AE Brown Bag Lunch: Zach D'Ambra, Carson Coursey, Tuna Ergan

Fri Oct 07 2022 11:00 AM to 12:00 PM
Guggenheim 442

The Daniel Guggenheim School of Aerospace Engineering

is proud to present the 

Brown Bag Lecture Series

featuring

Zach D'Ambra

(Advisor: Ellen Yi Chen Mazumdar)

and

 Carson D. Coursey

(Advisor: Brian Gunter)

and

Tuna Ergan

(Advisors: Michael Balchanos, Burak Bagdatli)

Friday, October 7
11am-12pm
Guggenheim 442

Refreshments provided


Zach D’Ambra will present:

"Digit Off-Axis Holography for Phase Distortion Removal"

Abstract: Holography is a technique used for the three-dimensional imaging of multiphase and particle heavy flows. The baseline technique, digital in-line holography (DIH), suffers from distortions caused by phase delays and index-of-refraction gradients. These distortions are present in environments where there are extreme conditions such as shockwaves and significant thermal gradients, rendering the DIH technique ineffective. Digital off-axis holography (DOH) was implemented as a solution to this problem. DOH is capable of distortion removal in these extreme environments for retrieval of size, position, and velocity information. In this work we show the application of DOH for distortion removal in high index-of-refraction gradient flows.


Carson D. Coursey will present:

"Implementing Angles Only Initial Orbit Determination (IOD), Validating Doppler IOD Techniques, and Investigating Software Defined Radio IOD"

Initial orbit determination is necessary to locate and reacquire orbiting satellites from the ground. Gauss’ initial orbital determination technique is applied to a dataset of satellite observations to determine if it is suitable for accurate reacquisition of a passing satellite. In the future, a novel IOD technique using solely Doppler measurements will be validated by applying the method to collected satellite observations. Additionally, a novel IOD method using software defined radio measurements of Doppler shift and angle is proposed.


Tuna Ergan will present:

"Modelling, Correlation, Calibration, and Animation for Advanced Race Car Analysis and Development"

After identifying the need for physical testing, along with its logistical and financial difficulties, as a significant barrier and limitation to make improvements in the field of motorsports and engineering tasks in general, an approach to mitigate this problem by reducing the need of working with a physical model has been proposed as a team. To achieve this goal, a roadmap consisting of first, Virtual Experiment Modelling, then, correlating and calibrating the virtual experiment results to real-life testing results, and finally integrating calibrated findings to the virtual experiment model and animating them on the vehicle model has been laid out. Personally focusing mainly on the calibration and correlation tasks, the use of Functional Mock-Up Units (FMUs) and Surrogate Models for calibration, along with Python libraries and Optimica software for optimization of calibration have been evaluated with different optimization methods. Results of this found that optimization with FMUs has been faster than optimization with surrogate models, but due to FMUs importing limited data, gradient-based optimization and various other alternatives have been failed to be performed, resulting in less accuracy and more room to grow. Beyond calibration, software to enable animation to have been explored and proof-of-concept demonstrators have been developed with identified software. Future goals have determined to be improving and extending the scope of calibration by correlating multiple sub-systems at the same time, as well as developing more specialized animation that visualizes the behavior of the correlated system and sub-systems better.

 

Location

Guggenheim 442