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Projects

Illumination Chamber Senior Design Project

The goal of this Senior Design Project was to create a contained table-top illumination system on a $300 budget.  Inside it would create relatively uniform irradiance of at least 5 W/cm^2  in a way that was sustainable, safe, and had camera viewing. This project was for a specific use in a laboratory and not designed for commercialization. It was designed to be easy to take apart and adjust it though as the project needs changed in the future.

These are examples of my AutoCAD and Python work for designing and planning this illumination system.

AutoCAD model of outside of illumination device, internal sample chamber, water-cooling heat exchanger, & LED array

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Python Simulation of LED placement with respect to illumination area and the resulting irradiance and uniformity

Here is a quick simulation used to gauge whether six 10W LEDs produced the irradiance needed for the project. Assuming these LEDs were about 20% efficient at converting electricity to light, then the simulation gives a good idea of the irradiance reaching different distances from the LEDs. Near the LEDs, it predicts about 6 W/cm^2 which accomplishes our goal of at least 5W/cm^2, though in this region the light is not very uniform. Near the bottom of the illumination area, the irradiance is much lower, however there is far greater uniformity in this region.

Model of irradiance expected near top of chamber
Model of irradiance expected at bottom of chamber

 

Computational Physics

Projects Included:

  • Internal energy & specific heat per lattice site calculation for the 2D ferromagnetic Ising 4×4 model with periodic boundary conditions, T in range 0.2 – 5 (in units of J/kB).
    • explicit generation of all 2^16 configurations (ie, exact evaluation)
    • Metropolis Monte Carlo simulation.
  • Simulating 2D Ising models with Metropolis algorithms to evaluate internal energy, specific heat, and magnetization.
  • Simulating a system of 16 Lennard-Jones particles in a 2D, periodic, 10×10 box to calculate speed distribution, diffusion coefficient, and phase.
  • Using 2D random-walks to find pair-correlation value of system, equilibrium positions, phase, and then eventually melting temperature of solid.
  • Using 3D random-walks to estimate fractal dimension of system.
  • Using Stochastic Methods to find stationary state of convection diffusion equation.
  • Modeling the energy band gap distribution of graphene to understand properties.

Equipment Control LabVIEW Project

During my Interdisciplinary Materials & Physics Research Experience for Undergraduates (REU) at Penn State University, I worked to build software that would take measurements from the equipment in the lab. I designed it such that a person could use any combination of GPIB-controlled equipment to take continuously-saved measurements. It also displayed that data graphically in real time. Using this adjustable LabVIEW program, we measured the resistivity of our superconducting devices at different magnetic field strength and direction to characterize the density of
states.

Below is a view of my code, as well as two short videos on how a user could set up the program for their specific experiment and watch it collect data.

Code for LabVIEW System that let’s the user pick any combination of equipment, configure the equipment, collect data from the equipment, save, and plot the data graphically in real time.

 

Mathematical Modeling for Physical and Mathematical Modeling

During this two-part class, we covered a variety of models. Here are a few examples:

  • model of heat generated during the hardening of cement
  • modeling steady state temperature distribution within metal rods as a result of heat source and air effects
  • diffusion model as it applied to the smell of a durian spreading across our classroom
  • Car speed as a function of density(traffic) along a road
  • oil spill spread near coast considering currents
  • sinko-streifer model with growth function to determine fish population and fish sizes
  • susceptible/infected/resistant (SIR) epidemic models for populations response to disease and vaccination

Additionally, used the following to improve our models:

  • Noise Filtering from audio files
  • sensitivity relations
  • determining solutions to partial differential equations
  • parameter estimation and optimization
  • Wave Equation, Doppler Effect,
  • Confidence Intervals, Standard Deviations
Sinko-Streifer Model for population of fish assuming born at different sizes, can only grow to a maximum size with time, and when they die they will be removed from the population u. In this model, no new fish are born or considered other than the initial population.
Susceptible/Infected/Recovered (SIR) Model assuming no vaccination
Susceptible/Infected/Recovered (SIR) Model assuming vaccination