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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hidden Processes During Seasonal Isolation of a High-Altitude Watershed

Biogeochemical processes capable of altering global carbon systems occur frequently in Earth’s Critical Zone–the area spanning from vegetation canopy to saturated bedrock–yet many of these phenomena are difficult to detect. Observation of these processes is limited by the seasonal inaccessibility of remote ecosystems, such as those in mountainous, snow- and ice-dominated areas. This isolation leads to a distinct gap in biogeochemical knowledge that ultimately affects the accuracy and confidence with which these ecosystems can be computationally modeled for the purpose of projecting change under different climate scenarios. To examine a high-altitude, headwater ecosystem’s role in methanogenesis, sulfate reduction, and groundwater-surface water exchange, water samples were continuously collected from the river and hyporheic zones (HZ) during winter isolation in the East River (ER), CO watershed. Measurements of continuously collected ER surface water revealed up to 50 μM levels of dissolved methane in July through September, while samples from 12 cm deep in the hyporheic zone at the same location showed a spring to early summer peak in methane with a strong biogenic signature (<65 μM, δ13C-CH4, -60.76‰) before declining. Continuously collected δ 18 O-H 2 O and δ 2 H-H 2 O isotopes from the water column exhibited similar patterns to discrete measurements, while samples 12 cm deep in the hyporheic zone experienced distinct fluctuations in δ 18 O-H 2 O, alluding to significant groundwater interactions. Continuously collected microbial communities in the river in the late fall and early winter revealed diverse populations that reflect the taxonomic composition of ecologically similar river systems, including taxa indicative of methane cycling in this system. These measurements captured several biogeochemical components of the high-altitude watershed in response to seasonality, strengthening our understanding of these systems during the winter months.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Support for Reactor Operators in Case of Cyber-Security Threats (NEUP Final Report)

Increasing use of digital technology in nuclear power plants (NPPs) poses cyber-security as a crucial threat to public safety and to continuous energy production. Cyber-security risks are comprised of complex known and unknown interactions between various entities, system vulnerabilities, network protocols, human users and malicious attacks. There is little understanding or research geared towards plant operators’ response under cyber-security threats and operational procedures to cope with such threats. This is particularly critical when a cyber-security event masquerades as a safety incident or an evolving accident. Instead of leading operators to the remediation of the accident, the masked cyber event may lead them to circumvent the safety mechanisms of the plant. Given these challenges, the objective of this project is to develop a tool to characterize abnormal NPP events as “cyber” or “safety” incidents, and to further develop this tool as a real-time operator aid to assist in response to the incident. This NEUP Final Report summarizes the research efforts towards the above goals and the results in the following three aspects. First, a methodology for classifying abnormal NPP events as cyber-attacks or safety incidents was developed. This provides the basis for responding to potential cyber-attacks. In this methodology, the causal relationships between various variables (i.e., physical variables, network variables, and control variables) for a nuclear system under consideration are modeled using dynamic Bayesian networks. Based on observations of certain variables (i.e., the evidence), inference is performed to identify the state of hidden variables, which helps indicate the occurrence of cyber-attacks or safety incidents. The proposed methodology was applied to the full scope NPP simulator system at The Ohio State University as a case study. The result shows that the proposed methodology is capable of distinguishing between cyber-attacks and safety incidents. Second, a game-theoretic approach for responding to cyber-attacks on nuclear power plants was developed. This is the second major goal of the project, and the method is used to recommend optimal responses to reactor operators in the case of cyber-attacks. We consider two cases in this research. In the first case, the interaction between the defender (i.e., the reactor operator) and the attacker is modeled as a two-player, nonzero-sum, Markov game with an infinite time horizon. In the second case, we consider a semi-Markov game between the defender and the attacker within a finite time horizon. The model in the second case enables us to consider the time sensitive response of the defender. In each case, the method for deriving the Nash equilibrium is developed. The Nash Equilibrium of the game provides the valid prediction of both players’ actions because no single player can benefit from unilaterally deviating from the equilibrium policy if the other player adheres to his/hers, hence it provides the best response of the defender to cyber-attacks. The proposed method in each case is demonstrated through a simplified benchmark digital feedwater control system. Third, a prototype display is developed to integrate the above information. The prototype display depicts the communication information to the operator and incorporates the cyber-security tool developed by the team into the interface. It consists of the regular display in current control rooms, as well as a panel used to indicate the occurrence of cyber-attacks or safety events and a panel used to guide the operator’s response to detected cyber-attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks: Preprint

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classical state estimation algorithms. In this paper, a new method so-called pruned physics-aware neural network (P2N2) is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one-year smart meter data of load consumptions for threephase power flow is developed to generate the measurement and voltage state data. The IEEE 123 node system is selected as the test network to benchmark the proposed algorithm against the classical weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS, in terms of data redundancy and estimation accuracy.

distribution systems state estimation↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the pruned physics-aware neural network (P2N2), is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one- year smart meter data of load consumptions for three-phase power flow is developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS in terms of data redundancy and estimation accuracy.

distribution system state estimation↗
NASA NTRS1990

CLIPS: A tool for corn disease diagnostic system and an aid to neural network for automated knowledge acquisition

This paper describes the building of a corn disease diagnostic expert system using CLIPS, and the development of a neural expert system using the fact representation method of CLIPS for automated knowledge acquisition. The CLIPS corn expert system diagnoses 21 diseases from 52 symptoms and signs with certainty factors. CLIPS has several unique features. It allows the facts in rules to be broken down to object-attribute-value (OAV) triples, allows rule-grouping, and fires rules based on pattern-matching. These features combined with the chained inference engine result to a natural user query system and speedy execution. In order to develop a method for automated knowledge acquisition, an Artificial Neural Expert System (ANES) is developed by a direct mapping from the CLIPS system. The ANES corn expert system uses the same OAV triples in the CLIPS system for its facts. The LHS and RHS facts of the CLIPS rules are mapped into the input and output layers of the ANES, respectively; and the inference engine of the rules is imbedded in the hidden layer. The fact representation by OAC triples gives a natural grouping of the rules. These features allow the ANES system to automate rule-generation, and make it efficient to execute and easy to expand for a large and complex domain.

Wu, Cathy↗
NASA NTRS1991

Cognitive consequences of clumsy automation on high workload, high consequence human performance

The growth of computational power has fueled attempts to automate more of the human role in complex problem solving domains, especially those where system faults have high consequences and where periods of high workload may saturate the performance capacity of human operators. Examples of these domains include flightdecks, space stations, air traffic control, nuclear power operation, ground satellite control rooms, and surgical operating rooms. Automation efforts may have unanticipated effects on human performance, particularly if they increase the workload at peak workload times or change the practitioners' strategies for coping with workload. Smooth and effective changes in automation requires detailed understanding of the congnitive tasks confronting the user: it has been called user centered automation. The introduction of a new computerized technology in a group of hospital operating rooms used for heart surgery was observed. The study revealed how automation, especially 'clumsy automation', effects practitioner work patterns and suggest that clumsy automation constrains users in specific and significant ways. Users tailor both the new system and their tasks in order to accommodate the needs of process and production. The study of this tailoring may prove a powerful tool for exposing previously hidden patterns of user data processing, integration, and decision making which may, in turn, be useful in the design of more effective human-machine systems.

Cook, Richard I.↗
DOE OSTI2025

A Flux-Tunable cavity for Dark matter detection

Developing a dark matter detector with wide mass tunability is an immensely desirable property, yet it is challenging due to maintaining strong sensitivity. Resonant cavities for dark matter detection have traditionally employed mechanical tuning, moving parts around to change electromagnetic boundary conditions. However, these cavities have proven challenging to operate in sub-Kelvin cryogenic environments due to differential thermal contraction, low heat capacities, and low thermal conductivities. Instead, we develop an electronically tunable cavity architecture by coupling a superconducting 3D microwave cavity with a DC flux tunable SQUID. With a flux delivery system engineered to maintain high coherence in the cavity, we perform a hidden-photon dark matter search below the quantum-limited threshold. A microwave photon counting technique is employed through repeated quantum non-demolition measurements using a transmon qubit. With this device, we perform a hidden-photon search and constrain the kinetic mixing angle to ${\varepsilon}< 8.2\times 10^{-15}$ in a tunable band from 5.672 GHz to 5.694 GHz. By coupling multimode tunable cavities to the transmon, wider hidden-photon searching ranges are possible.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1974

Geophysical, geochemical, and geological investigations of the Dunes geothermal system, Imperial Valley, California

The Dunes anomaly is a water-dominated geothermal system in the alluvium of the Salton Trough, lacking any surface expression. It was discovered by shallow-temperature gradient measurements. A 612-meter-deep test well encountered several temperature-gradient reversals, with a maximum of 105 C at 114 meters. The program involves surface geophysics, including electrical, gravity, and seismic methods, down-hole geophysics and petrophysics of core samples, isotopic and chemical studies of water samples, and petrological and geochemical studies of the cores and cuttings. The aim is (1) to determine the source and temperature history of the brines, (2) to understand the interaction between the brines and rocks, and (3) to determine the areal extent, nature, origin, and history of the geothermal system. These studies are designed to provide better definition of exploration targets for hidden geothermal anomalies and to contribute to improved techniques of exploration and resource assessment.

Elders, W. A.↗
NASA NTRS1991

Aids to School Bus Design

Navistar International Transportation Corporation, Chicago, IL, used three separate NASA-developed technologies in the design and testing of their 3000 Series Bus Chassis which was developed expressly for school bus applications. For structural analysis, they used the MSC/NASTRAN program which mathematically analyzes a design and predicts how it will hold up under stress. They also used the SPATE 9000 system for non-contact measurement of stress, load transfer mechanisms, detection of hidden flaws, and monitoring structural changes during fatigue testing. SPATE 9000 was based on infrared stress measurement technology developed by Langley Research Center. They also employed the Wyle Ride Quality Meter, which was developed by Langley to aid in passenger aircraft design by providing an accurate measurement of ride vibration and sound level. These numbers translate into a subjective discomfort level index. These technologies contribute to the company's 45-48 percent share of the school bus chassis market.

Source record↗
NASA NTRS1993

Markov Monitoring with Unknown States

Pattern recognition methods and hidden Markov models can be effective tools for online health monitoring of communications systems. Previous work has assumed that the states in the system model are exhaustive. This can be a significant drawback in real-world fault monitoring applications where it is difficult if not impossible to model all the possible fault states of the system in advance.

Markov models↗
DOE OSTI2022

Testing Hidden Assumptions of Representativeness in Reach-Scale Studies of Hyporheic Exchange

Field studies of hyporheic exchange in mountain systems are often conducted using short study reaches and a limited number of observations. It is common practice to assume these study reaches represent hyporheic exchange at larger scales or different sites and to infer general relationships among potential causal mechanisms from the limited number of observations. However, these assumptions of representativeness are rarely tested. In this study, we develop numerical models from four segments of mountain streams in different geomorphologic settings and extract shorter reaches to test how representative exchange metrics are in shorter reaches compared to their reference segments. We also map the locations of the representative reaches to determine if a pattern exists based on location. Finally, we compare variance of these shorter within-site reaches to 29 additional reaches across the same basin to understand the impacts of inferring causal mechanisms, for example, the expectation that wide and narrow valley bottoms will yield different hyporheic exchange patterns. Our results show that the location and length strategy of the study reach must be considered before assuming an exchange metric to be representative of anything other than the exact segment studied. Further, it is necessary to quantify within and between site variations before making causal inferences based on observable characteristics, such as valley width or stream morphology. Our findings have implications for future field practices and how those practices are translated into models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Detecting Volcanic Co2 Emissions Hidden in Tropical Volcanic Forests Using Fixed-Wing sUAS

CO2 emissions are among the earliest indicators of subsurface state change and reactivation of volcanic systems and these deep signals reflect in faint diffuse emissions variations on volcanic flanks. Monitoring vast areas of rainforests covering tropical volcanoes is challenging, and space-borne sensors like OCO-2 are not sensitive enough. Fortunately, trees exposed to mild enhancements of CO2 may experience fertilization and build excess CO2 into excess biomass (Cawse-Nicholson, et al. 2018, Biogeosciences). They also process excess CO2 through photosynthesis, measurable by fluorescence (Bogue et al., 2019, Biogeosciences). Furthermore, spaceborne remote sensing instruments have demonstrated increasing NDVI months to years before eruptions, unexplained by other factors (e.g., Houlie et al. 2006, EPSL; Seiler et al. 2017, PLoS One). By providing a means to map large areas of above-canopy CO2 variations over tropical rainforest on the flanks of actively degassing volcanoes, we enable to interpret the signals plants provide in response to excess CO2 with airborne and spaceborne hyperspectral remote sensing methods, including a possible future Surface Biology and Geology (SBG) NASA satellite mission.As a critical step to mature and test this concept, we deployed small unmanned aerial systems (sUAS) using a fixed-wing sUAS designed for autonomous operations and long endurance in extreme environments, without causing significant disturbance of the sampled air. We integrated an in-situ sensor to record the CO2 enhancement field above the forest canopy. Test flight results on the flanks of Turrialba volcano in Costa Rica above forest canopies covering known moderate gas seeps demonstrate the sUAS-borne detection and mapping capabilities of above-canopy elevated CO2 gradients. The strong detection capabilities and high detector signal stability resulted from key system design elements including RF shielding, mechanical stabilization, and calibration procedures. This highly robust system is readily applied for diffuse volcanic CO2 emission studies on active volcanoes covered by dense vegetation.

Volcanic↗
DOE OSTI2024

Ultrafast formation of topological defects in a two-dimensional charge density wave

Topological defects play a central role in dynamical systems undergoing a non-adiabatic transition. In solids, topological defects as a result of femtosecond laser excitation have attracted increasing interest not only because they are key to understanding phase transitions but also because they can generate a variety of hidden orders that are not accessible in thermal equilibrium. Despite the common occurrence of these defects in a non-equilibrium system, the fundamental limit on how fast they can emerge in solids and the generic pathway for defect creation at such fast timescales have remained open questions. Here we apply ultrafast electron diffraction to study the reciprocal-space signatures of transient defects in a two-dimensional charge density wave, where simultaneous measurements of both defect and phonon dynamics yield a microscopic view of defect formation in the femtosecond regime. We find that one-dimensional domain walls are generated well within 1 ps following photoexcitation, during which the defect growth is not dictated by the amplitude of the order parameter but is mediated by a non-thermal population of longitudinal optical phonons. In conclusion, our work provides a framework for the ultrafast engineering of topological defects that are coupled to specific collective modes, which will prove useful for the dynamical control of non-equilibrium phases in correlated materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Targeted assemblies of cas1 suggest CRISPR-Cas’s response to soil warming

Abstract There is an increasing interest in the clustered regularly interspaced short palindromic repeats CRISPR-associated protein (CRISPR-Cas) system to reveal potential virus–host dynamics. The universal and most conserved Cas protein, cas1 is an ideal marker to elucidate CRISPR-Cas ecology. We constructed eight Hidden Markov Models (HMMs) and assembled cas1 directly from metagenomes by a targeted-gene assembler, Xander, to improve detection capacity and resolve the diverse CRISPR-Cas systems. The eight HMMs were first validated by recovering all 17 cas1 subtypes from the simulated metagenome generated from 91 prokaryotic genomes across 11 phyla. We challenged the targeted method with 48 metagenomes from a tallgrass prairie in Central Oklahoma recovering 3394 cas1. Among those, 88 were near full length, 5 times more than in de-novo assemblies from the Oklahoma metagenomes. To validate the host assignment by cas1, the targeted-assembled cas1 was mapped to the de-novo assembled contigs. All the phylum assignments of those mapped contigs were assigned independent of CRISPR-Cas genes on the same contigs and consistent with the host taxonomies predicted by the mapped cas1. We then investigated whether 8 years of soil warming altered cas1 prevalence within the communities. A shift in microbial abundances was observed during the year with the biggest temperature differential (mean 4.16 °C above ambient). cas1 prevalence increased and even in the phyla with decreased microbial abundances over the next 3 years, suggesting increasing virus–host interactions in response to soil warming. This targeted method provides an alternative means to effectively mine cas1 from metagenomes and uncover the host communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AmeriFlux US-MZA Hidden Marsh, Montezuma NWR

This is the AmeriFlux version of the carbon flux data for the site US-MZA Hidden Marsh, Montezuma NWR. Site Description - This is a restored (from Ag) emergent marsh system located in the Montezuma National Wildlife Refuge. It is dominated by cattail most of the time, but experiences periodic, managed drying that produces other drier wetland vegetation types. In 2024, it was inundated and had abundant Typha and Phragmites.

Morin, Tim [SUNY ESF]↗
DOE OSTI2024

Hidden orders and phase transitions for the fully packed quantum loop model on the triangular lattice

Abstract Quantum loop and dimer models are prototypical correlated systems with local constraints, which are not only intimately connected to lattice gauge theories and topological orders but are also widely applicable to the broad research areas of quantum materials and quantum simulation. Employing our sweeping cluster quantum Monte Carlo algorithm, we reveal the complete phase diagram of the triangular-lattice fully packed quantum loop model. Apart from the known lattice nematic (LN) solid and the even$${{\mathbb{Z}}}_{2}$$ Z 2 quantum spin liquid (QSL) phases, we discover a hidden vison plaquette (VP) phase, which had been overlooked and misinterpreted as a QSL for more than a decade. Moreover, the VP-to-QSL continuous transition belongs to the (2 + 1)D cubic * universality class, which offers a lattice realization of the (fractionalized) cubic fixed point that had long been considered as irrelevant towards the O(3) symmetry until corrected recently by conformal bootstrap calculations. Our results are therefore of relevance to recent developments in both experiments and theory, and facilitate further investigations of hidden phases and transitions.

Physics↗
DOE OSTI2020

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗