Abbreviated Syllabus

AAE 550 Multidisciplinary Design Optimization
Prof. Crossley

Fall 2023

SYLLABUS

AAE 550 WEB PAGES

[Disclaimer]
[Course Description]
[Learning Outcomes]
[Teaching Philosophy]
[Prerequisites]
[Policies]

[Assessments, Homework and Project]
[Collaboration and Academic Integrity]
[Course Grades]

[Homepage] - [Syllabus] - [Schedule]

Disclaimer

This is an abbreviated syllabus to give a sense of the AAE 550 course. The official syllabus for the course is available for students enrolled in AAE 550 from Brightspace.

Course Description

AAE 550 is a fast-paced, graduate-level course that introduces students to the techniques of engineering design optimization, leading into topics needed for Multidisciplinary Design Optimization (MDO). The course also presents application of these techniques to solve engineering design problems.

To accomplish these two tasks, AAE 550 has two overlapping parts. The first part of the course exposes students to basic concepts about and to implementation of numerical optimization techniques, assuming that the student has little or no knowledge of these topics. The second part of the course uses this knowledge as the basis for students to investigate approaches for multiobjective and multidisciplinary optimization.

Learning Outcomes

In this course, you will:

acquire basic knowledge about optimization techniques
become familiar with techniques for engineering design optimization
understand which methods are appropriate for a given optimization application
develop the ability to formulate engineering problems as optimization problems that are appropriate for a chosen method
use the computer and available software to solve optimal engineering design problems
practice effective technical communication by writing a report documenting your final project

Teaching Philosophy

My approach to teaching engineering design optimization relies upon having students: (1) practice formulating problems, (2) examine important features of various optimization algorithms, and (3) use computer tools to solve optimization problems. This does not lend itself well to traditional exams, so the grade for AAE 550 will rely upon homework assignments (which essentially take the place of mid-term exams) and electronic assessments (which take the place of textbook question-based homework).

Prerequisites

Knowledge of linear algebra and multivariate calculus. Computer programming skills sufficient to use functions available in Matlab.

Some knowledge of basic statics and strength of materials might help with understanding example and homework problems, but this is not required. Appropriate equations and formulas for these problems will be provided.

Policies

Assessments, Homework and Project

Students are to complete and submit the electronic assessments and homework assignments by the class session listed in the calendar and in the assignments available on the Brightspace pages; generally the time for this is 11:59pm Eastern. Eight graded electronic assessments and five graded homework assignments are planned for the semester. This course will not have traditional examinations or a final exam.

Assessments

To provide some performance feedback quickly to students, we have used electronic assessments as a part of the course. These assessments use the Brightspace quiz tool. Our intent is that you would access these questions, select your answers and then submit them via the Brightspace quiz, the grading is automatic and feedback to you is nearly instantaneous. We intend to give you two attempts for each assessment question. After the first attempt, Brightspace shows your responses and your score. If you are unhappy with your score, you can choose to submit a second attempt before the due date. This, in theory, should allow you to score 100% on the second attempt. With a second attempt, the final score for the assessment will be an average of the two attempts, which is our closest analog to “partial credit” for the assessments.

We have used these assessments for quite a while now, but with the issues around the transition to Brightspace a few years ago and constant “upgrades” to Brightspace means there are unintended opportunities for us to introduce issues with the assessments. If we find an issue after we have released the questions, we will do everything we can to notify you of corrections and changes promptly.

Homework

AAE 550 also includes written homework assignments that focus on applications of optimization to engineering and / or engineering-like problems. These homework assignments are more project-like; in fact, these are analogous to take-home exams in terms of their contribution to your grade for the course. These generally require you to formulate an optimization problem and then solve it using methods covered in class. You will be asked to make observations about your experience solving these optimization problems and compare this to concepts presented in class to demonstrate that you have reached a high-level in meeting the educational objectives and learning goals for these topics. These homework assignments require a significant amount of time to complete; you will not complete these successfully if you start the night before they are due.

The homework assignments will be distributed via Brightspace. Students can download the assignment page and any sample programs or spreadsheets (when available). We will use Gradescope to grade these assignments. You will need to submit your work electronically to Gradescope, where the teaching assistants can retrieve it for grading; there will be a link from within Brightspace to Gradescope.

Upon completing the assignment, the work must be included in an electronic document that the student uploads and submits via Blackboard. It may take more three weeks to read, grade, and provide feedback for all assignments from 120+ students.

After the instructor and teaching assistants receive the last approved homework (see below for late assignment policy), we will provide a solution example on the Blackboard site.

Late Submittals

The Teaching Assistants and I will try to accommodate late submittals of assessments and homework, but this requires advance notice. You must notify the teaching team using the aae550ta@ecn.purdue.edu address three business days before the due date if you need extra time to submit your assignment for us to accept your late submittal without penalty. Without the three-business-day advance notice, late assessments or homework assignments may be accepted with a penalty unless there are extenuating circumstances beyond the student's control.

The Office of the Dean of Students (ODOS) student absence policies cover cases of grief/bereavement, military service, jury duty, parenting leave, or emergent medical care (see https://www.purdue.edu/advocacy/students/absence-policies.html). These are the only cases covered by ODOS.

Because I am concerned about fairness in the class, I do not make the solutions available until all students have turned in that assignment, including approved late submittals. If the TAs or I have posted the solutions, we will no longer accept late homework assignments.

Grading Comments / Requests for Re-grading

There are often questions or concerns about how your assignments are graded. The TAs and I understand this, and we will be willing to entertain questions about your grades and requests for re-grading. However, with a class of this size, we must receive these questions and / or re-grade requests no later than one week after the graded assignments have been returned.

Collaboration and Academic Integrity

Although AAE 550 has no typical exams, ideals of academic integrity do apply to the course, and Purdue’s Honor Pledge applies: “As a Boilermaker pursuing academic excellence, I pledge to be honest and true in all that I do. Accountable together - we are Purdue."

Collaboration with other students on assessments and homework is acceptable - even encouraged - because learning from peers is a valuable addition to the educational experience. However, each student is responsible for completing his / her / their own work. All submitted work must be demonstrably independent from that of other students (e.g., submitting the exact same code snippets and / or discussion about your results as another student is not acceptable).

Copying and plagiarism in the homework is not acceptable. The instructor and teaching assistants will use plagiarism detection software to screen work submitted by thestudents. All code snippets included in your homework and final project must be readable; i.e., cut and paste them into your document rather than using a screen capture.

You may talk with other students, give and receive advice about structuring your Matlab scripts or Excel worksheets, as well as give and receive advice about using Matlab functions and Excel functions.

Do not share scripts or worksheets that you develop yourself or that you have modified from the examples provided by the instructor. Do not simply copy and paste scripts that other students have developed or have modified from the examples provided by the instructor; this includes copying and pasting work from students who have taken the course in previous semesters whether you know them personally or have obtained older work from a commercial website. The work you submit must be demonstrably independent from that of other students, so that the instructor, teaching assistants and / or graders can reliably judge your mastery of the topics.

As generative Artificial Intelligence (AI) tools continue to develop, these are likely to be both transformative as well as disruptive. The information they provide can sometimes be incorrect or misleading; they can also provide shortcuts that keep you from working to understand content and have a meaningful learning experience. These tools can also generate example MATLAB scripts. Given the discussion above about collaboration, using an AI tool to help you in AAE 550 can provide you with the potential to better understand material in the course; however, you are still responsible for completing your own work. The AAE 550 course has been developed over time and the basic approach of the assessments and homework were set before AI tools became available; there should be no need to use these tools to perform well in the course.

If copying and / or plagiarism is detected, this will result minimally with a failing or zero grade for that particular assignment and, at the instructor’s discretion, may result in a failing grade for the course. Additionally, as recommended by the Provost’s office, all incidents of academic misconduct will be forwarded to the Office of Student Rights and Responsibilities (OSRR), where university penalties, including removal from the university, may be considered.

Students may report issues of abuse of or violation of academic integrity that they observe through the Office of the Dean of Students (http://www.purdue.edu/odos/) by calling 765-494-8778 or by email to integrity@purdue.edu

Course Grades

The final course grades will use the following weighting:

Items	Weighting
Assessments 1 - 8	36%
Homework 1 - 4	48%
Homework 5	16%

The final grades will use the plus/minus letter scheme. The following table converts the numerical percentage score, rounded to the nearest integer value (e.g. 91.55% becomes 92%, 91.45% becomes 91%) to the corresponding letter grades.

Score	Grade	Score	Grade	Score	Grade	Score	Grade
98 to 100%	A+	88 to 89%	B+	78 to 79%	C+	68 to 69%	D+
93 to 97%	A	83 to 87%	B	73 to 77%	C	63 to 77%	D
90 to 92%	A-	80 to 82%	B-	70 to 72%	C-	60 to 62%	D-

Each assessment and homework will be graded on a (points scored) / (points available) basis. Total points available will vary, but each assessment and homework will have equal weight as other assessments and homework assignments. Grade assignment will use the criterion (straight-scale) approach shown above, but the instructor reserves the right to curve the grades if appropriate. Under no circumstance will the scale be more stringent than the criterion given below (e.g. 93% or above will always earn an A), and the curve will never span more than one grade scale (e.g. the lowest A possible when grades are curved is 93%). A total score of 50% or lower will always fail.

Incompletes

A grade of incomplete (I) will be given only in unusual circumstances. To receive an "I" grade, a written request must be submitted prior to December 1, and approved by the instructor. The request must describe the circumstances, along with a proposed timeline for completing the course work. Submitting a request does not ensure that an incomplete grade will be granted. If granted, you will be required to fill out and sign an "Incomplete Contract" form that will be turned in with the course grades. Any requests made after the course is completed will not be considered for an incomplete grade.

Modified August 20, 2023