Search NASA⌕ Search

SEARCH · Search NASA

Results for “Human Factors, Simulation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Response time effects of alerting tone and semantic context for synthesized voice cockpit warnings

Some handbooks and human factors design guides have recommended that a voice warning should be preceded by a tone to attract attention to the warning. As far as can be determined from a search of the literature, no experimental evidence supporting this exists. A fixed-base simulator flown by airline pilots was used to test the hypothesis that the total 'system-time' to respond to a synthesized voice cockpit warning would be longer when the message was preceded by a tone because the voice itself was expected to perform both the alerting and the information transfer functions. The simulation included realistic ATC radio voice communications, synthesized engine noise, cockpit conversation, and realistic flight routes. The effect of a tone before a voice warning was to lengthen response time; that is, responses were slower with an alerting tone. Lengthening the voice warning with another work, however, did not increase response time.

Simpson, C. A.↗
NASA NTRS1991

Army-NASA aircrew/aircraft integration program: Phase 4 A(3)I Man-Machine Integration Design and Analysis System (MIDAS) software detailed design document

The Man-Machine Integration Design and Analysis System (MIDAS) is an integrated suite of software components that constitutes a prototype workstation to aid designers in applying human factors principles to the design of complex human-machine systems. MIDAS is intended to be used at the very early stages of conceptual design to provide an environment wherein designers can use computational representations of the crew station and operator, instead of hardware simulators and man-in-the-loop studies, to discover problems and ask 'what if' questions regarding the projected mission, equipment, and environment. This document is the Software Product Specification for MIDAS. Introductory descriptions of the processing requirements, hardware/software environment, structure, I/O, and control are given in the main body of the document for the overall MIDAS system, with detailed discussion of the individual modules included in Annexes A-J.

Banda, Carolyn↗
NASA NTRS2015

Avionics Configuration Assessment for Flightdeck Interval Management: A Comparison of Avionics and Notification Methods

Flightdeck Interval Management is one of the NextGen operational concepts that FAA is sponsoring to realize requisite National Airspace System (NAS) efficiencies. Interval Management will reduce variability in temporal deviations at a position, and thereby reduce buffers typically applied by controllers - resulting in higher arrival rates, and more efficient operations. Ground software generates a strategic schedule of aircraft pairs. Air Traffic Control (ATC) provides an IM clearance with the IM spacing objective (i.e., the TTF, and at which point to achieve the appropriate spacing from this aircraft) to the IM aircraft. Pilots must dial FIM speeds into the speed window on the Mode Control Panel in a timely manner, and attend to deviations between actual speed and the instantaneous FIM profile speed. Here, the crew is assumed to be operating the aircraft with autothrottles on, with autopilot engaged, and the autoflight system in Vertical Navigation (VNAV) and Lateral Navigation (LNAV); and is responsible for safely flying the aircraft while maintaining situation awareness of their ability to follow FIM speed commands and to achieve the FIM spacing goal. The objective of this study is to examine whether three Notification Methods and four Avionics Conditions affect pilots' performance, ratings on constructs associated with performance (workload, situation awareness), or opinions on acceptability. Three Notification Methods (alternate visual and aural alerts that notified pilots to the onset of a speed target, conformance deviation from the required speed profile, and reminded them if they failed to enter the speed within 10 seconds) were examined. These Notification Methods were: VVV (visuals for all three events), VAV (visuals for all three events, plus an aural for speed conformance deviations), and AAA (visual indications and the same aural to indicate all three of these events). Avionics Conditions were defined by the instrumentation (and location) used to present IM information to crews: (1) Integrated (IM information is embedded in extant PFD (Primary Flight Display), ND (Navigation Display), EICAS (Engine Indicating and Crew Alerting System) displays); (2) EFB_Aft (IM information is only supplied in an EFB and mounted in location similar to that for MITRE's UPS work); (3) EFB_Fore (IM information is only supplied in an EFB which is mounted more forward, under the side window), and (4) EFB_Aft plus use of an AGD (the same IM information is supplied in an EFB and on an AGD, both mounted in locations similar to that in MITRE's UPS work ). Twelve commercial pilot crews flew descent scenarios (VNAV Speed with the mode control panel (MCP) speed window open until flaps extended, then VNAV Path) in a commercial transport flight simulator with realistic visual scene and communications. The results of this study serve three practical aims: (1) contribute to the down-select of avionics configuration for future assessment of the ASTAR spacing algorithm at NASA; (2) provide information useful to the FAA Human Factors Division (ANG-C1)'s mission to identify issues pertinent to flight certification of, and flight standards; (3) identify methodological considerations in support of future FIM human-in-the-loop (HITL) investigations.

Latorella, Kara A.↗
NASA NTRS2016

Multifactor Interactions and the Air Traffic Controller: The Interaction of Situation Awareness and Workload in Association with Automation

Air traffic controllers (ATCOs) must maintain a consistently high level of human performance in order to maintain flight safety and efficiency. In current control environments, performance-influencing factors such as workload, fatigue and situation awareness (SA) can co-occur, and interact, to affect performance. However, multifactor influences and the association with performance are under-researched. This study utilized a high fidelity human in the loop enroute air traffic control simulation to investigate the relationship between workload, situation awareness and ATCO performance. The study aimed to replicate and extend Edwards, Sharples, Wilson and Kirwan's (2012) previous study and confirm multifactor interactions with a participant sample of ex-controllers. The study also aimed to extend Edwards et al.'s previous research by comparing multifactor relationships across 4 automation conditions. Results suggest that workload and SA may interact to produce a cumulative impact on controller performance, although the effect of the interaction on performance may be dependent on the context and amount of automation present. Findings have implications for human-automation teaming in air traffic control, and the potential prediction and support of ATCO performance.

Workload↗
DOE OSTI2021

Coordination and control – limits in standard representations of multi-reservoir operations in hydrological modeling

Abstract. Major multi-reservoir cascades represent a primary mechanism for dealing with hydrologic variability and extremes within institutionally complex river basins worldwide. These coordinated management processes fundamentally reshape water balance dynamics. Yet, multi-reservoir coordination processes have been largely ignored in the increasingly sophisticated representations of reservoir operations within large-scale hydrological models. The aim of this paper is twofold, namely (i) to provide evidence that the common modeling practice of parameterizing each reservoir in a cascade independently from the others is a significant approximation and (ii) to demonstrate potential unintended consequences of this independence approximation when simulating the dynamics of hydrological extremes in complex reservoir cascades. We explore these questions using the Water Balance Model, which features detailed representations of the human infrastructure coupled to the natural processes that shape water balance dynamics. It is applied to the Upper Snake River basin in the western US and its heavily regulated multi-reservoir cascade. We employ a time-varying sensitivity analysis that utilizes the method of Morris factor screening to explicitly track how the dominant release rule parameters evolve both along the cascade and in time according to seasonal high- and low-flow events. This enables us to address aim (i) by demonstrating how the progressive and cumulative dominance of upstream releases significantly dampens the ability of downstream reservoir rules' parameters to influence flow conditions. We address aim (ii) by comparing simulation results with observed reservoir operations during critical low-flow and high-flow events in the basin. Our time-varying parameter sensitivity analysis with the method of Morris clarifies how independent single-reservoir parameterizations and their tacit assumption of independence leads to reservoir release behaviors that generate artificial water shortages and flooding, whereas the observed coordinated cascade operations avoided these outcomes for the same events. To further explore the role of (non-)coordination in the large deviations from the observed operations, we use an offline multi-reservoir water balance model in which adding basic coordination mechanisms drawn from the observed emergency operations is sufficient to correct the deficiencies of the independently parameterized reservoir rules from the hydrological model. These results demonstrate the importance of understanding the state–space context in which reservoir releases occur and where operational coordination plays a crucial role in avoiding or mitigating water-related extremes. Understanding how major infrastructure is coordinated and controlled in major river basins is essential for properly assessing future flood and drought hazards in a changing world.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Subsonic Single Aft Engine (SUSAN) Flight Deck Experiment: Throttle and Engine Display Concepts

NASA is developing a new hybrid-electric aircraft concept called the SUbsonic Single Aft eNgine (SUSAN) Electrofan. The SUSAN airplane is being designed as a 180-passenger commercial regional jet and is expected to fly in the 2040 time frame. The flight deck of the SUSAN airplane is expected to resemble a typical two flightcrew transport. Research issues include pilot control and the display elements needed for the hybrid-electric propulsion system. This paper describes a research study conducted in a high-fidelity simulator where 14 commercial pilots evaluated throttle controls and engine display concepts for a proposed SUSAN flight deck.

hybrid electric aircraft↗
DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulated wildfire burned area over the CONUS during 2001-2020

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM). A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

Liu, Ye↗
NASA NTRS1994

Fusion interfaces for tactical environments: An application of virtual reality technology

The term Fusion Interface is defined as a class of interface which integrally incorporates both virtual and nonvirtual concepts and devices across the visual, auditory, and haptic sensory modalities. A fusion interface is a multisensory virtually-augmented synthetic environment. A new facility has been developed within the Human Engineering Division of the Armstrong Laboratory dedicated to exploratory development of fusion interface concepts. This new facility, the Fusion Interfaces for Tactical Environments (FITE) Facility is a specialized flight simulator enabling efficient concept development through rapid prototyping and direct experience of new fusion concepts. The FITE Facility also supports evaluation of fusion concepts by operation fighter pilots in an air combat environment. The facility is utilized by a multidisciplinary design team composed of human factors engineers, electronics engineers, computer scientists, experimental psychologists, and oeprational pilots. The FITE computational architecture is composed of twenty-five 80486-based microcomputers operating in real-time. The microcomputers generate out-the-window visuals, in-cockpit and head-mounted visuals, localized auditory presentations, haptic displays on the stick and rudder pedals, as well as executing weapons models, aerodynamic models, and threat models.

Haas, Michael W.↗
NASA NTRSDate not supplied

Crowd Sourcing Medical Data Collection Using Medical Students

OBJECTIVE We undertook an upgrade of the Evidence Library database of NASA HRP’s Integrated Medical Model, assessing 120 medical conditions which integrate with a novel probabilistic risk assessment (IMPACT) tool of medical risk and resource utilization for long duration exploration human spaceflight. This data collection process included a selection of these conditions crowd sourced over one year via three 4-week medical student electives at the University of Colorado School of Medicine (IDPT 8059 Space Medicine: Human Spaceflight Factors & Medical Risk Assessment). Students undertook a rapid systematic review of each medical condition, under close preceptors with backgrounds in clinical medicine, library science, epidemiology, biostatistics, and evidence-based medicine. As part of the elective, students also received instruction in core space medicine concepts, evidence based medicine and problem based learning sessions as a flight surgeon supporting a simulated Mars mission. METHODS The list of 120 medical conditions includes both common, terrestrial illness/injury (epistaxis, diverticulitis) as well as spaceflight-specific ones (space adaptation conditions, EVA-related injuries). A rapid systematic review process was developed that would allow students to find the data for determining disease incidence/prevalence, return to definitive care (often a surrogate such as hospitalization rates), loss of crew life, and treatment duration. Each data point required a tailored, specialized search process using different databases and corresponding specialized search filters. Databases were selected on their ability to provide high quality literature in an efficient manner and prioritized by their ability to provide graded evidence via a set rubrics specific to human spaceflight. Students were responsible for performing all literature searches and identifying the highest quality available evidence for each data point. Completed student data sheets underwent initial review by faculty preceptors followed by a secondary editing review by the ExMC Clinical Science Team. RESULTS Over the course of three electives, approximately 105 medical conditions were researched by students using spreadsheets with pre-crafted search strategies. Overall, this process was successful in allowing students to perform the preponderance of work to update incidence, treatment duration, return to definitive care, and loss of crew life data points. Students were successful in running searches, identifying the necessary data points within the literature, and determining the types of terrestrial data that most aligns with the astronaut population for successful completion of their tasks. Limitations included variable student experience with search methodologies [PubMed], differing values of evidence grading [best practice evidence based medicine vs. relevant to spaceflight], and students’ unfamiliarity with spaceflight specific conditions. CONCLUSION Finding the relevant literature for medical conditions in spaceflight within terrestrial databases in a systematic method is time consuming and not intuitive. However, the stepwise process that balanced sensitivity with specificity allowed for students to be highly successful in a short amount of time. Additionally, as the process was refined over the course of three electives, preceptors were better able to anticipate where students were likely to encounter barriers, which allowed the course to be adjusted to account for certain data points needing more time for completion. This replicable process may be an efficient way to accomplish rapid systematic reviews for a large volume of data in a short amount of time.

J Lemery↗
NASA NTRS2002

Analysis and Design of Crew Sleep Station for ISS

This paper details the analysis and design of the Temporary Sleep Station (TeSS) environmental control system for International Space Station (ISS). The TeSS will provide crewmembers with a private and personal space, to accommodate sleeping, donning and doffing of clothing, personal communication and performance of recreational activities. The need for privacy to accommodate these activities requires adequate ventilation inside the TeSS. This study considers whether temperature, carbon dioxide, and humidity within the TeSS remain within crew comfort and safety levels for various expected operating scenarios. Evaluation of these scenarios required the use and integration of various simulation codes. An approach was adapted for this study, whereby results from a particular code were integrated with other codes when necessary. Computational Fluid Dynamics (CFD) methods were used to evaluate the flow field inside the TeSS, from which local gradients for temperature, velocity, and species concentration such as CO (sub 2) could be determined. A model of the TeSS, containing a human, as well as equipment such as a laptop computer, was developed in FLUENT, a finite-volume code. Other factors, such as detailed analysis of the heat transfer through the structure, radiation, and air circulation from the TeSS to the US Laboratory Aisle, where the TeSS is housed, were considered in the model. A complementary model was developed in G189A, a code which has been used by NASA/JSC for environmental control systems analyses since the Apollo program. Boundary conditions were exchanged between the FLUENT and G189A TeSS models. G189A provides human respiration rates to the FLUENT model, while the FLUENT model provides local convective heat transfer coefficients to G189A model. An additional benefit from using an approach with both a systems simulation and CFD model, is the capability to verify the results of each model by comparison to the results of the other model. The G189A and FLUENT models were used to evaluate various ventilation designs for the TeSS over a range of operating conditions with varying crew metabolic load, equipment operating modes, ventilation flow rates, and with the TeSS doors open and closed. Results from the study were instrumental in the optimization of a design for the TeSS ventilation hardware. A special case was considered where failure of the TeSS ventilation system occurred. In this case, a study was conducted in order to determine the time required for the CO (sub 2) concentration inside the TeSS to increase to ISS limit values under transient conditions. A lumped-capacitance code, SINDA-FLUINT was used in this case to provide accurate predictions of the human reaction to the TeSS cabin conditions including core and skin temperatures and body heat storage. A simple two-dimensional CFD model of a crewmember inside the TeSS was developed in FLUENT in order to determine the volume envelope of the respired air from the human, which maintained a minimum velocity profile. This volume was then used in the SINDA-FLUINT model to facilitate the calculations of CO (sub 2) concentrations, dry bulb temperatures and humidity levels inside the TeSS.

Keener, John F.↗
DOE OSTI2023

Lighting for Health and Wellness Recommendations in Offices: A Circadian Lighting Pilot Project in Chicago, IL

In 2019 Cook County initiated a request for proposals for a pilot project aimed at designing and specifying an advanced tunable lighting system for government offices in Chicago, Illinois. The primary goal of the project was to replace the existing tubular LED (TLED) lighting system with a lighting system that supported the circadian rhythms and wellbeing their office employees. A secondary goal was to evaluate the potential impacts of the new lighting system, informing Cook County’s internal workplace design standards. Schuler Shook, a Chicago-based lighting design firm, was awarded the Circadian Lighting Pilot Project in October 2019 and invited Pacific Northwest National Laboratory (PNNL) to contribute as a research partner. Based on the initial project goals, Schuler Shook and PNNL established design objectives that would enable a human factors study in the office spaces, investigating the benefits and drawbacks of using a tunable LED lighting system to meet existing recommendations for lighting and health. The following report details the lighting design process through design development, with summaries of the relevant circadian lighting metrics, simulation tools, energy considerations, and research plan for the Circadian Lighting Pilot Project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model

Wildfires have shown increasing trends in both frequency and severity across the contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth system models (ESMs). Alternatively, fire models based on machine learning (ML), which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ELM2.1-XGBFire1.0) that integrates an eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran–C–Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001–2019, the ELM2.1-XGBFire1.0 outperforms process-based fire models in terms of spatial distribution and seasonal variations. The ELM2.1-XGBFire1.0 has proven to be a new tool for studying vegetation–fire interactions and, more importantly, enables seamless exploration of climate–fire feedback, working as an active component of E3SM.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Dual-Use Space Technology Transfer Conference and Exhibition, Volume 1

This document contains papers presented at the Dual-Use Space Technology Transfer Conference and Exhibition held at the Johnson Space Center February 1-3, 1994. Possible technology transfers covered during the conference were in the areas of information access; innovative microwave and optical applications; materials and structures; marketing and barriers; intelligent systems; human factors and habitation; communications and data systems; business process and technology transfer; software engineering; biotechnology and advanced bioinstrumentation; communications signal processing and analysis; new ways of doing business; medical care; applications derived from control center data systems; human performance evaluation; technology transfer methods; mathematics, modeling, and simulation; propulsion; software analysis and decision tools systems/processes in human support technology; networks, control centers, and distributed systems; power; rapid development perception and vision technologies; integrated vehicle health management; automation technologies; advanced avionics; ans robotics technologies. More than 77 papers, 20 presentations, and 20 exhibits covering various disciplines were presented b experts from NASA, universities, and industry.

Kumar Krishen↗
NASA NTRS2016

Genelab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASA's premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

bioinformatics↗
NASA NTRS2014

Upset Recovery Human Factors

Accident and incident analyses as well as industry group concerns and recommendations have justified taking a second look at proficiency standards related to upset recovery training and performance. Quite a number of factors and theories have been suggested-- leading the NASA Aviation Safety Program to reconsider manual handling skills in highly automated aircraft particularly in conditions that can potentially lead to Loss of Control events. Our team of Subject Matter Experts (SMEs) first identified 76 Basic Recovery Skills that were important for effective crew response under five different anomaly conditions. In addition to manual handling skills, the skill set included knowledge and cognitive skills, as well as decision making and management skills. Advanced Recovery Skills were identified by combining skills, integrating with crew resource management skills, and developing heuristics for decision making.Using the Advanced Recovery Skill set, the SMEs then developed a generic process flow starting from the problem discovery phase (e.g., identifying an anomaly) through the decision making and management phase (e.g., assessing response options), through the recovery phase (e.g., controlling the aircraft). The generic process flow was refined by testing it against six additional scenarios. The next part of the project was to develop an approach for assessing and revising a generic training curriculum (we used an operators Advanced Qualification Program (AQP) as a framework). Although many of the Basic and Advanced Recovery Skills could be found in the Job Task Listing, they were not always structured or combined in the most effective way. Recommendations were developed for assessing relevant aspects of the Job Task Listing and Continuing Qualification curriculum so that the more comprehensive set of Upset Recovery skillsincluding Human Factors--could be trained and assessed in the most appropriate and effective context. The existing AQP methodology provides a natural way to insert targeted Upset Recovery skills into its system of proficiency objectives, training devices, training activities, and ultimately, into the event sets of a simulator training scenario.

AQP training↗
NASA NTRS2016

GeneLab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASAs premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

spaceflight↗