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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Analysis of Logistics in Support of a Human Lunar Outpost

Strategic level analysis of the integrated behavior of lunar transportation system and lunar surface system architecture options is performed to inform NASA Constellation Program senior management on the benefit, viability, affordability, and robustness of system design choices. This paper presents an overview of the approach used to perform the campaign (strategic) analysis, with an emphasis on the logistics modeling and the impacts of logistics resupply on campaign behavior. An overview of deterministic and probabilistic analysis approaches is provided, with a discussion of the importance of each approach to understanding the integrated system behavior. The logistics required to support lunar surface habitation are analyzed from both 'macro-logistics' and 'micro-logistics' perspectives, where macro-logistics focuses on the delivery of goods to a destination and micro-logistics focuses on local handling of re-supply goods at a destination. An example campaign is provided to tie the theories of campaign analysis to results generation capabilities.

Cirillo, William↗
NASA NTRS2014

Challenges to Health During Deep Space Exploration Missions

Long duration missions outside of low Earth orbit will present unique challenges to the maintenance of human health. Stressors with physiologic and psychological impacts are inherent in exploration missions, including reduced gravity, increased radiation, isolation, limited habitable volume, circadian disruptions, and cabin atmospheric changes. Operational stressors such as mission timeline and extravehicular activities must also be considered, and these varied stressors may act in additive or synergistic fashions. Should changes to physiology or behavior manifest as a health condition, the rendering of care in an exploration environment must also be considered. Factors such as the clinical background of the crew, inability to evacuate to Earth in a timely manner, communication delay, and limitations in available medical resources will have an impact on the assessment and treatment of these conditions. The presentations associated with this panel will address these unique challenges from the perspective of several elements of the NASA Human Research Program, including Behavioral Health and Performance, Human Health Countermeasures, Space Radiation, and Exploration Medical Capability.

Watkins, S.↗
NASA NTRS1983

Conceptual models of information processing

The conceptual information processing issues are examined. Human information processing is defined as an active cognitive process that is analogous to a system. It is the flow and transformation of information within a human. The human is viewed as an active information seeker who is constantly receiving, processing, and acting upon the surrounding environmental stimuli. Human information processing models are conceptual representations of cognitive behaviors. Models of information processing are useful in representing the different theoretical positions and in attempting to define the limits and capabilities of human memory. It is concluded that an understanding of conceptual human information processing models and their applications to systems design leads to a better human factors approach.

Stewart, L. J.↗
NASA NTRS1998

Influence of gravity on cardiac performance

Results obtained by the investigators in ground-based experiments and in two parabolic flight series of tests aboard the NASA KC-135 aircraft with a hydraulic simulator of the human systemic circulation have confirmed that a simple lack of hydrostatic pressure within an artificial ventricle causes a decrease in stroke volume of 20%-50%. A corresponding drop in stroke volume (SV) and cardiac output (CO) was observed over a range of atrial pressures (AP), representing a rightward shift of the classic CO versus AP cardiac function curve. These results are in agreement with echocardiographic experiments performed on space shuttle flights, where an average decrease in SV of 15% was measured following a three-day period of adaptation to weightlessness. The similarity of behavior of the hydraulic model to the human system suggests that the simple physical effects of the lack of hydrostatic pressure may be an important mechanism for the observed changes in cardiac performance in astronauts during the weightlessness of space flight.

NASA Discipline Cardiopulmonary↗
NASA NTRSDate not supplied

The Current State and Future of the Former Central Nervous System Risk Managed by Space Radiation Element Part of NASA’s Human Research Program

Historically, the Space Radiation Element, as part of the NASA Human Research Program, was responsible for the scientific strategy and funding of research focused on characterizing and mitigating the effects of space radiation exposure on the central nervous system (CNS). During the past few years, there have been two major changes that have affected the management of the science associated with the CNS risk. The first major change was the integration of several related risks: CNS (C), behavioral medicine (B), and sensorimotor (S) to be managed as an integrated effort called CBS. CBS was formed to accelerate research on combined spaceflight stressors, specifically space radiation, microgravity, isolation, and confinement. CBS was managed by Human Factors and Behavioral Performance (HFBP) with support and inputs from Space Radiation as well as Human Health Countermeasures. Secondly, and more recently, the CNS risk was absorbed by the behavioral medicine risk currently also held by HFPB. As a result, the gaps associated with CNS were removed and aspects of them are represented in the BMED gaps (link). However, due to recent changes in element leadership, budget, and timeline, extensive restructuring of CBS is in progress. In addition, in an effort to harmonize datasets to be used in modeling efforts, an Animal Standardization TIM was held in June 2021 to standardize the type of behavioral and cognitive tasks as well as experimental set-ups used in animal models of future funded research. This presentation is an effort to communicate with the radiation research community concerning the strategy, management, and future of the former central nervous system risk and the role of the Space Radiation Element.

Janice A Zawaski↗
NASA NTRS2021

The Current State and Future of the Former Central Nervous System Risk Managed by Space Radiation Element Part of NASA’s Human Research Program

Historically, the Space Radiation Element, as part of the NASA Human Research Program, was responsible for the scientific strategy and funding of research focused on characterizing and mitigating the effects of space radiation exposure on the central nervous system (CNS). During the past few of years, there have been two major changes that have affected the management of the science associated with the CNS risk. The first major change was the integration of several related risks: CNS (C), Behavioral medicine (B), and sensorimotor (S) to be managed as an integrated effort called CBS. CBS was formed to accelerate research on combined spaceflight stressors, specifically space radiation, microgravity, isolation, and confinement. CBS was managed by Human Factors and Behavioral Performance (HFBP) with support and inputs from Space Radiation as well as Human Health Countermeasures. Secondly, and more recently, the CNS risk was absorbed by the behavioral medicine risk currently also held by HFPB. As a result, the gaps associated with CNS were removed and aspects of them are represented in the BMED gaps (link). However, due to recent changes in element leadership, budget, and timeline, extensive restructuring of CBS is in progress. In addition, in an effort to harmonize datasets to be used in modelling efforts, an Animal Standardization TIM was held in June 2021 to standardize the type of behavioral and cognitive tasks as well as experimental set-ups. This presentation is an effort to communicate with the radiation research community concerning the strategy, management, and future of the former central nervous system risk and the role of the Space Radiation Element.

central nervous system↗
NASA NTRS1987

A qualitative model of human interaction with complex dynamic systems

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Hess, Ronald A.↗
NASA NTRS1987

A Qualitative Model of Human Interaction with Complex Dynamic Systems

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Hess, Ronald A.↗
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety producing behaviors in addition to safety reducing behaviors. The panel will discuss opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors.

aviation safety↗
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety-producing behaviors in addition to safety-reducing behaviors. Opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors are discussed.

Aviation Safety↗
NASA NTRS1986

On the pilot's behavior of detecting a system parameter change

The reaction of a human pilot, engaged in compensatory control, to a sudden change in the controlled element's characteristics is described. Taking the case where the change manifests itself as a variance change of the monitored signal, it is shown that the detection time, defined to be the time elapsed until the pilot detects the change, is related to the monitored signal and its derivative. Then, the detection behavior is modeled by an optimal controller, an optimal estimator, and a variance-ratio test mechanism that is performed for the monitored signal and its derivative. Results of a digital simulation show that the pilot's detection behavior can be well represented by the model proposed here.

Morizumi, N.↗
NASA NTRSDate not supplied

Supportability Concepts for Crewed Deep Space Exploration

Supportability—defined as the set of system characteristics that influence the logistics and support required to enable safe and effective operations—will be a much larger driver of mass, risk, and crew time for future human space exploration due to the more challenging mission context. For Mars, systems must operate in a logistically isolated environment for much longer durations than previous missions, which results in a higher probability of system failure and therefore an increased need for maintenance or contingency options. Mars missions also lack access to quick aborts, which increases the consequences of an unrecoverable system failure. Together, this higher likelihood and consequence of failure results in an increase in supportability-related risk. Supportability analysis is an important part of systems development that helps designers better understand the impacts of system and mission decisions on risk, mass, and crew time. The real-world processes that drive maintenance requirements and other supportability-related characteristics are probabilistic, and therefore they require different conceptual approaches and models than are used for more deterministic aspects of space systems. This paper provides an overview of supportability analysis, addresses key concepts, and provides examples of how supportability analysis can be incorporated into system development. Specifically, system supportability involves stochastic processes, and therefore must be evaluated using probabilistic models. These models can be used to perform sensitivity analysis even if system characteristics are not yet fully defined. Failure rates cannot be measured directly, but tests provide valuable data that can help refine those estimates. Human spaceflight architectures are complex, and exhibit coupled behavior that should be examined with integrated systems analysis that includes an assessment of supportability.

Supportability↗
NASA NTRSDate not supplied

Supportability Concepts for Crewed Deep Space Exploration

Supportability—defined as the set of system characteristics that influence the logistics and support required to enable safe and effective operations—will be a much larger driver of mass, risk, and crew time for future human space exploration due to the more challenging mission context. For Mars, systems must operate in a logistically isolated environment for much longer durations than previous missions, which results in a higher probability of system failure and therefore an increased need for maintenance or contingency options. Mars missions also lack access to quick aborts, which increases the consequences of an unrecoverable system failure. Together, this higher likelihood and consequence of failure results in an increase in supportability-related risk. Supportability analysis is an important part of systems development that helps designers better understand the impacts of system and mission decisions on risk, mass, and crew time. The real-world processes that drive maintenance requirements and other supportability-related characteristics are probabilistic, and therefore they require different conceptual approaches and models than are used for more deterministic aspects of space systems. This paper provides an overview of supportability analysis, addresses key concepts, and provides examples of how supportability analysis can be incorporated into system development. Specifically, system supportability involves stochastic processes, and therefore must be evaluated using probabilistic models. These models can be used to perform sensitivity analysis even if system characteristics are not yet fully defined. Failure rates cannot be measured directly, but tests provide valuable data that can help refine those estimates. Human spaceflight architectures are complex, and exhibit coupled behavior that should be examined with integrated systems analysis that includes an assessment of supportability.

Supportability↗
NASA NTRS1973

On the physical basis of a theory of human thermoregulation.

Theoretical study of the physical factors which are responsible for thermoregulation in nude resting humans in a physical steady state. The behavior of oxidative metabolism, evaporative and convective thermal fluxes, fluid heat transfer, internal and surface temperatures, and evaporative phase transitions is studied by physiological/physical modeling techniques. The modeling is based on the theories that the body has a vital core with autothermoregulation, that the vital core contracts longitudinally, that the temperature of peripheral regions and extremities decreases towards the ambient, and that a significant portion of the evaporative heat may be lost underneath the skin. A theoretical basis is derived for a consistent modeling of steady-state thermoregulation on the basis of these theories.

Iberall, A. S.↗
NASA NTRS1989

Effects Of Vibrations On Grasp Control

Vibration powerful and specific stimulus to low-level reflex behavior. Report describes experiments on interactions between human operators and hand control device for control of extent of opening and gripping force of remote gripper. Major purpose of study to determine effects of vibrations in device upon ability of operators to control gripping force. Used beneficially in design of controls to provide warning signals preventing operators from commanding excessive, or perhaps insufficient forces.

Hannaford, Blake↗
NASA NTRS1994

Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Xu, Xiaoshu↗