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Munster, Drayton W.

Publications and source records attributed to Munster, Drayton W..

Thoracic Pressure Does Not Impact CSF Pressure via Compartment Compliance

Space acquired neuro-ocular syndrome (SANS) remains a difficult risk to characterize due to the complex multi-factorial etiology related to physiological responses to the spaceflight environment. Fluid shift and the resultant change on the Cardiovascular (CV) and cerebral spinal fluid systems (CSF) in the absence of gravity continue to be considered a contributing factor to the progression of SANS. In this study, we utilize a computational model of the CSF and CV interface to establish the sensitivity that intracranial pressure, and subsequently the optic nerve sheath pressure, exhibits due to variations in thoracic pressure, assuming the cranial perfusion pressure, i.e. mean arterial pressure (MAP) to central venous pressure (CVP), is known. Methods: The GRC Cross cutting computational modeling project created as model of the CSF and CV interaction within the cranial vault by extending the work of Stevens et al. [1] by modifying the representative anatomy to include a separate venous sinus, jugular veins, secondary veins and extra jugular pathways [2-3] to more adequately represent the vascular drainage pathways from the cranial vault (Figure 1). Assuming the MAP, CVP and thoracic pressure are known, we initiated this enhanced computational model assuming a supine positon and utilized a linear ramp to vary the thoracic pressure from the assumed supine state to the target pressure corresponding to set MAP and CVP values. The model generates the time based CSF pressure values (Figure2). Results and Conclusions: Following this analysis, CSF pressure shows significant independence from thoracic pressure changes (16 mmHg in thoracic pressure produces < 1mmHg change in CSF pressure), being mostly dependent on perfusion pressure. Similarly fluid redistribution is not predicted to be impacted over a level of 1mL. We note that this simulation represents an acute changes (order of 10's of minutes) and does not represent the long term effects.

Munster, Drayton W.

Static Controls Performance Tool for Lunar Landers

This document presents a static analysis tool used to evaluate the controllability of Lunar landers. This was created as part of the NASA Lunar Cargo Transportation and Landing by Soft Touchdown (Lunar CATALYST) program. This tool is capable of accepting typical design information such as location and direction of thrusters, maximum thruster forces, gravity vectors, and center of mass locations. The tool evaluates how far the center of gravity can move from its starting position while still maintaining control. This type of analysis is intended to support results produced by time domain simulations. The code created for this project was implemented in Python, and it was designed to be integrated into systems level optimization tools to yield first-cut results on optimal thruster placement.

Aretskin-Hariton, Eliot D.

Solution of Ordinary Differential Equations in Gradient-Based Multidisciplinary Design Optimization

A gradient-based approach to multidisciplinary design optimization enables efficient scalability to large numbers of design variables. However, the need for derivatives causes difficulties when integrating ordinary differential equations (ODEs) in models. To simplify this, we propose the use of the general linear methods framework, which unifies all Runge-Kutta and linear multistep methods. This approach enables rapid implementation of integration methods without the need to differentiate each one, even in a gradient-based optimization context. We also develop a new parallel time integration algorithm that enables vectorization across time steps. We present a set of benchmarking results using a stiff ODE, a non-stiff nonlinear ODE, and an orbital dynamics ODE, and compare integration methods. In a modular gradient-based multidisciplinary design optimization context, we find that the new parallel time integration algorithm with high-order implicit methods, especially Gauss-Legendre collocation, is the best choice for a broad range of problems.

Hwang, John T.