I have experience in both coding up custom script using MC simulations for optimization methods (brute force, ant colony, and particle swarm; along with MC methods in finite differencing methods). Here is a sampling of my work in Molflow+ and my code; this time in Python.
Modeling Molecular Transport Using Molflow+ (MS thesis): I ran MC simulations during the development of a vacuum effusion cell to maximize contamination to a collector plate in the lab during my MS thesis research on space-grade silicone response to LEO atomic oxygen exposure.
Master’s thesis research
Modeling Molecular Transport Using Molflow+ (CP Senior Design): I utilized Molflow+ to estimate the transport factor between sensitive scientific optical instruments and outgassing sources. These simulations helped validate the proposed system design against mission contamination requirements.
Cal Poly Senior Design Sequence
Stochastic Methods: Estimation of π using Monte Carlo brute force methods, Monte Carlo integration method, Ideal Gas, and Sensitivity Analysis
AERO 500 (Aerospace Modeling and Simulation)
Modified Brute Force and Genetic Algorithms: Solving the Knapsack problem using MBFA and Genetic Algorithms
AERO 500 (Aerospace Modeling and Simulation)
Biomimetic Algorithms: Ant Colony Optimization (five-node network) and using Particle Swarm Optimization to solve Ackley’s Function
AERO 500 (Aerospace Modeling and Simulation)