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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 469 records · Page 26

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NASA NTRS2010

Sizing Single Cantilever Beam Specimens for Characterizing Facesheet/Core Peel Debonding in Sandwich Structure

This technical publication details part of an effort focused on the development of a standardized facesheet/core peel debonding test procedure. The purpose of the test is to characterize facesheet/core peel in sandwich structure, accomplished through the measurement of the critical strain energy release rate associated with the debonding process. Following an examination of previously developed tests and a recent evaluation of a selection of these methods, a single cantilever beam (SCB) specimen was identified as being a promising candidate for establishing such a standardized test procedure. The objective of the work described here was to begin development of a protocol for conducting a SCB test that will render the procedure suitable for standardization. To this end, a sizing methodology was developed to ensure appropriate SCB specimen dimensions are selected for a given sandwich system. Application of this method to actual sandwich systems yielded SCB specimen dimensions that would be practical for use. This study resulted in the development of a practical SCB specimen sizing method, which should be well-suited for incorporation into a standardized testing protocol.

Ratcliffe, James G.↗

DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗

NASA NTRS1976

Advanced space system concepts and their orbital support needs (1980 - 2000). Volume 4: Detailed data. Part 2: Program plans and common support needs (a study of the commonality of space vehicle applications to future national needs

The methodology of alternate world future scenarios is utilized for selecting a plausible, though not advocated, set of future scenarios each of which results in a program plan appropriate for the respective environment. Each such program plan gives rise to different building block and technology requirements, which are analyzed for common need between the NASA and the DoD for each of the alternate world scenarios. An essentially invariant set of system, building block, and technology development plans is presented at the conclusion, intended to allow protection of most of the options for system concepts regardless of what the actual future world environment turns out to be. Thus, building block and technology needs are derived which support: (1) each specific world scenario; (2) all the world scenarios identified in this study; or (3) generalized scenarios applicable to almost any future environment. The output included in this volume consists of the building blocks, i.e.: transportation vehicles, orbital support vehicles, and orbital support facilities; the technology required to support the program plans; identification of their features which could support the DoD and NASA in common; and a complete discussion of the planning methodology.

Bekey, I.↗

NASA NTRS1994

Certification methodology applied to the NASA experimental radar system

The objective of the research is to apply selected FAA certification techniques to the NASA experimental wind shear radar system. Although there is no intent to certify the NASA system, the procedures developed may prove useful to manufacturers that plan to undergo the certification process. The certification methodology for forward-looking wind shear detection radars will require estimation of system performance in several FAA-specified microburst/clutter scenarios as well as the estimation of probabilities of missed and false hazard alerts under general operational conditions. Because of the near-impossibility of obtaining these results experimentally, analytical and simulation approaches must be used. Hazard detection algorithms were developed that derived predictive estimates of aircraft hazard from basic radar measurements of weather reflectivity and radial wind velocity. These algorithms were designed to prevent false alarms due to ground clutter while providing accurate predictions of hazard to the aircraft due to weather. A method of calculation of the probability of missed and false hazard alerts has been developed that takes into account the effect of the various algorithms used in the system and provides estimates of the probability of missed and false alerts per microburst encounter under weather conditions found at Denver, Kansas City, and Orlando. Simulation techniques have been developed that permit the proper merging of radar ground clutter data (obtained from flight tests) with simulated microburst data (obtained from microburst models) to estimate system performance using the microburst/clutter scenarios defined by the FAA.

Britt, Charles L.↗

DOE OSTI2016

Licensing basis event selection framework for advanced reactors - 18818

This paper describes key aspects of the risk-informed, performance-based licensing methodology as developed by the Department of Energy (DOE) for the Next Generation Nuclear Plant (NGNP) project. The discussion focuses on an event selection process using modular high temperature gas-cooled reactor (HTGR) design attributes and identifies how top-level regulatory criteria can be placed on a frequency-consequence (F-C) curve. Nuclear Regulatory Commission (NRC) staff evaluations of the proposed approach are included. In addition, important lessons learned during the NGNP prelicensing effort and their potential applications to alternative advanced reactor designs are presented. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2012

Screening tests for hazard classification of complex waste materials - Selection of methods

In this study we describe the development of an alternative methodology for hazard characterization of waste materials. Such an alternative methodology for hazard assessment of complex waste materials is urgently needed, because the lack of a validated instrument leads to arbitrary hazard classification of such complex waste materials. False classification can lead to human and environmental health risks and also has important financial consequences for the waste owner. The Hazardous Waste Directive (HWD) describes the methodology for hazard classification of waste materials. For mirror entries the HWD classification is based upon the hazardous properties (H1-15) of the waste which can be assessed from the hazardous properties of individual identified waste compounds or - if not all compounds are identified - from test results of hazard assessment tests performed on the waste material itself. For the latter the HWD recommends toxicity tests that were initially designed for risk assessment of chemicals in consumer products (pharmaceuticals, cosmetics, biocides, food, etc.). These tests (often using mammals) are not designed nor suitable for the hazard characterization of waste materials. With the present study we want to contribute to the development of an alternative and transparent test strategy for hazard assessment of complex wastes that is in line with the HWD principles for waste classification. It is necessary to cope with this important shortcoming in hazardous waste classification and to demonstrate that alternative methods are available that can be used for hazard assessment of waste materials. Next, by describing the pros and cons of the available methods, and by identifying the needs for additional or further development of test methods, we hope to stimulate research efforts and development in this direction. In this paper we describe promising techniques and argument on the test selection for the pilot study that we have performed on different types of waste materials. Test results are presented in a second paper. As the application of many of the proposed test methods is new in the field of waste management, the principles of the tests are described. The selected tests tackle important hazardous properties but refinement of the test battery is needed to fulfil the a priori conditions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1984

Development of a quantitative basis for selection of spectral features in a vegetation monitoring system

The development of an objective methodology for evaluation of alternative Landsat data preprocessing options, spectral transform features for monitoring vegetation, and feature summarization algorithms is presented. Based on estimates of spectral separability between a target class and its confusion classes, analysis of variance techniques are used to evaluate potential design options for large scale vegetation monitoring systems. Case studies are presented for early season and through the season spring small grains separation and for barley/other spring small grains separation. It is concluded that a basis for efficient, objective selection among alternative feature extraction approaches has been established for the large scale vegetation mapping/inventory problem. Although the approach has been demonstrated for the unitemporal class separability case, extensions to the multitemporal case are under development.

Phinney, D. E.↗

DOE OSTI2025

Materials Selection Principles for Designing Electro‐Thermal Neurons

Artificial neurons exhibiting volatile threshold switching and action potential‐like oscillations are crucial for brain‐inspired computing. While Complimentary Metal‐Oxide‐Semiconductor (CMOS)‐based strategies require hundreds of transistors to simulate each neuron, neuronal oscillations arise spontaneously in individual electro‐thermal devices due to nonlinearities like the Mott transition in VO 2 . Despite improved understanding of the physics, quantitative connections between neuronal performance and material properties remain under‐explored, preventing predictive neuron design and rational materials selection. In this work, a physics‐aware forward design methodology is developed for interrogating a wide palette of materials with properties varying by orders of magnitude, and their performance (high frequency, high dynamical reconfigurability and low power) under external circuit and device geometry constraints is assessed. The space of viable materials is identified to be much larger than previously recognized, with candidates from a range of materials classes, including Ge, GaP and MoS 2 . CMOS‐compatible performance (such as 100 GHz oscillating frequencies) can be achieved with CMOS‐compatible node sizes (≈10 nm). Finally, combinations of material properties yielding desired neuronal performance under uncertain design constraints are considered. This work solidifies forward design principles for electro‐thermal neuron devices, a necessary pre‐condition for inverse design from desired neuronal performance to required materials properties.

compact model↗

DOE OSTI2017

Renewable Energy Zones for Balancing Siting Trade-offs in India

India’s targets of 175 GW of renewable energy capacity by 2022, and 40% generation capacity from non-fossil fuel sources by 2030 will require a rapid and dramatic increase in solar and wind capacity deployment and overcoming its associated economic, siting, and power system challenges. The objective of this study was to spatially identify the amount and quality of wind and utility-scale solar resource potential in India, and the possible siting-related constraints and opportunities for development of renewable resources. Using the Multi-criteria Analysis for Planning Renewable Energy (MapRE) methodological framework, we estimated several criteria valuable for the selection of sites for development for each identified potential "zone", such as the levelized cost of electricity, distance to nearest substation, capacity value (or the temporal matching of renewable energy generation to demand), and the type of land cover. We find that high quality resources are spatially heterogeneous across India, with most wind and solar resources concentrated in the southern and western states, and the northern state of Rajasthan. Assuming India's Central Electricity Regulatory Commission's norms, we find that the range of levelized costs of generation of wind and solar PV resources overlap, but concentrated solar power (CSP) resources can be approximately twice as expensive. Further, the levelized costs of generation vary much more across wind zones than those across solar zones because of greater heterogeneity in the quality of wind resources compared to that of solar resources. When considering transmission accessibility, we find that about half of all wind zones (47%) and two-thirds of all solar PV zones (66%) are more than 25 km from existing 220 kV and above substations, suggesting potential constraints in access to high voltage transmission infrastructure and opportunities for preemptive transmission planning to scale up RE development. Additionally and importantly, we find that about 84% of all wind zones are on agricultural land, which provide opportunities for multiple-uses of land but may also impose constraints on land availability. We find that only 29% of suitable solar PV sites and 15% of CSP sites are within 10 km of a surface water body suggesting water availability as a significant siting constraint for solar plants. Availability of groundwater resources was not analyzed as part of this study. Lastly, given the possible economic benefits of transmission extensions or upgrades that serve both wind and solar generators, we quantified the co-location opportunities between the two technologies and find that about a quarter (28%) of all solar PV zones overlap with wind zones. Using the planning tools made available as part of this study, these multiple siting constraints and opportunities can be systematically compared and weighted to prioritize development that achieves a particular technology target. Our results are limited by the uncertainties associated with the input datasets, in particular the geospatial wind and solar resource, transmission, and land use land cover datasets. As input datasets get updated and improved, the methodology and tools developed through this study can be easily adapted and applied to these new datasets to improve upon the results presented in this study. India is on a path to significantly decarbonize its electricity grid through wind and solar development. A stakeholder-driven, systematic, and integrated planning approach using data and tools such as those highlighted in this study is essential to not only meet the country's RE targets, but to meet them in a cost-effective, and socially and environmentally sustainable way.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1993

KSC management training system project

The stated objectives for the summer of 1993 were: to review the Individual Development Plan Surveys for 1994 in order to automate the analysis of the Needs Assessment effort; and to develop and implement evaluation methodologies to perform ongoing program-wide course-to-course assessment. This includes the following: to propose a methodology to develop and implement objective, performance-based assessment instruments for each training effort; to mechanize course evaluation forms and develop software to facilitate the data gathering, analysis, and reporting processes; and to implement the methodology, forms, and software in at lease one training course or seminar selected among those normally offered in the summer at KSC. Section two of this report addresses the work done in regard to the Individual Development Plan Surveys for 1994. Section three presents the methodology proposed to develop and implement objective, performance-based assessment instruments for each training course offered at KSC.

Sepulveda, Jose A.↗

DOE OSTI2018

Cost benefit analysis for smart grid projects

The U.S. is unusual in that a definition of the term “smart grid” was written into legislation, appearing in the Energy Independence and Security Act (2007). When the recession called for stimulus spending and the American Recovery and Reinvestment Act (ARRA, 2009) was passed, a framework already existed for identification of smart grid projects. About $4.5B of the U.S. Department of Energy’s (U.S. DOE’s) $37B allocation from ARRA was directed to smart grid projects of two types, investment grants and demonstrations. Matching funds from other sources more than doubled the total value of ARRA-funded smart grid projects. The Smart Grid Investment Grant Program (SGIG) consumed all but $620M of the ARRA funds, which was available for the 32 projects in the Smart Grid Demonstration Program (SGDP, or demonstrations). Given the economic potential of these projects and the substantial investments required, there was keen interest in estimating the benefits of the projects (i.e., quantifying and monetizing the performance of smart grid technologies). Common method development and application, data collection, and analysis to calculate and publicize the benefits were central objectives of the program. For this purpose standard methods and a software tool, the Smart Grid Computational Tool (SGCT), were developed by U.S. DOE and a spreadsheet model was made freely available to grantees and other analysts. The methodology was intended to define smart grid technologies or assets, the mechanisms by which they generate functions, their impacts and, ultimately, their benefits. The SGCT and its application to the Demonstration Projects are described, and actual projects in Southern California and in China are selected to test and illustrate the tool. The usefulness of the methodology and tool for international analyses is then assessed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1990

Airborne MSS for land cover classification II

A basic methodology for land cover classification using airborne multispectral scanner (MSS) imagery is outlined. This includes waveband selection and radiometric calibration; correction for scan angle and atmosphere; training and classification and accuracy assessment. Refinements to this basic methodology include per-field sampling and the addition of low-pass filtering, image texture, prior probabilities and two dates of imagery. For a study area in upland England, eight land covers were classified with a mean accuracy of 52.6 percent using the basic methodology. This was increased to 79.0 percent by using a suitability refined methodology. Per-field sampling accounted for the largest proportion of this increase.

Curran, Paul J.↗

DOE OSTI2022

Metal‐Organic Framework (MOF) Morphology Control by Design

Abstract Exerting morphological control over metal‐organic frameworks (MOFs) is critical for determining their catalytic performance and to optimize their packing behavior in areas from separations to fuel gas storage. A mechanism‐based approach to tailor the morphology of MOFs is introduced and experimentally demonstrated for five cubic Zn 4 O‐based MOFs. This methodology provides three key features: 1) computational screening for selection of appropriate additives to change crystal morphology based on knowledge of the crystal structure alone; 2) use of additive to metal cluster geometric relationships to achieve morphologies expressing desired crystallographic facets; 3) potential for suppression of interpenetration for certain phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Ultrasound-driven fabrication of high-entropy alloy nanocatalysts promoted by alcoholic ionic liquids

High-entropy alloy nanoparticles (HEA-NPs) are highly underutilized in heterogeneous catalysis due to the absence of a reliable, sustainable, and facile synthetic method. Herein, we report a facile synthesis of HEA nanocatalysts realized via an ultrasound-driven wet chemistry method promoted by alcoholic ionic liquids (AILs). Owing to the intrinsic reducing ability of the hydroxyl group, AILs were synthesized and utilized as environmentally friendly alternatives to conventional reducing agents and volatile organic solvents in the synthetic process. Under high-intensity ultrasound irradiation, Au 3+ , Pd 2+ , Pt 2+ , Rh 3+ , and Ru 3+ ions were co-reduced and transformed into single-phase HEA (AuPdPtRhRu) nanocrystals without calcination. Characterization results reveal that the as-synthesized nanocrystals are composed of elements of Au, Pd, Pt, Rh, and Ru as expected. Compared to the monometallic counterparts such as Pd-NPs, the carbon-supported HEA nanocatalysts show superior catalytic performance for selective hydrogenation of phenol to cyclohexanone in terms of yield and selectivity. Our synthetic strategy provides an improved and facile methodology for the sustainable synthesis of multicomponent alloys for catalysis and other applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗

DOE OSTI2019

Acoustic emission Bayesian source location: Onset time challenge

Robust identification of the most accurate observed input data among a pool of observations is key in modeling and decision making. A statistically biased observed measurement deteriorates the predictive power of a model and affects decision-making ability based on the prediction of the model. When two competing methods of measurement are available, such as methods which identify arrival times in acoustic emission (AE) signals, a principal question is whether one of the two obtained datasets, or a combination of the two, should be used later on, for example, to localize an AE source. This question becomes more important when collecting not repeatable data such as AE signals created by a propagating crack. Here this paper considers an inverse source location problem in a concrete block to address the mentioned issue, a proposed methodology which also has wider application in competitive data selection. Elastic energy released by an AE event, such as a propagating crack, is recorded by acoustic emission data acquisition system. The onset time of AE signals is often used to locate the source of the event, and its accuracy directly affects the precision of source identification. This research proposes an innovative approach to select the most probable onset time obtained from two automatic picker methods. The proposed method selects the most probable onset times, which are observed by each picker for each sensor, in a probabilistic fashion. To validate the proposed method, the most accurate onset time observed by each picker is identified by visual inspection and is compared with the one is selected by the proposed method. Finally, the dataset is used for source location identification. Results show that picked onset times determined by the proposed method generate more accurate source identification when compared with coordinates obtained using each dataset individually.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Bayesian approach to strong lens finding in the era of wide-area surveys

ABSTRACT The arrival of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide-area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from $\mathcal {O}(10^3)$ to $\mathcal {O}(10^5)$. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong lens experts to remove false positives which is only set to increase with these surveys. In this work, we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science, and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false-positive rate of 10−3 can be achieved with a completeness of 46 per cent, compared to 34 per cent for the best individual classifier. Given the large number of galaxy–galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses and to help prioritize candidates for follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗