Identification of multibody vehicle models for crash analysis using an optimization methodology

This work proposes an optimization methodology for the identification of realistic multibody vehicle models, based on the plastic hinge approach, for crash analysis. The identification of the design variables and the objective function and constraints are of extreme importance for the success of the optimization. The characteristics of the plastic hinges are used as design variables while the objective functions are formulated with measures of the difference between the dynamic response of the model and a reference response. The sequential application of genetic and gradient-based optimization methods is used to solve the optimization problem constituting a systematic approach to the automatic identification of vehicle multibody models. The methodology is demonstrated with the identification of the multibody model of a large family car for side and front crash. The vehicle model is developed in the MADYMO multibody code which is linked with the optimization algorithms implemented in the Matlab Optimization Toolbox.     

Constrained multisine input signals for plant-friendly identification of chemical process systems

This paper considers the use of constrained minimum crest factor multisine signals as inputs for plant-friendly identification testing of chemical process systems. The methodology presented here effectively integrates operating restrictions, information-theoretic requirements, and state-of-the-art optimization techniques to design minimum crest factor multisine signals meeting important user-specified time and frequency domain properties. A series of optimization problem formulations relevant to problems in linear, nonlinear, and multivariable system identification are presented; these culminate with their application to the modeling of the Weischedel–McAvoy high-purity distillation column problem, a demanding nonlinear and highly interactive system. The effectiveness of these signals for modeling for control purposes and the ability to incorporate a priori nonlinear models in the signal design procedure are demonstrated in this distillation system case study.     

基于响应面法的汽车前防撞梁轻量化分析

将原来的汽车前防撞横梁材料替换成超高强度钢后,在确保低速碰撞性能基础上,利用响应面法进行轻量化分析.建立前防撞梁有限元模型,用LS-DYNA进行低速碰撞仿真.在此基础上以横梁和吸能盒的厚度作为变量进行试验设计.构建各项碰撞性能的2阶多项式响应面模型,并验证模型的有效性.以质量和吸能作为优化目标,建立多目标优化模型.与原设计相比,求出的优化方案在保证低速碰撞性能的基础上实现前防撞梁减重36%.     

Fitting nonlinear low-order models for combustion instability control

Tools for fitting low-complexity nonlinear models based on experimental data are examined through the example problem of finding a reduced-order model suitable for control of a combustion instability operating in a limit cycle. This proceeds in four parts; physical modeling, linear system identification, nonlinear analysis, and validation test design. It is shown how the nonlinear tools of describing functions, bifurcation methods and manifold analysis assist in developing a simple nonlinear model capable of describing the data and consistent with physical understanding. The system being modeled is a lean gas turbine combustor which exhibits a sustained mid-range (100–1000 Hz) limit cycle instability. The closed-loop experimental data does not contain a sufficiently rich spectrum for confident modeling in the first linear system identification phase. Despite the paucity of information quality, a grey-box nonlinear model is created and parametrized which provides an explanation both of the limit-cycle fundamental oscillation and of a high frequency nonharmonic signal also present. The model structure is explored and various operating conditions simulated to understand the model better.

The validation and/or refinement of this model is then considered. The model validation problem is important because of the poor information content of the periodic limit cycle data. The challenge is to provide a practically feasible, small excitation to the loop to improve identifiability and to provide qualitative tests of model performance. We examine this problem by considering the nonlinear dynamics of the model class and feasible excitation mechanisms.     

Identification of Neurofuzzy Models Using GTLS Parameter Estimation

In this paper, nonlinear system identification utilizing generalized total least squares (GTLS) methodologies in neurofuzzy systems is addressed. The problem involved with the estimation of the local model parameters of neurofuzzy networks is the presence of noise in measured data. When some or all input channels are subject to noise, the GTLS algorithm yields consistent parameter estimates. In addition to the estimation of the parameters, the main challenge in the design of these local model networks is the determination of the region of validity for the local models. The method presented in this paper is based on an expectation–maximization algorithm that uses a residual from the GTLS parameter estimation for proper partitioning. The performance of the resulting nonlinear model with local parameters estimated by weighted GTLS is a product both of the parameter estimation itself and the associated residual used for the partitioning process. The applicability and benefits of the proposed algorithm are demonstrated by means of illustrative examples and an automotive application.      

Parametric Identification of Multibody Models for Crash Victim Simulation

This paper concerns the parametric identification ofmultibody system models used to predict vehicleoccupant dynamics during crash impacts. The workconsiders the problem of identifying contactinteractions between a standard anthropomorphic dummyand its surrounding surfaces -- seat, belt, airbag, etc. -- directly from measured kinematic data. The central theme is that identification is viable, even in caseswhere there is only limited test data available, andwhere serious problems of ill-conditioning arise. Though the study is restricted to simulation data, and the models are simplified, the work has potential invehicle safety improvement, by providing validatedsystem models for use in restraint systemoptimisation. The methods presented may also prove useful in the wider class of multibody systemidentification problems.     

Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach

This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.     

Nonlinear dynamic system identification for automotive crash using optimization: A review

Literature on linear and nonlinear dynamic system identification is reviewed. The main motivation is to document the state-of-the-art, allowing one to propose further advancements in this field. The main problem is to identify system properties when experimental/numerical input and output data are specified. Parametric as well as nonparametric approaches for system identification are reviewed. For linear systems, both the first order and second order forms of the equations of motion are discussed. The use of first order form is more general as it can treat nonproportional structural damping as well. For nonlinear systems, the second order form of the equations of motion is used. A conclusion from the study is that more work is needed to develop identification formulations for nonlinear dissipative dynamic systems, especially for multi-degree of freedom systems. Received September 11, 2001     

Tolerance design optimization on cost–quality trade-off using the Shapley value method

Part tolerance design is important in the manufacturing process of many complex products because it directly affects manufacturing cost and product quality. It is significant to develop a reasonable tolerance scheme considering the demands of cost and quality to reduce the production risk and provide a guide for supplier management. Traditionally, some kinds of cost objective functions or variation propagation models are often applied in part tolerance design. Moreover, designers usually solve the tolerance design problem by constructing a single-objective model, dealing with several single-objective problems, or establishing a comprehensive evaluating function combining several optimization objectives with different weights. These approaches may not adequately consider the interdependent and the interactional relations of various demands and balance them. This paper presents a kind of tolerance design approach at the early design stage of automotive parts based on the Shapley value method (SVM) of coalitional game theory considering the demands of manufacturing cost and product quality. First the part tolerance design problem is defined. The measuring data in regular production is collected instead of working on specific objective functions or design models. Then how the SVM is adopted to solve the tolerance design problem is discussed. Lastly, a tolerance design example of a vehicle front lamp demonstrates the application and the performance of the proposed method.     

基于逆阵更新方法的局部非线性结构频响分析

针对含有非线性连接的大型局部非线性结构,采用描述函数表示其所连接的非线性内力,将非线性结构频响表示为拟线性动柔度矩阵(Quasilinear Receptance Matrix),提出一种逆阵更新(Inverse Matrix Updating Method,IMU)方法,将求解系统动柔度(频响特性)的高阶矩阵求逆转化为低阶矩阵的求逆,从而获得大型局部非线性结构主频响应的快速计算方法.仿真结果表明,本文的分析方法具有较好的稳定性,并能大幅提高大型局部非线性结构主频响应的计算效率.     

Method for solving nonlinear goal programming with interval coefficients using genetic algorithm

Traditional formulations on reliability optimization problems have assumed that the coefficients of models are known as fixed quantities and reliability design problem is treated as deterministic optimization problems. Because that the optimal design of system reliability is resolved in the same stage of overall system design, model coefficients are highly uncertainty and imprecision during design phase and it is usually very difficult to determine the precise values for them. However, these coefficients can be roughly given as the intervals of confidence.

In this paper, we formulated reliability optimization problem as nonlinear goal programming with interval coefficients and develop a genetic algorithm to solve it. The key point is how to evaluate each solution with interval data. We give a new definition on deviation variables which take interval relation into account. Numerical example is given to demonstrate the efficiency of the proposed approach.     

Multi-objective genetic programming approach for robust modeling of complex manufacturing processes having probabilistic uncertainty in experimental data

In this paper, a multi-objective uniform-diversity genetic programming (MUGP) algorithm deployed for robust Pareto modeling and prediction of complex nonlinear processes using some input-output data table. The uncertainties included in measured data are considered to obtain more robust models. The considered benchmarks are an explosive cutting and forming processes, in which the nonlinear behavior between the input and output of processes are detected using MUGP. For both case studies, a multi-objective modeling and prediction procedure firstly performed using deterministic data. Secondly, the same identification procedure carried out using probabilistic uncertainty in the experimental input-output data. The objective functions considered are namely, training error, prediction error and number of tree nodes (complexity of models) in the deterministic approach. Accordingly, the mean and standard deviation of training error and prediction error are considered in robust Pareto modeling and prediction of such processes. In this way, Pareto front of such modeling and prediction is first obtained for both explosive cutting and forming processes with deterministic data. Such Pareto front is then obtained using experimental input-output-data having probabilistic uncertainty in input parameters through a Monte Carlo simulation (MCS) approach. In addition, it has been shown that for both cases, the trade-off models obtained from deterministic data have significant biases when tested on data with probabilistic uncertainty. Finally, the obtained results of such multi-objective robust model identification show promising results in terms of compensating uncertainty in the experimental input-output-data.     

Metamodeling development for reliability-based design optimization of automotive body structure

Metamodels are commonly used in reliability-based design optimization (RBDO) due to the enormously expensive computation cost of numerical simulations. However, for large-scale design optimization of automotive body structure, with the increasing number of design variable and enhanced nonlinearity degree of structural performance, polynomial response surface which is commonly used for vehicle design optimization often suffers exponentially increased computation burden and serious loss of approximation accuracy. In this paper, support vector regression, along with other four complex metamodeling techniques including moving least square, artificial neural network, radial basis function and Kriging, is investigated for approximating frontal crashworthiness performance which is one of the most highly nonlinear performances. It aims at testing support vector regression and providing advanced metamodeling technique for RBDO of automotive body structure. Approximation results are compared in both accuracy and computational efficiency. Based on the frontal crashworthiness example, it is found that support vector regression and moving least square are preferable techniques to approximate structural performances with good accuracy. But support vector regression is recommended for its computational efficiency and better approximation potential. Moreover, the ensemble of support vector regression, moving least square, Kriging and artificial neural network is an effective alternative and is proved, in the RBDO example for the lightweight design of front body structure, to outperform any other single metamodel. The remarkable predominance indicates that the ensemble of support vector regression, moving least square, Kriging and artificial neural network holds great potential in approximating highly nonlinear performances for RBDO of automotive body structure.     

Development of generic multibody road vehicle models for crashworthiness

The crashworthiness analysis of road vehicles requires detailed data of the vehicles that the automotive manufacturers are, generally, unable to release due to commercial or legal restrictions. In the development of passive safety subsystems or substructures, the overall crash response of a vehicle model used to support it, must mimic that of the real vehicle; if this exists, regardless of any particular constructive detail of its structure provided that it is not located in the vicinity of such subsystem. This work proposes a methodology for the development of multibody models of road vehicles, for passive safety analysis, which include all general structural and mechanical features of real vehicles and start by exhibiting impact dynamic responses similar to the top of line vehicles. These vehicle models, designated as generic, do not require the knowledge of most of the particular details of the design of the real vehicle, which the manufacturers are unable to release, but can be adjusted to have crash responses similar to those of the real vehicle. Based on an existing finite element model of a car, which has all constructive features of vehicles of the chosen class, a multibody model is built applying the plastic hinge approach. By using a selected number of crash scenarios, defined in international standards such as the EuroNCAP, selected parameters of the vehicle multibody model are adjusted to ensure a good correlation between its impact responses and those of the finite element model. The crash responses are measured in terms of structural deformations, velocities and accelerations, occupant injury measures and structural energy absorption capabilities. Assuming that the plastic hinge constitutive equations of the multibody model are not exactly known, their parameters are used here as the multibody vehicle model that are adjusted. The methodology proposed is demonstrated by its application to the identification of the vehicle multibody model of a large family car for which the reference vehicle is available as a detailed finite element model.     

A multi-variate Hammerstein model for processes with input directionality

A new formulation of a block-structured model based on the Hammerstein operator is presented for the identification of multi-variate systems with input directionality. In contrast to the existing formulations for multi-variate Hammerstein models, the proposed structure offers the possibility to independently model the dynamic and nonlinear characteristics of the system and at the same time preserves the possibility to use the new efficient algorithms developed for the identification of single input Hammerstein models. Further, the formulation allows for a representation of arbitrary static nonlinear coupling of input variables with a considerably lower amount of parameters compared to existing formulations. The new model structure is applied to the identification of a fluid catalytic cracking (FCC) unit and significantly outperforms all previous multi-variate Hammerstein model structures by reducing the prediction error by over 50%.     

Analysis of a high-resolution optical wave-front control system

We consider the formulation and analysis of a problem of automatic control: correcting for the distortion induced in an optical wave front due to propagation through a turbulent atmosphere. It has recently been demonstrated that high-resolution optical wave-front distortion suppression can be achieved using feedback systems based on high-resolution spatial light modulators and phase-contrast techniques. We examine the modeling and analysis of such adaptive optic systems, and show that under certain conditions, the nonlinear dynamical system models obtained are gradient systems (with energy functions that also serve as Lyapunov functions). These gradient systems (employing fixed phase-contrast sensors) serve as a starting point for understanding the design of practical high-resolution wave-front correction systems, in which the phase-contrast sensor itself is subject to control.     

山东省流动地磁数据管理系统设计与实现

主要是介绍了山东省流动地磁数据管理系统的设计思路、系统开发的技术与方法,以及系统各个模块的功能,采用Delphi7．0为前台开发工具,用Access2000作为后台数据库来完成流动地磁数据管理系统数据库设计。实现了在WindowsXP系统下对流动地磁数据的录入、编辑、查询、报表、打印等功能。与以往使用的流动地磁数据管理系统相比,功能更强,操作更方便快捷,提高了流动地磁数据处理速度。由于所有数据Table位于一个模块中,非常便于管理维护和迁移升级,是流动监测数据处理工作上的一个飞跃。     

Problems of structural optimization for post-buckling behaviour

A proposal of a new approach to the optimal design of structures under stability constraints is presented. It is shown that the standard problem of maximization of the instability load may be modified so as to obtain a specified post-critical behaviour of the designed structure. The modified optimal structure represents stable post-buckling behaviour either locally, that is, in the vicinity of the critical point, or for a specified range of generalized displacements. First, some rigid–elastic finite-degree-of-freedom models are optimized, giving an insight into the modified design problems. Then a classification of the new optimization problems is presented. Various forms of instability are taken into account and a broad selection of objective as well as constraint functions is proposed. Based on the presented classification and following the proposed optimization concept, detailed formulations of nonlinear problems of design for post-buckling behaviour are given.