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aeropython

Classical Aerodynamics of potential flow using Python and Jupyter Notebooks

cover-letter-for-monolith-materials

Attached is the cover letter for the position CFD intern at Monolith Materials. If you have any other questions, please do not hesitate to contact me! Thank you so much for considering my application!

fluid-jupyter-notebooks

A set of in-progress Jupyter notebooks that seek to ease computations of compressible flow and provide visualizations of potential flow and simple solutions to the Navier-Stokes equations.

hopf-bifurcation-project

Based on the Holling-Tanner predator-prey model, the goal of this project was to analyze data of a population of wolves from Yellowstone National Park in order to determine how to stimulate a healthy oscillatory cycle. Obtaining this value was found through the utilization of linear algebra to find the Hopf bifurcation values of the parameters in the model.

mae-250a-hw-6-jupyter-notebooks

magnetostriction-measurement

Code that performed the magnetic field sweep and data processing are provided here.

mangrove-project

In order to promote mangrove growth, a matrix model of its regeneration after disturbances was obtained from a research paper written by Peter J. Clarke entitled "The population dynamics of the mangrove Avicennia marina; demographic synthesis and predictive modeling." To accomplish this, concepts from linear algebra were applied, using Python to perform the computational legwork.

matlab-2d-fluid-simulation

This project utilizes smoothed particle hydrodynamics and spatial hashing to simulate a grid of interacting fluid particles. Although this program is unlike traditional computation fluid dynamics (CFD) programs, it still simulates the motion of fluid on an atomic level.

numerical-solvers

This repository contains a collection of programs compiled that solve a number of numerical methods, ranging from Newton's method to the discrete Galerkin method.

self-parking-simulation

For the final project of CEE/MAE M20, this script simulates a self-parking car that navigates from the left edge to the closest parking spot in a lot of 20 rows (10 on top and 10 on bottom). The parked cars that populate the lot are filled in pseudorandomly, ranging from 1 to 19 cars. It further generates an animation at 30 frames per second for 10 seconds, where the first frame of the video displays the car's initial position (in UCLA blue) and final destination (in red) along with its travel path. The travel path always consists of three segments: a horizontal segment, quarter-circle rotation, and vertical path to the lot after completing its rotation.

tanms-research

trajectory-code