Witold Pawlus

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.

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.

Parameters identification of induction motor dynamic model for offshore applications

The paper presents a technique to identify parameters of the LuGre dynamic friction model applied to represent mechanical losses of an induction motor. This method is based on Artificial Neural Networks (ANNs) system identification which is able to estimate parameters of nonlinear mathematical models. Within the presented approach, the network is first trained to associate model parameters with predicted friction torque, being given the reference motor speed. When this process completes, the inverse operation is performed and the network delivers estimated parameters of the model based on the reference friction torque. These parameters are then integrated with the dynamic model of the induction motor to form a complete virtual simulator of an electrical actuation system. The advantages and practical significance of the proposed technique are illustrated by an example of a scaled version of an induction motor used in an offshore pipe handling machine. It is demonstrated that the model of this system accurately simulates behavior of the experimental motor in the presence of speed and current reference profiles which resemble the ones characterized by offshore conditions. Hence, the model could be successfully applied in simulation based control system design.

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.

Drivetrain design optimization for electrically actuated systems via mixed integer programing

The proposed paper presents a method to optimally select components of a drivetrain for an electrically actuated machine. A simple mathematical model of the machine is established and inequality constraints which determine the choice of drivetrain components are formulated. Elements to be picked (namely, a motor, a gearbox, and a drive) are taken from a discrete set of data provided in the catalogs of industrial motors and drives manufacturers. By solving an optimization problem, a combination of components which both satisfy design requirements and minimize the total drivetrain cost is selected. The operation of the selected drivetrain is verified against the motor loadability curves. In addition, feasibility of other possible drivetrain configurations is checked and benchmarked with the optimal solution. Practical significance of the current work is demonstrated on a winch mechanism which is a popular part of many engineering applications, however, methods presented here could easily be adapted to other machines and industries. The results of the current work allow to reduce conservatism when designing actuation systems, while still satisfying the safety requirements specified by the designer. The system operating conditions are therefore effectively shifted to be closer to the constraints, which results in increasing the overall efficiency of the design and proving its cost-effectiveness.

Torque peak reduction and overload monitoring of induction motors in offshore drilling operations

Current drivetrain design procedures for electric actuation systems operating in offshore conditions typically consider two major requirements: to provide sufficient steady-state and maximum motor torques. As a result of this, no information regarding the transient state (e.g. during motor acceleration) is utilized when selecting drivetrain components. This leads to potentially dangerous situations when motors undergo saturation in these regions due to too high dynamic loads. A common reason for this (apart from lack of information about transient state when designing a drivetrain) is applying trapezoidal reference motion profiles that cause discontinuities in system acceleration and infinite values of jerk. To overcome this drawback, the current paper presents an application of a trigonometric scurve trajectory to lower values of torque peaks of motors operating in offshore drilling equipment. In addition, we show a method to monitor time spent by motors in overload regions. These two combined contributions allow to avoid unexpected motors saturation and verify if the components selected for a given electric actuation system will provide for a smooth machine operation. Practical significance of the presented work is demonstrated on a gripper arm mechanism of a full-scale offshore pipe handling machine. The results show not only a serious reduction in motor torque peak values when applying the trigonometric motion profiles (up to two times lower values), but also adaptation of the overall motor torque shapes to avoid too high overloads during the steady-state operation.

EKF-based estimation and control of electric drivetrain in offshore pipe racking machine

A typical challenge for electric drivetrains is to reduce the number of sensors required for control action or system monitoring. This is particularly important for electric motors operating in offshore conditions, since they work in hostile environment which often damages data acquisition systems. Therefore, this paper deals with verification and validation of the extended Kalman filter (EKF) for sensorless indirect field-oriented control (IFOC) of an induction motor operating in offshore conditions. The EKF is employed to identify the speed of the induction motor based on the measured stator currents and voltages. The estimated speed is used in the motor speed control mode instead of a physical encoder signal. In addition, we utilize a stationary frame model of the induction machine to assess the fidelity level of the EKF-based estimation of rotor fluxes. The experimental setup is used to validate accuracy of the EKF-based state estimation and motor speed control. The importance of the current work is demonstrated on an example of a full-scale offshore drilling equipment. Real-world speed and load profiles sustained by the gripper arm of the vertical pipe racking machine are scaled down and reproduced by the experimental setup. The proposed EKF algorithm accurately estimates both speed and electromagnetic torque experienced by the reference full-scale electric drivetrain, creating a potential to reduce the number of data acquisition devices in similar type of equipment.

Temperature Rise Estimation of Induction Motor Drives Based on Loadability Curves to Facilitate Design of Electric Powertrains

Thermal protection limits are equally important as mechanical specifications when designing electric drivetrains. However, properties of motor drives like mass/length of copper winding or heat dissipation factor are not available in producers’ catalogs. The lack of this essential data prevents the effective selection of drivetrain components and makes it necessary to consult critical design decisions with equipments suppliers. Therefore, in this paper, the popular loadability curves that are available in catalogs become a basis to formulate a method that allows to estimate temperature rise of motor drives. The current technique allows for evaluating a temperature rise of a motor drive for any overload magnitude, duty cycle, and ambient temperature, contrary to using a discrete set of permissible overload conditions that are provided by manufacturers. The proposed approach is based on industrially adopted practices, greatly improves flexibility of a design process, and facilitates communication in a supplier–customer dialog.