Theory & Simulation (TS) Projects

Solar Analysis

Manager: Emma Peavler – In-House & Partnership with the REA Solar Team 

Learning ObjectivesSimulation Programming (Python, Matlab, and/or Mathematica), Report Writing

Applications closed

As the effects of non-renewable energy resources (i.e. coal, oil etc) are compounding the issue of global warming, the creation of practical and manufacturable renewable energy sources is necessary for the future of humanity. This experiment group will be designing, simulating and creating solar energy storage devices using lithium ion battery cells (18650) and various solar panels. In this experiment team members will learn and use basic programming knowledge to simulate the efficiency of various solar panels and optimize a design that allows for the maximum return on investment for each solar pack. Members will also be able to engineer and build the energy storage packs for optimized use.

LabVIEW Interfacing

Manager: TBA – In-House

Learning ObjectivesLabVIEW, Measurement System Design, Sensor Control

LabVIEW programming is used to control various measurement devices such as thermocouples, photosensors, strain gauges, and potentiometers to take measurements of properties of physical systems. Team members will set up these measurement device systems and program them to take such measurements. They will then test these systems to measure temperature, light, pressure, displacement, and other properties of interest.

Cyclotron Motion Simulation

Manager: Jared Rivera – In-House

Learning Objectives: Programming (Python)

Applications Closed

Cyclotron motion is the motion that charged particles undergo in a magnetic field such that they move outward in a spiral path. Team members use Python to simulate the motion of charged particles undergoing cyclotron motion. They will then apply this simulation to physical systems to predict outcomes of potential experiments, one example of which includes determining plasma wave behavior in the atmosphere using cyclotron accelerators, and another example being determining particle trajectories in a particle collider.

Particle Collider Simulation with Mathematica

Manager: TBA – In-House

Learning Objectives: Programming (Python, Matlab, and/or Mathematica), Data Analysis

Particle colliders are used to smash together particles to produce conditions similar to that of the Big Bang. Team members will use a programming language (Python, Matlab, and/or Mathematica) to simulate particle collisions that produce phenomena such as such as quark-gluon plasmas, and analyze real data from CERN. They will test this program using a particle accelerator to determine the success of the simulation.

Efficient Electricity Generation via Novel Methods

Manager: Mercedeh Khazaieli – In-House

Learning Objectives: Programming (Python)

Applications: Closed

To meet increasing energy demands, scientists are researching electricity generation using novel methods such as thermoelectrics, photovoltaics, wave power, and other methods. Thermoelectric materials generate electricity from temperature differences between their layers, photovoltaics generate electricity using light, and wave power is a way to utilize wind waves to generate power. Team members will create simulations of these methods using Python, optimizing for maximized efficiency. These simulations can be used as a basis for actual power generators in the future.

Modeling Quantum Systems with Machine Learning

Manager: Joshua Wong – In-House

Learning Objectives: Programming (Python), Neural Networks

Applications Open

Electrons are all over the place, and they’re complicated! If only we could somehow model their wavefunctions… but with so many in a given molecule, that’s practically impossible! I wonder, is there an easier way to model a complicated wavefunction…? Yes! We can use machine learning to do this!

A Course in Introductory Programming for Physics

Manager: Suyash Kumar – In House

Learning Objectives: Simulation, Programming (Python)

Applications Closed

In Physics, and in Math, we often encounter situations where all cannot be said on pen and paper. Mathematical equations are, though compact and convenient, abstract, and are understanding of Physics can certainly benefit from visualization. Thereby, programming becomes an indispensable tool for us for ensuring that the power of visualization remains at our disposal. The goal of this course is to learn programming in Python for Physics simulation.

Solid State Simulations

Manager: Ahmad Bosset Ali – Guidance from Prof. Brown

Learning Objectives: Programming (Python, C++)

Applications: Closed

The focus of our group is to simulate Solid State Physics which seeks to establish generalized descriptions of materials properties, by observing specific atomic crystal formations; which can undergo phase transitions from a variety of factors, such as temperature or pressure.  In our specific laboratory we sought to simulate various material properties, that are Stochastic in nature, by using the Markov Chain Monte Carlo method of analysis, this simply means that we incrementally change our observed systems till it matches the energy or other factors in a process we would see occur in nature naturally. Utilizing the law of large numbers, we seek culminate our results and create accurate models of Solid-State phenomena.