About Antoine Moats
My Story
My undergraduate experience has been an exploration of the different fields of engineering and applied physics. My research experiences have focused on using simulations to accelerate experimentation in the fields of quantum computation, mechanics, and materials characterization. As part of my hands-on engineering coursework, I have worked with components such as band pass filters, Op-Amps, transistors, strain gauges, and microcontrollers, cultivating a solid foundation in the practical application of electronics and sensor technologies. As I prepare for the next stage of my career, I am eager to leverage my experiences in modeling and experiments to innovate technologies that push the boundaries of human capabilities.
(Citation for above figures [1, 2, 3])
Research Projects
Research @ Tsinghua University
Research @ Tsinghua University
Research @ Tsinghua University
Intrigued by a modern physics class on quantum mechanics, I conducted computational research under Dr. Rebing Wu at Tsinghua University on single flux quantum gate transformations. The simulation was of an anharmonic oscillator exposed to a sequence of voltage pulses that change the probability, ideally, in a way that results in a swap of the probabilities of the two lowest eigenstates. [4, 5]
Research @ USC
Research @ Tsinghua University
Research @ Tsinghua University
During my junior year, I started working on the simulation of origami patterns. Under the guidance of Professor Plucinsky at the University of Southern California, I have written an algorithm that determines whether a given 2D origami pattern is compatible with a 3D pattern using a spring approximation. [4]
Research @ UT-ORII
Research @ Tsinghua University
Research @ UT-ORII
At the University of Tennessee-Oak Ridge Innovation Institute I was given the opportunity to utilize a supercomputing cluster to model compositionally complex oxides (CCO) under Dr. Haixuan Xu using Density Functional Theory (DFT). Together with his graduate student, we were tasked with using DFT to determine why one of the CCOs that was
At the University of Tennessee-Oak Ridge Innovation Institute I was given the opportunity to utilize a supercomputing cluster to model compositionally complex oxides (CCO) under Dr. Haixuan Xu using Density Functional Theory (DFT). Together with his graduate student, we were tasked with using DFT to determine why one of the CCOs that was synthesized and experimentally characterized had unique electromagnetic properties. To simulate the material we utilized quantum ESPRESSO and Monte Carlo simulations. [1, 6]
AME 441: Senior Design Laboratory
Background
Inspired by riblets, direct numerical studies conducted by Gomez et al. (2019) have shown that porous materials with optimal permeabilities and anisotropic ratios have the ability to reduce skin friction drag by an estimated 20-25 percent. However, due to the emerging nature of the topic, no experimental geometries exist that have yielded improvements compared to a smooth wall. [7]
Solution
Our experiments seek to replicate similar, scaled geometries of those in the literature and utilize dynamic similarity to determine whether the tested geometries perform better at larger scales. To create and test these geometries, resin based 3D printing, channel flow experiments, and Ansys Fluent simulations were be conducted at the USC Fluid-Structure Interactions lab. [4]
Class Projects
AME 508
ASTE 331B
ASTE 331B
AME 508 is a computational physics and machine learning course. My final project involved utilizing a deep neural network (DNN) to predict the intrinsic dielectric breakdown for any insulating material based on eight parameters. The data for the insulating materials were posted in an open source database as previous researchers collected
AME 508 is a computational physics and machine learning course. My final project involved utilizing a deep neural network (DNN) to predict the intrinsic dielectric breakdown for any insulating material based on eight parameters. The data for the insulating materials were posted in an open source database as previous researchers collected and used the data for training. My final report includes the comparison of a DNN and a random forest regression model. [2]
ASTE 331B
ASTE 331B
ASTE 331B
ASTE 331B is a second semester of a two semester spacecraft systems engineering course that entails a semester long group project. As part of this class, I simulated an orbit that provides maximal converge of the North Pole using STK and the thermal subsystem of the spacecraft using Thermal Desktop. [8, 9]
ASTE 475
ASTE 331B
ASTE 475
ASTE 475 is a rocket propulsion course where I simulated a Quasi-1D rocket nozzle with normal shocks, expansion fans and oblique shocks at the exit. As part of this project, I had to program my own root solvers for the Area-Mach number relation in MATLAB using Newton's method. While Newton's method was more efficient than the bisection so
ASTE 475 is a rocket propulsion course where I simulated a Quasi-1D rocket nozzle with normal shocks, expansion fans and oblique shocks at the exit. As part of this project, I had to program my own root solvers for the Area-Mach number relation in MATLAB using Newton's method. While Newton's method was more efficient than the bisection solver, it proved to be more difficult due to the steep gradient at certain Mach numbers. To combat this, I used a scaling parameter associated with the derivative term. [4]
Works Cited (paGe 1)
[1] Lattices were created in VESTA
[2] Lichtenberg figure from Bert Hickman
[3] 2D contour plot made in Paraview
[4] Plots made in MATLAB
[5] Quantum oscillator plots were taken from https://www.researchgate.net/figure/Harmonic-and-anharmonic-systems-and-their-suitability-as-qubits-a-In-the-quadratic_fig2_335845744