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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Component Importance and Interdependence Analysis for Transmission, Distribution and Communication Systems

For critical infrastructure restoration planning, the real-time scheduling and coordination of system restoration efforts, the key in decision-making is to prioritize those critical components that are out of service during the restoration. For this purpose, there is a need for component importance analysis. While it has been investigated extensively for individual systems, component importance considering interdependence among transmission, distribution and communication (T&D&C) systems has not been systematically analyzed and widely adopted. In this study, we propose a component importance assessment method in the context of interdependence between T&D&C networks. Analytic methods for multilayer networks and a set of metrics have been applied for assessing the component importance and interdependence between T&D&C networks based on their physical characteristics. The proposed methodology is further validated with integrated synthetic Illinois regional transmission, distribution, and communication (T&D&C) systems, the results reveal the unique characteristics of component/node importance, which are strongly affected by the network topologies and cross-domain node mapping.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

High Power Density Cost-Effective MVDC Aircraft Cable

To make the power density of electric aircraft closer to conventional aircraft, an electric power system (EPS) with high power delivery and low system mass is necessary. As an essential component of aircraft EPS, cables are necessary to transmit power from one node to another. Virginia Tech will design high-power density, cost-effective ±5 kV cables for twin-aisle all-electric aircraft. Innovations include conductors with increased current-carrying capacity and a multilayer, multifunctional insulation system made of exceptionally high thermal conductivity materials. Designed for DC voltage, the new insulator will allow fewer partial discharge events and provide improved electromagnetic interference protection.

42 ENGINEERING↗
DOE OSTI2024

Orienting Strained Interfaces designed to Direct Energy Flow (Final Technical Report)

This proposal aims to establish a transformative paradigm for oxide heterostructures with an exceptionally large number of interfaces designed to direct energy flow through controlled interface orientation, enabling fast ion transport. A primary focus is on unveiling the key role of interfacial strain in oxygen ion migration at low temperatures. The central challenge is to create, understand, utilize self-assembled vertical heteroepitaxial nanostructures with the goal of obtaining and understanding fast ion transport properties by modulating interfacial strain. The specific objectives are: (1) to synthesize multilayer thin films and vertical heteroepitaxial nanostructures with fluorite Gd-doped CeO 2 (GDC) and bixbyite RE 2 O 3 (RE = Y and Sm), (2) to evaluate the interfacial strain states under various temperatures and ambient conditions, (3) to understand the effect of interfacial strain on ionic conductivity of the proposed nanostructures, (4) to understand the three-dimensional (3D) atomic structure of the proposed material design and interface phenomena at the atomic scale.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigation of new superconducting materials for the next generation high-performance RF superconducting cavities for particle accelerators

In this DOE-funded project DE-SC0010081-020 Old Dominion University (ODU) in collaboration with University of Wisconsin (UW) and Jefferson Laboratory have investigated both experimentally and theoretically electromagnetic response and losses in multilayered superconducting structures made of new SRF materials which can push the field and Q performance limits of accelerating cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

BOTTLE: Hybrid Chemical-Mechanical Separation and Upcycling of Mixed Plastic Waste

The main objective of this project is to develop a hybrid mechanical-chemical recycling technology for multilayered and laminated plastics. We aimed to separate and upcycle up to more than 80% of the two main constituents of such structures, polyolefins and polyesters, for a significantly lower cost and at higher energetic efficiency and much larger throughputs than chemical recycling. At the end of the project, the team was able to: a) Develop an extrusion-based separation technology that resorts to polyester depolymerizaton and extraction and allows for more than 90% of the polyester to be separated in the melt from the main polyolefin stream. b) Depolymerize the separated PET to more than 90%, which facilitates its posterior repolymerization and guarantees its retention in the polymer value-chain. c) Develop a zeolite-induced extrusion-based technology able to conduct continuous catalytic cracking of polyolefins, including highly contaminated PCR streams, at temperatures as low as 350 OC. d) Show, using LCA/TEA analysis that the two technologies are much more advantageous techno-economically and over the material’s life cycle than existing recycling technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dynamic Probabilistic Risk Assessment Based Response Surface Approach for FLEX and Accident Tolerant Fuels for Medium Break LOCA Spectrum

After the Fukushima Daiichi Accident, the safety features such as accident tolerant fuel (ATF) and diverse and flexible coping strategies (FLEX) for existing nuclear fleets are being investigated by the US Department of Energy under the Light Water Reactor Sustainability Program. This research is being conducted to quantify the risk-benefit of these safety features. Dynamic probabilistic risk assessment (DPRA)-based response-surface approach has been presented to quantify the FLEX and ATF benefits by estimating the risk associated with each option. ATFs with multilayered silicon carbide (SiC), iron-chromium-aluminum, and chromium-coated zirconium cladding were considered in this study. While these ATF candidates perform better than the current zirconium cladding (Zr), they may introduce additional failure modes in some operating conditions. The fuel failure analysis modules (FAMs) were developed to investigate ATF performance. The dynamic risk assessments were performed using RAVEN, a DPRA tool, coupled with RELAP5 and FAMs. A cumulative distribution function-based index provided a mean of comparing the benefits of safety enhancements. For medium break loss of coolant accidents, FLEX operational timing window for each fuel type was estimated. Among these ATF candidates, SiC-type ATF was the most beneficial candidate for an increased safety margin than Zr-based fuel and was found to complement FLEX strategies in terms of risk and coping time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

17 WIND ENERGY↗
DOE OSTI2018

Ionically Paired Layer-by-Layer Hydrogels: Water and Polyelectrolyte Uptake Controlled by Deposition Time

Despite intense recent interest in weakly bound nonlinear (“exponential”) multilayers, the underlying structure-property relationships of these films are still poorly understood. This study explores the effect of time used for deposition of individual layers of nonlinearly growing layer-by-layer (LbL) films composed of poly(methacrylic acid) (PMAA) and quaternized poly-2-(dimethylamino)ethyl methacrylate (QPC) on film internal structure, swelling, and stability in salt solution, as well as the rate of penetration of invading polyelectrolyte chains. Thicknesses of dry and swollen films were measured by spectroscopic ellipsometry, film internal structure—by neutron reflectometry (NR), and degree of PMAA ionization—by Fourier-transform infrared spectroscopy (FTIR). The results suggest that longer deposition times resulted in thicker films with higher degrees of swelling (up to swelling ratio as high as 4 compared to dry film thickness) and stronger film intermixing. The stronger intermixed films were more swollen in water, exhibited lower stability in salt solutions, and supported a faster penetration rate of invading polyelectrolyte chains. These results can be useful in designing polyelectrolyte nanoassemblies for biomedical applications, such as drug delivery coatings for medical implants or tissue engineering matrices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Miniature low-pass filter in low-loss 9k7 LTCC

DuPont 9k7 low-temperature cofired ceramic (LTCC) is a low-loss, or high-quality-factor Q, tape system targeting at radio frequency (RF) applications. This paper reports on the effect of a critical process parameter, the heating rate, on the densification and dielectric properties of the 9k7 LTCC. The role of competing densification and crystallization during the sintering of 9k7 is discussed. The high Q of DuPont 9K7 can be used to improve RF system performance, for example a better receiver noise figure, by designing embedded passive RF components such as inductors, capacitors and filters. Furthermore, miniaturized multilayer low-pass filters (LPF) with a wide stopband were fabricated to showcase the technology.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Growing Cutting-edge X-ray Optics

Ever imagined that an Xbox controller could help open a window into a world spanning just one billionth of a meter? Brookhaven Lab's Ray Conley grows cutting-edge optics called multilayer Laue lenses (MLL) one atomic layer at a time to focus high-energy x-rays to within a single nanometer. To achieve this focusing feat, Ray uses a massive, custom-built atomic deposition device, an array of computers, and a trusty Xbox controller. These lenses will be deployed at the Lab's National Synchrotron Light Source II, due to begin shining super-bright light on pressing scientific puzzles in 2015.

36 MATERIALS SCIENCE↗
DOE OSTI2018

2018 Technology Innovation Program Carbon Nanomaterial Enabled Ultra Conductive Metal Composites

Carbon nanomaterial enabled UCC metal composites combine ORNL’s strength in nanomaterial research, electron microscopy, and scalable material processing together with novel methods to produce a scalable assembly of Cu-CNT multilayer composites. These composites enable higher electrical conductivities than that of pure Cu. Using scalable, cost-effective, and commercially viable solution-based processing methods, our technological platform achieves a high degree of CNT alignment and coating stability, which demonstrates a novel technological platform to reproducibly produce carbon nanomaterial enabled ultra conductive conductors for a broad range of electrical systems and industrial applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Performance of Low-Complexity Spectrally Selective One-Dimensional Mirrors for Photovoltaic Thermal Management: Preprint

Operation at elevated temperatures is detrimental to the performance of crystalline Si solar modules. One method of reducing module operating temperature is selective reflection of sub-band gap photons, which can otherwise only be absorbed parasitically. We numerically optimize the design of a series of multilayer photonic mirrors based on real materials using a previously developed optimization routine. Combined ray tracing and finite element simulations reveal the ability of each mirror to increase energy yield and decrease operating temperature. The best design outperforms a conventional glass antireflection coating, contains only nine layers, and maintains performance regardless of geographic location.

14 SOLAR ENERGY↗
DOE OSTI2025

Evaluating Direct and Indirect Influence on EV Charging Stations Across the US

The adoption of new technology for electric vehicles (EV) and mobility applications can bring underappreciated vulnerabilities to the power grid. One area of potential fraud and adversarial influence is through the business ecosystem of startups that own and deploy EV technology. Yet, there are no models or analyses that map the network of organizations and people that have direct and indirect influence over technologies currently deployed in the grid. To fill this gap, we develop a multilayer network model to measure direct and indirect influence on EV charging stations. First, we create and adversarial socio-technical network (ASTN) model via a data fusion pipeline for different US regions of interest (ROI). Then, we develop an integrated ASTN for Chicago, Los Angeles, New York, and Philadelphia. We rank EV charging companies direct influence within each geographic region as well as indirect influence via social network analysis. While some companies have strong direct and indirect influence (i.e., ChargePoint) others show a mismatch between their influence over charging stations and their position within the social network. For example, Tesla has strong direct influence on stations and weak indirect influence over competitors. In contrast, 7Charge has weak direct influence over stations, but strong indirect influence over competitors.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2017

Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

Here, recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R 2 = 0.60 ( RMSE = 18.72 W m -2 ) for all 27 sites and R 2 =0.62 ( RMSE 5 18.21 W m -2 ) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R 2 values of only 0.50 ( RMSE 5 20.74 W m -2 ) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

58 GEOSCIENCES↗
DOE OSTI2017

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. As such, in this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

58 GEOSCIENCES↗
DOE OSTI2015

Lasers, extreme UV and soft X-ray

Three decades ago, large ICF lasers that occupied entire buildings were used as the energy sources to drive the first X-ray lasers. Today X-ray lasers are tabletop, spatially coherent, high-repetition rate lasers that enable many of the standard optical techniques such as interferometry to be extended to the soft X-ray regime between wavelengths of 10 and 50 nm. Over the last decade X-ray laser performance has been improved by the use of the grazing incidence geometry, diode-pumped solid-state lasers, and seeding techniques. The dominant X-ray laser schemes are the monopole collisional excitation lasers either driven by chirped pulse amplification (CPA) laser systems or capillary discharge. The CPA systems drive lasing in neon-like or nickel-like ions, typically in the 10 – 30 nm range, while the capillary system works best for neon-like argon at 46.9 nm. Most researchers use nickel-like ion lasers near 14 nm because they are well matched to the Mo:Si multilayer mirrors that have peak reflectivity near 13 nm and are used in many applications. As a result, the last decade has seen the birth of the X-ray free electron laser (XFEL) that can reach wavelengths down to 0.15 nm and the inner-shell Ne laser at 1.46 nm.

42 ENGINEERING↗