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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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NASA NTRS2018

Natural and Induced Environment Around the International Space Station (ISS) as Observed During On-Orbit Operations of the Robotic External Leak Locator (RELL)

The Robotic External Leak Locator (RELL) was deployed to the International Space Station (ISS) with the goal of detecting and locating on-orbit leaks around the ISS. Three activities to characterize the background natural and induced environment of ISS were performed with RELL as part of the on-orbit validation and demonstration conducted in November and December 2016. The first demonstration activity pointed RELL directly in the ram (+X) and wake (-X) directions for one orbit each. The ram facing measurements showed high partial pressure for mass-to-charge ratio 16, corresponding to atomic oxygen (AO), as well as the presence of mass-to-charge ratio 17. RELL's view in the wake-facing direction included more ISS structure and several Environmental Control and Life Support System (ECLSS) on-orbit vents were detected, including the Carbon Dioxide Removal Assembly (CDRA), Russian segment ECLSS, and Sabatier vents. The second demonstration activity pointed RELL at three faces of the P1 Truss segment. Effluents from ECLSS and European Space Agency (ESA) Columbus module on-orbit vents were detected by RELL. The partial pressures of mass-to-charge ratios 17 and 18 remained consistent with the first on-orbit activity of characterizing the natural environment. The third demonstration activity involved RELL scanning an Active Thermal Control System (ATCS) radiator. Three locations along the radiator were scanned and the angular position of RELL with respect to the radiator was varied. Mass-to-charge ratios 16 and 17 both had upward shifts in partial pressure when pointing toward the Radiator Beam Valve Modules (RBVMs), likely corresponding to a known, small ammonia leak.

Fox, Katie L.↗
NASA NTRS2018

Modeling the Distribution and Type of High-Latitude Natural Wetlands for Methane Studies

High latitude (>50N) natural wetlands emit a substantial amount of methane to the atmosphere, and are located in a region of amplified warming. Northern hemisphere high latitudes are characterized by cold climates, extensive permafrost, poor drainage, short growing seasons, and slow decay rates. Under these conditions, organic carbon accumulates in the soil, sequestering CO2 from the atmosphere. Methanogens produce methane from this carbon reservoir, converting stored carbon into a powerful greenhouse gas. Methane emission from wetland ecosystems depends on vegetation type, climate characteristics (e.g, precipitation amount and seasonality, temperature, snow cover, etc.), and geophysical variables (e.g., permafrost, soil type, and landscape slope). To understand how wetland methane dynamics in this critical region will respond to climate change, we have to first understand how wetlands themselves will change and therefore, what the primary controllers of wetland distribution and type are. Understanding these relationships permits data-anchored, physically-based modeling of wetland distribution and type in other climate scenarios, such as paleoclimates or future climates, a necessary first step toward modeling wetland methane emissions in these scenarios. We investigate techniques and datasets for predicting the distribution and type of high latitude (>50N) natural wetlands from a suite of geophysical and climate predictors. Hierarchical clustering is used to derive an empirical methane-centric wetland model. The model is applied in a multistep process first to predict the distribution of wetlands from relevant geophysical parameters, and then, given the predicted wetland distribution, to classify the wetlands into methane-relevant types using an expanded suite of climate and biogeophysical variables. As the optimum set of predictor variables is not known a priori, the model is applied iteratively, and each simulation is evaluated with respect to observed high-latitude wetlands.

Romanski, Joy↗
NASA NTRS2019

A Preliminary Study on the Use of a NASA Developed Coatings Technology for Protecting Natural Science Collections from Molecular Contaminants

Many museum conservators and collection managers are faced with the challenge of molecular contaminants that can promote the degradation of specimens on display in exhibits or in cabinets at storage facilities. This has prompted the need to explore innovative techniques to alleviate the presence of chemical species that originate from atmospheric off-gassing of materials or cross-contamination among collection items. For example, the Smithsonian Institution's National Museum of Natural History (NMNH) has tackled this problem for many years, specifically targeting contaminants, such as mercury vapor, at its Museum Support Center (MSC) storage facility in Suitland, Maryland. Similarly, the presence of molecular contaminants poses a significant threat for NASA science and exploration missions. The deposition of chemical species on sensitive surfaces can degrade the performance and operational lifetime of satellites, telescopes, and instruments. As a result, a sprayable zeolite-based coatings technology was designed to passively capture molecular contaminants and reduce the risks associated with material outgassing in vacuum environments for aerospace applications. This technology, called the Molecular Adsorber Coating (MAC), was developed at NASA Goddard Space Flight Center (GSFC). MAC has been extensively used during thermal vacuum chamber testing of various spaceflight hardware and components, such as for the James Webb Space Telescope (JWST). The coating is also planned to fly aboard upcoming NASA missions to address on-orbit outgassing concerns within instrument and laser cavities. Recently, the MAC technology was evaluated as a possible solution for protecting the Smithsonian Institution's natural science specimens, specifically its mineral ore and botany collections at the MSC storage facility. The initial year-long study between NASA GSFC and NMNH involved investigating the effectiveness of the MAC technology in capturing molecular contaminants that are present within the collections and storage cabinets at ambient, non-vacuum conditions. The work included sample fabrication, installation and retrieval efforts, testing efforts and associated challenges, preliminary findings, and future plans for the multi-year project.

conservation↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRS2019

Cross-Program Design Specification for Natural Environments (DSNE) Revision G

The DSNE completes environment-related specifications for architecture, system-level, and lower-tier documents by specifying the ranges of environmental conditions that must be accounted for by NASA ESD Programs. To assure clarity and consistency, and to prevent requirements documents from becoming cluttered with extensive amounts of technical material, natural environment specifications have been compiled into this document. The intent is to keep a unified specification for natural environments that each Program calls out for appropriate application.

Barry C Roberts↗
NASA NTRS2020

Modeling Uncertainty in Large Natural Resource Allocation Problems

The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. This study introduces a novel numerical method to solve large-scale dynamic stochastic natural resource allocation problems that cannot be addressed by conventional methods. The method is illustrated with an application focusing on the allocation of global land resource use under stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields.

Cai, Yongyang↗
NASA NTRS2019

Additional Findings from the Common Research Model Natural Laminar Flow Wind Tunnel Test

An experimental investigation of the Common Research Model with Natural Laminar Flow (CRM-NLF) took place in the National Transonic Facility (NTF) at the NASA Langley Research Center in 2018. The 5.2% scale semispan model was designed using a new natural laminar flow design method, Crossflow Attenuated NLF (CATNLF). CATNLF enables laminar flow on typical transport wings with high sweep and Reynolds number by reshaping the wing airfoils to obtain specific pressure distribution characteristics that control the crossflow growth near the leading edge. The CATNLF method also addresses Tollmien- Schlichting transition, attachment line transition, and Görtler vortices. During the wind tunnel test, data were acquired to address three primary test objectives: validate the CATNLF design method, characterize the NTF laminar flow testing capabilities, and establish best practices for laminar flow wind tunnel testing. The present paper provides both experimental and computational data to understand the CRM-NLF laminar flow characteristics, as well as address the three primary test objectives. The effects of angle of attack and Reynolds number on the CRM-NLF laminar flow extent are studied, and the dominant transition mechanism is evaluated at a variety of test conditions. Critical N-factors are calculated for the NTF environment, and a discussion on best practices for laminar flow wind tunnel testing is provided. The CRM-NLF in the NTF provided initial confirmation of the ability of the CATNLF method to suppress crossflow growth and enable significant extents of laminar flow on transport wings with high sweep and Reynolds numbers.

Lynde, Michelle N.↗
NASA NTRS2020

Cross-Program Design Specification for Natural Environments (DSNE) Revision H

The DSNE completes environment-related specifications for architecture, system-level, and lower-tier documents by specifying the ranges of environmental conditions that must be accounted for by NASA ESD Programs. To assure clarity and consistency, and to prevent requirements documents from becoming cluttered with extensive amounts of technical material, natural environment specifications have been compiled into this document. The intent is to keep a unified specification for natural environments that each Program calls out for appropriate application.

Frank B Leahy↗
NASA NTRSDate not supplied

Design of a Crossflow Attenuated Natural Laminar Flow Flight Test Article

A natural laminar flow test article has been designed and analyzed for a flight test at the NASA Armstrong Flight Research Center using the F-15 aircraft with Centerline Instrument Pylon (CLIP) testbed. The flight test aims to experimentally validate the new Crossflow Attenuated Natural Laminar Flow (CATNLF) design method in a flight environment. The CATNLF design method uses geometry shaping to produce specific pressure distributions that enable laminar flow on vehicle components with high sweep and high Reynolds numbers by attenuating the crossflow growth that typically leads to premature transition on such components. The CATNLF test article, which will be mounted vertically below the F-15 for flight testing, is designed to be representative of a transonic transport wing and accounts for transition mechanisms due to crossflow, Tollmien-Schlichting, and attachment line transition instabilities. The test article is predicted to support 53% surface area of laminar flow on the suction-side at the design point and maintains significant laminar flow extents at near-cruise, off-design conditions. The design was performed, flight matrix selected, and instrumentation chosen with two primary test goals in mind: to confirm the effectiveness of the CATNLF method in attenuating crossflow growth on a representative transonic transport wing, and to investigate surface requirements needed for laminar flow applications.

Michelle N. Lynde↗
NASA NTRSDate not supplied

Exploring Multimodal Interactions in Human-Autonomy Teaming Using a Natural User Interface

The creation of a multimodal, natural user interface to facilitate multi-agent interaction is essential to establishing trust among human and machine teammates in multi-agent systems. Trust is being researched, along with trustworthiness, as a path to certification of autonomous systems by the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project at NASA. The Autonomous Mission Experimental Logistics Interactive Assistant (AMELIA) is a natural user interface that enables multimodal interaction and is designed for rapid mission planning. AMELIA is an intelligent system that considers the user’s preferred communication strategies, as well as the time-critical aspect of the multi-agent system decision-making process. Twenty-four participants planned a multi-agent search and rescue mission, with the aid of an intelligent assistant. The results show that while the combined use of touch and speech was faster than speech alone, the single modality, touch, was still the most efficient. Future research should investigate additional input technologies.

Lisa R Le Vie↗
NASA NTRSDate not supplied

Lidar-Polarimeter Retrieval OSSEs using a Nature Run in Support of NASA's Aerosols, Clouds, Convection and Precipitation (ACCP) Study

The 2017 Decadal Survey (DS) highlighted Earth System Science themes, science and application questions, and several high priority objectives that have led to the inclusion of Aerosols (A) and Clouds-Convection-Precipitation (CCP) as Designated Observables (DOs). On June 1, 2018, several NASA centers (GSFC, LaRC, JPL, MSFC, GRC and ARC) submitted a joint Study Plan to the NASA Earth Science Division for the Aerosol (A) and Cloud, Convection, and Precipitation (CCP) Pre-formulation Study (ACCP). The DS and the ACCP team recognized the science merit in combining the A and CCP DOs for both enhancing the ability to address a number of science objectives and also to provide an expanded capability to address additional objectives beyond those addressed by individual DOs. A critical element of the ACCP observing strategy is to make extensive use of new passive and active sensors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. Central to this observing system design is the adoption of a Value Framework in which quantitative assessment of the science benefits of space- and air-borne assets is a key element. Given pre-defined ACCP science objectives and geophysical variables with desired accuracies, ACCP relies on a spectrum of Observing System Simulation Experiments (OSSEs) aimed at addressing pixel level retrieval uncertainties and sampling trade-offs. In this talk we will discuss a subset of Retrieval OSSEs being considered for ACCP, namely, synergistic lidar-polarimeter retrievals based on observation simulations from the GEOS-5 Nature Run (G5NR). Starting with aerosol states from the GEOS-5 Nature Run (G5NR) sampled along specific satellite orbits, we simulate polarized radiances at the desired polarimeter wavelengths with the Vector Linearized Direct Ordinate Radiative Transfer (VLIDORT) model, alongside the lidar signal for the relevant lidars with realistic error characterization. Next, inversions are performed with the Generalized Retrieval of Aerosol and Surface Properties (GRASP) system and the accuracy of the retrieved geophysical variables are assessed. In this presentation we will highlight results for key architectures being considered for ACCP with emphasis on geophysical variables of particular relevance to ACCP’s aerosol science objectives.

A Da Silva↗
NASA NTRSDate not supplied

Exploring Multimodal Interactions in Human-Autonomy Teaming Using a Natural User Interface

The creation of a multimodal, natural user interface to facilitate multi-agent interaction is essential to establishing trust among human and machine teammates in multi-agent systems. Trust is being researched, along with trustworthiness, as a path to certification of autonomous systems by the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project at NASA. The Autonomous Mission Experimental Logistics Interactive Assistant (AMELIA) is a natural user interface that enables multimodal interaction and is designed for rapid mission planning. AMELIA is an intelligent system that considers the user’s preferred communication strategies, as well as the time-critical aspect of the multi-agent system decision-making process. Twenty-four participants planned a multi-agent search and rescue mission, with the aid of an intelligent assistant. The results show that while the combined use of touch and speech was faster than speech alone, the single modality, touch, was still the most efficient. Future research should investigate additional input technologies.

Lisa Renee Le Vie↗
NASA NTRS2020

Exploring the human-nature dynamics of Hunga Tonga Hunga Ha'apai, Earth's newest landmass

This paper examines the human-nature dynamics of volcanic eruptions through a multidisciplinary exploration of the recently-formed Hunga Tonga Hunga Ha‘apai (HTHH) landmass in the Kingdom of Tonga. HTHH was formed in early 2015 in the Ha‘apai island group in southwestern Tonga. This landmass has persisted longer than expected, providing a rare opportunity to examine pathways of erosion and biological colonization, and offering a glimpse into the cultural dynamics of a continuously changing Polynesian seascape. In 2018 and 2019, a collaborative partnership between the Kingdomof Tonga, Sea Education Association (SEA), and the United States National Aeronautics and Space Administration (NASA) ran expeditions to HTHH to calibrate satellite observations via field, ship, and drone-based measurements. In accessing HTHH via SEA's sailing school vessel (SSV) the Robert C. Seamans, a team of scientists and students combined fieldwork in anthropology, oceanography, and physical volcanology to study micro- and macro-scale dynamics of this classical surtseyan eruption site. In this pathfinding paper, we discuss the value of involving students in the process of real-time scientific discovery with on-the-fly adaptive hypothesis testing; this collaborative project provided mutual benefit to initiatives in science and education by offering robust data collection, while expanding environmental and cultural literacy. Additionally, we integrate collaborative research on HTHH with ethnographic research to understand how this ever changing “sea of islands” or intimately interconnected island spaces impact social-cultural relations within Oceania. We emphasize the importance of such multidisciplinary research in understanding the complexity of human-nature relations in a rapidly changing world.

Emily B. Hite↗
NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Documentation and Information Science↗
NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Documentation and Information Science↗
NASA NTRS2022

Integrating FRET with Copilot: Automated Translation of Natural Language Requirements to Runtime Monitors

Runtime verification (RV) enables monitoring systems at runtime, to detect property violations early and limit their potential consequences. To provide the level of assurance required for ultra-critical systems, monitor specifications must faithfully reflect the original mission requirements, which are often written in ambiguous natural language. This paper presents an end-to-end framework to capture requirements in structured natural language and generate monitors that capture their semantics faithfully. We leverage NASA’s Formal Requirement Elicitation Tool (FRET), and the RV system Copilot. We extend FRET with mechanisms to capture additional information needed to generate monitors, and introduce OGMA, a new tool to bridge the gap between FRET and Copilot. With this framework, users can write requirements in an intuitive format and obtain real-time C monitors suitable for use in embedded systems. Our tool chain is available as open source.

FRET↗
NASA NTRS2022

Groundwater Variability Across India, Under Contrasting Human and Natural Conditions

Characterizing local to regional scale water cycles and water resources will be crucial for achieving the United Nations' water-related Sustainable Developmental Goals. However, quantification and understanding of groundwater extraction across scales have been hampered by inadequate water usage reporting and limited information on irrigation practices. Here we analyze observations from ∼15,000 groundwater monitoring wells and the Gravity Recovery and Climate Experiment satellites together with irrigation, agricultural, and meteorological datasets to show how drought-induced coupling between natural and anthropogenic groundwater storage variations has caused sustainability challenges in India, the world's biggest consumer of groundwater for irrigation. Notably, the mechanisms and consequences of such coupling differ significantly depending on aquifer types. In Andhra Pradesh's hard rock aquifer, groundwater declines have been limited, despite the nearly constant water scarcity that its farmers face. Moreover, its free farm power policy involves an annual irrigation energy consumption of 26 billion kWh that costs US$ 2.5 billion, possibly unparalleled compared to any other part of the world of similar size (0.27 million km2). In West Bengal's highly permeable alluvial aquifer, the water table is declining rapidly (15 cm/yr) due to a policy that encourages irrigation. Situated between these two states, Odisha's aquifer shows substantial resilience to drought, owing to the state's relatively natural landscape and forest restoration policy. The findings of this study provide new insights to understand the divergent aspects of groundwater irrigation in north versus south India, which can enable development of adaptation and mitigation strategies to avert the looming water crisis.

Dileep K Panda↗
NASA NTRS2023

From Natural Language Requirements to the Verification of Programmable Logic Controllers: Integrating FRET into PLCverif

PLCverif is an actively developed project at CERN, enabling the formal verification of Programmable Logic Controller (PLC) programs in critical systems. In this paper, we present our work on improving the formal requirements specification experience in PLCverif through the use of natural language. To this end, we integrate NASA’s FRET, a formal requirement elicitation and authoring tool, into PLCverif. FRET is used to specify formal requirements in structured natural language, which automatically translates into temporal logic formulae. FRET’s output is then directly used by PLCverif for verification purposes. We discuss practical challenges that PLCverif users face when authoring requirements and the FRET features that help alleviate these problems. We present the new requirement formalization workflow and report our experience using it on two critical CERN case studies.

FRET↗