations, spatio-temporal features, explanatory variables) and process them in order to find efficient ways to describe the car crash dynamics and link criteria/quantities of interest with some explanatory variables. One of the improvements made in the last months is the use of the so-called Empirical Interpolation Method (EIM, [Barrault et al.]) to identify the few time instants and spatial nodes of the FE-mesh (referred to as magic points) that "explain" the behavior of the body during the crash, within a dimensionality reduction approach. The EIM

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“…Guo and Hesthaven presented a non-intrusive data-driven reduced order modelling method [27,28]. The NIROMs have been successfully applied into various problems like fluid structure interactions [29], non-linear problems [30], car crash simulation [31] and compressor blades design [32].…”

Section: Introductionmentioning

confidence: 99%

Error estimation of the parametric non-intrusive reduced order model using machine learning

Xiao

2019

Computer Methods in Applied Mechanics and Engineering

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A novel error estimation method for the parametric non-intrusive reduced order model (P-NIROM) based on machine learning is presented. This method relies on constructing a set of response functions for the errors between the high fidelity full model solutions and P-NIROM using machine learning method, particularly, Gaussian process regression method. This yields closer solutions agreement with the high fidelity full model. The novelty of this work is that it is the first time to use machine learning method to derive error estimate for the P-NIROM. The capability of the new error estimation method is demonstrated using three numerical simulation examples: flow past a cylinder, dam break and 3D fluvial channel. It is shown that the results are closer to those of the high fidelity full model when considering error terms. In addition, the interface between two phases of dam break case is captured well if the error estimator is involved in the P-NIROM.

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

Cited by 35 publications

References 38 publications

Adaptive intervention logic for automated driving systems based on injury risk minimization

Adaptive intervention logic for automated driving systems based on injury risk minimization

Toward new methods for optimization study in automotive industry including recent reduction techniques

Error estimation of the parametric non-intrusive reduced order model using machine learning

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