Course Meeting Times

Lectures: 2 sessions / week; 1.5 hours / session

Course Description

This course provides an introduction to numerical methods and computational techniques arising in aerospace engineering. Applications are drawn from aerospace structures, aerodynamics, dynamics and control, and aerospace systems. Techniques covered include numerical integration of systems of ordinary differential equations; numerical discretization of partial differential equations; and probabilistic methods for quantifying the impact of variability. Specific emphasis is given to finite volume methods in fluid mechanics, and finite element methods in structural mechanics.

Course Objectives

Students successfully completing 16.90 should have:

  1. A conceptual understanding of computational methods commonly used for analysis and design of aerospace systems.
  2. A working knowledge of computational methods including experience implementing them for model problems drawn from aerospace engineering applications.
  3. A basic foundation in theoretical techniques to analyze the behavior of computational methods.


This course is being offered with a new pedagogy, which was first used in 2012. Our primary goal is to improve student learning. In addition, we hope to create an environment that allows some flexibility for students to participate in activities that may require some time away from the classroom (e.g., participating in an engineering competition, presenting at a conference, etc).

To achieve these goals, we will:

  • Require student preparation for class through look-ahead reading and problem sets.
  • Leverage student preparation to utilize class time for active engagement with the faculty and teaching assistants including: Concept questions; question & answer sessions; problem solving; small group exercises; project work; etc.
  • Allow students flexibility to remotely observe and engage in many of the classroom activities. If as the semester proceeds you have suggestions for how we can better meet these goals, please let us know! 

Tips for OCW Users

Learning Strategies

You could begin by completing all the readings in a Unit, then work through all of the Unit's sample problems in sequence, and then finally do all of the homework assignments.

Alternatively, you may find it more effective to try the relevant Unit sample problems and/or homework assignments as you finish each portion of the Unit reading. You can use the measurable outcome tags to identify these relationships: they appear at the top of all Unit pages, just beneath the title).

Either approach is fine: use whatever way you think is more effective for the way you learn!

Navigating the Unit Readings

When you mouse over the navigation bar at the top of any Unit page, the titles of each numbered subsection will pop up in a small window. This pop up title feature is also active on the "Back" and "Continue" buttons.

Having trouble reading an equation on these Unit pages, due to small font size? Double-click on the equation and a zoomed-in view will pop up, more clearly showing all of the superscripts and subscripts.


The subject total grade will be based on the letter grades from the homework, projects, and exams. The weighting of the individual letter grades is as follows:

Homeworks (embedded reading questions and problem sets combined) 12%
Projects (each worth 12%, 3 total) 36%
Oral midterm exam 25%
Oral final exam 25%
Classroom participation 2%

For the subject letter grade, we adhere to the MIT grading guidelines that give the following description of the letter grades:

  • Exceptionally good performance demonstrating a superior understanding of the subject matter, a foundation of extensive knowledge, and a skillful use of concepts and / or materials.
  • Good performance demonstrating capacity to use the appropriate concepts, a good understanding of the subject matter, and an ability to handle the problems and materials encountered in the subject.
  • Adequate performance demonstrating an adequate understanding of the subject matter, an ability to handle relatively simple problems, and adequate preparation for moving on to more advanced work in the field.
  • Minimally acceptable performance demonstrating at least partial familiarity with the subject matter and some capacity to deal with relatively simple problems, but also demonstrating deficiencies serious enough to make it inadvisable to proceed further in the field without additional work.