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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Unified Memory: GPGPU-Sim/UVM Smart Integration

CPU/GPU heterogeneous compute platforms are an ubiquitous element in computing and a programming model specified for this heterogeneous computing model is important for both performance and programmability. A programming model that exposes the shared, unified, address space between the heterogeneous units is a necessary step in this direction as it removes the burden of explicit data movement from the programmer while maintaining performance. GPU vendors, such as AMD and NVIDIA, have released software-managed runtimes that can provide programmers the illusion of unified CPU and GPU memory by automatically migrating data in and out of the GPU memory. However, this runtime support is not included in GPGPU-Sim, a commonly used framework that models the features of a modern graphics processor that are relevant to non-graphics applications. UVM Smart was developed, which extended GPGPU-Sim 3.x to in- corporate the modeling of on-demand pageing and data migration through the runtime. This report discusses the integration of UVM Smart and GPGPU-Sim 4.0 and the modifications to improve simulation performance and accuracy.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2019

A Control Framework for Autonomous Smart Grids for Space Power Applications

With the National Aeronautics and Space Administration's (NASA) rising interest in lunar surface operations and deep space exploration, there is a growing need to move from traditional ground-based mission operations to more autonomous vehicle level operations. In lunar surface operations, there are periods of time where communications with ground-based mission control could not occur, forcing vehicles and a lunar base to completely operate independent of the ground. For deep space exploration missions, communication latency times increase to greater than 15 minutes making real-time control of critical systems difficult, if not near impossible. These challenges are driving the need for an autonomous power control system that has the capability to manage power and energy. This will ensure that critical loads have the necessary power to support life systems and carry out critical mission objectives. This paper presents a flexible, hierarchical, distributed control methodology that enables autonomous operation of smart grids and can integrate into a higher level autonomous architecture.

Csank, Jeffrey T.↗
NASA NTRS2019

A Control Framework for Autonomous Smart Grids for Space Power Applications

With the National Aeronautics and Space Administration's (NASA) rising interest in lunar surface operations and deep space exploration, there is a growing need to move from traditional ground-based mission operations to more autonomous vehicle level operations. In lunar surface operations, there are periods of time where communications with ground-based mission control could not occur, forcing vehicles and a lunar base to completely operate independent of the ground. For deep space exploration missions, communication latency times increase to greater than 15 minutes making real-time control of critical systems difficult, if not near impossible. These challenges are driving the need for an autonomous power control system that has the capability to manage power and energy. This will ensure that critical loads have the necessary power to support life systems and carry out critical mission objectives. This paper presents a flexible, hierarchical, distributed control methodology that enables autonomous operation of smart grids and can integrate into a higher level autonomous architecture.

Carbone, Marc A.↗
DOE OSTI2021

Iridium metallene oxide for acidic oxygen evolution catalysis

Exploring new materials is essential in the field of material science. Especially, searching for optimal materials with utmost atomic utilization, ideal activities and desirable stability for catalytic applications requires smart design of materials’ structures. Herein, we report iridium metallene oxide: 1 T phase-iridium dioxide (IrO 2 ) by a synthetic strategy combining mechanochemistry and thermal treatment in a strong alkaline medium. This material demonstrates high activity for oxygen evolution reaction with a low overpotential of 197 millivolt in acidic electrolyte at 10 milliamperes per geometric square centimeter (mA cm geo –2 ). Together, it achieves high turnover frequencies of 4.2 s UPD –1 (3.0 s BET –1 ) at 1.50 V vs. reversible hydrogen electrode. Furthermore, 1T-IrO 2 also shows little degradation after 126 hours chronopotentiometry measurement under the high current density of 250 mA cm geo –2 in proton exchange membrane device. Theoretical calculations reveal that the active site of Ir in 1T-IrO 2 provides an optimal free energy uphill in *OH formation, leading to the enhanced performance. The discovery of this 1T-metallene oxide material will provide new opportunities for catalysis and other applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

West Virginia University Industrial Assessment Center (Final Progress Report)

The Industrial Assessment Center at West Virginia University has been successful in workforce development, and in generation of energy savings for manufacturing facilities during the period 2016 to 2021. Graduate and undergraduate students have benefited from energy assessment experience and most of them have found good positions as energy engineers and analysts in the industrial sector. Several peer reviewed research papers in archival journals as well as conference papers on the topic of energy engineering and assessment have been published. The implemented energy savings for manufacturing facilities have been significant in the areas of lighting, compressed air, process heating, steam, HVAC, chillers and cooling towers, and motors. In addition, water use reduction, smart manufacturing applications to save energy, cyber security evaluation, energy management, productivity improvements and waste reduction opportunities that lead to energy intensity reductions have been explored. The students have obtained an opportunity to interact with energy efficiency and productivity improvement professionals at various conferences and workshops and their research has generated important results. In summary, the West Virginia University Industrial Assessment Center (WVU-IAC) has fulfilled its mission in regard to workforce development, energy efficiency for manufacturing facilities, and laid a strong platform enhancing sustainability through reductions in carbon emissions achieved through energy efficiency and energy management initiatives and reductions in water usage and waste reduction. During the project period (2016-2021) the WVU-IAC has made numerous energy efficiency, water use reduction, waste reduction, and productivity improvement recommendations, a significant number of them having been implemented by the manufacturing facilities. The research adds significant understanding to the area of energy efficiency, water and waste reduction, and productivity improvement. The technical effectiveness and economic feasibility of the methods and techniques investigated and demonstrated through this project have been exemplary, resulting in significant recommended and implemented resource savings and reductions in carbon emissions. The project has been of significant benefit to the public owing to replication of results within the industrial sector, thus reducing operating costs for businesses that result in increase of growth and employment, as well as community benefits in terms of reductions in carbon emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Additive Manufacturing of Self‐Sensing Carbon Fiber Composites

Carbon fiber-reinforced polymer (CFRP) composites have gained substantial attention across various industries owing to their exceptional mechanical properties and lightweight nature. The emergence of additive manufacturing technologies brings new opportunities to the industry, offering advantages such as design freedom, rapid prototyping, and customization. However, the fabrication of CFRP composites through 3D printing techniques poses challenges pertaining to low resolution and limitations in complex geometry realization. This work introduces digital light processing printing as a versatile, high-resolution method ideal for CFRP composite fabrication. Furthermore, the development and characterization of CFRP are focused on and the manipulation of mechanical properties through variations in matrix resins and fiber loadings is investigated, showcasing the versatility of CFRP composites for tailored applications. Additionally, the integration of self-sensing capabilities in CFRP structures is explored, which opens up opportunities for applications in smart components for automotive and structural health monitoring.

carbon fiber composites↗
DOE OSTI2021

Programmable Site‐Specific Functionalization of DNA Origami with Polynucleotide Brushes

Abstract Combining surface‐initiated, TdT (terminal deoxynucleotidyl transferase) catalyzed enzymatic polymerization (SI‐TcEP) with precisely engineered DNA origami nanostructures (DONs) presents an innovative pathway for the generation of stable, polynucleotide brush‐functionalized DNA nanostructures. We demonstrate that SI‐TcEP can site‐specifically pattern DONs with brushes containing both natural and non‐natural nucleotides. The brush functionalization can be precisely controlled in terms of the location of initiation sites on the origami core and the brush height and composition. Coarse‐grained simulations predict the conformation of the brush‐functionalized DONs that agree well with the experimentally observed morphologies. We find that polynucleotide brush‐functionalization increases the nuclease resistance of DONs significantly, and that this stability can be spatially programmed through the site‐specific growth of polynucleotide brushes. The ability to site‐specifically decorate DONs with brushes of natural and non‐natural nucleotides provides access to a large range of functionalized DON architectures that would allow for further supramolecular assembly, and for potential applications in smart nanoscale delivery systems.

Yang, Yunqi↗
DOE OSTI2021

Programmable Site-Specific Functionalization of DNA Origami with Polynucleotide Brushes

Combining surface-initiated, TdT (terminal deoxynucleotidyl transferase) catalyzed enzymatic polymerization (SI-TcEP) with precisely engineered DNA origami nanostructures (DONs) presents an innovative pathway for the generation of stable, polynucleotide brush-functionalized DNA nanostructures. Here we demonstrate that SI-TcEP can site-specifically pattern DONs with brushes containing both natural and non-natural nucleotides. The brush functionalization can be precisely controlled in terms of the location of initiation sites on the origami core and the brush height and composition. Coarse-grained simulations predict the conformation of the brush-functionalized DONs that agree well with the experimentally observed morphologies. We find that polynucleotide brush-functionalization increases the nuclease resistance of DONs significantly, and that this stability can be spatially programmed through the site-specific growth of polynucleotide brushes. The ability to site-specifically decorate DONs with brushes of natural and non-natural nucleotides provides access to a large range of functionalized DON architectures that would allow for further supramolecular assembly, and for potential applications in smart nanoscale delivery systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Neuro-Spark: A Submicrosecond Spiking Neural Networks Architecture for In-Sensor Filtering

Neuro-Spark, which is a new neuromorphic architecture with a field-programmable gate array (FPGA) implementation for ultrafast spiking neural network (SNN) inference at the edge, facilitates smart-pixel in-sensor filtering for high-energy physics experiments at the Large Hadron Collider (LHC). Utilizing the evolutionary optimization for neuromorphic systems (EONS) training method, we generate compact SNN models with 91% signal efficiency, akin to convolutional neural networks but with half the parameters. However, deploying near the detector poses a challenge because the SNN must handle a sustained input data rate exceeding 1013 GB/s. To overcome this, we propose a novel hardware architecture that uses high-level synthesis to construct a tuned architecture for the EONS-trained SNN. In addition to the analysis and validation with an AMD Xilinx Artix-A7 FPGA, our solution consumes only ç24% of FPGA LUT and flipflops. We also introduce an innovative quantization method that reduces FPGA resource utilization by ç15% without compromising accuracy. Our FPGA implementation achieves computing latency of ç10 ns for smart-pixel application inference on an edge FPGA.

Miniskar, Narasinga Rao↗
DOE OSTI2020

Multi-View Convolutional Neural Network for Data Spoofing Cyber-Attack Detection in Distribution Synchrophasors

Security of Distribution Synchrophasors Data (DSD) is of paramount importance as the data is used for critical smart grid applications including situational awareness, advanced protection, and dynamic control. Unfortunately, the DSD are attractive targets for malicious attackers aiming to damage grid. Data spoofing is a new class of deceiving attack, where the DSD of one Phasor Measurement Units (PMUs) is tampered by other PMUs thereby spoiling measurement based applications. In order to address this issue, a source authentication based data spoofing attack detection method is proposed using Multi-view Convolutional Neural Network (MCNN). First, common components embedded in raw frequency measurements from DSD are removed by Savitzky-Golay (SG) filter. Second, fast S transform (FST) is utilized to extract representative spatial fingerprints via time frequency analysis. Third, the spatial fingerprint is fed to MCNN, which combines dilated and standard convolutions for automatic feather extraction and source identification. Finally, according to the output of MCNN, spoofing attack detection is performed via threshold criterion. Extensive experiments with actual DSD from multiple locations in FNET/Grideye are conducted to verify the effectiveness of the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Prolate and oblate chiral liquid crystal spheroids

Liquid crystals are known to exhibit intriguing textures and color patterns, with applications in display and optical technologies. This work focuses on chiral materials and examines the palette of morphologies that arises when microdroplets are deformed into nonspherical shapes in a controllable manner. Specifically, geometrical confinement and mechanical strain are used to manipulate orientational order, phase transitions, and topological defects that arise in chiral liquid crystal droplets. Inspired by processes encountered in nature, where insects and animals often rely on strain and temperature to alter the optical appearance of dispersed liquid crystalline elements, chiral droplets are dispersed in polymer films and deformation induced by uniaxial or biaxial stretching. Our measurements are interpreted by resorting to simulations of the corresponding systems, thereby providing an in-depth understanding of the morphologies that arise in these materials. The reported structures and assemblies offer potential for applications in smart coatings, smart fabrics, and wearable sensors.

42 ENGINEERING↗
DOE OSTI2025

BuildingQA: A Benchmark for Natural Language Question Answering over Building Knowledge Graphs

Graph-based representations of building metadata using ontologies like Brick are vital for smart building applications, but querying them remains a challenge for practitioners. Knowledge Graph Question Answering (KGQA) systems, meant to retrieve answers from natural language questions, traditionally require large-scale training data, making them ill-suited for the specialized and data-scarce building domain. The advent of Large Language Models (LLMs) offers a paradigm shift, enabling zero-shot natural language querying without building/domain-specific training. Yet, there is no standardized benchmark for building-specific KGQA which can guide and validate research in this area. To address this gap, our work makes three primary contributions. First, we introduce the BuildingQA Benchmark Dataset, constructed through a multi-stage process of collecting practitioner data, augmenting it with LLMs for linguistic diversity, and curating a final set of 188 questions across 4 buildings. Second, we characterize the benchmark's complexity and ambiguity, introducing a novel method to quantify its "lexical gap" and providing a four-stage diagnostic framework for analyzing how systems fail. Third, we benchmark zero-shot LLM-powered KGQA systems to establish baseline performance and analyze their failure modes. Our evaluation reveals that top-performing systems achieve a maximum F1 score of only 0.38. This result does not indicate a failure of these powerful systems, but rather underscores the unique challenges posed by our benchmark. It demonstrates a critical performance gap, showing that current methods successful on general KGs struggle with the specific lexical and structural nuances of the building domain. BuildingQA1 thus provides the benchmark dataset and foundational analysis needed to drive the development of novel, domain-aware methods required to unlock the use of semantic data in buildings.

Mulayim, Ozan Baris↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1996

CMOS Active-Pixel Image Sensor With Simple Floating Gates

Experimental complementary metal-oxide/semiconductor (CMOS) active-pixel image sensor integrated circuit features simple floating-gate structure, with metal-oxide/semiconductor field-effect transistor (MOSFET) as active circuit element in each pixel. Provides flexibility of readout modes, no kTC noise, and relatively simple structure suitable for high-density arrays. Features desirable for "smart sensor" applications.

Fossum, Eric R.↗
NASA NTRS1996

Piezoelectric Actuator/Sensor Technology at Rockwell

We describe the state-of-the art of piezoelectric materials based on perovskite and tungsten bronze families for sensor, actuator and smart structure applications. The microstructural defects in these materials have been eliminated to a large extent and the resulting materials exhibit exceedingly high performance for various applications. The performance of Rockwell actuators/sensors is at least 3 times better than commercially available products. These high performance actuators are being incorporated into various applications including, DOD, NASA and commercial. The multilayer actuator stacks fabricated from our piezoceramics are advantageous for sensing and high capacitance applications. In this presentation, we will describe the use of our high performance piezo-ceramics for actuators and sensors, including multilayer stacks and composite structures.

Neurgaonkar, Ratnakar R.↗