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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Translating Material-Level Characterization of Carbon-Nanotube-Reinforced Composite Gridlines to Module-Level Degradation

Cell cracks in PV modules caused by poor handling during shipping and installation as well as from extreme weather events can lead to gradual or immediate power degradation. To directly address cell-crack-induced degradation, we have formulated a carbon nanotube additive for commercial screen printed silver pastes. We have shown in previous work that these metal matrix composites have little to no effect on the cell's efficiency while enhancing the metallization's fracture toughness and electrical gap-bridging capability. In this work, we focus on translating materials level characterization techniques to module level degradation. We found that we get conflicting results from two different methods of measuring the metallization's ability to electrically bridge gaps in cracked solar cells. Mini-module stress testing is currently underway to determine which materials characterization correlates well with the min-module degradation characteristics.

carbon nanotubes↗
DOE OSTI2023

Cadmium Telluride Accelerator Consortium (CTAC)

The United States is the leader in cadmium telluride (CdTe) photovoltaic (PV) manufacturing, and CdTe is the second most-deployed solar technology in the world. The CdTe Accelerator Consortium (CTAC) was formed to enhance U.S. technology leadership and competitiveness in CdTe photovoltaics (PV) including increasing cell efficiencies, decreasing module costs, and maintaining or increasing domestic CdTe PV material and module production. Leadership for the 3-year consortium was selected by an NREL team through a competitive solicitation. CTAC was formally announced on August 1st by U.S. Secretary of Energy Jennifer M. Granholm and U.S. Representative Marcy Kaptur at an event held at University of Toledo. We will give an overview of CTAC research directions along with their goals and activities. We can also discuss how CTAC relates to the National Renewable Energy Laboratory (NREL) and to the U.S. Manufacturing of Advanced Cadmium Telluride Photovoltaics Consortium (US-MAC). Visit our poster to find out who leads CTAC and how you can get involved.

costs↗
DOE OSTI2019

Enabling Secure Grid Information Sharing through Hash Calendar-based Blockchain Infrastructures

The power grid is becoming increasingly interconnected, as information from grid operations must be accessible to an array of engineers, grid regulators, and market operators. However, there remain challenges ensuring the appropriate integrity and authenticity of the grid information. While traditional cryptographic protocols, like public key infrastructure, provide security for in-transit communication, they come with key exchanges and cost to get public key certificates from a centralized authority. To address these challenges, this paper demonstrates how a hash calendar-based blockchain can be used to provide improved security for communication with the use of distributed ledgers. It demonstrates the security requirements and threats to this communication, and then provides a theoretic definition of the process. Furthermore, it introduces a use-case of the proposed process within the WSU Smart City Testbed DMS with the Guardtime’s Keyless Signature Infrastructure (KSI) architecture.

Kaur, Kudrat J.↗
DOE OSTI2025

A Flang Plugin for Fortran Feature Characterization

As new compute systems are developed, there is still a need to compile and execute codes authored in Fortran on these leading edge systems. In order to achieve this, development of compilers that support the latest hardware is continuously under development. Though the specification of Fortran is extensive, it is helpful to compiler authors to be able to prioritize the development of key features in order to get certain codes deemed important, e.g., applications of interest to leadership computing facilities, executable on leading edge compute systems. Identifying key features though is largely done through querying software experts or users of the Fortran applications of interest, who then manually report what features are and are not present. This exercise can both time consuming and error prone. To automate this process, we present a compiler plugin to Flang, the Fortran frontend for LLVM. This plugin is a tool that operates on the parse tree representation generated by Flang and detects key features based on walking parse tree nodes that correspond to features of interest. We show the result of our tool on four applications, three of which were manually profiled by software experts. We show the discrepancies between our tool and the manual characterization of the three applications, as well as generate a characterization for an application not yet profiled. We intend to open-source our tool in order to invite the community to benefit from the tool and make contributions for other features.

Cabrera, Anthony [ORNL]↗
DOE OSTI2020

Quantifying Load Uncertainty Using Real Smart Meter Data

As we get closer to customers in distribution systems, load stochasticity increases. In the past, due to lack of real-time data, the comprehensive knowledge of load behavior was limited, and simplistic assumptions had to be made for distribution system modeling and analysis, especially in the processes of network design and expansion. With the deployment of Advanced Metering Infrastructure (AMI), ample real-time smart meter data has become available to utilities. In this paper, using real hourly smart meter data, we have quantified load uncertainty in terms of average, maximum and maximum noncoincident demands on a daily basis, as well as load factor and diversity factor. These uncertainty metrics are examined for individual residential, commercial and industrial customers, as well as distribution transformers serving residential customers. This paper provides a benchmark on load uncertainty quantification for practicing engineers and researchers.

Bu, Fankun↗
DOE OSTI2025

Traffic Shaping to Traffic Engineering in Time-Sensitive OT Network

Modern industrial automation systems increasingly depend on network infrastructures for time-critical communication, driving the need for solutions that guarantee timely and reliable data delivery. IEEE 802.1 Time-Sensitive Networking (TSN) holds significant promise for converging Information Technology (IT) and Operational Technology (OT) networks, enabling interoperability and supporting the coexistence of mixed-critical traffic crucial for Industry 4.0 and IIoT. To achieve deterministic communication, TSN employs various traffic shapers such as the Time-Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), and Credit-Based Shaper (CBS). However, the effective deployment of TSN in industrial automation faces several challenges. These include the non-trivial mapping of diverse industrial traffic types to specific shapers, the complexity of optimizing shaper configurations. We present a model for effective traffic engineering within TSN enabled OT Network. Our experiments also demonstrate how shaping of certain traffic types get affected in absence of precise time synchronization and propose possible solutions based on experiment results. Based on our experimental results we provide recommendations on how traffic type assignments should be done and which traffic shaping mechanisms should be used for a particular traffic type.

Sarker, Taposh Kumer [University of Texas at El Pa↗
DOE OSTI2023

Assessment and Performance of Flexible Quench Antenna Array Diagnostics for Superconducting Magnets

FNAL has been developing multiple versions of flexible quench antennas (flex-QA), including some specially optimized for high sensitivity and/or high resolution, to characterize quench events and transients during current ramping in superconducting magnets. A fundamental feature in our use of these is the creation of grid-like structures of sensitive elements to cover coil surfaces, with the aim of getting precise localization of magnetic flux-change events. The flex-QA are coupled with fast data-acquisition, allowing comprehensive analysis of signals at the relevant fine time scales. In addition to arrays of various flex-QA types being used during cryogenic testing of superconducting magnets, we also are utilizing a newly developed room temperature test stand to better understand QA response characteristics. The data from actual superconducting magnet tests, warm test stand measurements, and simulation data on the same QA designs allows us to draw conclusions on operational feasibility and plan better for improvements of our sensors. In this paper we present data from the multiple tests performed and analysis results. Flex-QA designs are compared, and their features, options, and optimization discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Construction of a New MgB 2 Coating System for 1.3-GHz Superconducting RF Cavities at LANL

After many years of evaluating MgB 2 films prepared with various techniques for the application to superconducting radio-frequency (SRF) cavities, we have decided to build a system to coat full-size 1.3-GHz elliptical cavities. This paper describes the design and construction of the system. Additionally, we briefly describe experimental results with a small system and first tests with the new large system. In conclusion, we were able to obtain superconducting samples with a T c of up to 38 K with the small system, but we have not been able to get any superconducting samples with the new system yet.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

A restricted Boltzmann machine (RBM) is a generative model that could be used in effectively balancing a cybersecurity dataset because the synthetic data a RBM generates follows the probability distribution of the training data. RBM training can be performed using contrastive divergence (CD) and quantum annealing (QA). QA-based RBM training is fundamentally different from CD and requires samples from a quantum computer. We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training. Our analysis of the ISCX data shows that the dataset is imbalanced. We present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the undersampling of benign instances. The imbalanced training dataset is divided into five sub-datasets that are trained separately. A majority voting is then performed to get the result. Our results show the majority vote increases the classification accuracy up from 90.24% to 95.68%, in the case of CD. For the case of QA, the classification accuracy increases from 74.14% to 80.04%. In the second scheme, a RBM is used to generate synthetic data to balance the training dataset. We show that both QA and CD-trained RBM can be used to generate useful synthetic data. Balanced training data is used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better than other classifiers. They both show an accuracy of 93%. Our results show a proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques. Further, we show that synthetic data generated from a RBM can be used to balance the original dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗
DOE OSTI2024

Equitable Transactive Market Design to Coordinate Networked Microgrids with Mixed Ownership

This paper presents an inter-microgrid peer-topeer (P2P) transactive market mechanism to support the coordination of multiple microgrids in a mixed-ownership environment, while enabling prosumers to actively participate in the market to get their own benefits. An equitable P2P transactive energy market is designed, in which the energy burden to customers within microgrids is fairly distributed by leveraging a peer-to-peer communication network among MG owners and the distribution system operator (DSO). To reach the market settlement, a consensus-based distributed optimization algorithm is introduced to enable each MG owner and the DSO to distributedly determine the cleared price which is compensated by a household-income-based discount factor to encourage the energy-burden equity among customers in networked microgrids service territory. Numerical results on the modified 123 node test feeder including 6 microgrids are used to demonstrate the operation of the introduced equitable transactive energy market.

Vu, Thanh Long↗
DOE OSTI2022

Hierarchical Distributed Optimal Power Flow of HV and MV Distribution Networks With Continuous and Discrete Devices

With large-scale distributed photovoltaics (PVs) being integrated into distribution networks (DNs), coordinated optimal power flow (OPF) of high voltage (HV) and medium voltage (MV) DNs should be investigated to optimally dispatch the distributed PVs and other network devices. Here, this paper presents a hierarchical distributed OPF method for HV and MV DNs with on-load tap changers, reactive power compensators, feeder switches and distributed PVs. A hierarchical master-slave control architecture is applied to implement coordinated OPF of two-layer DNs. The HV master problem and MV subproblems are transformed into mixed-integer convex problems respectively with second order cone programming and LinDistFlow approximation. Since there is no efficient distributed algorithm to solve such OPF models with integer subproblems, a novel distributed algorithm is proposed in this paper to efficiently solve the hierarchical coordinated OPF model with integer subproblems in a distributed manner. In the proposed algorithm, the coordinated OPF model is solved in a branch-and-bound framework, where in each branch node generalized Benders decomposition (GBD) algorithm is applied to decompose the coordinated OPF model into a master problem and relaxed subproblems and solves them iteratively to get optimal solution. The GBD optimal and feasible cutting planes generated in a branch node are proved to be valid for its descendants. Moreover, three acceleration techniques are introduced into the proposed algorithm to improve computational efficiency. Finally, the effectiveness and accuracy of the proposed method are verified via simulation tests in Jinzhai DNs of China.

42 ENGINEERING↗
DOE OSTI2022

Towards Optimal and Executable Distribution Grid Restoration Planning With a Fine-Grained Power-Communication Interdependency Model

Distribution service restoration (DSR) under natural disasters is always a critical and challenging problem for utility companies. An effective solution must not ignore the power-communication interdependency as various systems are getting increasingly connected in the Smart Grid era. In this paper, we propose a two-layer distribution system model with both power and communication components. Based on this model, we formulate the restoration process as a routing problem that schedules the path and action sequence of utility crews that involves repairing damaged components, closing power switches, and enabling communication paths between the control center and remote field devices. Further, we develop a simulation-based method to quantitatively evaluate the restoration process with public reference models of large-scale power systems. The experimental results show that our method improves the total restored energy up to 57.6% and reduces the recovery time up to 63% by considering the power-communication interdependency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Cut–and–chip harvester material capacity and fuel performance on commercial-scale willow fields for varying ground and crop conditions

Shrub willow (Salix spp.) is capable of producing commercially attractive amounts of biomass in short rotations, but harvesting costs and logistics remain a concern. There is a particular need for information about harvesting operations on larger, commercial short–rotation woody crop systems. Another recent issue on commercial fields in northern New York is commercial growers conducting harvests during the growing season rather than the recommended dormant season when fields may be too wet to harvest. This study evaluated and modeled the in–field performance of a cut–and–chip harvester for almost 700 wagonloads of chips operating in commercial willow fields in a wider array of crop and field conditions than have been previously reported. Analysis indicated that the time of harvest (leaf–on or leaf–off) and whether site conditions were wet or dry affected the harvester's material capacity. Mean material capacity was greatest for leaf–off, dry conditions (71.8 Mg/hr) and lowest for leaf–on harvests, which were similar for wet (30.4 Mg/hr) and dry conditions (29.7 Mg/hr). Mean crop specific fuel consumption ranged between 1.3 and 3.3 L/Mg, but can get considerably higher for standing biomasses below 40 Mg/ha. Wet ground conditions and leaf–on harvests tend to decrease material capacity and increase fuel consumption as the harvester has to divert power to forward movement and material processing. Relationships for material capacity and fuel consumption based on standing biomass, time of harvest and ground conditions will be essential for evaluating and modeling the economic and environmental impacts of commercial–scale willow operations.

09 BIOMASS FUELS↗
DOE OSTI2024

A modern concept of Lagrangian hydrodynamics

Here, we offer a modern interpretation of Lagrangian hydrodynamics as employed in Lagrangian simulations of compressible fluid flow. Our main result is to show that artificial viscosity, traditionally viewed as a numerical artifice to control unphysical oscillations in flows with shocks, actually represents a physical process and is necessary to derive accurate simulations in any compressible flow. We begin by reviewing the origins of two numerical devices, artificial viscosity and finite-volume methods. We proceed to construct a mathematical (PDE) model that incorporates those numerics and in which a new length scale, the observer, arises representing the discretization. Associated with that length scale, there are new inviscid fluxes that are the artificial viscosity as first formulated by Richtmyer and an artificial heat flux postulated by Noh but typically not included in Lagrangian codes. We discuss the connection of our results to bivelocity hydrodynamics. We conclude with some speculation as to the direction of future developments in multidimensional Lagrangian codes as computers get faster and have larger memories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Study of Energy Saving Analysis for Different Industries

This study analyzes the energy consumption and saving performance in the industries in the U.S.A. All energy assessments implemented were for facilities whose annual energy consumptions were less than 9,000,000 kWh (small- and medium-sized industries) that belong to the manufacturing industries with Standard Industrial Classification (SIC) codes ranging from 2000 to 3999 in addition to SIC codes starting with 49. In this study, assessments are classified based on the SIC codes with recommendations analysis for each classification to get a better idea of what recommendations were suggested in each major industrial sector, knowing that 68 assessments were made, and their SIC ranged from 14 to 49. In addition, this study could be considered as a guide for energy engineers and other personnel involved in the energy assessment process. The information investigated can give a better prediction for composing better energy-demanding industries and minimizing energy consumption. More than 61 energy assessments were conducted for manufacturing facilities and analyzing the data gathered and processed. Through the research, the Fabricated Metal industry achieved the highest average kWh savings and cost savings within the industries studied in this study. According to the average gigajoule (GJ) savings, the fabricated metal industry ranked second within the studied industries. Conversely, Food and Kindred Products achieved the highest GJ energy savings within the studied industries. Overall, lighting, motors, compressors, and heating, ventilation, and air conditioning (HVAC) were the most contributing industries in a total of 547 recommendations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A Study of Cost-Saving Potential of Load Flexibility Measures in Grid-Interactive Multifamily Buildings

With recent advances in smart technologies, more and more smart devices are penetrating the residential and commercial buildings market. The introduction of these smart devices is also helping IoT companies emerge with load aggregator roles in the sector. With more utility companies on the track of supporting OpenADR protocols, the aggregators could play a significant role in providing load flexibilities by automatically responding to demand response (DR) events and coordinating load flexibility measures between customers. This would benefit utility companies by reducing stress on the grid during critical peak demand hours as well as customers by allowing them to utilize utility rate structures advantageous to those able to reduce electric usage during high-demand hours. This study evaluates cost and energy savings from adopting multiple load flexibility measures in multifamily buildings. Combinations of different load flexibility measures, including space temperature floating, light dimming, automatic window shading, and water heater temperature floating, are considered. The simulations are performed using OpenStudio®, an open-source U.S. Department of Energy (DOE) simulation platform. For the case study, we used a midrise apartment building with weather conditions from Denver, Colorado. To compare climate zone differences, simulations were also performed for Los Angeles, California, and Chicago, Illinois. Initial results indicate that the application of automated load flexibility measures without careful consideration of dispatching strategies and DR program enrollments could significantly affect the savings. To get meaningful cost savings, aggregators need to encourage tenant awareness to curtail energy usage through occupant behavior in addition to dispatching automatic load flexibility measures. The outcomes from this study are believed to help load aggregators understand the risks and benefits of load flexibility opportunities.

building energy modeling↗
DOE OSTI2022

Experimental Characterization of Hygrothermal Aging: Competition Between Thermo-Oxidation and Hydrolysis Phenomena

Abstract In this work, we have presented a comparison between effects of accelerated thermo-oxidative, hydrolytic and hygrothermal aging on the mechanical and chemical properties of a polyurethane based (PUB) adhesive. The adhesive is a flexible adhesive which is extensively used in automobile industry as a glass sealant. During service life it gets exposed to the hygrothermal environment and the integrity of matrix interface is most vulnerable. This study focuses on weighing and comparing effects of different aging environments on material behavior; and it shows that hygrothermal aging environment results as a competition between two sub-aging phenomena i.e., thermo-oxidation and hydrolytic aging. Samples were exposed to 0%RH, 80%RH and submerged condition in distilled water. Uni-axial tensile tests, and cross-link density analysis by swelling test were carried out on as-received and aged samples. The aging process was conducted at three different temperatures (60°C, 80°C and 95°C) and for four different exposure durations of 1, 10, 30, and 90 days. This work confirms that the effect of hygrothermal aging is a result of competition between thermo-oxidation and hydrolytic aging environments. The total mechanical and environmental damage incurred by specimens is a collaborative effect of all exposure environments involved i.e., aging time, temperature, oxygen, water and humidity. The results are in agreement with the chemical test outcomes as well.

Shaafaey, Mamoon↗