AE Brown Bag Presents: Nathaniel Berger and Matthew Wallace​

Fri Feb 28 2020 12:30 PM to 01:30 PM
Guggenheim 442
GT-AE tradition in which select undergraduates and graduate students present their research before their peers and mentors

The Daniel Guggenheim School of Aerospace Engineering

is proud to present the

Brown Bag Lunch

featuring

Nathaniel Berger

(Advisor: Prof. Dimitri Mavris​)

and

Matthew Wallace​

(Advisor: Prof. Evangelos Theodorou​​)

 

Friday, February 28
12:30 - 1:30 PM
Guggenheim 442
Pizza


Nathaniel Berger will present

"Developing Preliminary Sizing Optimization and Safety Analysis Tool for Wing Substructure​"

Early design decisions made during the conceptual and preliminary design phases of a development program are largely influential to the final weight, design, and cost of the aircraft. This presentation discusses the development and integration of a tool designed to optimize the weights of primary substructure groups within a user-defined wingbox. The optimization process is constrained to ensure all relevant structural margins of safety are satisfied under various shear and bending loads experienced during flight. The resulting optimal weights are summed to return to the user an evaluation of the ideal weight of the analyzed wingbox. This allows for fast and accurate sizing considerations early in the design process.


Matthew Wallace will present

"Optimal Control for In-Host Virus Models"

Mathematical virus models bear a close resemblance to dynamical systems common in aerospace engineering.  This similarity enables the use of optimal control methods to determine improved treatment regimes.  The talk will give an overview of ordinary differential equation and continuous time markov chain models (a type of jump process) for viral in-host infections.  Control emerges by interrupting infection of new cells with medication.  The cost function encodes minimizing amount of medication, and thus side effects, while still eliminating the viral population.  Differential dynamic programming will be discussed for the ordinary case and model predictive path integral for the stochastic case.

 

 

 

Location

Guggenheim 442