Kjell G. Robbersmyr

Application of viscoelastic hybrid models to vehicle crash simulation

This paper presents an application of physical models composed of springs, dampers and masses in various arrangements to simulate a real car collision with a rigid pole. Equations of motion of these systems are being established and subsequently solutions to obtain differential equations are formulated. We begin with a general model consisting of two masses, two springs and two dampers and illustrate its application to modelling fore-frame and aft-frame of a vehicle. Hybrid models, as being particular cases of two-mass–spring–damper model, are elaborated afterwards and their application to predict results of real collision is shown. Models’ parameters are obtained by fitting their response equations to the real vehicles crush coming from the data measurement analysis. For the full-scale experiment and created models we perform comparative analysis of both kinematic and energy responses.

SIGNAL ANALYSIS AND PERFORMANCE EVALUATION OF A VEHICLE CRASH TEST WITH A FIXED SAFETY BARRIER BASED ON HAAR WAVELETS

Authors version of an article published in the journal: International Journal of Wavelets, Multiresolution and Information Processing. Also available from the publisher at: http://dx.doi.org/10.1142/s0219691311003979

Data-based modeling of vehicle collisions by nonlinear autoregressive model and feedforward neural network

Vehicle crash test is the most direct and common method to assess vehicle crashworthiness. Visual inspection and obtained measurements, such as car acceleration, are used, e.g. to examine impact severity of an occupant or to assess overall car safety. However, those experiments are complex, time-consuming, and expensive. We propose a method to reproduce car kinematics during a collision using nonlinear autoregressive (NAR) model which parameters are estimated by the use of feedforward neural network. NAR model presented in this study is derived from the more general one - nonlinear autoregressive with moving average (NARMA). Suitability of autoregressive systems for data-based modeling was confirmed by application of neural networks with a NAR model to experimental data - measurements of vehicle acceleration during a crash test. This model allows us to predict the kinematic responses (acceleration, velocity, and displacement) of a given car during a collision. The major advantage of this approach is that those plots can be obtained without additional teaching of a network.

Signal reconstruction, modeling and simulation of a vehicle full-scale crash test based on Morlet wavelets

Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this paper a novel wavelet-based approach is introduced to reproduce acceleration pulse of a vehicle involved in a crash event. The acceleration of a colliding vehicle is measured in its center of gravity-this crash pulse contains detailed information about vehicle behavior throughout a collision. Three types of signal analysis are elaborated here: time domain analysis (i.e. description of kinematics of a vehicle in time domain), the frequency analysis (identification of the parameters of the crash pulse in frequency domain), and the time-frequency analysis, which comprises those techniques that study a signal in both the time and frequency domains simultaneously, using Morlet wavelet properties. Determination of time of occurrence of particular frequency components included in the measured acceleration pulse and further analysis of the obtained scalegram are based on the reproduction of each crash pulse component, according to the frequencies identified in the acceleration signal. Finally, by using the superposition principle, those major signal components are combined, yielding the reproduced crash pulse. The comparative analysis between the current methods outcome, the responses of models established previously by using different approach and the behavior of a real car is performed and reliability of the actual methods and tools is evaluated.

Data-Based Modeling of Vehicle Crash Using Adaptive Neural-Fuzzy Inference System

Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathematical point of view. In order to establish a mathematical model of a vehicle crash, one needs to consider various areas of research. For this reason, to simplify the analysis and improve the modeling process, in this paper, a novel adaptive neurofuzzy inference system (ANFIS-based) approach to reconstruct kinematics of colliding vehicles is presented. A typical five-layered ANFIS structure is trained to reproduce kinematics (acceleration, velocity, and displacement) of a vehicle involved in an oblique barrier collision. Subsequently, the same ANFIS structure is applied to simulate different types of collisions than the one which was used in the training stage. Finally, the simulation outcomes are compared with the results obtained by applying different modeling techniques. The reliability of the proposed method is evaluated thanks to this comparative analysis.

A fuzzy logic approach to modeling a vehicle crash test

This paper presents an application of fuzzy approach to vehicle crash modeling. A typical vehicle to pole collision is described and kinematics of a car involved in this type of crash event is thoroughly characterized. The basics of fuzzy set theory and modeling principles based on fuzzy logic approach are presented. In particular, exceptional attention is paid to explain the methodology of creation of a fuzzy model of a vehicle collision. Furthermore, the simulation results are presented and compared to the original vehicle’s kinematics. It is concluded which factors have influence on the accuracy of the fuzzy model’s output and how they can be adjusted to improve the model’s fidelity.

Sliding-mode observer based sensor-less control of a small wind energy conversion system

Small wind turbines are becoming an attractive solution for household applications. These micro generation units can be used as standalone applications or grid connected applications. However to get the full potential benefits of these wind turbines, systems should be low cost and reliable. Introducing the wind speed and rotor speed sensors at the generator shaft may reduce the reliability of small wind turbines. In this study, a grid connected sensor-less 5 kW small wind energy conversion system has been studied. The maximum power point tracking method of the wind turbine is totally independent from wind speed and rotor speed measurements. Optimum rotor speed and actual rotor speed are estimated using output current and voltage of the generator. To estimate the optimum rotor speed of the wind turbine, power signal feedback method has been used. Moreover, a sliding-mode observer is designed to estimate the rotor speed. Performance of the sliding-mode observer system has been compared with the measured rotor speed based wind energy conversion system. The simulation results show the effectiveness of the proposed sensor-less control system for the system under consideration.

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

In this paper mathematical modeling of a vehicle crash test based on a single-mass is studied. The model under consideration consists of a single-mass coupled with a spring and/or a damper. The parameters for the spring and damper are obtained by analyzing the measured acceleration in the center of gravity of the vehicle during a crash. A model with a nonlinear spring and damper is also proposed and the parameters will be optimized with different damper and spring characteristics and optimization algorithms. The optimization algorithms used are interior-point and firefly algorithm. The objective of this paper is to compare different methods used to establish a simple model of a car crash and validate the results against real crash data.

Optimization of Vehicle-to-Vehicle Frontal Crash Model Based on Measured Data Using Genetic Algorithm

In this paper, a mathematical model for vehicle-to-vehicle frontal crash is developed. The experimental data are taken from the National Highway Traffic Safety Administration. To model the crash scenario, the two vehicles are represented by two masses moving in opposite directions. The front structures of the vehicles are modeled by Kelvin elements, consisting of springs and dampers in parallel, and estimated as piecewise linear functions of displacements and velocities, respectively. To estimate and optimize the model parameters, a genetic algorithm approach is proposed. Finally, it is observed that the developed model can accurately reproduce the real kinematic results from the crash test.

A state-space approach to mathematical modeling and parameters identification of vehicle frontal crash

In this paper a state-space estimation procedure that relies on the time-domain analysis of input and output signals is used for mathematical modeling of vehicle frontal crash. The model is a double-spring–mass–damper system, whereby the front mass and real mass represent the chassis and the passenger compartment, respectively. It is observed that the dynamic crash of the model is closer to the dynamic crash from experimental when the mass of the chassis is greater than the mass of the passenger compartment. The dynamic crash depends on pole placement and the estimated parameters. It is noted that when the poles of the model are closer to zero, the dynamic crash of the model is far from the dynamic crash from the experimental data. The stiffness and damping coefficients play an important role in the dynamic crash.