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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Enhancing Neural Network Decision-Making with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. This explainability encourages people to be more inclined to justifiably trust machine decision-making.

Loc Tran↗
DOE OSTI2018

The Solar Energy Innovation Network

The Solar Energy Innovation Network assembles diverse teams of stakeholders to research cutting-edge solutions for a more reliable and resilient grid. Administered by the National Renewable Energy Laboratory, it brings together cities, states, utilities, non-profits, and others, from across the United States who are implementing novel applications of solar energy and other advanced energy technologies.

14 SOLAR ENERGY↗
NASA NTRS2004

Time Changes of the European Gravity Field from GRACE: A Comparison with Ground Measurements from Superconducting Gravimeters and with Hydrology Model Predictions

We investigate the time-variable gravity changes in Europe retrieved from the initial GRACE monthly solutions spanning a 18 month duration from April 2002 to October 2003. Gravity anomaly maps are retrieved in Central Europe from the monthly satellite solutions we compare the fields according to various truncation levels (typically between degree 10 and 20) of the initial fields (expressed in spherical harmonics to degree 120). For these different degrees, an empirical orthogonal function (EOF) decomposition of the time-variable gravity field leads us to its main spatial and temporal characteristics. We show that the dominant signal is found to be annual with an amplitude and a phase both in agreement with predictions in Europe modeled using snow and soil-moisture variations from recent hydrology models. We compare these GRACE gravity field changes to surface gravity observations from 6 superconducting gravimeters of the GGP (Global Geodynamics Project) European sub-network, with a special attention to loading corrections. Initial results suggest that all 3 data sets (GRACE, hydrology and GGP) are responding to annual changes in near-surface water in Europe of a few microGal (at length scales of approx.1000 km) that show a high value in winter and a summer minimum. We also point out that the GRACE gravity field evolution seems to indicate that there is a trend in gravity between summer 2002 and summer 2003 which can be related to the 2003 heatwave in Europe and its hydrological consequences (drought). Despite the limited time span of our analysis and the uncertainties in retrieving a regional solution from the network of gravimeters, the calibration and validation aspects of the GRACE data processing based on the annual hydrology cycle in Europe are in progress.

Hinderer, J.↗
NASA NTRS1988

Computing motion using resistive networks

Recent developments in the theory of early vision are described which lead from the formulation of the motion problem as an ill-posed one to its solution by minimizing certain 'cost' functions. These cost or energy functions can be mapped onto simple analog and digital resistive networks. It is shown how the optical flow can be computed by injecting currents into resistive networks and recording the resulting stationary voltage distribution at each node. These networks can be implemented in cMOS VLSI circuits and represent plausible candidates for biological vision systems.

Koch, Christof↗
NASA NTRS2008

Damage Diagnosis in Semiconductive Materials Using Electrical Impedance Measurements

Recent aerospace industry trends have resulted in an increased demand for real-time, effective techniques for in-flight structural health monitoring. A promising technique for damage diagnosis uses electrical impedance measurements of semiconductive materials. By applying a small electrical current into a material specimen and measuring the corresponding voltages at various locations on the specimen, changes in the electrical characteristics due to the presence of damage can be assessed. An artificial neural network uses these changes in electrical properties to provide an inverse solution that estimates the location and magnitude of the damage. The advantage of the electrical impedance method over other damage diagnosis techniques is that it uses the material as the sensor. Simple voltage measurements can be used instead of discrete sensors, resulting in a reduction in weight and system complexity. This research effort extends previous work by employing finite element method models to improve accuracy of complex models with anisotropic conductivities and by enhancing the computational efficiency of the inverse techniques. The paper demonstrates a proof of concept of a damage diagnosis approach using electrical impedance methods and a neural network as an effective tool for in-flight diagnosis of structural damage to aircraft components.

Ross, Richard W.↗
DOE OSTI2022

SEIN: Breaking Barriers Resilient Energy System Analysis [Slides]

The Solar Energy Innovation Network (SEIN) is a collaborative research program that supports multi-stakeholder teams in researching and sharing solutions to real-world challenges associated with solar energy adoption. The Breaking Barriers project was selected to participate in the Solar Energy Innovation Network, Round 2, and was led by Groundswell, a D.C.-based clean energy project developer. The Breaking Barriers team included Partnership for Southern Equity, Atlanta University Center campus facility managers and professors, the City of Atlanta's Neighborhood Planning Unit T, and Georgia Power Company. The project aimed to design and construct innovative urban energy resiliency hubs integrating microgrid technology, solar generation, and energy storage in Atlanta colleges and communities. The hubs will help these historically Black colleges and universities (HBCUs) and the energy-burdened broader community in West Atlanta be more resilient, in addition to informing new course curricula at Atlanta University Center campuses. With many possible options for the system's battery size, the Breaking Barriers team needed insight into the relationships between BESS size, economic performance, and resilience at Spelman College's Manley Center. These insights are crucial for entering procurement negotiations with project developers, establishing resilience capabilities that the HBCU campuses can plan around, and guiding the team's fundraising targets. This analysis includes estimates of PV and battery performance, costs, savings, and resilience for multiple battery sizes. In order to provide power during a grid outage, the resilient energy system also needs to be connected to the Manley Center in a safe and island-able manner (electrically isolated from the grid). Analysis of potential electrical configurations and estimated setup costs is key to successfully entering a required interconnection agreement with Georgia Power, as well as informing requests for engineering firms to construct the system. This analysis includes conceptual options and rough order of magnitude cost estimates for electrically interconnecting the resilient energy system to the Manley Center and the grid. The Breaking Barriers team used this analysis to select preferred system characteristics and design for the resilient energy system.

14 SOLAR ENERGY↗
DOE OSTI2025

Dynamic Interfacial Architectures: Cruciferin‐Stabilized Oil/Water Interfaces for Sustainable Emulsions

Stabilizing oil-water interfaces in emulsions by plant-based proteins provides sustainable and tunable ways for designing emulsions with specific properties, for food, healthcare, and pharmaceuticals. Cruciferin, a protein from rapeseed, has great potential as green emulsifier, but details about its structure and mobility at oil-water interfaces are largely unknown. Here, these properties are studied with small angle neutron and x-ray scattering, and neutron spin echo spectroscopy, analyzed by atomistic modelling of scattering curves and coarse-grained modelling, to gain insight into interface coverage, and molecular conformation and mobility at the interface. Cruciferin assumes trimeric conformations at the interface, as in solution, but with its protrusions from the central core of the subunits (“arms”) more compressed. Interfacial mobility is only marginally lower than in solution, indicating the arms still transiently extend and preserve a network, for the first time revealing the mechanism how cruciferin forms highly elastic 2d gel-like oil-water interfaces, as observed in macroscopic rheology. The high interfacial mobility may help in self-repairing non-stabilized interfacial fractions, reducing coalescence. These findings provide a deeper molecular level understanding of proteins at oil-water interfaces, which can stimulate development of new plant-based emulsion products, and contribute to the global protein transition.

cruciferin↗
DOE OSTI2017

Electrocatalytic properties of three new POMs-based inorganic–organic frameworks with flexible zwitterionic dicarboxylate ligands

Three POMs–based inorganic–organic frameworks, namely, [Cu{sub 2}(L){sub 2}(SiW{sub 12}O{sub 40})(H{sub 2}O){sub 2}]·6H{sub 2}O (1), [Co{sub 2}(L){sub 2}(SiW{sub 12}O{sub 40})(H{sub 2}O){sub 8}]·8H{sub 2}O (2) and [Ni{sub 2}(L){sub 2}(SiW{sub 12}O{sub 40})(H{sub 2}O){sub 8}]·6H{sub 2}O (3), (L=1,1′-(1,4-phenylene-bis(methylene))-bis(pyridine-3-carboxylic acid)), have been synthesized and characterized by elemental analyses, IR, XRPD, TG, and single crystal X-ray diffraction. Compound 1 contains 1D double chains decorated by (Cu{sub 2}(L){sub 4}(H{sub 2}O){sub 2}) units and the 1D chains and POMs are stacked to yield 2D frameworks. Compound 2 displays a 2D network constructed from 1D zigzag chains and POMs arranged in ABAB mode. Compound 3 consists of big square girds and the POMs are dispersed in the middle of the two adjacent girds, forming 2D networks. Compounds 1–3 all show structural integrity in aqueous solutions at different pH values and in common organic solvents. Additionally, the fluorescence and electrochemical properties of compounds 1–3 are also investigated. Compounds 1–3 exhibit good electrocatalytic activities for the reduction of NaNO{sub 2} and H{sub 2}O{sub 2}. - Highlights: • Compound 1–3 all contain different 1D chains. The noncovalent interaction of metal–organic moieties from compounds 1–3 and POMs to construct three new host–guest supramolecular compounds. • Compounds 1–3 show good electrocatalytic activities towards the reduction of NaNO{sub 2} and H{sub 2}O{sub 2}. • Compounds 1–3 all show structural integrity in aqueous solutions at different pH values and in common organic solvents. • Compounds 1–3 may be promising luminescent materials due to their luminescent properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Identification of Common Types of Plastics by Vibrational Spectroscopic Techniques

Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS) account for most plastic use worldwide, with production nearing 380 million tons annually. A considerable portion enters municipal solid waste and landfills, creating long-term environmental concerns. Scaling recycling operations requires automated sorting technologies, with spectroscopy and machine learning offering promising solutions. In this study, a six-class convolutional neural network (CNN) was developed for plastic identification using vibrational spectroscopies. Raman Scattering (RS) spectra collected from recycling samples enabled accurate chemical differentiation while assessing the influence of visible features such as color. A CNN trained on RS data achieved 100% classification accuracy. To strengthen field applicability, Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy was incorporated, achieving 95% accuracy with a similar CNN model. These findings demonstrate the potential of integrating spectroscopy with deep learning for reliable plastic classification, advancing development of scalable, field-ready recycling technologies.

Garcia Tovar, Maria P.↗
DOE OSTI2019

Optimal charging management and infrastructure planning for free-floating shared electric vehicles

Recently, major car-sharing service providers have begun to include electric vehicles (EVs) in free-floating car-sharing fleets. For these EVs, downtime due to charging, including time spent traveling to and waiting in queues at charging stations in a sparse charging infrastructure network, is a major barrier to sustainable operation. An intuitive solution to overcome this barrier is to increase the number of fast-charging stations in the fleet service area. This paper studies the relationship between fleet vehicle downtime and the number of charging stations by modeling the fleet operations of a major car-sharing service provider. An integer programming model is developed that jointly optimizes charging station allocation, in terms of the number and location of charging stations, and the assignment of EVs to charging stations. Case study results showed that fleet vehicle charging time comprises 72-75% of the total downtime spent on charging trips. This indicates that charging time, as opposed to travel time or waiting time, is the dominant component of total fleet downtime for charging. The study also shows that by adding 5 to 20 new charging stations reduces total fleet downtime and travel time during charging trips by 2-4% and 26-49%, respectively. Although adding new charging stations to the fleet service area reduces total charging trip travel time significantly, it does not reduce total downtime significantly. Results also show that if the EV battery state of the charge (SOC) threshold—below which a charging trip is initiated—is less than 18%, not all EVs in the car-sharing fleet can be charged using the existing direct current fast chargers (DCFCs) in the fleet’s service area. Furthermore, this implies that adequate charging infrastructure coverage is required to ensure that EVs of a given range can operate in the car-sharing fleet.

25 ENERGY STORAGE↗
DOE OSTI2025

Broadband Infrared Carbon Nanotube Linear Photodetector Arrays

Carbon nanotubes (CNTs) possess exceptional optoelectronic properties, including broadband absorption and high absorption coefficients, making them promising candidates for photodetector applications. However, conventional designs often rely on single or aligned nanotubes, which restrict device scalability and require complex fabrication techniques. Here, in this work, we address these limitations by utilizing CNT-suspended solutions to fabricate large-area photodetectors based on CNT networks via vacuum filtration and direct laser scribing. CNT networks are uniformly formed onto low-thermal-conductance polymeric filter papers that serve as substrates and mitigate the need for suspended structures, reducing the fabrication complexity and cost. The CNT films are patterned into single devices and 49-pixel linear arrays by using a laser-cutting process, enabling scalable and cost-effective production. The resulting devices operate as bolometers and exhibit broad spectral sensitivity extending from visible to midwave infrared, showing stable operation at room temperature. We further demonstrate proof-of-concept IR imaging using linear arrays.

IR imaging↗
DOE OSTI2016

Interplay of Ion–Water and Water–Water Interactions within the Hydration Shells of Nitrate and Carbonate Directly Probed with 2D IR Spectroscopy

The long-range influence of ions in solution on the water hydrogen-bond (H-bond) network remains a topic of vigorous debate. Recent spectroscopic and theoretical studies have, for the most part, reached the consensus that weakly coordinating ions only affect water molecules in the first hydration shell. In this work, we apply ultrafast broadband two-dimensional infrared (2D IR) spectroscopy to aqueous nitrate and carbonate in neat H 2 O to study the solvation structure and dynamics of ions on opposite ends of the Hofmeister series. By exciting both the water OH stretches and ion stretches and probing the associated cross-peaks between them, we are afforded a comprehensive view into the complex nature of ion hydration. We show in aqueous nitrate that weak ion–water H-bonding leads to water–water interactions in the ion solvation shells dominating the dynamics. In contrast, the carbonate CO stretches show significant mixing with the water OH stretches due to strong ion–water H-bonding such that the water and ion modes are intimately correlated. Further, the excitonic nature of vibrations in neat H 2 O, which spans multiple water molecules, is an important factor in describing ion hydration. Finally, we attribute these complex dynamics to the likely presence of intermediate-range effects influenced by waters beyond the first solvation shell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning

Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small-scale features (e.g., microcracks in shales) as an effective medium. Although this fracture-matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, e.g., microcrack clogging during mineral reactions. Here, we employ deep learning in place of effective medium theory to upscale physical processes in small-scale features. Specifically, we consider reactive transport in a fracture-microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which the microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is firstly employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.

58 GEOSCIENCES↗
DOE OSTI2018

Transactive control for connected homes and neighbourhoods

Smart in-home devices, if integrated with each other and with the electric grid, can offer a disruptive solution to managing energy supply and demand. Such transactive energy networks could turn each home from a passive energy consumer into an intelligent, active energy storage and service provider for the future utility grid.

Wang, Na↗
DOE OSTI2018

Modeling ocean, rail, and truck transportation flows to support policy analysis

Freight transportation represents about 9.5% of GDP in the U.S., it is responsible for about 8% of greenhouse gas emissions, and supports the import and export of about 3.6 trillion in international trade. It is therefore important that the national freight transportation system is designed and operated efficiently. Hence, this paper develops a mathematical model to estimate international and domestic freight flows across ocean, rail, and truck modes, which can be used to study the impacts of changes in our infrastructure, as well as the imposition of new user fees and changes in operating policies. The model integrates a user equilibrium-based logit argument for path selection with a system optimal argument for rail network operations. This leads to the development of a unique solution procedure that is demonstrated in a large-scale analysis focused on all intercity freight and U.S export/import containerized freight. The model results are compared with the reported flow volumes. Here, the model is applied to two case studies: a disruption of the seaports of Los Angeles and Long Beach (LA and LB) similar to the impacts that would be felt in an earthquake; and implementation of new user fees at the California ports.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Fabrication of chitosan/polyacrylonitrile blend and semi-IPN hydrogel with epichlorohydrin

The present study is focused on the preparation of chitosan (CS)/polyacrylonitrile (PAN) blend and semi-interpenetrating polymer network (sIPN). Blend CS/PAN hydrogel films (HFs) were prepared by solution blending and casting technique. CS in the blend was crosslinked with epichlorohydrin (ECH) to prepare sIPN. The developed CS/PAN blend and sIPN hydrogels were characterized with Field Emission Scanning Electron Microscopy (FE-SEM), Fourier transform infrared spectroscopy (FTIR), Thermagravimetric analysis (TGA), and Differential Scanning Calorimeter (DSC). The result showed good miscibility between CS and PAN and crosslinking of CS in the blend. The swelling of the different blended and sIPN hydrogels samples were examined at room temperature (T{sub r}). Blend (C80/P20) sample showed highest swelling (∼2400%) and fair degree of stability (∼28% until 96 h), whereas sIPN hydrogel exhibited relatively low degree of swelling (∼244%) and high degree of aqueous (∼85 % until 96 h), and thermal (onset temperature 304°C) stabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Security Enhancement of Network Constraint Grid-Edge Energy Management System

Network constrained grid edge energy management system (EMS) provides economic solution for active and reactive power dispatch of distributed energy resources (DERs) at the grid edge level. Grid edge EMS ensures secure interconnection of a circuit segment to the distribution system by maintaining grid code requirements (e.g. IEEE 1547–2018). Grid edge EMS is dependent on communication to receive load measurement, which brings a risk of unobservable false data injection attacks (FDIAs). To mitigate the risk, this paper proposes a framework to enhance resilient operation of grid edge EMS by detecting the unobservable FDIAs on loads and replacing them with forecasted values. In this work, a two-step detection algorithm is proposed. In first step, conventional residual based algorithm is deployed. Autoencoder (AE) based data driven mechanism is included in second step to detect the presence of unobservable FDIAs. After ensuring the presence of FDIA, its specific location is detected by checking the maximum residue values till the predefined threshold value is reached. Detected false data injected loads are then replaced with forecasted load values following long-short term memory (LSTM) based forecast to ensure resilient performance of grid edge EMS in the presence of attacks. This proposed security enhancement framework for grid edge EMS is evaluated in IEEE 13 bus system with three integrated DERs. Numerical simulation shows the validation of the proposed framework by reducing voltage violation in real operation of grid edge EMS.

cyber attack detection↗
DOE OSTI2020

Formal methods approach to the charging facility location problem for battery electric vehicles

Battery electric vehicles (BEVs) are becoming more prevalent as improvements in battery technology and energy management continue to be made. As the number of electric vehicles grows, the demand for fast-charging stations is expected to increase dramatically. Thus, building new charging station infrastructure efficiently will be key to reducing upfront costs while meeting consumer demands. In this work, we propose a method for choosing a set of charging station locations that are optimized based on a set of given common vehicle demand points. As part of this solution, we also offer a novel abstraction of the road network on which energy-efficient paths that account for charge-time delays may be found. The current algorithm chooses the optimal charging locations for a single agent which has a route objective specified using temporal logic. To demonstrate the proposed method, the running example shows how a charging station could be chosen for an electric delivery vehicle. Simulations were run on sample road networks with a given set of demand points to service and potential charging station locations to compare. The method is shown to successfully rank potential charging stations in terms of their expected average charging time cost.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗