Multidisciplinary Design Optimization (MDO)

Aaryan's current research at Purdue, developing a multidisciplinary design optimization framework for rapid electric aircraft conceptual design.

Built a modular MDO algorithm that connects sizing, performance, and mission-level constraints in one workflow so early design iterations stay rigorous while moving quickly. The toolchain uses Python and MATLAB for the optimization loops, sensitivity checks, and design-space exploration that drive the conceptual-design trades, structured so individual disciplines can be swapped or extended without rebuilding the whole architecture.

Result: An abstract on "A Modular MDO Framework for Rapid Electric Aircraft Design" was accepted to the AIAA Aviation Forum 2026.

Applied optimization for the Purdue SAE Aero Design Team during the competition aircraft conceptual design stage.

Put the same modular MDO framework to work on the team's conceptual design workflow, using it to compare candidate aircraft configurations against sizing, performance, and mission-level constraints in one place. Running the trades through a single optimization loop let the team weigh configurations consistently rather than evaluating them in isolation, turning early design questions into faster, better-supported decisions.

Result: The framework let the team compare configurations and make faster design decisions during the conceptual stage.

AAE 550 Multidisciplinary Design Optimization, named on this site as a graduate-level course, taken as an undergraduate in Purdue's aerospace engineering program.

Carried AAE 550 as an undergraduate, working through the formal MDO methods behind the research: framing objectives and constraints, driving optimization algorithms, and running sensitivity and design-space studies. Aaryan deliberately loaded this graduate course alongside AAE 514 Intermediate Aerodynamics on top of the standard aerospace curriculum, grounding the applied framework work in the underlying optimization theory.

Result: AAE 550 appears in the Academia page course list, and the site describes it as a graduate-level course taken as an undergraduate.

TurboFan Engine Assembly, a fully parametric engine modeled as a personal project, used here as an applied optimization example.

Ran bypass ratio optimization studies and performance parameter calculations on the fully parametric TurboFan engine assembly, which is modeled end to end in Creo Parametric. It serves here as a compact, hands-on example of applying design optimization to a real engine model rather than to an abstract test case.

Result: The complete parametric assembly, gallery, and project files are browsable on the project page.