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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Optoelectronic Properties of C60 and C70 Fullerene Derivatives: Designing and Evaluating Novel Candidates for Efficient P3HT Polymer Solar Cells

Ten novel fullerene-derivatives (FDs) of C60 and C70 had been designed as acceptor for polymer solar cell (PSC) by employing the quantitative structure-property relationship (QSPR) model, which was developed strategically with a reasonably big pool of experimental power conversion efficiency (PCE) data. The QSPR model was checked and validated with stringent parameter and reliability of predicted PCE values of all designed FDs. They were assessed by the applicability domain (AD) and process randomization test. The predicted PCE of FDs range from 7.96 to 23.01. The obtained encouraging results led us to the additional theoretical analysis of the energetics and UV-Vis spectra of isolated dyes employing Density functional theory (DFT) and Time-dependent-DFT (TD-DFT) calculations using PBE/6-31G(d,p) and CAM-B3LYP/6-311G(d,p) level calculations, respectively. The FD4 is the best C60-derivatives candidates for PSCs as it has the lowest exciton binding energy, up-shifted lowest unoccupied molecular orbital (LUMO) energy level to increase open-circuit voltage (VOC) and strong absorption in the UV region. In case of C70-derivatives, FD7 is potential candidate for future PSCs due to its strong absorption in UV-Vis region and lower exciton binding energy with higher VOC. Our optoelectronic results strongly support the developed QSPR model equation. Analyzing QSPR model and optoelectronic parameters, we concluded that the FD1, FD2, FD4, and FD10 are the most potential candidates for acceptor fragment of fullerene-based PSC. The outcomes of tactical molecular design followed by the investigation of optoelectronic features are suggested to be employed as a significant resource for the synthesis of FDs as an acceptor of PSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Added value of site load measurements in probabilistic lifetime extension: a Lillgrund case study

Site-specific fatigue estimation is an essential part of wind turbine lifetime extension, with various methods depending on data availability. The present study compares probabilistic lifetime extension assessment results for rotor blades with and without load measurements. It also addresses two key questions in such assessments: the applicability of the Frandsen model for estimating waked turbulence under complex and mixed wake conditions and the extrapolation of mid-term data over longer time periods. The case study wind turbine is SWT-2.3-93, located at the edge of the Lillgrund wind farm, situated in the Øresund Strait between Denmark and Sweden. The turbine is extensively instrumented, with 5 years of data available from its supervisory control and data acquisition (SCADA) system. Although the Frandsen turbulence estimates deviate in a different manner from measurements at below- and above-rated mean wind speeds, the model remains a conservative approach for fatigue load prediction and reliability. In the current case study, the site-specific assessment using strain gauge measurements yields a 33 % higher annual fatigue reliability index after 35 years compared to a scenario based on the Frandsen estimation combined with ambient environmental data and a generic aeroelastic model. The results also demonstrate that the sensitivity of fatigue reliability to load uncertainty is negligible when load measurements are used directly but relatively high when relying on the Frandsen model in combination with a generic aeroelastic model. Overall, the high variability of the lifetime extension in different scenarios of data availability and accuracy shows the importance and added value of high-quality measurements combined with wind-farm-level SCADA and a model updated in real time (digital twins).

17 WIND ENERGY↗
DOE OSTI2011

End-to-End Network QoS via Scheduling of Flexible Resource Reservation Requests

Modern data-intensive applications move vast amounts of data between multiple locations around the world. To enable predictable and reliable data transfer, next generation networks allow such applications to reserve network resources for exclusive use. In this paper, we solve an important problem (called SMR3) to accommodate multiple and concurrent network reservation requests between a pair of end-sites. Given the varying availability of bandwidth within the network, our goal is to accommodate as many reservation requests as possible while minimizing the total time needed to complete the data transfers. We first prove that SMR3 is an NP-hard problem. Then we solve it by developing a polynomial-time heuristic, called RRA. The RRA algorithm hinges on an efficient mechanism to accommodate large number of requests by minimizing the bandwidth wastage. Finally, via numerical results, we show that RRA constructs schedules that accommodate significantly larger number of requests compared to other, seemingly efficient, heuristics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Performance Prediction and Validation: Data, Frameworks, and Considerations

Improving the predictability and reliability of wind power generation and operations will reduce costs and potentially establish a framework to attract new capital into the distributed wind sector, a key cost reduction requirement highlighted in results from the distributed wind future market assessment conducted with dWind. Quantifying and refining the accuracy of project performance estimates will also directly address several of the key challenges identified by industry stakeholders in 2015 as part of the distributed wind resource assessment workshop and be cross-cutting for several other facets of the distributed wind portfolio. This presentation covers the efforts undertaken in 2016 to address these topics.

17 WIND ENERGY↗
DOE OSTI2021

FCIC Task 5--Preprocessing

the objective of this project is to develop science-based design and operation principles (as informed by TEA and LCA), which result in preprocessing units to operate predictably and reliably, and are capable of scalable performance.

09 BIOMASS FUELS↗
DOE OSTI2022

Wind Energy Accomplishments & Midyear Performance Report: Fiscal Year 2022

2022 marks 43 years of innovation through the National Wind Technology Center at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) Flatirons Campus. Scientists, engineers, analysts, and researchers continue to build on NREL's legacy of innovation at this world-class hub for wind energy technology research and development. Today, the nation's goals to tackle climate change and revitalize the U.S. economy through increased investment in clean energy - particularly in land-based, offshore, and distributed wind energy - are more ambitious than ever. From advancing wind energy science, technologies, and materials to demonstrating thought leadership on a worldwide scale, NREL's Wind Energy Program played a powerful role in maximizing the impact of wind energy in the first half of Fiscal Year (FY) 2022. NREL's technical expertise, research capabilities, and industry understanding continued to support DOE's efforts to champion clean energy, addressing market and deployment barriers and driving down costs with more efficient, reliable, and predictable wind energy systems. This report provides an overview of the achievements NREL made on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners between October 1, 2021, and March 31, 2022.

accomplishments↗
DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2022

Four decades ago, construction was just beginning on experimental turbines at the National Wind Technology Center (NWTC). Today, the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) facility is the centerpiece of the laboratory's Flatirons Campus, a world-class hub for renewable energy research. The nation's shift to 100% clean electricity by 2035 will require a mix of renewable energy sources and strategies - and together, wind and solar energy could account for 60% to 80% of that clean energy resource. In Fiscal Year (FY) 2022, NREL scientists, engineers, and analysts contributed to these visionary goals through their wind energy research. As wind innovations push into new areas, NREL continues to play a vital role in advancing technology and addressing deployment barriers in pursuit of more efficient, reliable, and predictable wind energy systems. FY 2022 wind research and development explored the potential for dramatic growth in land-based systems, the launch of the nation's first commercial-scale offshore installations, and transmission infrastructure buildout. Land-based wind energy is one of the most cost-effective electricity supply options - but utility-scale deployment will require up to 10 times the current number of turbines. An NREL plan addressed this need to accelerate U.S. wind technology rollout at distributed and utility scales. Another project conducted by NREL and the Pacific Northwest National Laboratory (PNNL) helps position the nation's first major offshore wind corridor for success. The WETO-funded Atlantic Offshore Wind Transmission Study is evaluating options for balancing electricity supply and demand, while supporting resilience of the grid and marine industries. WETO, NREL, and other partners are working to enable the enormous supply chain and workforce changes the U.S. wind energy industry will need to meet net-zero-carbon-emissions targets. As part of a seminal series of DOE-funded supply chain studies, NREL analysts reported on the trade-offs involved in manufacturing large volumes of wind technologies, while addressing cost, workforce, and logistics issues. All of this research is supported by NREL's outstanding research teams, tools, data, and facilities. A WETO-funded international wind energy field campaign, the American WAKE experimeNt (AWAKEN), has brought together experts from NREL, PNNL, and Sandia National Laboratories to create the world's most comprehensive set of high-resolution data on wind energy atmospheric phenomenon. This study could lead to more accurate predictions of losses from turbine-to-turbine wake interactions, eventually helping wind plants capture more energy and operators save millions of dollars. In addition, NREL researchers developed testing, modeling, and analysis tools to improve the security of power grids by identifying wind power plant dynamic stability problems. A new Stochastic Soaring Raptor Simulator (SSRS) protects golden eagles from turbine encounters by predicting flight paths. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2022 (between October 1, 2021, and September 30, 2022).

accomplishments↗
DOE OSTI2013

Successful Deployment of System for the Storage and Retrieval of Spent/Used Nuclear Fuel from Hanford K-West Fuel Storage Basin-13051

In 2012, a system was deployed to remove, transport, and interim store chemically reactive and highly radioactive sludge material from the Hanford Site's 105-K West Fuel Storage Basin that will be managed as spent/used nuclear fuel. The Knockout Pot (KOP) sludge in the 105-K West Basin was a legacy issue resulting from the spent nuclear fuel (SNF) washing process applied to 2200 metric tons of highly degraded fuel elements following long-term underwater storage. The washing process removed uranium metal and other non-uranium constituents that could pass through a screen with 0.25-inch openings; larger pieces are, by definition, SNF or fuel scrap. When originally retrieved, KOP sludge contained pieces of degraded uranium fuel ranging from 600 microns (μm) to 6350 μm mixed with inert material such as aluminum hydroxide, aluminum wire, and graphite in the same size range. In 2011, a system was developed, tested, successfully deployed and operated to pre-treat KOP sludge as part of 105-K West Basin cleanup. The pretreatment process successfully removed the vast majority of inert material from the KOP sludge stream and reduced the remaining volume of material by approximately 65 percent, down to approximately 50 liters of material requiring management as used fuel. The removal of inert material resulted in significant waste minimization and project cost savings because of the reduced number of transportation/storage containers and improvement in worker safety. The improvement in worker safety is a result of shorter operating times and reduced number of remote handled shipments to the site fuel storage facility. Additionally in 2011, technology development, final design, and cold testing was completed on the system to be used in processing and packaging the remaining KOP material for removal from the basin in much the same manner spent fuel was removed. This system was deployed and successfully operated from June through September 2012, to remove and package the last of the SNF fragments from the 105-K West Basin, ending the long and complex history of the KOP sludge materials. The planning and execution of this project demonstrated how the graded application of DOE O 413.3B, Program and Project Management for the Acquisition of Capital Assets[1], DOE-STD-1189-2008, Integration of Safety into the Design Process[2], and DOE G 413.3-4, U.S. Department of Energy Technology Readiness Assessment Guide[3], can positively affect the outcome of project implementation in the DOE Complex. Provided herein are relevant information, ideas, and tools for use by other projects facing similar issues in managing high risk waste streams in a complex regulatory environment. Positive aspects are also provided of appropriate time spent in detailed planning, design, and testing in non-hazardous environments to reduce project risks in both cost and safety performance, as well as improving confidence in meeting project goals through predictable and reliable performance. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

An Overview of Project Planning for Hot-Isostatic Pressure Treatment of High-Level Waste Calcine for the Idaho Cleanup Project - 12289

The Calcine Disposition Project is responsible for retrieval, treatment by hot-isostatic pressure, packaging, and disposal of highly radioactive calcine stored at the Idaho Nuclear Technology and Engineering Center at the Idaho National Laboratory Site in southeast Idaho. In the 2009 Amended Record of Decision: Idaho High-Level Waste and Facilities Disposition Final Environmental Impact Statement the Department of Energy documented the selection of hot-isostatic pressure as the technology to treat the calcine. The Record of Decision specifies that the treatment results in a volume-reduced, monolithic waste form suitable for transport outside of Idaho by a target date of December 31, 2035. That target date is specified in the 1995 Idaho Settlement Agreement to treat and prepare the calcine for transport out of Idaho in exchange for allowing storage of Navy spent nuclear fuel at the INL Site. The project is completing the design of the calcine-treatment process and facility to comply with Record of Decision, Settlement Agreement, Idaho Department of Environmental Quality, and Department of Energy requirements. A systems engineering approach is being used to define the project mission and requirements, manage risks, and establish the safety basis for decision making in compliance with DOE O 413.3B, 'Program and Project Management for the Acquisition of Capital Assets'. The approach draws heavily on 'design-for-quality' tools to systematically add quality, predict design reliability, and manage variation in the earliest possible stages of design when it is most efficient. Use of these tools provides a standardized basis for interfacing systems to interact across system boundaries and promotes system integration on a facility-wide basis. A mass and energy model was developed to assist in the design of process equipment, determine material-flow parameters, and estimate process emissions. Data generated from failure modes and effects analysis and reliability, availability, maintainability, and inspectability analysis were incorporated into a time and motion model to validate and verify the capability to complete treatment of the calcine within the required schedule. The Calcine Disposition Project systems engineering approach, including use of industry-proven design-for-quality tools and quantitative assessment techniques, has strengthened the project's design capability to meet its intended mission in a safe, cost-effective, and timely manner. Use of these tools has been particularly helpful to the project in early design planning to manage variation; improve requirements and high-consequence risk management; and more effectively apply alternative, interface, failure mode, RAMI, and time and motion analyses at the earliest possible stages of design when their application is most efficient and cost effective. The project is using these tools to design and develop HIP treatment of highly radioactive calcine to produce a volume-reduced, monolithic waste form with immobilization of hazardous and radioactive constituents. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Laying the Foundation for a Predictive Theory of Electroweak Nuclear Phenomena

Reliable model-independent predictions of nuclear processes such as beta-decay and neutrinonucleus scattering are important for many fundamental science and mission-related investigations; For example, accurate descriptions of as beta-decay are required for modeling the anti-neutrino spectrum of nuclear reactor, whether for trying to detect new physics such as sterile neutrinos, or remotely identifying the purpose of that reactor. For these activities, we need accurate calculations, as well as careful and thorough analyses of theoretical error and uncertainty in the underlying models and the many-body methods and codes used in solving that model. Unfortunately, modern theoretical calculations of these processes are inadequate for this task. At the heart of this inadequacy is the interaction between nucleons and other particles known as nuclear currents. Historically, descriptions of nuclear currents took into account only the simplest of particle interactions. Recently, the development of an effective field theory known as chiral EFT has been used to derive large corrections to these particle interactions. However, the complex nature of these corrections inhibited their use in ab initio methods of predicting nuclear processes. We developed a novel method to address these complexities and applied it in a series of benchmark and demonstrative calculations using state-of-the-art quantum few-nucleon solvers. Specifically, we studied the role of two-nucleon currents in electroweak processes using light nuclei (i.e., nuclei containing fewer than five nucleons) as a workbench to compute these processes and quantify their uncertainties using Bayesian extensions of the EFT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of machine learning (ML), specifically neural networks, has grown significantly and has spurred research in its applicability to digital instrumentation and control systems (DI&C). While ML models have shown promise in operational contexts, the trustworthiness of using such algorithms has not been adequately assessed. Failures of ML integrated systems are not well understood, and the lack of comprehensive risk modeling can degrade the trustworthiness in these systems. In recent reports by the National Institute for Standards and Technology (NIST) [1] and the Nuclear Regulatory Commission (NRC) [2], they indicate that trustworthiness in ML is a critical barrier and will play a vital role in the safe, accountable, and secure operation of intelligent systems. Thus, in this work, we demonstrate a dynamic model-agnostic method to quantify the relative reliability of AI/ML predictions by incorporating out-of-distribution (OOD) detection on the training dataset. It is well documented that most ML algorithms excel at interpolation (or near-interpolation) tasks but experience significant performance degradation at extrapolation. The method, referenced as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets which can used to the relative reliability of AI/ML predictions. LADDR is then demonstrated on a feedforward neural network based digital twin used for the prediction of safety significant factors during a loss-of-flow transient. LADDR is used to demonstrate how training data can be used as evidence to support the relative reliability of ML/AI predictions enhancing the overall trustworthiness of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Ecosystem biogeochemistry model parameterization: Do more flux data result in a better model in predicting carbon flux?

Reliability of terrestrial ecosystem models highly depends on the quantity and quality of thedata that have been used to calibrate the models. Nowadays, in situ observations of carbon fluxes areabundant. However, the knowledge of how much data (data length) and which subset of the time seriesdata (data period) should be used to effectively calibrate the model is still lacking. This study uses theAmeriFlux carbon flux data to parameterize the Terrestrial Ecosystem Model (TEM) with an adjoint-baseddata assimilation technique for various ecosystem types. Parameterization experiments are thus conductedto explore the impact of both data length and data period on the uncertainty reduction of the posteriormodel parameters and the quantification of site and regional carbon dynamics. We find that: the modelis better constrained when it uses two-year data comparing to using one-year data. Further, two-year datais sufficient in calibrating TEM’s carbon dynamics, since using three-year data could only marginallyimprove the model performance at our study sites; the model is better constrained with the data thathave a higher‘‘climate variability’’than that having a lower one. The climate variability is used to measurethe overall possibility of the ecosystem to experience all climatic conditions including drought and extremeair temperatures and radiation; the U.S. regional simulations indicate that the effect of calibration datalength on carbon dynamics is amplified at regional and temporal scales, leading to large discrepanciesamong different parameterization experiments, especially in July and August. Our findings areconditioned on the specific model we used and the calibration sites we selected. The optimal calibrationdata length may not be suitable for other models. However, this study demonstrates that there may exist athreshold for calibration data length and simply using more data would not guarantee a better modelparameterization and prediction. More importantly, climate variability might be an effective indicator ofinformation within the data, which could help data selection for model parameterization. As a result, we believe ourfindings will benefit the ecosystem modeling community in using multiple-year data to improve modelpredictability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

ASME V\&V challenge problem: Surrogate-based V&V

The process of verification and validation can be resource intensive. From the computational model perspective, the resource demand typically arises from long simulation run times on multiple cores coupled with the need to characterize and propagate uncertainties. In addition, predictive computations performed for safety and reliability analyses have similar resource requirements. For this reason, there is a tradeoff between the time required to complete the requisite studies and the fidelity or accuracy of the results that can be obtained. At a high level, our approach is cast within a validation hierarchy that provides a framework in which we perform sensitivity analysis, model calibration, model validation, and prediction. The evidence gathered as part of these activities is mapped into the Predictive Capability Maturity Model to assess credibility of the model used for the reliability predictions. With regard to specific technical aspects of our analysis, we employ surrogate-based methods, primarily based on polynomial chaos expansions and Gaussian processes, for model calibration, sensitivity analysis, and uncertainty quantification in order to reduce the number of simulations that must be done. The goal is to tip the tradeoff balance to improving accuracy without increasing the computational demands.

42 ENGINEERING↗
DOE OSTI2025

The Reliability of Hybrid Functionals for Accurate Fundamental and Optical Gap Prediction of Bulk Solids and Surfaces

Hybrid functionals have been considered insufficiently reliable for the prediction of band gaps in solids and surfaces. We revisit this issue with a new generation of optimally tuned range-separated hybrid functionals, focusing on the reconstructed Si(111)-(2×1) and Ge(111)-(2×1) surfaces. We show that certain hybrid functionals can accurately predict the surface-state and bulk fundamental and optical gaps, as well as projected band structures of these surfaces, by combining ground-state and time-dependent density functional theory.

Sagredo, Francisca↗
DOE OSTI2024

Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics

Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.

15 GEOTHERMAL ENERGY↗