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Wayne Mullinax

Publications and source records attributed to Wayne Mullinax.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Interfacial thermodynamics of cryogenic fluids: The effect on non-condensable gas on fluid storage

Propellant tanks that contain cryogenic fluids (CFs) in low gravity conditions are usually pressurized with non-condensable (NC) gases for fast extraction. Unfortunately, the presence of NC gases causes CFs to exhibit higher boil-offs compared to pure CF systems. For optimal utilization of the cryogenic fuels, these higher boil-off rates must be minimized. Our goal is to quantify the effects NC gases have on the evaporation and condensation dynamics of CFs to develop strategies that mitigate the higher boil-offs. The hypothesis is that the NC gas accumulates around the CF’s liquid vapor interface in the Knudsen layer, creating a kinetic barrier for both evaporation and condensation. Testing this hypothesis with experimental methods is difficult due to the transient nature of the Knudsen layer, which is only a few nanometers thick. Furthermore, experimental capabilities are limited in low gravity conditions and extremely expensive. Accordingly, we approach this problem from a theoretical perspective. In this study, we employ molecular dynamics (MD) simulations to probe the interfacial mechanisms that affect CF’s evaporation and condensation at varying concentrations of NC gases. Specifically, we use nitrogen (N2) and oxygen (O2) as our CFs and neon (Ne) as our NC gas. Using MD simulations, we show that Ne accumulates at N2 liquid vapor interface across a wide range of Ne concentrations, thereby impeding mass transport of N2. Our simulations allow for direct computation of the molar flux as well as the mass accommodation coefficient (MAC) which can then be used as input parameters to continuum fluid dynamics (CFD) models for optimal storage tank design.

Michael Robert DeLyser↗
NASA NTRSDate not supplied

Surrogate Optimization for Quantum Circuits

Variational quantum Eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has yet to be realized with few claims of quantum advantage and high resource estimates mainly due to the need for optimization in the presence of noise. Finding algorithms and methods to improve the convergence is essential to accelerate the capabilities of near-term hardware for VQE or more broad applications of hybrid methods in which optimization is required. To this goal we look to use modern approaches recently developed in circuit simulations and stochastic classical optimization that can be combined in a surrogate optimization approach to classical circuits. Using an approximate state vector simulator, we efficiently calculate an approximate Hessian, fed as an input for a detailed quantum circuit simulator. We demonstrate the capabilities of such an approach with and without sampling noise. We also show that this method outperforms Powell in the presence of quantum circuit shot noise by a factor of 2-4

quantum computing↗
NASA NTRSDate not supplied

A Look at the Truths and Misconceptions of the Variational Quantum Eigensolver and the Implications of Overparameterization

In this work, we investigate loss landscapes of the variational quantum eigensolver (VQE) by quantifying the number of local minima through empirical analyses. We focus on minimal models in chemistry and physics so that we can do a complete analysis using more computationally expensive tools. We employ Hessian eigenvalue calculations and the nudged elastic band algorithm to characterize these landscapes. Our results expand upon the existing literature by highlighting the optimization challenges faced by VQE. We find that, as the number of parameters in our ansatz increases, the number of basins increases while the corresponding loss function values converge toward the global minimum value. This observation implies that overparameterization may lead to an ``effective convexity'' in VQE loss landscapes, a phenomenon supported by theoretical and numerical work in classical machine learning.

quantum computing↗