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UVM Theses and Dissertations

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Format:
Online
Author:
Allen, Luke Daniel
Dept./Program:
Mechanical Engineering
Year:
2016
Degree:
MS
Abstract:
Current simulations of atmospheric entry into both Mars and Earth atmospheres for the design of thermal protections systems (TPS) typically invoke conservative assumptions regarding surface-catalyzed recombination and the amount of energy deposited on the surface. The need to invoke such assumptions derives in part from lack of adequate experimental data on gas-surface interactions at trajectory relevant conditions. Addressing this issue, the University of Vermont's Plasma Test and Diagnostics Laboratory has done extensive work to measure atomic specie consumption by measuring the concentration gradient over various material surfaces. This thesis extends this work by attempting to directly diagnose molecular species production in air plasmas. A series of spectral models for the A-X and B-X systems of nitric oxide (NO), and the B-X system of boron monoxide (BO) have been developed. These models aim to predict line positions and strengths for the respective molecules in a way that is best suited for the diagnostic needs of the UVM facility. From the NO models, laser induced fluorescence strategies have been adapted with the intent of characterizing the relative quantity and thermodynamic state of NO produced bysurface-catalyzed recombination, while the BO model adds a diagnostic tool for the testing of diboride-based TPS materials. Boundary layer surveys of atomic nitrogen and NO have been carried out over water-cooled copper and nickel surfaces in air/argon plasmas. Translation temperatures and relative number densities throughout the boundary layer are reported. Additional tests were also conducted over a water-cooled copper surface to detect evidence of highly non-equilibrium effects in the form of excess population in elevated vibrational levels of the A-X system of NO. The tests showed that near the sample surface there is a much greater population in the v'' = 1ground state than is predicted by a Boltzmann distribution.