SEARCH · Search NASA
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.
Through recent usability studies, the issue of relevance became increasingly clear in the Earthdata Search Client. After all, if a user can't find the data they are looking for, nothing else we do matters. This presentation walks through usability testing findings and recent relevance improvements made to the Earthdata Search Client.
Microbial production of succinic acid (SA) at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation for over three decades. Here, we metabolically engineer the acid-tolerant yeast Issatchenkia orientalis for SA production, attaining the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further perform batch fermentation using sugarcane juice medium in a pilot-scale fermenter (300×) and achieve 63.1 g/L of SA, which can be directly crystallized with a yield of 64.0%. Finally, we simulate an end-to-end low-pH SA production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce greenhouse gas emissions by 34–90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for production of organic acids.
Explore the source record for details and available documents.
Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.
There is a growing need for rapid and effective access to information in large electronic documentation systems. Access can be facilitated if information relevant in the current problem solving context can be automatically supplied to the user. This includes information relevant to particular user profiles, tasks being performed, and problems being solved. However most of this knowledge on contextual relevance is not found within the contents of documents, and current hypermedia tools do not provide any easy mechanism to let users add this knowledge to their documents. We propose a compositional relevance network to automatically acquire the context in which previous information was found relevant. The model records information on the relevance of references based on user feedback for specific queries and contexts. It also generalizes such information to derive relevant references for similar queries and contexts. This model lets users filter information by context of relevance, build personalized views of documents over time, and share their views with other users. It also applies to any type of multimedia information. Compared to other approaches, it is less costly and doesn't require any a priori statistical computation, nor an extended training period. It is currently being implemented into the Computer Integrated Documentation system which enables integration of various technical documents in a hypertext framework.
Impacts-relevant Earth system data refers to observational and ESM data that are downscaled, debiased, validated, and provisioned for use by decision-makers. Impacts-relevant Earth system data is essential for mitigation and adaptation planning across a variety of regions and sectors. A vast number of these data products have emerged in recent years, which has led to confusion among stakeholders and scientists as to the best product to use. With no standard evaluation protocol available for these products, the decision on which product to use was sometimes made because it was pragmatic rather than the best product to use. This project sought to develop foundational capabilities around impacts-relevant data products that would support more informed selection and application of these products. This work has been immensely successful, driving several academic publications and supported the development of a community of practice around impacts-relevant data products. Over the project’s three years we have addressed six tasks: First, the development of standard evaluation metrics for impacts-relevant climate data; second, the development of a novel suite of atmospheric river metrics; third, the development of novel metrics for precipitation feature analysis; fourth, the development of novel metrics for assessing co-variances between temperature and precipitation; fifth, the development of a dashboard for interactive examination of impacts-relevant climate data; and sixth, the establishment of a community of practice around impacts-relevant climate data that will continue beyond the conclusion of this project.
An exploratory study was conducted to identify commercial aviation incidents that are relevant to a "controlled flight into terrain" (CFIT) accident using a NASA-developed text processing method. The QUORUM method was used to rate 67820 incident narratives, virtually all of the narratives in the Aviation Safety Reporting System (ASRS) database, according to their relevance to two official reports on the crash of American Airlines Flight 965 near Cali, Colombia in December 1995. For comparison with QUORUM's ratings, three experienced ASRS analysts read the reports of the crash and independently rated the relevance of the 100 narratives that were most highly rated by QUORUM, as well as 100 narratives randomly selected from the database. Eighty-four of 100 QUORUM-selected narratives were rated as relevant to the Cali accident by one or more of the analysts. The relevant incidents involved a variety of factors, including, over-reliance on automation, confusion and changes during descent/approach, terrain avoidance, and operations in foreign airspace. In addition, the QUORUM collection of incidents was found to be significantly more relevant than the random collection.
Introduction: Analogue environments assist the NASA Human Research Program (HRP) in developing capabilities to mitigate high risk issues to crew health and performance for space exploration. The Habitat Demonstration Unit (HDU) is an analogue habitat used to assess space-related products for planetary missions. The Exploration Medical Capability (ExMC) element at the NASA Johnson Space Center (JSC) was tasked with developing planetary-relevant medical scenarios to evaluate the concept of operations for mitigating medical issues in such an environment. Methods: Two medical scenarios were conducted within the simulated planetary habitat with the crew executing two space flight-relevant procedures: Eye Examination with a corneal injury and Skin Laceration. Remote guidance for the crew was provided by a flight surgeon (FS) stationed at a console outside of the habitat. Audio and video data were collected to capture the communication between the crew and the FS, as well as the movements of the crew executing the procedures. Questionnaire data regarding procedure content and remote guidance performance also were collected from the crew immediately after the sessions. Results: Preliminary review of the audio, video, and questionnaire data from the two scenarios conducted within the HDU indicate that remote guidance techniques from an FS on console can help crew members within a planetary habitat mitigate planetary-relevant medical issues. The content and format of the procedures were considered concise and intuitive, respectively. Discussion: Overall, the preliminary data from the evaluation suggest that use of remote guidance techniques by a FS can help HDU crew execute space exploration-relevant medical procedures within a habitat relevant to planetary missions, however further evaluations will be needed to implement this strategy into the complete concept of operations for conducting general space medicine within similar environments
The Adaptive Indexing and Retrieval Agent (ARNIE) is a code library, designed to be used by an application program, that assists human users in retrieving desired information in a hypertext setting. Using ARNIE, the program implements a computational model for interactively learning what information each human user considers relevant in context. The model, called a "relevance network," incrementally adapts retrieved information to users individual profiles on the basis of feedback from the users regarding specific queries. The model also generalizes such knowledge for subsequent derivation of relevant references for similar queries and profiles, thereby, assisting users in filtering information by relevance. ARNIE thus enables users to categorize and share information of interest in various contexts. ARNIE encodes the relevance and structure of information in a neural network dynamically configured with a genetic algorithm. ARNIE maintains an internal database, wherein it saves associations, and from which it returns associated items in response to a query. A C++ compiler for a platform on which ARNIE will be utilized is necessary for creating the ARNIE library but is not necessary for the execution of the software.
Impacts-relevant Earth system data refers to observational and ESM data that are downscaled, debiased, validated, and provisioned for use by decision-makers. Impacts-relevant Earth system data is essential for mitigation and adaptation planning across a variety of regions and sectors. A vast number of these data products have emerged in recent years, which has led to confusion among stakeholders and scientists as to the best product to use. With no standard evaluation protocol available for these products, the decision on which product to use was sometimes made because it was pragmatic rather than the best product to use. This project sought to develop foundational capabilities around impacts-relevant data products that would support more informed selection and application of these products. This work has been immensely successful, driving several academic publications and supported the development of a community of practice around impacts-relevant data products.
The Medical Operational Support Team (MOST) was tasked by the JSC Space Medicine and Life Sciences Directorate (SLSD) to incorporate medical simulation into 1) medical training for astronaut-crew medical officers (CMO) and medical flight control teams and 2) evaluations of procedures and resources required for medical care aboard the International Space Station (ISS). Development of evidence-based medical scenarios that mimic the physiology observed during spaceflight will be needed for the MOST to complete these two tasks. The MOST used a human patient simulator, the ISS-like resources in the Medical Simulation Laboratory (MSL), and evidence from space operations, military operations and medical literature to develop space relevant medical scenarios. These scenarios include conditions concerning airway management, Advanced Cardiac Life Support (ACLS) and mitigating anaphylactic symptoms. The MOST has used these space relevant medical scenarios to develop a preliminary space medical training regimen for NASA flight surgeons, Biomedical Flight Controllers (Biomedical Engineers; BME) and CMO-analogs. This regimen is conducted by the MOST in the MSL. The MOST has the capability to develop evidence-based space-relevant medical scenarios that can help SLSD I) demonstrate the proficiency of medical flight control teams to mitigate space-relevant medical events and 2) validate nextgeneration medical equipment and procedures for space medicine applications.
This one-year project was selected by NASA's Science Innovation Fund in FY17 to address Corrosion on Mars which is a problem that has not been addressed before. Corrosion resistance is one of the most important properties in selecting materials for landed spacecraft and structures that will support surface operations for the human exploration of Mars. Currently, the selection of materials is done by assuming that the corrosion behavior of a material on Mars will be the same as that on Earth. This is understandable given that there is no data regarding the corrosion resistance of materials in the Mars environment. However, given that corrosion is defined as the degradation of a metal that results from its chemical interaction with the environment, it cannot be assumed that corrosion is going to be the same in both environments since they are significantly different. The goal of this research is to develop a systematic approach to understand corrosion of spacecraft materials on Mars by conducting a literature search of available data, relevant to corrosion in the Mars environment, and by performing preliminary laboratory experiments under relevant simulated Martian conditions. This project was motivated by the newly found evidence for the presence of transient liquid brines on Mars that coincided with the suggestion, by a team of researchers, that some of the structural degradation observed on Curiosity's wheels may be caused by corrosive interactions with the brines, while the most significant damage was attributed to rock scratching. An extensive literature search on data relevant to Mars corrosion confirmed the need for further investigation of the interaction between materials used for spacecraft and structures designed to support long-term surface operations on Mars. Simple preliminary experiments, designed to look at the interaction between an aerospace aluminum alloy (AA7075-T73) and the gases present in the Mars atmosphere, at 20degC and a pressure of 700 Pa, showed that there is an interaction between the small amount of oxygen present in the Mars gas and the alloy when there is a scratch that removes the protective aluminum oxide film. Further studies are needed to consider many other important components of the Mars environment that can affect this interaction such as: the effect of oxidants, the effect of radiation on their oxidizing properties and the possible catalytic effects of the clays present in the Martian regolith. The results of this one-year project provide strong justification for further investigation of the corrosion mechanism of materials relevant to long-term surface operations in support of future human exploration missions on Mars.
Datalink is currently operational for departure clearances and in oceanic environments and is currently being tested in high altitude domestic enroute airspace. Interaction with even simple datalink clearances may create more workload for flight crews than the voice system they replace if not carefully designed. Datalink may also introduce additional complexity for flight crews with hundreds of uplink messages now defined for use. Finally, flight crews may lose airspace awareness and operationally relevant information that they normally pickup from Air Traffic Control (ATC) voice communications with other aircraft (i.e., “party-line” transmissions). Once again, automation may be poised to increase workload on the flight deck for incremental benefit. Datalink implementation to support future air traffic management concepts needs to be carefully considered, understanding human communication norms and especially, the change from voice- to text-based communications modality and its effect on pilot workload and situation awareness. Increasingly autonomous systems, where autonomy is designed to support human-autonomy teaming, may be suited to solve these issues. NASA is conducting research and development of increasingly autonomous systems, utilizing machine-learning algorithms seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. Increasingly autonomous systems offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. Two increasingly autonomous systems concepts - a traffic data manager and a conversational co-pilot - were developed to intelligently address the datalink issues in a complex, future state environment with significant levels of traffic. The system was tested for suitability of datalink usage for terminal airspace. The traffic data manager allowed for automated declutter of the Automatic Dependent Surveillance-Broadcast (ADS-B) display. The system determined relevant traffic for display based on machine learning algorithms trained by experienced human pilot behaviors. The conversational co-pilot provided relevant audio air traffic control messages based on context and proximity to ownship. Both systems made use of the connected aircraft concepts to provide intelligent context to determine relevancy above and beyond proximity to ownship. A human-in-the-loop test was conducted in NASA Langley Research Center’s Integration Flight Deck B-737-800 simulator to evaluate the traffic data manager and the conversational co-pilot. Twelve airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to the procedural events. This paper details the flight crew performance and evaluation during the events.
Seawater electrolysis alleviates freshwater demand to produce clean hydrogen while eliminating the need for water purification steps. The anodic process, seawater oxidation, typically requires high overpotentials and yields low selectivity to oxygen via the oxygen evolution reaction (OER), primarily due to the competing chlorine evolution reaction (CER) and hypochlorite evolution reaction (HCER) in pH-neutral conditions. Here, combining in situ surface-enhanced Raman characterization, grand canonical density functional theory-based calculations, and kinetic Monte Carlo simulations, we report the evolution of surface adsorbate configurations driven by applied potential and pH during seawater-relevant OER over IrO 2 , a highly OER-active and chloride-corrosion-resistant catalyst. As a result, the chemical properties of active sites, and thereby the kinetics of OER and CER/HCER, are effectively tuned. However, it is revealed that there is no optimal combination of potential and pH to achieve both high activity and high selectivity for seawater-relevant OER. To address this limitation, we establish a correlation between activity/selectivity and surface adsorbate configurations, enabling the optimization of highly active and OER-selective IrO 2 -based catalysts in seawater-relevant oxidation by modulating the local adsorbate environment of active sites.
Sandia will construct a cold flow receiver test platform in order to characterize falling particle curtain receivers at commercially relevant scales. While Sandia has extensive experience in R&D of falling particle curtain receivers, most have been at pilot scale and smaller - on the order of 1 MW th with characteristic dimensions of nominally 1-2 m to adequately collect solar energy from the heliostat field at the National Solar Thermal Test Facility (NSTTF). However, scaling up receivers to commercially relevant scales (25 MW th and above) will require a thorough understanding of particle curtain dynamics at larger scales, especially longer drop heights, for design certainty. The goal of this project will be to construct a cold falling particle curtain test rig capable of simulating particle characteristics that are expected in a commercial scale CSP plant, namely the drop height, curtain thickness, and particle mass flow rate (normalized by length of curtain). This will enable data collection on curtain opacity and spread, both of which are correlated to receiver efficiency and reliable construction, for commercially relevant scales. It will also permit validation of numerical models that will enable detailed receiver characterization and design past currently validated scales.
Sandia will construct a cold flow receiver test platform in order to characterize falling particle curtain receivers at commercially relevant scales. While Sandia has extensive experience in R&D of falling particle curtain receivers, most have been at pilot scale and smaller - on the order of 1 MWth with characteristic dimensions of nominally 1-2 m to adequately collect solar energy from the heliostat field at the National Solar Thermal Test Facility (NSTTF). However, scaling up receivers to commercially relevant scales (25 MWth and above) will require a thorough understanding of particle curtain dynamics at larger scales, especially longer drop heights, for design certainty. The goal of this project will be to construct a cold falling particle curtain test rig capable of simulating particle characteristics that are expected in a commercial scale CSP plant, namely the drop height, curtain thickness, and particle mass flow rate (normalized by length of curtain). This will enable data collection on curtain opacity and spread, both of which are correlated to receiver efficiency and reliable construction, for commercially relevant scales. It will also permit validation of numerical models that will enable detailed receiver characterization and design past currently validated scales.
Within this paper, control-relevant vehicle design concepts are examined using a widely used 3 DOF (plus flexibility) nonlinear model for the longitudinal dynamics of a generic carrot-shaped scramjet powered hypersonic vehicle. Trade studies associated with vehicle/engine parameters are examined. The impact of parameters on control-relevant static properties (e.g. level-flight trimmable region, trim controls, AOA, thrust margin) and dynamic properties (e.g. instability and right half plane zero associated with flight path angle) are examined. Specific parameters considered include: inlet height, diffuser area ratio, lower forebody compression ramp inclination angle, engine location, center of gravity, and mass. Vehicle optimizations is also examined. Both static and dynamic considerations are addressed. The gap-metric optimized vehicle is obtained to illustrate how this control-centric concept can be used to "reduce" scheduling requirements for the final control system. A classic inner-outer loop control architecture and methodology is used to shed light on how specific vehicle/engine design parameter selections impact control system design. In short, the work represents an important first step toward revealing fundamental tradeoffs and systematically treating control-relevant vehicle design.
NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.