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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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An overview presentation of the H-3PO laboratory for a Virginia Tech: Wearable Bioinstrumentation guest lecture.
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— Motion and visual cue influences are critical in any simulator system, as they impact multiple aspects of the human’s neurovestibular and visual systems. Cues of real motion proceeds to the brain before cues of visual change. It is important therefore for simulator motion cues to exist and to match those realistically with those of the real vehicle to provide transferable training of the activity for operations. The United Kingdom’s Royal Air Force Institute of Aviation Medicine (1989)[1] stated that motion platforms are the only simulation devices capable of fully stimulating the body motion sensors. They confirmed that motion platforms can impart accelerations to the whole body and therefore exercise the automatic motion feedback-loop that operators are used to. With both visual and motion cues handling the vehicle becomes more realistic. Strachan (2019)[1] confirms motion cueing from a well set-up motion platform has been found to be important especially in conditions such as night or reduced visibility where motion cues may be more relied upon. As of this writing, the only lunar rover motion simulator is housed at the General Motors (GM) Milford Proving Ground, which only simulates the motions of a traditional car. A NASA Test Team proposed to complete a motion-based simulation system for NASA’s Lunar Terrain Vehicle (LTV) which resides at Johnson Space Center’s Systems Engineering Simulator facility. This activity integrated existing fixed base simulation capabilities with a newly procured six-degree of freedom motion base platform for the design, development, evaluation, and training associated with the LTV project. This would incorporate a South Pole Lunar virtual reality simulation using Lunar Reconnaissance Orbiter 5m/pixel high-resolution imagery and Unreal 5.2 Virtual Shadow Maps, combined with a virtual reality (VR) headset, integrated within a rover cockpit on a Mikrolar Motion Platform for evaluating human performance and vehicle handling qualities for concept roving vehicle designs. The motion-based system received its Human Rating Certification on March 2023. To begin testing the new facility, a preliminary handling qualities study was conducted. The objectives were to determine if there is a correlation amongst three handling quality methods of performance for a lunar vehicle and to understand the effects of a simulated 1/6-g loads and visuals with a motion platform on the operator while in a 1-g physical environment. The capability this system provides directly benefits the NASA’s Extravehicular Activity and Human Surface Mobility Program and NASA’s Flight Operations Directorate in evaluating the driving qualities of vehicle concepts, human performance related to the operating of a lunar surface vehicle, and assessment of suit related driving impacts. It would also provide direct operational benefit by providing a first-in class and unique simulation platform for the lunar astronaut training curriculum. The empirical knowledge of rover and human performance on this scale is paramount as there is currently no other lunar surface simulator with these capabilities. The result of this study ensures a broadly applicable method of testing for shirtsleeve, unpressurized and pressurized suited rover handling qualities.
The United States Department of Energy is sponsoring the Light Water Reactor Sustainability program, which has the overall objective of supporting the near-term and the extended operation of commercial nuclear power plants. One key research and development (R&D) area in this program is the Risk-Informed Safety Margin Characterization pathway, which combines probabilistic risk simulation with thermohydraulic simulation codes to define and manage safety margins. The R&D efforts to date, however, have not included robust simulations of human operators, and how the reliability of human performance or lack thereof (i.e., human errors) can affect risk-margins and plant performance. This paper describes current and planned research efforts to address the absence of robust human reliability simulations and thereby increase the fidelity of simulated accident scenarios.
Many aircraft accidents/incidents investigations cite crew error as a causal factor (Boeing Commercial Airplane Group 1996). Human factors experts suggest that crew error has many underlying causes and should be the start of an accident investigation and not the end. One of those causes, the flight deck design, is correctable. If a flight deck design does not accommodate the human's unique abilities and deficits, crew error may simply be the manifestation of this mismatch. Pilots repeatedly report that they are "behind the aircraft" , i.e., they do not know what the automated aircraft is doing or how the aircraft is doing it until after the fact. Billings (1991) promotes the concept of "human-centered automation"; calling on designers to allocate appropriate control and information to the human. However, there is much ambiguity regarding what it mean's to be human-centered. What often are labeled as "human-centered designs" are actually designs where a human factors expert has been involved in the design process or designs where tests have shown that humans can operate them. While such designs may be excellent, they do not represent designs that are systematically produced according to some set of prescribed methods and procedures. This paper describes a design concept, called Wings, that offers a clearer definition for human-centered design. This new design concept is radically different from current design processes in that the design begins with the human and uses the human body as a metaphor for designing the aircraft. This is not because the human is the most important part of the aircraft (certainly the aircraft would be useless without lift and thrust), but because he is the least understood, the least programmable, and one of the more critical elements. The Wings design concept has three properties: a reversal in the design process, from aerodynamics-, structures-, and propulsion-centered to truly human-centered; a design metaphor that guides function allocation and control and display design; and a deliberate distinction between two fundamental functions of design, to complement and to interpret human performance. The complementary function extends the human's capabilities beyond his or her current limitations - this includes sensing, computation, memory, physical force, and human decision making styles and skills. The interpretive (or hermeneutic, Hollnagel 1991) function translates information, functionality, and commands between the human and the aircraft. The Wings design concept allows the human to remain aware of the aircraft through natural interpretation. It also affords great improvements in system performance by maximizing the human's natural abilities and complementing the human's skills in a natural way. This paper will discuss the Wings design concept by describing the reversal in the traditional design process, the function allocation strategy of Wings, and the functions of complementing and interpreting the human.
Human performance characteristics in manual control tasks, and techniques for data analysis and systems simulation
Applications of concepts of information theory to assessment of human performance in compensatory tracking tasks
Designing a planetary suit is very complex and often requires difficult trade-offs between performance, cost, mass, and system complexity. In order to verifying that new suit designs meet requirements, full prototypes must eventually be built and tested with human subjects. Using computer models early in the design phase of new hardware development can be advantageous, allowing virtual prototyping to take place. Having easily modifiable models of the suit hard sections may reduce the time it takes to make changes to the hardware designs and then to understand their impact on suit and human performance. A virtual design environment gives designers the ability to think outside the box and exhaust design possibilities before building and testing physical prototypes with human subjects. Reductions in prototyping and testing may eventually reduce development costs. This study is an attempt to develop computer models of the hard components of the suit with known physical characteristics, supplemented with human subject performance data. Objectives: The primary objective was to develop an articulating solid model of the Mark III hip bearings to be used for evaluating suit design performance of the hip joint. Methods: Solid models of a planetary prototype (Mark III) suit s hip bearings and brief section were reverse-engineered from the prototype. The performance of the models was then compared by evaluating the mobility performance differences between the nominal hardware configuration and hardware modifications. This was accomplished by gathering data from specific suited tasks. Subjects performed maximum flexion and abduction tasks while in a nominal suit bearing configuration and in three off-nominal configurations. Performance data for the hip were recorded using state-of-the-art motion capture technology. Results: The results demonstrate that solid models of planetary suit hard segments for use as a performance design tool is feasible. From a general trend perspective, the suited performance trends were comparable between the model and the suited subjects. With the three off-nominal bearing configurations compared to the nominal bearing configurations, human subjects showed decreases in hip flexion of 64%, 6%, and 13% and in hip abduction of 59%, 2%, and 20%. Likewise the solid model showed decreases in hip flexion of 58%, 1%, and 25% and in hip abduction of 56%, 0%, and 30%, under the same condition changes from the nominal configuration. Differences seen between the model predictions and the human subject performance data could be attributed to the model lacking dynamic elements and performing kinematic analysis only, the level of fit of the subjects with the suit, the levels of the subject s suit experience.
In the past several years, several international organizations have begun to collect data on human performance in nuclear power plant simulators. The data collected provide a valuable opportunity to improve human reliability analysis (HRA), but these improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this paper, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.
Introduction: Designing a planetary suit is very complex and often requires difficult trade‐offs between performance, cost, mass, and system complexity. To verify that new suit designs meet requirements, full prototypes must be built and tested with human subjects. However, numerous design iterations will occur before the hardware meets those requirements. Traditional draw‐prototype‐test paradigms for research and development are prohibitively expensive with today's shrinking Government budgets. Personnel at NASA are developing modern simulation techniques that focus on a human‐centric design paradigm. These new techniques make use of virtual prototype simulations and fully adjustable physical prototypes of suit hardware. This is extremely advantageous and enables comprehensive design down‐selections to be made early in the design process. Objectives: The primary objective was to test modern simulation techniques for evaluating the human performance component of two EMU suit concepts, pivoted and planar style hard upper torso (HUT). Methods: This project simulated variations in EVA suit shoulder joint design and subject anthropometry and then measured the differences in shoulder mobility caused by the modifications. These estimations were compared to human‐in‐the‐loop test data gathered during past suited testing using four subjects (two large males, two small females). Results: Results demonstrated that EVA suit modeling and simulation are feasible design tools for evaluating and optimizing suit design based on simulated performance. The suit simulation model was found to be advantageous in its ability to visually represent complex motions and volumetric reach zones in three dimensions, giving designers a faster and deeper comprehension of suit component performance vs. human performance. Suit models were able to discern differing movement capabilities between EMU HUT configurations, generic suit fit concerns, and specific suit fit concerns for crewmembers based on individual anthropometry
This report describes the interim progress for research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and elsewhere throughout the plant, along with a greater use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human-performance-related organizational and technical design issues are identified and addressed. This report describes modeling tools and techniques, based on sociotechnical system theory, to support these design goals and their application in the current research effort. The report is intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control and feedback relationships amongst the system’s technical and organizational components. Up to this point, we have employed a Causal Analysis based on STAMP (CAST) technique to examine a performance- and safety-related incident at an industry partner’s plant that involved the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. Our ongoing analysis is focused on identifying near-term process improvements and longer-term design requirements for an optimized IAE system. The latter analyses will employ a second STAMP-derived technique, System-Theoretic Process Analysis (STPA). STPA is a useful modeling tool for generating and analyzing actual or potential information control structures. Finally, we have begun modeling plantwide organizational relationships and processes. Organizational system modeling will supplement our CAST and STPA findings and provide a basis for mapping out a plantwide information control architecture. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the initiating event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. We present two preliminary information automation models. The proactive issue resolution model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system. From our results, we have generated a set of preliminary system-level requirements and safety constraints. These requirements will be further developed over the remainder of our project in collaboration with nuclear industry subject matter experts and specialists in the technical systems under consideration. Additionally, we will continue to pursue the system analyses initiated in the first part of our effort, with a particular emphasis on STPA as the main tool to identify weak or weakening control structures that affect the resilience of organizations and programs. Our intent is to broaden the scope of the analysis from an individual use case to a related set of use cases (e.g., maintenance tasks, compliance tasks) with similar human-system performance challenges. This will enable more generalized findings to refine the Proactive Issue Resolution and IAE models, as well as their system-level requirements and safety constraints. We will use organizational system modeling analyses to supplement STPA findings and model development. We conclude the report with a set of summary recommendations and an initial draft list of system-level requirements and safety constraints for optimized information automation systems.
As automation and advanced technologies are introduced into transport systems ranging from the Next Generation Air Transportation System termed NextGen, to the advanced surface transportation systems as exemplified by the Intelligent Transportations Systems, to future systems designed for space exploration, there is an increased need to validly predict how the future systems will be vulnerable to error given the demands imposed by the assistive technologies. One formalized approach to study the impact of assistive technologies on the human operator in a safe and non-obtrusive manner is through the use of human performance models (HPMs). HPMs play an integral role when complex human-system designs are proposed, developed, and tested. One HPM tool termed the Man-machine Integration Design and Analysis System (MIDAS) is a NASA Ames Research Center HPM software tool that has been applied to predict human-system performance in various domains since 1986. MIDAS is a dynamic, integrated HPM and simulation environment that facilitates the design, visualization, and computational evaluation of complex man-machine system concepts in simulated operational environments. The paper will discuss a range of aviation specific applications including an approach used to model human error for NASA s Aviation Safety Program, and what-if analyses to evaluate flight deck technologies for NextGen operations. This chapter will culminate by raising two challenges for the field of predictive HPMs for complex human-system designs that evaluate assistive technologies: that of (1) model transparency and (2) model validation.
Medical physics is learned in a combination of activities including classroom sessions, individual study, small-group collaborative problem solving, and direct experience in the laboratory or clinical environment. Each type of learning activity is characterized by its effectiveness in producing the desired knowledge for the learner and the cost in terms of resources and human effort required providing it. While learning and teaching is a human activity, modern technology provides a variety of tools that can be used to enhance human performance. The class or conference room is the common setting for educational sessions in both academic institutions and continuing education conferences and programs such as those sponsored by the AAPM. A major value of a class/conference room program is efficiency by bringing a group of learners together to share in a common learning experience under the guidance of one or more experienced learning facilitators (lecturers or presenters). A major challenge is that the class/conference room is separated from the real world of medical physics. The design of an educational activity needs to take into consideration the desired outcomes with respect to what the learners should be able to do. The distinction is that of being able to apply the knowledge to perform specific physics functions rather than just knowing and being able to recall facts, and perhaps do well on written examinations. These are different types of knowledge structures within the human brain and distinctly different learning activities to develop each. Much of medical physics education, especially at the post-graduate and continuing education level, is for the purpose of enhancing the ability of physicists and other related professionals to perform applied procedures and tasks and requires specific types of knowledge.In this session we will analyze various learning activity models, the values and limitations of each, and how they can be used in medical physics education. An example we will use is optimizing CT image quality and dose which is an important topic for medical physicists, radiologists and residents, along with technologists. The knowledge structure for this is best developed by a combination of learning activities including class/conference discussions, individual study and review, and direct observation and interaction in the clinical setting under the direction of a knowledgeable leader.The function of the human brain will be considered with respect to learning experiences that contribute to effective medical physics knowledge structures. The characteristics of various types of educational activities will be compared with respect to their effectiveness for producing desired outcomes along with their limitations. Emphasis will be given to the design of highly-effective classroom/conference presentations, and activities will be demonstrated with an emphasis on using technology to enhance human performance of both learners and the learning facilitators. Learning Objectives: Develop and provide highly effective medical physics educational sessions. Use technology to enhance human performance in the educational process. Identify and analyze various models of educational activities Select and use educational activities that contribute value to the medical physics profession.