courses

New course offering: Complex Systems Design & Integration
This graduate level course examines advanced design methodologies applied to complex aerospace systems to analyze, understand, and optimize their behavior. Includes instruction in the design of experiments, surrogate modeling, machine learning, sensitivity analysis, and multicriteria decision-making techniques. Applications include sustainable aircraft systems design, such as electrified aircraft propulsion sizing and synthesis. Offers projects that allow students to address open-ended, real-world problems across a broad spectrum of sustainable aerospace system applications.
Last updated on Jan 1, 2024 1 min read courses
New course offering: Complex Systems Design & Integration
AEROSP 740 - Complex Systems Design & Integration
Examines computational methods for the analysis, understanding, and optimization of complex aerospace systems. Covers design of experiments, linear and non-linear regression techniques and surrogate modeling, sensitivity analysis, multi-dimensional trade studies, and surrogate-based optimization. Topics also include parametric physics-based modeling of advanced concepts, including principles of electrified aircraft propulsion systems (encompassing all-, hybrid-, and turbo-electric), energy-based aircraft mission analysis, and methods for co-optimization of aircraft, propulsion system, and energy and power management. Click here for more info.
AEROSP 481 - Aircraft Design
Multidisciplinary integration of aerodynamics, performance, stability and control, propulsion, structures and aeroelasticity in a system approach aimed at designing an aircraft for a set of specifications. Team based projects are overseen and graded by faculty and may also involve mentoring by representatives from external organizations.
AEROSP 388 - Aerospace Tools & Methods (MBSE)
Comprehensive teachings in Aerospace tools and methods, and statistical modeling, featuring Model-Based Systems Engineering (MBSE). Physical and virtual verification and validation (V&V) methods will be taught, emphasizing correlation of virtual modeling to physical testing. Root cause analysis and Six Sigma tools and process are included to put V&V into context.