Engineering Commons^™

Development Of Reduced Order Models Using Reservoir Simulation And Physics Informed Machine Learning Techniques, Mark V. Behl Jr

LSU Master's Theses

Reservoir simulation is the industry standard for prediction and characterization of processes in the subsurface. However, simulation is computationally expensive and time consuming. This study explores reduced order models (ROMs) as an appropriate alternative. ROMs that use neural networks effectively capture nonlinear dependencies, and only require available operational data as inputs. Neural networks are a black box and difficult to interpret, however. Physics informed neural networks (PINNs) provide a potential solution to these shortcomings, but have not yet been applied extensively in petroleum engineering.

A mature black-oil simulation model from Volve public data release was used to generate training data …

Generation Of Warm Dense Plasma On Solar Panel Infrastructure In Exo-Atmospheric Conditions, Harrison C. Wenzel

Theses and Dissertations

The use of a weaponized thermo-nuclear device in exo-atmospheric conditions would be of great impact on the material integrity of orbiting satellite infrastructure. Particular damage would occur to the multi-layered, solar cell components of such satellites. The rapid absorption of X-ray radiation originating from a nuclear blast into these layers occurs over a picosecond time scale and leads to the generation of Warm Dense Plasma (WDP). While incredibly difficult and costly to replicate in a laboratory setting, a collection of computational techniques and software libraries may be utilized to simulate the intricate atomic and subatomic physics characteristics of such an …