Note that all of these treatments will be introductory, and we will not get to the most recent developments. The final will be at the standard time: Wedneday, May 6, at 2pm. The midterm take place in class on Tuesday, March 3. All readings for the course will come from materials that are freely available online although some will require access from inside Yale or the VPN.
Our main references will be. First, add the course in Canvas. Then fill out the enrollment survey. Yijiang Huang. UG: 4. Required of:. Preference Given to:. The projects will parallel the lecture content. The overall aim is to teach general tools and methods in the lectures, while allowing students to apply these tools to a specific application that is aligned with their background and interests. For their projects and homework assignments, the students will be free to choose the platform and software of their choice.
In terms of using optimization software, we recommend the following alternatives:. This tool is state-of-the-art and is used in many large corporations that focus on the design and development of large, complex engineering systems.
The program can be "wrapped around" any user specific simulation code e. The origin of this tool is the "Software Robot" developed by Dr.
PHX ModelCenter 3 : This is a visual environment for process integration to support your design projects. With PHX ModelCenter, you can quickly create an engineering process and then perform complex design exploration techniques to find the best design.
PHX ModelCenter is adaptable, and works well with groups whose design processes change frequently. PHX ModelCenter automates the process of running the hundreds of design programs you use during a typical design project. Using PHX ModelCenter design data is automatically passed from one program to another, freeing you to concentrate on the results of the design and not the drudgery of running individual programs.
It is the responsibility of the students to create for themselves the computational infrastructure that suits them best. There will be less emphasis on this point, however, since proficiency in these tools takes a long time to acquire and many of these codes have steep learning curves. Hence, the emphasis of the course is rather on learning the process of setting up, solving and interpreting multidisciplinary problems, rather than on creating physical models of very high fidelity as would be expected in an industry environment.
Part a : Small, simple problems to be solved individually, many just by hand or with a calculator. Goal is to ensure learning of the key ideas regardless of chosen project. Some problems might require more extensive computation. Part b : Application of theory to a project of your choice from either existing class projects or a project related to your research. We expect team sizes between two and four students. The assignments are due bi-weekly.
The class project is our main means of assessing whether you can learn the material at a deeper level and apply it to a graduate level research project. There are two major deliverables here towards the end of the term:.
A key component of this course is learning how to integrate different models from various disciplinary fields together into a single macro-model.
All too often specialists in different fields structures, fluids, propulsion, controls etc. Also frequently lacking is an understanding of how such design decisions impact system lifecycle cost and program risk. Understanding of and fluency in integrated, multidisciplinary modeling is essential to the success of contemporary and future complex systems. A system is a physical or virtual object that is composed of more than one element and that exhibits some behavior or performs some function as a consequence of interactions between these constituent elements.
This course focuses on engineering design problems e. As such, students should have a background and interest in engineering and system or product design and have had previous exposure to optimization. The course will be a good complement to existing courses in product development and system architecture, which do not typically present a multitude of quantitative methods and tools.
Optimization is a mathematical method and gives rise to a number of algorithmic tools. As such it represents a bridge, which enables the use of integrated multidisciplinary models to do more effective design engineering work. It should be stressed that the use of optimization is not intended to remove the human from the design loop. Rather, optimization enables engineers and system architects to explore vast design spaces, often resulting in non-intuitive insights.
This may result in system designs that are more cost-effective compared to previously considered traditional designs. Don't show me this again.
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