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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 73 records · Page 4

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NASA NTRS1974

Turbulence flight director analysis and preliminary simulation

A control column and trottle flight director display system is synthesized for use during flight through severe turbulence. The column system is designed to minimize airspeed excursions without overdriving attitude. The throttle system is designed to augment the airspeed regulation and provide an indication of the trim thrust required for any desired flight path angle. Together they form an energy management system to provide harmonious display indications of current aircraft motions and required corrective action, minimize gust upset tendencies, minimize unsafe aircraft excursions, and maintain satisfactory ride qualities. A preliminary fixed-base piloted simulation verified the analysis and provided a shakedown for a more sophisticated moving-base simulation to be accomplished next. This preliminary simulation utilized a flight scenario concept combining piloting tasks, random turbulence, and discrete gusts to create a high but realistic pilot workload conducive to pilot error and potential upset. The turbulence director (energy management) system significantly reduced pilot workload and minimized unsafe aircraft excursions.

Johnson, D. E.↗

NASA NTRS1978

Use of reward-penalty structures in human experimentation

The use of motivational techniques in human performance research is reviewed and an example study employing a reward-penalty structure to simulate the motivations inherent in a real-world situation is presented. Driver behavior in a decision-making driving scenario was studied. The task involved control of an instrumented car on a cooperative test course. Subjects were penalized monetarily for tickets and accidents and rewarded for saving driving time. Two groups were assigned different ticket penalties. The group with the highest penalties tended to drive more conservatively. However, the average total payoff to each group was the same, as the conservative drivers traded off slower driving times with lower ticket penalties.

Stein, A. C.↗

NASA NTRS1981

Defining the subjective experience of workload

Flight scenarios that represent different types and levels of pilot workload are needed in order to conduct research about, and develop measures of, pilot workload. In order to be useful, however, the workload associated with such scenarios and the component tasks must be determined independently. An initial study designed to provide such information was conducted by asking a panel of general aviation pilots to evaluate flight-related tasks for the overall, perceptual, physical, and cognitive workload they impose. These ratings will provide the nucleus for a data base of flight-related primary tasks that have been independently rated for workload to use in workload assessment research.

Hart, S. G.↗

NASA NTRS1993

The application of active side arm controllers in helicopters

Eurocopter Deutschland (ECD) started simulation trials to investigate the particular problems of Side Arm Controllers (SAC) applied to helicopters. Two simulation trials have been performed. In the first trial, the handling characteristics of a 'passive' SAC and the basic requirements for the application of an 'active' SAC were evaluated in pilot-in-the-loop simulations, performing the tasks in a realistic scenario representing typical phases of a transport mission. The second simulation trial investigated the general control characteristics of the 'active' in comparison to the 'passive' control principle. A description of the SACs developed by ECD and the principle of the 'passive' and 'active' control concept is given, as well as specific ratings for the investigated dynamic and ergonomic parameters effecting SAC characteristics. The experimental arrangements, as well as the trials procedures of both simulation phases, are described and the results achieved are discussed emphasizing the advantages of the 'active' as opposed to the 'passive' SAC concept. This also includes the presentation of some critical aspects still to be improved and proposals to solve them.

Knorr, R.↗

NASA NTRS2001

Robot and Human Surface Operations on Solar System Bodies

This paper presents a comparison of robot and human surface operations on solar system bodies. The topics include: 1) Long Range Vision of Surface Scenarios; 2) Human and Robots Complement Each Other; 3) Respective Human and Robot Strengths; 4) Need More In-Depth Quantitative Analysis; 5) Projected Study Objectives; 6) Analysis Process Summary; 7) Mission Scenarios Decompose into Primitive Tasks; 7) Features of the Projected Analysis Approach; and 8) The "Getting There Effect" is a Major Consideration. This paper is in viewgraph form.

Weisbin, C. R.↗

NASA NTRS1997

Automation Bias and Countermeasures in Flight Crews

Results of recent research investigating the use of automated systems have indicated the presence of automation bias, a term describing errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing. Automation commission errors, i.e., errors made when decision makers take inappropriate action because they over-attend to automated information or directives, and automation omission errors, i.e., errors made when decision makers do not take appropriate action because they are not informed of an imminent problem or situation by automated aids, can result from this tendency. In a series of studies, participants who perceived themselves as "accountable" for their strategies of interaction with automation were significantly more likely to verify its correct functioning, and committed significantly fewer automation-related errors than those who did not report this perception. In this study, we focus on two manipulations to encourage verification behaviors within cockpit crews. The first, an information/training manipulation, will focus on explicit information on automation bias, and training for verification of automated functioning. The second manipulation will consist of display prompts to verify automated functioning. Participants (glass-cockpit crews) will fly a series of approaches on the Mini-ACFS, a two-person pan-task flight simulator. Approach scenarios include several automation "events," that is, glitches, malfunctions, or inappropriate recommendations, that can be caught if cross-checked with other cockpit indicators. We anticipate that these manipulations will ameliorate automation bias. Final data analysis will be completed prior to the OSU symposium, and is expected to support the hypotheses that: a) reduced errors are associated with verification behaviors; b) crews will be more likely to verify under training/display conditions than when in the control condition; and c) effects will persist when subjects return for a follow-up exercise 6-9 weeks after their first session. Implications for training and automation design will be discussed.

Mosier, Kathleen, L.↗

NASA NTRS2021

Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

crop yield projections↗

NASA NTRSDate not supplied

Wi-Fi Signal Survey of the International Space Station by Autonomous Free-Flying Robot

This paper analyzes Wi-Fi signal propagation inside pressurized modules of the International Space Station (ISS). Various flight datasets were collected by the Astrobee fleet of autonomous free-flying robots developed by the NASA Ames Research Center (ARC). The enclosed module spaces of the ISS pose unique challenges for Wi-Fi signal propagation, and understanding these challenges is crucial for optimizing connectivity in such environments. The survey data presented are valuable for optimizing wireless access point (WAP) locations and conditions. A computational method is developed to analyze the wireless communication system, RF coverage, and module compositions. The method is both rigorous and practical for assessing the ISS wireless system performance. The computational tools in this paper serve as a complementary approach to direct, on-orbit measurements, which is a complicated and expensive task. In some operational scenarios, ground measurements may be intractable due to the size of the spacecraft. Results from this research will aid future space missions and commercial spacecraft, providing insights into optimal WAP placements and robust wireless communication systems.

Astrobee↗

NASA NTRSDate not supplied

Wi-Fi Signal Survey of the International Space Station by Autonomous Free-Flying Robot

International Space Station↗

DOE OSTI2025

Synergistic learning with multi-task DeepONet for efficient PDE problem solving

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Finally, our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.

42 ENGINEERING↗

NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Imprint Human Performance Models to Inform Crew Size

HUMANS TO MARS, BUT HOW MANY? USING IMPRINT HUMAN PERFORMANCE MODELS TO INFORM CREW SIZE Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The custom-built crew expertise model results will be presented in the training sub-tag. Here we present results of the three IMPRINT models. Two of these models were built using IMPRINT’s Operations Model capability, with detailed task networks for specific systems and scenarios allowing for a comprehensive mental workload analysis on certain crew positions. The third model was built using IMPRINT’s Force Model capability, allowing for a higher-level manpower analysis based on crew engagement in activities performed over a longer Mars transit scenario, and how those activities may be impacted by unplanned events and new tasking unique to the Mars mission. Differences between the models will be presented, as well as examples of model results and a discussion of how those results may inform future Mars mission manpower requirements, system design, and operating procedures.

Donna L Dempsey↗

NASA NTRS1977

Orbital construction support equipment

Approximately 200 separate construction steps were defined for the three solar power satellite (SPS) concepts. Detailed construction scenarios were developed which describe the specific tasks to be accomplished, and identify general equipment requirements. The scenarios were used to perform a functional analysis, which resulted in the definition of 100 distinct SPS elements. These elements are the components, parts, subsystems, or assemblies upon which construction activities take place. The major SPS elements for each configuration are shown. For those elements, 300 functional requirements were identified in seven generic processes. Cumulatively, these processes encompass all functions required during SPS construction/assembly. Individually each process is defined such that it includes a specific type of activity. Each SPS element may involve activities relating to any or all of the generic processes. The processes are listed, and examples of the requirements defined for a typical element are given.

Source record↗

NASA NTRS2015

A Validated Task Analysis of the Single Pilot Operations Concept

The current day flight deck operational environment consists of a two-person Captain/First Officer crew. A concept of operations (ConOps) to reduce the commercial cockpit to a single pilot from the current two pilot crew is termed Single Pilot Operations (SPO). This concept has been under study by researchers in the Flight Deck Display Research Laboratory (FDDRL) at the National Aeronautics and Space Administration's (NASA) Ames (Johnson, Comerford, Lachter, Battiste, Feary, and Mogford, 2012) and researchers from Langley Research Centers (Schutte et al., 2007). Transitioning from a two pilot crew to a single pilot crew will undoubtedly require changes in operational procedures, crew coordination, use of automation, and in how the roles and responsibilities of the flight deck and ATC are conceptualized in order to maintain the high levels of safety expected of the US National Airspace System. These modifications will affect the roles and the subsequent tasks that are required of the various operators in the NextGen environment. The current report outlines the process taken to identify and document the tasks required by the crew according to a number of operational scenarios studied by the FDDRL between the years 2012-2014. A baseline task decomposition has been refined to represent the tasks consistent with a new set of entities, tasks, roles, and responsibilities being explored by the FDDRL as the move is made towards SPO. Information from Subject Matter Expert interviews, participation in FDDRL experimental design meetings, and study observation was used to populate and refine task sets that were developed as part of the SPO task analyses. The task analysis is based upon the proposed ConOps for the third FDDRL SPO study. This experiment possessed nine different entities operating in six scenarios using a variety of SPO-related automation and procedural activities required to guide safe and efficient aircraft operations. The task analysis presents the roles and responsibilities in a manner that can facilitate testing future scenarios. Measures of task count and workload were defined and analyzed to assess the impact of transitioning to a SPO environment.

task analysis↗

NASA NTRS2002

Energy Absorbing Seat System for an Agricultural Aircraft

A task was initiated to improve the energy absorption capability of an existing aircraft seat through cost-effective retrofitting, while keeping seat-weight increase to a minimum. This task was undertaken as an extension of NASA ongoing safety research and commitment to general aviation customer needs. Only vertical crash scenarios have been considered in this task which required the energy absorbing system to protect the seat occupant in a range of crash speeds up to 31 ft/sec. It was anticipated that, the forward and/or side crash accelerations could be attenuated with the aid of airbags, the technology of which is currently available in automobiles and military helicopters. Steps which were followed include, preliminary crush load determination, conceptual design of cost effective energy absorbers, fabrication and testing (static and dynamic) of energy absorbers, system analysis, design and fabrication of dummy seat/rail assembly, dynamic testing of dummy seat/rail assembly, and finally, testing of actual modified seat system with a dummy occupant. A total of ten full scale tests have been performed including three of the actual aircraft seat. Results from full-scale tests indicated that occupant loads were attenuated successfully to survivable levels.

Kellas, Sotiris↗

NASA NTRS2013

An Onboard ISS Virtual Reality Trainer

Prior to the retirement of the Space Shuttle, many exterior repairs on the International Space Station (ISS) were carried out by shuttle astronauts, trained on the ground and flown to the station to perform these repairs. After the retirement of the shuttle, this is no longer an available option. As such, the need for the ISS crew members to review scenarios while on flight, either for tasks they already trained or for contingency operations has become a very critical subject. In many situations, the time between the last session of Neutral Buoyancy Laboratory (NBL) training and an Extravehicular Activity (EVA) task might be 6 to 8 months. In order to help with training for contingency repairs and to maintain EVA proficiency while on flight, the Johnson Space Center Virtual Reality Lab (VRLab) designed an onboard immersive ISS Virtual Reality Trainer (VRT), incorporating a unique optical system and making use of the already successful Dynamic Onboard Ubiquitous Graphical (DOUG) graphics software, to assist crew members with current procedures and contingency EVAs while on flight. The VRT provides an immersive environment similar to the one experienced at the VRLab crew training facility at NASA Johnson Space Center. EVA tasks are critical for a mission since as time passes the crew members may lose proficiency on previously trained tasks. In addition, there is an increased need for unplanned contingency repairs to fix problems arising as the ISS ages. The need to train and re-train crew members for EVAs and contingency scenarios is crucial and extremely demanding. ISS crew members are now asked to perform EVA tasks for which they have not been trained and potentially have never seen before.

Miralles, Evelyn↗

NASA NTRS1979

Mathematical concepts for modeling human behavior in complex man-machine systems

Many human behavior (e.g., manual control) models have been found to be inadequate for describing processes in certain real complex man-machine systems. An attempt is made to find a way to overcome this problem by examining the range of applicability of existing mathematical models with respect to the hierarchy of human activities in real complex tasks. Automobile driving is chosen as a baseline scenario, and a hierarchy of human activities is derived by analyzing this task in general terms. A structural description leads to a block diagram and a time-sharing computer analogy.

Johannsen, G.↗

NASA NTRSDate not supplied

Natural Language Processing Methods for Air Traffic Management Text and Speech Data

This presentation discusses two efforts of the NARI AI/ML Intern team during the Fall 2021 OSTEM Internship term. For Letters of Agreement (LoA), we have studied how LoAs are structured and explored the question ‘What is an LoA constraint?’ To do this, our approach is data-driven, iterative, and assisted by machine learning when available. In this presentation, we will walk through our tasks of manually scanning through documents, performing a preliminary entity labelling task, and our unsupervised analysis on LoA procedures sections. After this research phase, we define the smallest constraint unit in an LoA, and start to perform entity extraction. Looking towards constraint extraction, we are also exploring the use of a one-class support vector machine (OneClassSVM) model to identify patterns within the data. The second effort of our team this term is focused on Air Traffic Control System Command Center (ATCSCC) advisory meetings, and the subsequent advisory documents that get published from their content. These advisory documents are important to give readily accessible summaries of daily operations, so that data centers, airline officials, and other stakeholders can easily understand the context of these meetings in real time. In applying machine learning to this scenario, two natural language processing tasks are used. First is developing machine learning models to convert the meeting speech data into text. With this text, use of extractive and abstractive text summarization models are used to automatically generate preliminary versions of the advisory documents.

Natural Language Processing↗

DOE OSTI2024

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years due to the growing interest in deploying RL agents in real-world scenarios. A critical aspect of risk awareness involves modelling highly rare risk events (rewards) that could potentially lead to catastrophic outcomes. These infrequent occurrences present a formidable challenge for data-driven methods aiming to capture such risky events accurately. While risk-aware RL techniques do exist, they suffer from high variance estimation due to the inherent data scarcity. Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value distribution. To achieve this, we formulate the extreme values of the state-action value function distribution as parameterized distributions, drawing inspiration from the principles of extreme value theory (EVT). We propose an extreme value theory based actor-critic approach, namely, Extreme Valued Actor-Critic (EVAC) which effectively addresses the issue of infrequent occurrence by leveraging EVT-based parameterization. Importantly, we theoretically demonstrate the advantages of employing these parameterized distributions in contrast to other risk-averse algorithms. Our evaluations show that the proposed method outperforms other risk averse RL algorithms on a diverse range of benchmark tasks, each encompassing distinct risk scenarios.

Wang, Yu↗