The first part of the lecture for this week introduces the module and its assessment structure, the second part will be looking at and explaining the requirement for simulation in modern development processes. By way of revision in the third part we will be reviewing differential equations; what they are, what do they do and how to do numerical integration.
Why numerical integration? It can help us find a solution where it is too difficult or not possible to find an analytical one. What is a solution? Well, we normally describe a dynamic system in terms of a differential equation that shows how various terms are related as time progresses. As we engineer systems we are often interested in actual quantities for some given parameter values, inputs and initial conditions not just how the terms are related through some difficult-to-think-about equation. Working out what is actually happening in the system given some initial conditions and parameter values e.g. spring stiffness, mass, etc helps us to evaluate the consequences of the engineering decisions we make i.e. to meet requirements should I use spring with stiffness x or y?
In the second week we will be continuing our review of some of the more theoretical fundamentals of the module, that ultimately allow us to represent the behaviour of complex systems using numerical dynamic models. In the first part of the lecture we will be looking at linearity.
Whether or not a system is linear, or at least can be represented linearly, is important because this enables to describe and analyse the system using less complex mathematical methods. In reality very few real systems are actually linear but in many cases they can be approximated as such.
Following from a quick look at linearity we will review the State Space Representation of a system. This particular way of formulating the system equations is one of many but it is perhaps one of the best known. In state space form we can make use of many different analytical methods to probe and understand the behaviour of the system we are studying and determine the consequences of the engineering decisions we make.
In week three we will continue looking at Ride Dynamics by finishing off the last part of the lecture and starting the Tutorial and Computer Lab on Suspension System Simulation. The lab will give you an opportunity to put what you have learnt into practice in the lectures by creating a simple quarter car model and running some simulations to observe, as you change parameters, how the system response changes.
In the second topic covered this week, Using Eigenvalues and Eigenvectors, we will start off by reviewing what eigenvalues and eigenvectors are. Following this we will see they help us to understand the type of response to expect from a system and how systems behaviour can be invested using them.
This week we will investigate aspects of Drivetrain Dynamics. In this topic we will be attempting to understand the drivetrain as a vibrational system comprised of a number of components that each contribute to the response of the system (and vehicle) to excitation input.
Drivetrain dynamics are important since the drivetrain contributes significantly to the overall vehicle performance. The drivetrain is key to ensuring a response that meets engineering targets, as torque is applied from an electric machine or internal combustion engine. Managing the torque pulsations from an engine or large changes in torque input from an electric machine present a significant challenge that require some interesting analysis methods!
This week we will be looking at the fundamental physics and derivation of equations required for the modelling of engines, most of which is related to a fundamental knowledge of thermodynamics and fluid mechanics. Making some assumptions we can generate fairly accurate models of the engine as a torque producing device.
In next week’s laboratory we will be looking at how we can, using our knowledge of engine physics, build a representative engine model. Data for more accurate parameterisation of this will be obtained in our straight line performance test at MIRA.
This week we will be looking at the fundamental physics and derivation of equations required for the modelling of drivetrain components. Implementing these as modular components within Simulink means that they can be used for modelling several different drivetrain architectures by connecting and parameterising them in different ways.
During the laboratory session we will be creating an example model of a front wheel drive vehicle that will be used later within the coursework.
The tuning of parameters in physical models is a key step that is often required later on in the model development process. The tuning of parameter ensures that the model best represents the physics of the system, this is important since the model will be used for engineering purposes.
This week we will make use of the a slightly modified version of the vehicle model that you have developed over the last few weeks to practice parameter tuning. Using the MATLAB/Simulink Parameter Estimation GUI and some sample data we will be tuning key model parameters to better represent the system described by the data.