When talking about modelling it is natural to talk about simulation. Simulation is the imitation of the operation of a real-world process or systems over time. The objective is to generate a history of the model and the observation of that history helps us understand how the real-world system works, not necessarily involving the real-world into this process. A system (or process) model takes the form of a set of assumptions concerning its operation.
When computing the performance of a vehicle in longitudinal motion (maximum speed, gradeability, fuel consumption, braking, etc.), the vehicle is modelled as a rigid body, or in an even simpler way, as a point mass. The presence of suspensions and the compliance of tires are then neglected and motion is described by a single equation, the equilibrium equation in the longitudinal direction. If the x-axis is assumed to be parallel to the ground, the longitudinal equilibrium equation reduces to m¨ = x
A basic characteristic of a vehicle structural response in crash testing and model simulation is the “crash signature,” commonly referred to as the crash pulse  (numbers refer to references at the end of each chapter). This is the deceleration time history at a point in the vehicle during impact. The crash pulse at a point on the rocker panel at the B-pillar is presumed to identify the significant structural behavior and the gross motion of the vehicle in a frontal impact. Other locations, such as the radiator and the engine, are frequently chosen to record the crash pulse...
To supplement full scale dynamic testing of vehicle crashworthiness, mathematical models and laboratory tests (such as those using a Hyge sled or a vehicle crash simulator) are frequently employed. The objective of these tests is the prediction of changes in overall safety performance as vehicle structural and occupant restraint parameters are varied. To achieve this objective, it is frequently desirable to characterize vehicle crash pulses such that parametric optimization of the crash performance can be defined.
In general, it is the BEV (not the )V) that describes the VTV crash severities in a complete manner. Only in the fixed barrier test condition or when the stiffness ratio equals the mass ratio, will BEV (or crash severity index) be the same as )V (or crash momentum index). This relationship can be proved by simply making Rm = Rk and substituting into Eq. (7.52). Then BEV1/Vclose = )V/Vclose = 1/(1+Rm). 7.6.3 Crash Severity Assessment by a Power Curve Model This section presents a model with power curve force deflection, as shown in Fig. 7.28. The model is used...
A crash pulse is the time history of the response of a vehicle system subjected to an impact or excitation. The dynamic characteristics of the system can be described by using a “hardware” or a “software” model. A “hardware” model is a system consisting of masses interconnected by energy absorbers (springs and dampers). This will be presented in Chapters 4 and 5. The present chapter covers the use of a “software” model utilizing digital convolution theory for crash pulse prediction.
The solution to a problem with an impact or excitation model having more than two masses and/or any number of non-linear energy absorbers becomes too complex to solve in a closed form. Then, numerical evaluation and integration techniques are necessary to solve for the dynamic responses. Models such as the two non-isomorphic (with different structural configuration) hybrid or standard solid models, the combination of two hybrid models, and special cases with point masses will be treated first in closed-form.
In Chapters 4 and 5, efforts were directed toward analyzing the transient response and parametric relationships of a dynamic system under impact and/or excitation conditions. The basis for modeling such a dynamic system is Newton’s Second Law. In this chapter, the principle of impulse and momentum and the principle of energy derived from Newton’s Second Law are utilized to solve impulsive loading problems. The solutions to such dynamic problems do not directly involve the time variable. On the subject of impulse and momentum, the basic principles are reviewed first.
Equating the total energy at the start and end (potential and kinetic) yields:
(6.66) which is a quadratic in * and can be rearranged as follows:
Given the values of W = 5 lbs, h = 3.0 feet and k = 20 lbs/ft, *S = 5/20 = 0.25 ft. Substituting this in the above equation gives for the maximum deformation * = 1.5 feet or 6 *S. 184.108.40.206 Drop Test Using a Spring Having Finite Weight Let us repeat the drop test, but now assume that the bar has appreciable mass, Wb, as shown in Fig. 6.41. The uniform bar-mass model is...
Impact Effect of Moving Vehicles
56.1 56.2 Mingzhu Duan
Quincy Engineering, Inc.
56.3 56.4 56.5
Philip C. Perdikaris
Case Western Reserve University
Introduction Consideration of Impact Effect in Highway Bridge Design Consideration of Impact Effect in Railway Bridge Design Free Vibration Analysis
Structural Models • Free Vibration Analysis
Forced Vibration Analysis under Moving Load
Dynamic Response Analysis • Summary of Bridge Impact Behavior
The study of braking on straight road is performed using mathematical models similar to those seen in Chapter 23 for longitudinal dynamics. But in this case, the presence of suspensions and the compliance of tires are neglected and the motion is described by the longitudinal equilibrium equation (23.1) alone m¨ = x
Apart from cases in which the vehicle is slowed by the braking eﬀect of the engine, which can dissipate a non-negligible power (lower part of the graph of Fig. 22.
Over the past several years, the automobile industry has been in a period of dramatic turbu-
lence. There have been large movements in gasoline prices, taking retail prices to their highest
real values ever (Energy Information Administration, Short Term Energy Outlook, January
2007). The Ford F150 pickup fell from its longstanding position as the highest selling car in
the U.S. in favor of the Honda Civic (Automotive News, June 5, 2008), while GM lost its
position to Toyota as the worldwide leader in new car sales (Barron's, January 21, 2009).
Current average fuel economy levels vary consid-
erably by country. Across the OECD the average
figure in 2005 was around 8 litres per 100 km for
new cars (including SUVs and minivans and in-
cluding both gasoline and diesel vehicles). With a
50% fuel economy improvement, the average new
car performance in OECD markets would improve
to around 4 litres per 100 km (about 90 g/km of
So far, hybrid-testing research has centered on dry-mill production, the
lower investment technique of choice for the new cooperatively owned
plants. The seed companies are targeting their incentive programs on dry
mills. Monsanto’s program, “Fuel Your Profits,” provides the participating
ethanol plant with high-tech equipment that profiles the genetics of
incoming corn and is calibrated to maximize ethanol yield (Rutherford). As
an incentive, Monsanto gives rebates on E85 vehicles (those designed to run
on 85%-ethanol fuel) and fueling stations.
Less than twenty years ago photolithography and medicine were total strangers to one
another. They had not yet met, and not even looking each other up in the classiﬁeds. And
then, nucleic acid chips, microﬂuidics and microarrays entered the scene, and rapidly these
strangers became indispensable partners in biomedicine.
As recently as ten years ago the notion of applying nanotechnology to the ﬁght against dis-
ease was dominantly the province of the ﬁction writers.
Mobile Robotic Systems
26.1 26.2 Introduction Fundamental Issues
Deﬁnition of a Mobile Robot • Stanford Cart • Intelligent Vehicle for Lunar/Martian Robotic Missions • Mobile Robots — Nonholonomic Systems
Nenad M. Kircanski
University of Toronto
Dynamics of Mobile Robots Control of Mobile Robots
This subsection is devoted to modeling and control of mobile robotic systems. Because a mobile robot can be used for exploration of unknown environments due to its partial or complete autonomy is of fundamental importance.
Motor vehicles, like most machines, have a general bilateral symmetry. Only hypotheses can be advanced to explain why this occurs. Certainly to have a symmetry plane simpliﬁes the study of the dynamic behavior of the system, for it can be modelled, within certain limits, using uncoupled equations. However, the reason is likely to be above all an aesthetic one: symmetry is considered an essential feature in most deﬁnitions of beauty.
During the last few years the Internet has grown tremendously and has penetrated all aspects of everyday
life. Starting off as a purely academic research network, the Internet is now extensively used for education, for
entertainment, and as a very promising and dynamic marketplace, and is envisioned as evolving into a vehicle
of true collaboration and a multi-purpose working environment.