 Let's get to the point. Now we're going to see what are we going to model or to learn how to model in this course, why we're going to do it, and how we're going to do it. First of all, the what question, all important. In this course, we are going to treat the engineering models of systems and processes. Of course, you can think of an engine or a power plant as a system. Why we're going to do that? This will guide our modeling effort. We're going to do it because of a purpose we have to define very precisely. The success of our endeavor depends very much on how precisely we define the purpose. And of course, that requires defining what we are going to model. In general, we humans have models of things or representations of realities when we want to understand how it works. And then how we're going to do it? We're going to do it with a different or a set of mathematical models. And of course, we will go quite deep into that. And thanks to the use of computers that allow us to run the models, obtain simulation results, and understand from them. Now, before we start, I need to tell you some definitions. Definitions are a little bit boring, but bear with me. I will read them out loud for you and comment on that such that they become clear. What is a system? A system is a group of independent but interrelated elements comprising a unified whole. It is physically defined by its boundary. A bit abstract? Let's see what it is. Let's think of an aero engine, for instance. An aero engine is a system, it forms a unified whole, and it's formed by components, the compressor, the combustor, and the turbine, which are all tightly interrelated. Of course, in order to define a system, we have to define where it starts and where it ends. For instance, we might be interested in modeling the aero engine, but we do not care about the wing or the aeroplane. We just develop a thermal model of that system. What is a process? A set of physical, chemical, sometimes biological transformations of material and energy, and a set of operational procedures implemented in a controlled system. What is a process? Let's think, for instance, of a power plant based on the steam ranking cycle. A process can be steam that evaporates, that's a physical process. It is a transformation of material and energy. Of course, in energy engineering, we do not leave the processes go wherever they want, but we have to actually tightly control them. Think of a combustor and the combustion that occurs within it. We definitely need to control it to avoid, for instance, explosion. What is a model? A model is a simplified representation of a system or process in time and or space. We will see that in detail later. Intended to promote the understanding of the real system. I think this is quite self-effident as of now. We, for instance, develop the model of an aero engine to design it, to understand how it works, to improve its performance. What is a simulation? The use of a model in such a way that it enables the understanding of interaction otherwise hidden. Now, you can easily understand that the complexity of what happens in an engine, in a power plant, in whatever is so huge that by just staring at it and even having a very good physical understanding of what happens, we would never be able to tell what are the relations among the different variables. We do need mathematical equations for that. Now, I already mentioned some examples. Let's go through a list of things you will learn how to model in this course. Of course, engines. Would they be flying or on earth? Track engines, turbofan engines, even rocket engines. And I would advise you to try to save some minutes and think about other examples yourself. Maybe those who motivate you to further learn about mathematical modeling. Examples of power systems, you see them listed there. Maybe you already heard of fuel cells, but maybe you didn't hear about very exciting technologies that we are studying here at Tudel, supercritical carbon dioxide turbo generators. You'll hear about that more later. In general, these can be classified as thermal energy conversion systems. In that sense, the things you learn in this course are not limited to power and propulsion, but you can think of other applications whereby thermal energy is converted to obtain a certain defined purpose. Some examples. I hope they will get you excited. What you see here is a state-of-the-art turbofan engine. It is an extremely complex and possibly the most efficient machine that we have ever devised and obtained. On the right-hand side, you see the graphical user interface of a software you are going to learn how to use in this course. It's called GSP, the Gas Turbine Simulation Program, and some of the elements that compose always the software, like, for instance, the process flow diagram, that colorful scheme. Another example of gas turbine, in this case a terrestrial one, this is also a wonder because it achieves incredibly high efficiency thanks to that bulky element you see on the top of the gas turbine, which is called the recuperator. And again, on the right-hand side, you see the representation in GSP of such a system. As I mentioned, you can also end up modeling systems that do not provide propulsion on power, but thermal conversion systems. And this is out of some research we're doing at the moment on the environmental control system for aircraft, which is also a very heavy energy consumer on board. And therefore, there is a lot of interest in making it efficient. And this is the graphical user interface of another program that has been developed with the purpose of studying and improving these kind of systems. When we come to terrestrial applications, something I am very fond of is the organic ranking cycle turbo generator. This is a turbine system for the generation of electricity that can use as primary thermal energy source, renewable energy. Therefore, you understand it's extremely actual and compelling. A fuel cell is also a very modern energy conversion system based on electrochemical reactions. It can be used both on board of an airplane, top left of the chart, or as a power plant on the bottom left of the chart. And you see here the graphical user interface of a modelica model. And you will hear a lot more about modelica in the coming modules. Now, why do we do modeling? Let's define some types of engineering problems. We do it, of course, when we need to design a system. It is, therefore, the first thing we do when we want to analyze the feasibility, for instance, of a novel concept. But actually, the art of modeling got to an extremely sophisticated level. Nowadays, in some areas, one can claim to develop a virtual prototype, avoiding the need of actually building hardware, but relying on a computer for very accurate predictions. We, of course, care a lot about pollution when it comes to propulsion and power systems. That has to do with sustainability. And we can develop highly accurate models to predict the emissions of engines. All important is the control of systems. Or, for instance, you can imagine how important control is in the case of emissions. For this reason, we developed models, system models, that allow us to conceptually get to very efficient strategies for the control of systems and manage operation, regular operation, start option down, or even emergency situations. Troubleshooting. Once the system is realized, very often, especially if it's a new system, there are problems. And models can help us understand if there are faults, how to solve them, how to solve malfunctions. Other types of modeling. Safety is, of course, extremely important, imagine on an airplane. Models can help us prevent Azzardo's operation by understanding those complex reactions that happen when an Azzardo's situation realizes. They can help us, also, in the unfortunate case of an accident, to try to understand what caused the accident or to estimate the effects of accidents. Models are, of course, very important in that. Operator training. All these systems require highly trained operators in order to properly have these propulsion and power systems work. Imagine the aero engine. The pilot needs to understand how it works. And, of course, needs to be trained to all the operations that are needed for proper functions. For instance, they need models and training operators to start up and shut down the engines properly, or also just properly operate the system in normal conditions. But, very importantly, they need to understand how to react in case of an emergency. And, of course, that is not something that can be reproduced in reality. Again, think of the example of a pilot on an airplane. You do not want to train the pilot in an emergency situation by itself, but you would like him to have a simulator and train him to react to an emergency situation in a non-Azardous condition. And, as I said before, also it is important to study the environmental impact. Assess the emissions through models is also very important. Now let's have a look at the why question. We apply models throughout a project. You see there the time span of a project, and each phase comes with each special type of model. At first, we have to design the system. Therefore, we need a model for system analysis in order to get to probably the optimal solution for the given problem. In the second phase of the project, whereby we need to design the control, we need a model in order to test different concepts or different strategies to control our system. Once we have realized the system, initially the operation will not be optimal. And again, we can rely on a special type of models to fine tune the operation of the system and get to the right parameters of the control system. Finally, once the system is deployed in the field, again we can use models to further optimize the operation of the system. Now let's have a look, again within the why question, to a correspondence that there is between a set of people that we call the users and another set of people that we call the developers. And probably you can become one of the developers or one of the users by attending this course. The users are those who provide the requirements for models and you can see we can go from the level of development at the very bottom of these two triangular charts where you have researchers developing very sophisticated models that can be used by manufacturers or other researchers to come to a new prototype, for instance. Then you have the testing of this prototype which requires a different type of models. Again, the maturity of the software gets to a higher level. And finally, you have the actual deployed system. The users of the deployed systems are the engine operators or the airframe designers. And again, you have an even more mature level of software which is actually most often commercial software which is sold and maintained and interacted about with the users of this software. Now, with respect to the how question, we used to say that the modeling effort is usually an iterative one. And you see here an example of what we call the modeling loop where this iteration occurs. First of all, in the top left of the chart, you have a real world problem. That helps you to define the requirements from which you develop a mathematical model that you implement into a software. You run simulations. You obtain a solution with very sophisticated visual tools that allows you to interpret and analyze the results with which you should solve the problem. Now, unfortunately, very often one iteration is not enough to solve the problem because you have to go back to square zero or to one of the other squares and improve your model until you get to the solution of the problem. What are the possible approaches to modeling? I have devised three categories. Well, this is my personal subdivision. In this course, we will be focusing on physical equations modeling. Our models will be based for sure on conservation equations of which you see an example written out there. But sometimes, as you remember from the very first slide of module one, models are too complicated and we do not have physical equations for all of them. This is why sometimes people resort to actually experimental data that are needed in order to obtain a reliable model. Sometimes, and we can use still very sophisticated modeling techniques like linearized model and control theory in order to obtain a set of equations which we usually call transfer functions that still allow us to predict the outcome of a certain input based on experimental data. This is the case, for instance, if the model is linear and you might have had a sum of that in previous courses. Finally, there are cases in which the situation is just too complex and this was the case, for instance, many years ago with fuel cells. The electrochemistry was so complicated that the only way to understand how they worked was to actually build a scaled model and measure almost about everything. Now, in all of this, you understand computers play a major role. And that role has to be understood very carefully. A computer is used for model development, for simulation and for analysis in all those these phases. It is always at the base of the what if analysis we do in order to be able to get to the solution of the problem. We need specific software to model the system. It can be a pre-programmed commercial software or something that we learn to develop in this course. And here, a very important word, the caution. Be careful that the powerful CPUs of today can induce mistake through a faulty behavior which is trial and error. You always want to avoid continuously trying things without understanding what is happening there. That is a true recipe to waste a humongous amount of time. With respect to this, I would like to stimulate to read a PDF document we have posted on Brightspace which will tell you better what I mean by that. Have we succeeded in getting artificial intelligence or something that is independent from human thinking? I do not think so.