Search NASA⌕ Search

SEARCH · Search NASA

Results for “reliability data”

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.

At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Near-real-time environmental monitoring and large-volume data collection over slow communication links

Climate change studies are one of the most important aspects of modern science and related experiments are getting bigger and more complex. One such experiment is the Spruce and Peatland Responses Under Changing Environments conducted in northern Minnesota. The SPRUCE experimental mission is to assess ecosystem-level biological responses of vulnerable, high-carbon terrestrial ecosystems to a range of climate warming manipulations and an elevated CO 2 atmosphere. This manipulation experiment generates a lot of observational data and requires a reliable on-site data collection system, dependable methods to transfer data to a robust scientific facility, and real-time monitoring capabilities. Furthermore this publication shares our experience of establishing a near-real-time data collection and monitoring system via a satellite link using the not very well-known possibilities of PakBus protocol.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce. In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-based integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM's parameterized representation of total runoff. Results show that (1) ELM's total runoff was the same order of magnitude as the ATS simulations, and both models were similarly variable over time; (2) minor adjustments to coefficients in ELM's runoff parameterization improved the match between the ATS simulation and ELM's parameterized representation of annual and seasonal total runoff; (3) overall, runoff responses in ATS and ELM are more similar in flat hillslope environments compared to steep hillslopes; and (4) shallower active layer thicknesses and higher precipitation simulations resulted in lower correlations between the two models due to greater total runoff. By incorporating the optimized runoff coefficients from the Sagavanirktok River basin into ELM, the simulated total runoff better matched the streamflow observations at a small watershed located on the Seward Peninsula of Alaska. Our findings revealed important insights into the effectiveness of runoff parameterizations in land surface models and pathways for improving runoff coefficients in typical Arctic regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

The IAEA Coordinated Research Program on HTGR Reactor Physics, Thermal-hydraulics and Depletion Uncertainty Analysis: Description of the Benchmark Test Cases and Phases

The continued development of High Temperature Gas Cooled Reactors (HTGRs) requires verification of design and safety features with reliable high fidelity physics models and robust, efficient, and accurate codes. The uncertainties in the HTR analysis tools are today typically assessed with sensitivity analysis and then a few important input uncertainties (typically based on a PIRT process) are varied in the analysis to find a spread in the parameter of importance. However, one wish to apply a more fundamental approach to determine the predictive capability and accuracies of coupled neutronics/thermal-hydraulics and depletion simulations used for reactor design and safety assessment. Today there is a broader acceptance of the use of uncertainty analysis even in safety studies and it has been accepted by regulators in some cases to replace the traditional conservative analysis. Finally, there is also a renewed focus in supplying reliable covariance data (nuclear data uncertainties) that can then be used in uncertainty methods. Uncertainty and sensitivity studies are therefore becoming an essential component of any significant effort in data and simulation improvement. In order to address uncertainty in analysis and methods in the HTGR community the IAEA launched a Coordinated Research Project (CRP) on the HTGR Uncertainty Analysis in Modelling early in 2012. The project is built on the experience of the OECD/NEA Light Water Reactor (LWR) Uncertainty Analysis in Best-Estimate Modelling (UAM) benchmark activity, but focuses specifically on the peculiarities of HTGR designs and its simulation requirements. Two benchmark problems were defined with the prismatic type design represented by the MHTGR-350 design from General Atomics (GA) while a 250 MW modular pebble bed design, similar to the INET (China) and indirect-cycle PBMR (South Africa) designs are also included. In the paper more detail on the benchmark cases, the different specific phases and tasks and the latest status and plans are presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2011

Performance Characterization for Fusion Co-design Applications

Magnetic fusion is a long-term solution for producing electrical power for the world, and the large thermonuclear international device (ITER) being constructed will produce net energy and a path to fusion energy provided the computer modeling is accurate. To effectively address the requirements of the high-end fusion simulation community, application developers, algorithm designers, and hardware architects must have reliable simulation data gathered at scale for scientifically valid configurations. This paper presents detailed benchmarking results for a set of magnetic fusion applications with a wide variety of underlying mathematical models including both particle-in-cell and Eulerian codes using both implicit and explicit numerical solvers. Our evaluation on a petascale Cray XE6 platform focuses on profiling these simulations at scale identifying critical performance characteristics, including scalability, memory/network bandwidth limitations, and communication overhead. Overall results are a key in improving fusion code design, and are a critical first step towards exascale hardware-software co-design — a process that tightly couples applications, algorithms, imple- mentation, and computer architecture.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Three Modeling Tools Accelerate the Development of Marine Energy Technologies

Enough power flows through U.S. rivers and oceans to meet up to 60% of the country's electricity needs. But before today's early-stage marine energy technologies can harness some of that power, they need to overcome a few challenges, like a volatile ocean and high development costs. And to do that, technology developers need reliable tools to understand and overcome the challenges they face. That's why researchers at the National Renewable Energy Laboratory (NREL) have developed several open-source, validated, and customizable numerical tools for technology developers. With these tools, the marine energy community can assess their devices' potential power output, predict how their technologies might handle extreme loads (like extreme waves), and learn how changing one component might affect their device's performance. The lab's models generate highly accurate, reliable, robust data that can help marine energy devices leap from the computer screen to the ocean and, eventually, the power grid.

current energy↗
DOE OSTI2011

Towards a Scalable and Reliable Real Time In-Network Data Analysis Infrastructure

The smart grid applications requires real time analysis, response within the order of milliseconds and high-reliability because of the mission critical structure of the power grid system. The only way to satisfy these requirements is in network data analysis and build-in redundancy routing for failures. To achieve this, we propose a data dissemination system that builds routes using network flow algorithms, have in network processing of the data and utilize data encoding to cope with high latencies.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Reliable noise measure in time-gated NMR data

Time gating is a commonly used approach in the pre-processing of nuclear magnetic resonance data before inversion. Gating suppresses spurious signals that can degrade recovered decay time distributions and therefore often stabilizes inversion. However, care must be taken in applying this technique when assumptions of uncorrelated Gaussianity break down and reduce stacking efficiency. If not properly accounted for, unreliable noise estimates introduce inversion artefacts through over- or underfitting of the data. Block-bootstrap resampling of noise realization proxies obtained through data phasing can be used to generate reliable noise estimates for the windows. Benefits of the approach are demonstrated through inversion of synthetic and borehole data. Finally, analysis confirms that bootstrapped noise metrics are more reliable under variable noise conditions and result in more stable inversion results.

58 GEOSCIENCES↗
DOE OSTI2024

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. Further, there are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

CIRFT Data Update and Data Analyses for Spent Nuclear Fuel Vibration Reliability Study

The objective of this research is to collect experimental data on spent nuclear fuel (SNF) from pressurized water reactors (PWRs), including the H. B. Robinson Nuclear Power Station (HBR), Catawba Nuclear Station, North Anna Nuclear Power Station (NA), and the Limerick Nuclear Power Station (LMK) boiling water reactor (BWR).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

CIRFT Data Update and Data Analyses for Spent Nuclear Fuel Vibration Reliability Study

The objective of this research is to collect experimental data on spent nuclear fuel (SNF) from pressurized water reactors (PWRs), including the H. B. Robinson Nuclear Power Station (HBR), Catawba Nuclear Station, North Anna Nuclear Power Station (NA), and the Limerick Nuclear Power Station (LMK) boiling water reactor (BWR). Data will be collected under simulated transportation environments using the cyclic integrated reversible-bending fatigue tester (CIRFT), an enabling hot-cell testing technology developed at Oak Ridge National Laboratory (ORNL). These data will be used to support ongoing SNF modeling activities and to address regulatory issues associated with SNF transport.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Gaining insight into the $T\,_2^*$– T 2 relationship in surface NMR free-induction decay measurements

One of the primary shortcomings of the surface nuclear magnetic resonance (NMR) freeinduction decay (FID) measurement is the uncertainty surrounding which mechanism controls the signal’s time dependence. Ideally, the FID-estimated relaxation time T$*\atop{2}$ that describes the signal’s decay carries an intimate link to the geometry of the pore space. In this limit the parameter T$*\atop{2}$ is closely linked to a related parameter T 2 , which is more closely linked to pore-geometry. If T$*\atop{2}$ T 2 the FID can provide valuable insight into relative pore-size and can be used to make quantitative permeability estimates. However, given only FID measurements it is difficult to determine whether T$*\atop{2}$ is linked to pore geometry or whether it has been strongly influenced by background magnetic field inhomogeneity. If the link between an observed T$*\atop{2}$ and the underlying T 2 could be further constrained the utility of the standard surface NMR FID measurement would be greatly improved. We hypothesize that an approach employing an updated surface NMR forward model that solves the full Bloch equations with appropriately weighted relaxation terms can be used to help constrain the T$*\atop{2}$ –T 2 relationship. Weighting the relaxation terms requires estimating the poorly constrained parameters T 2 and T 1 ; to deal with this uncertainty we propose to conduct a parameter search involving multiple inversions that employ a suite of forward models each describing a distinct but plausible T$*\atop{2}$ –T 2 relationship. We hypothesize that forward models given poor T 2 estimates will produce poor data fits when using the complex-inversion, while forward models given reliable T 2 estimates will produce satisfactory data fits. By examining the data fits produced by the suite of plausible forward models, the likely T$*\atop{2}$ –T 2 can be constrained by identifying the range of T 2 estimates that produce reliable data fits. Synthetic and field results are presented to investigate the feasibility of the proposed technique.

58 GEOSCIENCES↗
DOE OSTI2021

Inter-well connectivity detection in CO 2 WAG projects using statistical recurrent unit models

Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO 2 EOR projects utilizing the water-alternating-gas (WAG) process. SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure. The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. This study results in a reliable and efficient data analytics framework that is well-suited for large field applications.

42 ENGINEERING↗
DOE OSTI2023

Predictive Analytics for Hydropower Fleet Intelligence

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

Yucesan, Yigit↗
DOE OSTI2016

Decommissioning costs - some international perspectives and initiatives. Presentation for Panel discussion. Session No. 024: 'Getting a Handle on Decommissioning Costs - a Global Perspective'

Financing for decommissioning: Are the cost estimates valid? Are the financing arrangements suitable? Are the fund management systems appropriate? Cost estimates are estimated, analysed and updated on a regular basis, but cost comparisons are not straight-forward. There is an incomplete understanding of variability between estimates. Multiple internal and external factors impact estimates: Institutional, legal and policy, purpose, Strategies and plans, overall context, Facility characteristics and history Contingencies and risk, Limited actual cost data. The financing arrangements: there exists a range of systems in place for the implementation of Funding Policy and Strategy, but there are Considerable variations in: Calculating funding requirements, contributions/provisions, Allocating risks and requirements for securities Managing funds and accounting, adjusting the system, Managing disbursements, Reporting and supervision. All cost estimates for future decommissioning activities include considerable uncertainties. These uncertainties will gradually diminish with growing experience from current and planned projects. Different costing methods have different data requirements. Their reliability depends on the extent to which various data are available and applicable to the specific case being considered. Independent of the assessment method, some uncertainty is inevitable in all estimates of future costs. Analysis of the costing method may be useful in order to locate the key uncertainties in each specific estimate. NEA's Decommissioning Cost Estimation Group (DCEG) was Created in 2008 to foster the exchange of information and experience. It Aims to enhance the credibility, reliability, and auditability of the cost estimation process. It Helps define international good practices in decommissioning cost estimation. However, some major challenges remain. Weaknesses in: Understanding relations between estimates and actual costs, Lack of relevant cost data, sharing data. How might be addressed: Improved sharing of cost data, Systematic analyses of estimates and actual costs, Developing benchmarking approaches.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

The influence of cloud cover on the reliability of satellite-based solar resource data

Satellite-based solar resource data are often developed and validated by using binary cloudiness categories: clear sky or overcast cloudy sky. To investigate the reliability of solar resource data in partially cloudy conditions, we estimate cloud fraction using two distinct algorithms: a physical retrieval model using surface observed global horizontal irradiance (GHI) and direct normal irradiance (DNI) and a temporal average of cloud mask data estimated by the observed DNI. Our analysis reveals a significant presence of scattered clouds, broken clouds, and mismatches between satellite- and surface-based cloud data at 17 surface sites across the contiguous United States, though confidently clear and cloudy conditions collectively account for more than 70 % of the data. Solar radiation is computed using the National Solar Radiation Database (NSRDB) algorithm and validated using surface observations. Here, our findings suggest that, in the presence of scattered clouds, NSRDB data for clear-sky conditions can be subject to significant overestimation. In cloudy-sky conditions classified by satellite data, DNI computed by the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) can be underestimated when limited clouds are detected by surface observations. The bias observed in several cloudiness categories indicates that the NSRDB is exceptionally accurate in confidently clear conditions. However, clear-sky conditions with scattered clouds and mismatched cloud data contribute significantly to the overall uncertainties in the NSRDB. Therefore, future improvements in solar resource data should involve development and implementation of satellite-derived cloud fraction and should consider a novel radiative transfer model accounting for amplified cloud reflection. The evaluation within cloudiness categories also provides a physical rationale for the superior performance of FARMS-DNI compared to the Direct Insolation Simulation Code (DISC) in both cloudy-sky and all-sky conditions.

14 SOLAR ENERGY↗