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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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Estimating the Reliability of Electronic Parts in High Radiation Fields

Radiation effects on materials and electronic parts constrain the lifetime of flight systems visiting Europa. Understanding mission lifetime limits is critical to the design and planning of such a mission. Therefore, the operational aspects of radiation dose are a mission success issue. To predict and manage mission lifetime in a high radiation environment, system engineers need capable tools to trade radiation design choices against system design and reliability, and science achievements. Conventional tools and approaches provided past missions with conservative designs without the ability to predict their lifetime beyond the baseline mission.This paper describes a more systematic approach to understanding spacecraft design margin, allowing better prediction of spacecraft lifetime. This is possible because of newly available electronic parts radiation effects statistics and an enhanced spacecraft system reliability methodology. This new approach can be used in conjunction with traditional approaches for mission design. This paper describes the fundamentals of the new methodology.

radiation↗
DOE OSTI2020

Impact analysis of personalized thermostat demand response

Demand response (DR) aims to curtail peak electric load to avoid running inefficient power plants, reduce costly transmission capacity increases, and improve grid reliability. One method of DR for residential settings involves the utility or third part remotely adjusting thermostat temperatures. Cooling setpoints may be increased 2°F – 6°F for hours, irrespective of occupant comfort. To maintain their comfort, occupants override these setpoint changes impacting the reliability of DR services provided to the grid. This paper presents a preliminary impact analysis of replacing the current DR approach of uniform thermostat setbacks for an entire population with personalized and context aware DR based on a dynamic model of occupant thermal comfort behavior. Over 151k interactions with ecobee thermostats are analyzed to predict the impact of such personalized DR on overrides, energy curtailment magnitude, and reliability.

Kane, Michael↗
DOE OSTI2020

Predicting the chemical reactivity of organic materials using a machine-learning approach

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database, i.e. data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium–oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium–oxygen batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2015

Accelerated Test Method for Corrosion Protective Coatings Project

This project seeks to develop a new accelerated corrosion test method that predicts the long-term corrosion protection performance of spaceport structure coatings as accurately and reliably as current long-term atmospheric exposure tests. This new accelerated test method will shorten the time needed to evaluate the corrosion protection performance of coatings for NASA's critical ground support structures. Lifetime prediction for spaceport structure coatings has a 5-year qualification cycle using atmospheric exposure. Current accelerated corrosion tests often provide false positives and negatives for coating performance, do not correlate to atmospheric corrosion exposure results, and do not correlate with atmospheric exposure timescales for lifetime prediction.

Technology Portfolio System↗
DOE OSTI2023

Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations

There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability and vertical variations in microphysical processes in low-level warm clouds. In this study, the warm-cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary layer cloud structure, major macrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft-observed ones in the summer but are overestimated by a factor of 2 in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations in droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of subgrid variations in cloud properties in CAM6 cloud microphysics, which further suppresses the scheme's sensitivity to individual warm-rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces an aerosol indirect effect that is too strong. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Tenderness Tester

Space telemetry has been transferred to food processing in the Armour;Tenderorneter, an instrument that predicts the tenderness of meat. The space component of the instrument is a sensitive, highly reliable strain gage originally produced for NASA's Surveyor lunar lander and other space programs by BLH Electronics, Waltham, Mass. Several years ago Armour & Co. began to develop a method of testing a hanging carcass to predict how tender the meat would be after cooking; no such method then existed. After considerable experimentation, Armour came up with a manifold-mounted group of-needle-like probes, which when stuck into a carcass, could measure the degree to which the meat resisted penetration. This provided a basis for predicting tenderness, but the development required one more . step; a device that could translate meat resistance into an electrical readout. Armour found it in the BLH strain gage. The resulting Tenderometer, now a standard and important part of Armour's meat processing operation, includes a large, 10-pronged fork which is plunged into a carcass and a cable-connected, handheld electronic device that translates the sensings of the prongs into a tenderness reading on a dial. The instrument is used by Armour to select and guarantee a premium line of beef known as TesTender, whose annual sales run into tens of millions of pounds.

Source record↗
DOE OSTI2017

A Theoretical Investigation of the Chemical Behaviour of Irradiated U-Mo/Mg Fuel

A significant international effort has been put forth to replace Highly Enriched Uranium (HEU) fuel used in research and test reactors with Low Enriched Uranium (LEU) fuel to reduce proliferation concerns. Furthermore, some fuel processing issues have been encountered with some LEU candidate fuel designs. In order to address both of the foregoing issues, new fuel designs must be developed. One of these designs has U-Mo particles dispersed in a Mg matrix (U-Mo/Mg) that is cladded in an Al-alloy. While characterization analyses and thermophysical measurements have been reported for fresh U-Mo/Mg fuel, the effects of irradiation are not yet well known nor are they understood. Given that earlier international campaigns that investigated U-Mo/Al as a candidate fuel design resulted in consistent failures due to the formation of an interaction layer between the U-Mo fuel particles and Al meat, it seems prudent to theorize and predict any phases that may form as a result of irradiation of U-Mo/Mg fuel. The formation of these phases and any interaction that they may have with the cladding may potentially affect the reliability of this fuel design. This short paper provides a preliminary theoretical framework for the prediction of minor phases that may appear in irradiated U-Mo/Mg research reactor fuel, which should be confirmed via postirradiation examination at a later time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An algorithmic approach to predicting mechanical draft cooling tower fan speeds from infrasound signals

Mechanical draft cooling towers (MDCTs) serve a critical heat management role in a variety of industries. For nuclear reactors in particular, the consistent, predictable operation of MDCTs is required to avoid damage to infrastructure and reduce the potential for catastrophic failure. Accurate, reliable measurement of MDCT fan speed is therefore an important maintenance and safety requirement. To that end, we have developed an algorithm for automatically predicting the rotational speeds of multiple, simultaneously operating fan rotors using contactless, infrasound measurements. The algorithm is based on identifying the blade passing frequencies (BPFs), their harmonics, as well as the motor frequencies (MFs) for each fan in operation. Using the algorithm, these frequencies can be automatically identified in the acoustic waveform’s short-time Fourier transform spectrogram. Attribution is aided by a set of filters that rely on the unique spectral and temporal characteristics of fan operation, as well as the intrinsic frequency ratios of the BPF harmonics and the BPF/MF signals. The algorithm was tested against infrasound data acquired from infrasound sensors deployed at two research reactors: the Advanced Test Reactor (ATR) located at Idaho National Laboratory (INL) and the High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL). After manually identifying the MDCT gearbox ratio, the algorithm was able to quickly yield fan speeds at both reactors in good agreement with ground truth. Ultimately, this work demonstrates the ease by which MDCT fans may be monitored in order to optimize operational conditions and avoid infrastructure damage.

42 ENGINEERING↗
DOE OSTI2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg 12 Al 8 shows excellent ductility and Mg 5 Al 27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

A Numerical Tool for the Design of Enhanced Geothermal Reservoirs based on Modeling of Thermo-Hydro-Mechanical Processes and Explicit Representation of 3D Discrete Fracture Networks

This project has developed a coupled thermo-hydro-mechanical numerical modeling tool for prediction of energy production from Enhanced Geothermal Reservoirs (EGS). The tool is unique because it can explicitly represent thousands of pre-existing natural fractures. The capabilities developed in this project were built on the already existing expertise of Itasca arising from thirty years of research and development in discrete element modeling of fractured rocks. A thermal module suitable for analysis of different heat transfer mechanisms in fractured rocks percolated by fluids was developed and coupled with the already existing hydro-mechanical calculation in 3DEC, a commercial software for analysis of jointed rock based on the discrete element method (DEM). The heat transfer module simulates heat conduction, heat advection by moving fluid in rock fractures, and convective heat exchange at the fracture walls between the rock and the moving fluid. Techniques for spatial discretization of heat transfer in EGS-type problems were evaluated, and a boundary layer meshing approach was developed. Also, given the significant differences in time scale of physical processes involved, temporal integration schemes for coupled advection-diffusion processes and coupled thermal-hydro-mechanical processes were developed by taking advantage of both implicit and explicit time integration and also optimizing the coupling intervals. These improvements were necessary for modeling EGS reservoirs. Also, the DFN capacities in 3DEC were extended to include a larger number of fractures. Research was carried out on approaches to simplify the DFN so that the model can meet computational demands, while preserving thermo-hydro-mechanical characteristics of the DFN. The developed modules were coupled with the already existing capabilities of 3DEC. The results were presented as a submitted conference paper (Riahi et al., 2018a). A series of benchmark and test examples showed the developed capabilities. Finally, the developed tool was tested for a large-scale reservoir model for longer periods of injection and production. The thermal draw-down curves were generated and presented. The project successfully met all the milestones laid out in the proposal. Results demonstrated the feasibility of developing a fast and reliable software for the EGS community to use for assessment and prediction of reservoir performance. Some of its applications include assessment of stimulated volume and produced temperature and power. It can also be used for improved engineering, design and optimization of EGS reservoirs as well as other unconventional resources such as shale gas. Finally, the 3D analysis presented in this study showed that simulating EGS reservoirs with tens of thousands of fractures is feasible, and confirmed that the developed tool can serve toward meeting the goals and objectives of Geothermal Technologies Office in overcoming technical barriers associated with commercial EGS development.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling

We introduce a novel semi-supervised learning approach, named Teacher-Student Bayesian Optimization (TSBO ), integrating the teacher-student paradigm into BO to minimize expensive labeled data queries for the first time. TSBO incorporates a teacher model, an unlabeled data sampler, and a student model. The student is trained on unlabeled data locations generated by the sampler, with pseudo labels predicted by the teacher. The interplay between these three components implements a unique selective regularization to the teacher in the form of student feedback. This scheme enables the teacher to predict high-quality pseudo labels, enhancing the generalization of the GP surrogate model in the search space. To fully exploit TSBO , we propose two optimized unlabeled data samplers to construct effective student feedback that well aligns with the objective of Bayesian optimization. Furthermore, we quantify and leverage the uncertainty of the teacher-student model for the provision of reliable feedback to the teacher in the presence of risky pseudo-label predictions. TSBO demonstrates significantly improved sample-efficiency in several global optimization tasks under tight labeled data budgets. The implementation is available at https://github.com/reminiscenty/TSBO-Official.

Yin, Yuxuan↗
NASA NTRS2013

Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers

Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.

prognosis↗
DOE OSTI2025

An Alternative Ensemble Streamflow Prediction Approach Using Improved Subseasonal Precipitation Forecasts from the North America Multi-Model Ensemble Phase II

In this article, streamflow forecasting at a subseasonal time scale (10–30 days into the future) is important for various human activities. The ensemble streamflow prediction (ESP) is a widely applied technique for subseasonal streamflow forecasting. However, ESP’s reliance on the randomly resampled historical precipitation limits its predictive capability. Available dynamical subseasonal precipitation forecasts provide an alternative to the randomly resampled precipitation in ESP. Prior studies found the predictive performance of raw subseasonal precipitation forecast is limited in many regions such as the central south of the United States, which raises questions about its effectiveness in assisting streamflow forecasting. To further assess the hydrologic applicability of dynamical subseasonal precipitation forecasts, we test the subseasonal precipitation forecast from North America Multi-Model Ensemble Phase II (NMME-2) at four watersheds in the central south region of the United States. The subseasonal precipitation forecasts are postprocessed with bias correction and spatial disaggregation (BCSD) to correct bias and improve spatial resolution before replacing the randomly resampled precipitation in ESP for streamflow predictions. The performance of the resulting streamflow predictions is benchmarked with ESP. Evaluation is conducted using Kling–Gupta Efficiency (KGE), continuous ranked probability score (CRPS), probability of detection (POD), false alarm ratios (FARs), as well as reliability diagrams. Our results suggest that BCSD-corrected subseasonal precipitation forecasts lead to overall improved streamflow predictions due to added skills in winter and spring. Our results also suggest that BCSD-corrected subseasonal precipitation forecasts lead to improved predictions on the occurrence of high-percentile streamflow values above 75%. Overall, BCSD-corrected subseasonal precipitation has shown promising performance, highlighting its potential broader applications for river and flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Selective Sampling for Sensor Type Classification in Buildings

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2010

Using Facility Condition Assessments to Identify Actions Related to Infrastructure

To support cost effective, quality research it is essential that laboratory and testing facilities are maintained in a continuous and reliable state of availability at all times. NASA Langley Research Center (LaRC) and its maintenance contractor, Jacobs Technology, Inc. Research Operations, Maintenance, and Engineering (ROME) group, are in the process of implementing a combined Facility Condition Assessment (FCA) and Reliability Centered Maintenance (RCM) program to improve asset management and overall reliability of testing equipment in facilities such as wind tunnels. Specific areas are being identified for improvement, the deferred maintenance cost is being estimated, and priority is being assigned against facilities where conditions have been allowed to deteriorate. This assessment serves to assist in determining where to commit available funds on the Center. RCM methodologies are being reviewed and enhanced to assure that appropriate preventive, predictive, and facilities/equipment acceptance techniques are incorporated to prolong lifecycle availability and assure reliability at minimum cost. The results from the program have been favorable, better enabling LaRC to manage assets prudently.

Rubert, Kennedy F.↗
NASA NTRS1983

A general computer model for predicting the performance of gas sorption refrigerators

Projected performance requirements for cryogenic spacecraft sensor cooling systems which demand higher reliability and longer lifetimes are outlined. The gas/solid sorption refrigerator is viewed as a potential solution to cryogenic cooling needs. A software model of an entire gas sorption refrigerator system was developed. The numerical model, evaluates almost any combination and order of refrigerator components and any sorbent-sorbate pair or which the sorption isotherm data are available. Parametric curves for predicting system performance were generated for two types of refrigerators, a LaNi5-H2 absorption cooler and a Charcoal-N2 adsorption cooler. It is found that precooling temperature and heat exchanger effectiveness affect the refrigerator performance. It is indicated that gas sorption refrigerators are feasible for a number of space applications.

Sigurdson, K. B.↗
DOE OSTI2019

Stars that Move Together Were Born Together

It is challenging to reliably identify stars that were born together outside of actively star-forming regions and bound stellar systems. However, conatal stars should be present throughout the Galaxy, and their demographics can shed light on the clustered nature of star formation and the dynamical state of the disk. In previous work we presented a set of simulations of the Galactic disk that followed the clustered formation and dynamical evolution of 4 billion individual stars over the last 5 Gyr. The simulations predict that a high fraction of comoving stars with physical and 3D velocity separation of Δr < 20 pc and Δv < 1.5 km s -1 are conatal. In this Letter, we use Gaia DR2 and LAMOST DR4 data to identify and study comoving pairs. We find that the distribution of relative velocities and separations of pairs in the data is in good agreement with the predictions from the simulation. We identify 111 comoving pairs in the solar neighborhood with reliable astrometric and spectroscopic measurements. These pairs show a strong preference for having similar metallicities when compared to random field pairs. We therefore conclude that these pairs were very likely born together. The simulations predict that conatal pairs are born in clusters that follow the overall cluster mass function and in relatively young (<1 Gyr) star clusters. Gaia will eventually deliver well-determined metallicities for the brightest stars, enabling the identification of thousands of conatal pairs due to disrupting star clusters in the solar neighborhood.

79 ASTRONOMY AND ASTROPHYSICS↗

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