(Georgia Institute of Technology, 2009-05-26)
Chua, Zarrin K.
Despite two decades of manned spaceflight development, the recent thrust for increased human exploration places significant demands on current technology. More information is needed in understanding how human control affects mission performance and most importantly, how to design support systems that aid in human-system collaboration. This information on the general human-system relationship is difficult to ascertain due
to the limitations of human performance modeling and the breadth of human actions in a particular situation. However, cognitive performance can be modeled in limited, well-defined scenarios of human control and the resulting analysis on these models can provide preliminary information with regard to the human-system relationship. This investigation examines the critical case of lunar Landing Point Redesignation (LPR) as a case study to further knowledge of the human-system relationship and to improve the design of support systems to assist astronauts during this task. To achieve these objectives, both theoretical and experimental practices are used to develop a task execution time model and subsequently inform this model with observations of simulated astronaut behavior. The experimental results have established several major conclusions. First, the method of LPR task execution is not necessarily linear, with tasks performed in parallel or neglected entirely. Second, the time to complete the LPR task and the overall accuracy of the landing site is generally robust to environmental and scenario factors such as number of points of interest, number of identifiable terrain markers, and terrain expectancy. Lastly, the examination of the overall tradespace between the three main criteria of fuel consumption, proximity to points of interest, and safety when comparing human and analogous automated
behavior illustrates that humans outperform automation in missions where safety and nearness to points of interest are the main objectives, but perform poorly when fuel is the most critical measure of performance.
Improvements to the fidelity of the model can be made by transgressing from a deterministic to probablistic
model and incorporating such a model into a six degree-of-freedom trajectory simulator. This paper briefly summarizes recent technological developments for manned spaceflight, reviews previous and current efforts in implementing LPR, examines the experimental setup necessary to test the LPR task modeling, discusses the significance of findings from the experiment, and also comments on the extensibility of the LPR task and experiment results to human Mars spaceflight.
(Georgia Institute of Technology, 2009-05)
Cordell, Christopher E., Jr.
Supersonic retropropulsion provides an option that can potentially enhance drag characteristics of
high mass entry, descent, and landing systems. Preliminary flow field and vehicle aerodynamic
characteristics have been found in wind tunnel experiments; however, these only cover specific
vehicle configurations and freestream conditions. In order to generate useful aerodynamic data that
can be used in a trajectory simulation, a quicker method of determining vehicle aerodynamics is
required to model supersonic retropropulsion effects. Using computational fluid dynamics, flow
solutions can be determined which yield the desired aerodynamic information. The flow field
generated in a supersonic retropropulsion scenario is complex, which increases the difficulty of
generating an accurate computational solution. By validating the computational solutions against
available wind tunnel data, the confidence in accurately capturing the flow field is increased, and
methods to reduce the time required to generate a solution can be determined. Fun3D, a
computational fluid dynamics code developed at NASA Langley Research Center, is capable of
modeling the flow field structure and vehicle aerodynamics seen in previous wind tunnel
experiments. Axial locations of the jet terminal shock, stagnation point, and bow shock show the
same trends which were found in the wind tunnel, and the surface pressure distribution and drag
coefficient are also consistent with available data. The flow solution is dependent on the
computational grid used, where a grid which is too coarse does not resolve all of the flow features
correctly. Refining the grid will increase the fidelity of the solution; however, the calculations will
take longer if there are more cells in the computational grid.