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Techniques for mapping submerged sea grasses using aircraft supported remote sensors are described. The 21 channel solid state array spectroradiometer was successfully used as a remote sensor in the experiment in that the system operated without problem and obtained data. The environmental conditions of clear water, bright sandy bottom and monospecific vegetation (Thalassia) were ideal.
Fiber optic components in the space environment were examined. The ability to operate over long periods of time without degradation of performance are determined.
Research into the impact of personality factors on groups in various settings is reviewed as an introduction to a brief discussion of personality and group behavior research needs relevant to the space program. Significant findings of some earlier research are summarized, and methodological problems are touched on. The study of intergroup and intragroup conflict in a stressful environment, as exemplified particularly by undersea habitats, is seen as being of consequence for long-term space missions. It is concluded that adequate research can only be conducted as an adjunct to data collection from operational stressful environments, and not from laboratory experiments.
Results of the OEX program are summarized with emphasis on the information on entry aerothermodynamic phenomena derived from Space Shuttle operations. The discussion focuses on OEX experiment complement and operational history, freestream environment and vehicle attitude data, aerodynamic force and moment data, aerodynamic surface data, and vehicle configuration data. Attention is also given to orbiter aerodynamic performance, stability and control, high-altitude atmospheric density variability, direct simulation Monte Carlo validation, orbital drag variation, and computational fluid dynamic technique validation.
Miscellaneous tasks related to mission operations and data analysis for the Burst and Transient Source Experiment on the Gamma Ray Observatory, to collection, analysis, and interpretation of data from the Marshall Space Flight Center Very Low Frequency transient monitoring program, and to compilation and analysis of induced radioactivity data were performed. The results are summarized and relevant references are included.
Accurate prediction of hardware and flow characteristics within the Space Shuttle Main Engine (SSME) during transient and main-stage operation requires a significant integration of ground test data, flight experience, and computational models. The process of integrating SSME test measurements with physical model predictions is commonly referred to as data reduction. Uncertainties within both test measurements and simplified models of the SSME flow environment compound the data integration problem. The first objective of this effort was to establish an acceptability criterion for data reduction solutions. The second objective of this effort was to investigate the data reduction potential of the ROCETS (Rocket Engine Transient Simulation) simulation platform. A simplified ROCETS model of the SSME was obtained from the MSFC Performance Analysis Branch . This model was examined and tested for physical consistency. Two modules were constructed and added to the ROCETS library to independently check the mass and energy balances of selected engine subsystems including the low pressure fuel turbopump, the high pressure fuel turbopump, the low pressure oxidizer turbopump, the high pressure oxidizer turbopump, the fuel preburner, the oxidizer preburner, the main combustion chamber coolant circuit, and the nozzle coolant circuit. A sensitivity study was then conducted to determine the individual influences of forty-two hardware characteristics on fourteen high pressure region prediction variables as returned by the SSME ROCETS model.
A one-dimensional, atmospheric boundary layer model is coupled to a thermodynamic ice model to estimate the surface turbulent fluxes over thick sea ice. The principal forcing parameters in this time-dependent model are the air temperature, humidity, and wind speed at a specified level (either at 2 m or at 850 mb) and the downwelling surface radiative fluxes. The free parameters. are the air temperature, humidity, and wind speed profiles below the specified level, the surface skin temperature, the ice temperature profile, and the surface turbulent fluxes. The goal is to determine how well we can estimate the turbulent surface heat and momentum fluxes using forcing parameters from atmospheric temperatures and radiative fluxes retrieved from the TIROS-N Operational Vertical Sounder (TOVS) data. Meteorological observations from the Lead Experiment (LeadEx, April 1992) ice camp are used to validate turbulent fluxes computed with the surface observations and the results are used to compare with estimates based on radio-sonde observations or with estimates based on TOVS data. We find that the TOVS-based estimates of the stress are significantly more accurate than those found with a constant geostrophic drag coefficient, with a root-mean-square error about half as large. This improvement is due to stratification effects included in the boundary layer model. The errors in the sensible heat flux estimates, however, are large compared to the small mean values observed during the field experiment.
Personal journals maintained by NASA astronauts during six-month expeditions onboard the International Space Station were analyzed to obtain information concerning a wide range of behavioral and human factors issues. Astronauts wrote most about their work, followed by outside communications (with mission control, family, and friends), adjustment to the conditions, interactions with crew mates, recreation/leisure, equipment (installation, maintenance), events (launches, docking, hurricanes, etc.), organization/management, sleep, and food. The study found evidence of a decline in morale during the third quarters of the missions and identified key factors that contribute to sustained adjustment and optimal performance during long-duration space expeditions. Astronauts reported that they benefited personally from writing in their journals because it helped maintain perspective on their work and relations with others. Responses to questions asked before, during, and after the expeditions show that living and working onboard the ISS is not as difficult as the astronauts anticipate before starting their six-month tours of duty. Recommendations include application of study results and continuation of the experiment to obtain additional data as crew size increases and operations evolve.
The hazards of spaceflight to the human system are present in in varying degrees on different spaceflight platforms: altered gravity, isolation and confinement, distance from Earth, radiation, and hostile closed environments. A strategic view of the fidelity of hazards experienced on different platforms can shape the testing plans for human research and technology demonstration related to crew health and performance. Working across the international partnership, the International Space Station (ISS) is planning periods of modified operations to improve the fidelity of Mars simulations. To understand transit durations as an independent variable, a series of 1-year, 6-month and 30-45 day missions are being planned on ISS. Standard measurements across ISS missions of different durations, lunar missions and ground analogs offers the opportunity to distinguish different hazards and their effects in the context of the relevance to future mission concepts. The ISS partnership is planning for additional test cases that are aligned with Mars mission plans. (1) Evaluating crew performance capabilities when transitioning to gravity after long durations in microgravity representative of a Mars transit. (2) Simulating crew medical care under Mars-like autonomous operations. (3) Identification and testing of operations under communications delay and autonomy expected for Mars missions and the linked effects on behavioral health and performance of the crew. Artemis missions serve as a valuable analogs for Mars surface operations, with partial gravity and deep space radiation hazards, but with crewmembers that are probably more physically capable than their counterparts would be after a Mars transit. Getting unobtrusive data from early Artemis missions, and knowledge gained from operational experience as Artemis operations develop can improve engineering design, medical requirements and countermeasures, and ultimately ensure mission success on Mars. Linking Mars architectures with the plans and capabilities for ISS and Artemis allows us to plan to most operationally relevant tests of crew health and performance on current spaceflight missions to inform planning for future missions to Mars. By using human spaceflight platforms as well as ground simulation in an integrated way, the international community can improve exploration readiness, develop countermeasures and reduce risks of future human space missions.
As part of NASA’s new Earth Action strategy, the Wildland Fire initiative was established, which includes both the NASA Wildland Fire Program (WFP) and the FireSense project. NASA has over 50 years of experience generating data and technology to enhance fire science and operational management. The WFP’s mission is threefold: 1) assemble communities of practice through collaborative efforts with government, academia, and the private sector; 2) co-develop knowledge and applications with relevant partners and stakeholders in the wildfire community; and 3) improve wildland fire management through the transitioning of NASA data, technology, tools, and science to stakeholder organizations. The WFP is focusing on supporting proactive fire management, including situational awareness, preparedness, and risk mitigation. This will be accomplished through selected projects that identify management challenges, relevant to partners and end users, and the NASA data that will be utilized to deliver innovative solutions to enhance the management of wildland fires. Examples include: i) investigation of evaporative stress from OpenET to help predict the risk of wildfire occurrence in watersheds; ii) incorporation of space based LiDAR for the generation of 3-dimensional forest fuel metrics, used to improve wildfire risk and behavior models; iii) integration of global, multi-platform geostationary active-fire data in near-real-time into NASA’s Fire Information for Resource Management System (FIRMS); and iv) identification of post-fire ecohydrological conditions using thermal, multispectral, synthetic aperture radar (SAR), and hyperspectral remotely-sensed data to improve flood hazard forecast models. The FireSense project is a US-focused 5-year project that will focus on delivering NASA’s unique Earth science and technological capabilities to operational agencies, striving towards enhancing fire fighting and air quality management. The project will include airborne campaigns and new technology that will likely have global implications. Initial stakeholder engagement led FireSense to focus on four use-cases focused on the characterization and measurement of: (i) pre-fire fuels conditions, (ii) active fire-dynamics; (iii) post-fire impact and threats; and iv) air quality impacts and forecasting, each-developed with identified stakeholders.
This project leverages the Concept Exploration Laboratory (CEL) to collect, warehouse, and augment data relevant to human lunar exploration as a platform for NA (S&MA) to develop operational data integration techniques. The project capitalizes on 16+ years of CEL experience applied to NASA, DoD, the City of Houston, the State of Texas, and private industry. The integrated data will be utilized in the two scenarios described in a definition of concept for development of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, and may be useful as pathfinders for the Digital Transformation Program.
This paper provides an analysis of Flight Technical Error (FTE) from recent SATS experiments, called the Higher Volume Operations (HVO) Simulation and Flight experiments, which NASA conducted to determine pilot acceptability of the HVO concept for normal operating conditions. Reported are FTE results from simulation and flight experiment data indicating the SATS HVO concept is viable and acceptable to low-time instrument rated pilots when compared with today s system (baseline). Described is the comparative FTE analysis of lateral, vertical, and airspeed deviations from the baseline and SATS HVO experimental flight procedures. Based on FTE analysis, all evaluation subjects, low-time instrument-rated pilots, flew the HVO procedures safely and proficiently in comparison to today s system. In all cases, the results of the flight experiment validated the results of the simulation experiment and confirm the utility of the simulation platform for comparative Human in the Loop (HITL) studies of SATS HVO and Baseline operations.
The International Criticality Safety Benchmark Evaluation Projects (ICSBEP) has over two decades of experience under the auspices of the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) to identify, evaluate, and compile a comprehensive data set of criticality safety benchmark data [1]. The activities of the ICSBEP have led to the continued success of the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook) [2]. The contents of the ICSBEP Handbook include benchmark specifications derived from nuclear facilities around the world, which have been utilized to support criticality safety efforts and also integral testing of nuclear data [3]. The 2020 edition of the ICSBEP Handbook will include data from 582 evaluations containing benchmark specifications for 5,053 critical, subcritical, or near-critical configurations. There are seven criticality-alarm-placement/shielding evaluations containing a total of 45 benchmark configurations, and ten fundamental physics evaluations containing a total of 237 measurements relevant to criticality safety applications. The purpose of this paper is to summarize those benchmarks relative to validation needs in shielding applications currently found within the ICSBEP Handbook. The current and future plans of shielding benchmarks with regards to the ICSBEP will also be discussed.
Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.
Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.
Data-driven modeling has become a key building block in computational science and engineering. However, data that are available in science and engineering are typically scarce, often polluted with noise and affected by measurement errors and other perturbations, which makes learning the dynamics of systems challenging. Here, in this work, we propose to combine data-driven modeling via operator inference with the dynamic training via roll outs of neural ordinary differential equations. Operator inference with roll outs inherits interpretability, scalability, and structure preservation of traditional operator inference while leveraging the dynamic training via roll outs over multiple time steps to increase stability and robustness for learning from low-quality and noisy data. Numerical experiments with data describing shallow water waves and surface quasi-geostrophic dynamics demonstrate that operator inference with roll outs provides predictive models from training trajectories even if data are sampled sparsely in time and polluted with noise of up to 10%.
A survey of commercial helicopter-operating experiences was conducted using a helicopter flight recorder in order to provide a basis for extending helicopter design and service-life criteria. These data are representative of 182 flight hours accumulated during 1414 flights comprised of the separate legs of the total route structure employed. The operating experiences are presented in terms of the time spent within different airspeed brackets, within the classifiable flight conditions of climb, en route, and descent, at various rates of climb and descent, and at different rotor rotational speeds. The results indicated that the helicopter spent a majority of the flight time at airspeeds either below 40 knots or above 100 knots. Rates of climb and descent were concentrated at values below 5.1 m/s (1000 ft/min) particularly for higher airspeeds. Normal acceleration experiences were low, both in the total number and peak value realized; however, an extremely large number of pitch angular-velocity experiences were noted. Rotor rotational speeds were normal with no occurrences above the upper red-line limit.
This research examines the relationships between aircrew compliance with procedures and operator errors. The data for this analysis were generated by reexamination of a 1976 experiment in full mission simulation conducted by Dr. H. P. Ruffell Smith (1979) for the NASA-Ames Research Center. The character of individual operators, the chemistry of crew composition, and complex aspects of the operational environment affected procedural compliance by crew members. Associations between enumerated operator errors and several objective indicators of crew coordination were investigated. The correspondence among high operator error counts and infrequent compliance with specific crew coordination requirements was most notable when copilots were accountable for control of flight parameters.