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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bio-inspired Bistable Piezoelectric Energy Harvester for Powering Animal Telemetry Tags: Concept Design and Preliminary Experimental Validation

This paper presents the concept design, preliminary experimental validation, and performance evaluation of a novel bio-inspired bi-stable piezoelectric energy harvester for self-powered animal telemetry tags. The overall concept design, which includes a bio-inspired attachment and a bi-stable piezoelectric energy harvester, is introduced firstly with a specific application example of marine fish tracking. The self-powered telemetry tag can be externally deployed on fish (dorsal fin) to monitor fish habitats, population, and underwater environment. Inspired by the Venus flytrap’s rapid shape transition, a bistable piezoelectric energy harvester is developed to scavenge energy from fish maneuvering and the surrounding fluid flow for a sustainable power supply. The bistability of the harvester is characterized by the measured force-displacement curve and double potential wells. A bluff body is integrated to the free end of the bistable piezoelectric energy harvester to enhance the structure-fluid interaction for the large-amplitude snap-through vibrations and higher voltage output. Controlled laboratory experiments are conducted in a water tank on the bio-inspired bi-stable piezoelectric energy harvester using a servo motor system to simulate fish swing motion at various conditions to evaluate the power generation performance. The preliminary underwater experimental results demonstrated that the proposed bio-inspired bi-stable piezoelectric could effectively converter fish swing motions into electricity. 17.25 mJ energy was obtained within 130 s under the peak-to-peak swing angle of 30o at 1.5 Hz in the capacitor charging experiments.

Qian, Feng↗
DOE OSTI2021

Evaluating the Performance of Integer Sum Reduction in SYCL on GPUs

SYCL is a promising programming model for heterogeneous computing—allowing a single-source code to target devices from multiple vendors. One significant task performed on these accelerators is a primitive operation for integer sum reduction. This paper presents several SYCL implementations of integer sum reduction—using atomic functions, shared local memory, vectorized memory accesses and parameterized workload sizes—to compare the performance and maturity of SYCL against open-source vendor-specific implementations of the same reduction. For a sufficiently large number of integers, tuning the parameters of our SYCL implementations achieves 1.4X speedup over the open-source implementations on an Intel UHD630 integrated GPU. The SYCL reduction is 3% faster than the templated reduction in Thrust, and 0.3% faster than the device reduction in CUB on an Nvidia P100 GPU. The SYCL reduction is 1.9% faster than the templated reduction in Thrust, and 0.4% faster than the device reduction in CUB on an Nvidia V100 GPU.

Jin, Zheming↗
DOE OSTI2025

ORNL Portable Diagnostic Package for Evaluating Mirror Performance

Oak Ridge National Laboratory (ORNL) and Realta Fusion successfully executed a one-year collaborative effort to deploy the Portable Diagnostic Package (PDP) on the Wisconsin HTS Axisymmetric Mirror (WHAM) and to assess future diagnostic needs. The PDP consists of an optical emission spectroscopy (OES) subsystem and a Thomson scattering (TS) subsystem. Within the first two months, the PDP OES subsystem was installed, integrated with WHAM timing, and handed over to trained Realta personnel. It immediately delivered multi-channel impurity surveys and the first spatially resolved plasma-rotation profiles inferred from C III Doppler shifts. The TS subsystem was commissioned within the first six months; transportation of the class IV laser system, approval of the safety plan, and the integration with WHAM timing with 100 ns resolution. The TS subsystem provided the first localized electron temperature measurements in WHAM commissioning shots. Leveraging these results, the team has discussed a diagnostic concept map for Realta’s next-step facility, Anvil, identifying line-of-sight constraints, required temporal resolution, and hardware upgrades.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Ultra-clear Aerogels for Insulated Glass in Refrigeration Doors (CRADA Final Report)

Display refrigerators commonly employ glass doors to provide product visibility; however, these doors exhibit significantly poorer thermal insulation than solid doors, resulting in increased heat transfer and higher energy consumption. To address this limitation, this project investigated the use of silica aerogel as a transparent insulating medium for refrigerator glass doors, with the objective of enhancing thermal performance without compromising visual clarity. In Phase I of the project, we assessed the feasibility of silica aerogel insulation through laboratory and full-scale studies, including material characterization, durability testing, and heat transfer measurements, along with the development and performance evaluation of prototype glass doors under realistic operating conditions. In Phase II, the project focused on scaling the technology toward practical deployment. This included scaling up the manufacturing process to achieve a surface area roughly 20 times larger for commercial applications, developing full-scale prototypes that integrated silica aerogel into standard display refrigerator doors, and conducting field evaluations to assess thermal performance, energy savings, and operational durability under real-world conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Evaluating the Performance of Random Forest and Iterative Random Forest Based Methods when Applied to Gene Expression Data

Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3. We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.

iRF-Loop, expression network, Populus Trichocarpa↗
DOE OSTI2024

Evaluating the Performance and Reliability of Screen-Printable Fire-Through Copper Paste on PERC Solar Cells

A bifacial silicon heterojunction solar cell demands approximately 210 mg usage of silver paste (9 busbars, 24.5%, bifacial, M6 size wafer). Copper is an excellent alternative to silver: 100x cheaper, similar elecvtrical resistivities, and 1000x more abundant. Successful demonstration of large area selective emitter PERC solar cells using Cu fire-through paste with FF approximately 75% and approximately 19%. Paste chemistry results in oxide-based Cu diffusion barrier, leading to good reliability of the devices: 1000h DHT giving a 3.5% efficiency drop. The additional series resistance still needs to be lowered. Printed and fired cell performance is governed mostly by pFF, especially J02 ; same for DHT degradation. Further improvement in printing and firing optimization for lower series resistance and higher FF.

Ag↗
DOE OSTI2023

Performance portability for the CMS Reconstruction with Alpaka

For CMS, Heterogeneous Computing is a powerful tool to face the computational challenges posed by the upgrades of the LHC, and will be used in production at the High Level Trigger during Run 3. In principle, to offload the computational work on non-CPU resources, while retaining their performance, different implementations of the same code are required. This would introduce code-duplication which is not sustainable in terms of maintainability and testability of the software. Performance portability libraries allow to write code once and run it on different architectures with close-to-native performance. The CMS experiment is evaluating performance portability libraries for the near term future.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Evaluation of Distributed Power Apportioning with Net Load Management Engine in Microgrids Using Power Hardware-in-the-Loop Simulation

This article presents the performance evaluation of ratio consensus-based distributed power apportioning engine along with centralized net load management (NLM) engine that ensures viable and stable operation of an islanded microgrid. Managing net load variability in a microgrid with high penetrations of uncertain renewable generation and ever-changing load demands is a crucial need in order to ensure viable and stable operation of the microgrid. Centralized “dispatch-rule”-based and/or multi-agent-based distributed control of distributed energy resources (DERs) in microgrid are well accepted for microgrid by adapting ANSI/ISA-95-based hierarchical control architecture. In the application where microgrid network has large geographical span with multiple DERs dispersed in the network, high penetration of uncertain renewable energy resources, and ever-changing load demands, a judicious selection of techniques/solutions for managing net-load resources for maintaining viability and stability is required. With this motivation, this article proposes a novel solution to mitigate the challenges by incorporating a mixed centralized NLM engine and distributed power apportioning control of DERs and loads. A power-hardware-in-the-loop (PHIL) -based experiment is conducted with the centralized NLM engine and the distributed power apportioning engine along with two commercial inverters. The experimental results validates the efficacy of the proposed method in ensuring viability and stability of a microgrid.

hardware-in-the-loop simulation↗
DOE OSTI2022

Individualized empirical baselines for evaluating the energy performance of existing buildings

The evaluation of building energy performance requires a baseline for comparison. Common empirical baselines are usually used for existing buildings since they are fast and convenient. However, the same type of building at the same location will receive the same baseline despite their difference in usage. Individualized baselines by creating building energy models are possible solutions, but it is labor intensive and time-consuming. To fill the gap, this study is to develop individualized empirical baselines for existing buildings in a fast way. First, common empirical baselines are created based on survey data. Then, to get training samples, building energy models for large-scale existing buildings are created and simulated. So finally, based on simulation results, mathematical models to get individualized empirical baselines in a fast way are created. U.S. medium office buildings were used as an example to demonstrate the method. We developed 30 mathematical models for medium office buildings in two vintages (constructed before 1980 and after 1980) and 15 climate zones. The mean absolute percentage errors (MAPE) between the individualized empirical baselines and the modeled baselines for those 30 mathematical models are all lower than 5.5%. An engineer can obtain the individualized empirical baseline for an existing building in a few seconds by using the open-source tool we developed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Abstract Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration “surrogates,” which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from “global” chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using “global” surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using “expert-selected” surrogates ( n = 3) instead of “global” surrogates ( n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases. Graphical Abstract

Pu, Shirley (ORCID:0000000201223797)↗
DOE OSTI2022

Ames National Laboratory Annual Site Environmental Report for CY2021

The primary purpose of this report is to summarize the performance of Ames National Laboratory’s environmental programs, present highlights of significant environmental activities, and confirm compliance with environmental regulations and requirements for calendar year 2021. This report is a working requirement of Department of Energy Order 231.1B, Environment, Safety and Health Reporting. It includes descriptions of the Laboratory’s site, mission, the status of its compliance with applicable environmental regulations, its planning and activities to maintain compliance, and a comprehensive review of its environmental protection, surveillance and monitoring activities. Ames National Laboratory is located on the campus of Iowa State University (ISU) and occupies 13 buildings owned by the Department of Energy (DOE). See the Laboratory’s Web page for location and Laboratory overview. The Laboratory also leases space in ISU owned buildings. In 2021, the Laboratory accumulated and disposed of hazardous waste under a U.S. Environmental Protection Agency (EPA) issued generator number. All waste was handled according to applicable EPA, State, and local regulations and DOE Orders. The Laboratory operates as a Small Quantity Generator (SQG) of hazardous waste. There were no radiological air emissions or exposures to the general public due to Laboratory activities in 2021 (See U.S. Department of Energy Air Emissions Annual Report in Appendix A.) The Laboratory has an established Environmental Radiological Protection Program (Plan 10200.041) per DOE Order 458.1 requirements. Plans, policies, and procedures are in place to protect the public and the environment against undue risk from radiation associated with DOE radiological activities. As indicated in prior Site Environmental Reports, formal pollution prevention awareness, waste minimization and recycling programs have been in practice since 1990, with improvements implemented most recently in 2017 with Iowa State University’s shift toward single-stream recycling. Included in recycling efforts are items such as batteries, monitors, corrugated cardboard, lamps, miscellaneous electronic office equipment, mixed paper, newsprint, food/beverage containers, and laboratory glassware. Ames National Laboratory also recycles/reuses salvageable metal, used oil, and foamed polystyrene peanuts, and encourages chemical redistribution and sharing among research groups. Ames National Laboratory reported its contractual performance to DOE-Ames Site Office (AMSO) through the Laboratory’s Performance Evaluation Measurement Plan (PEMP), and a performance level of “B+” was achieved in 2021 for Sustain Excellence and Enhance Effectiveness of Integrated Safety, Health, and Environmental Protection As reported in Site Environmental Reports for prior years, the Laboratory’s Environmental Management System (EMS) has been integrated into the Laboratory’s Integrated Safety Management System (ISMS) since 2005. The integration of EMS into Laboratory business practices allows the Laboratory to systematically review, address and respond to environmental impacts. In addition to DOE-identified objectives and targets, the EMS Steering Committee recommends annual environmental goals for the Laboratory. Due to the COVID-19 pandemic and limited onsite work staff, goals of reducing water usage and travel/commuting to promote the reduction of scope 3 greenhouse gases were achieved. All contract deliverables and environmental compliance activities were still met during this time.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation

This paper presents the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., the loss of a large generation unit). This NLM engine is particularly important for microgrid systems because it provides a high-speed, cost-optimal control solution to coordinate grid-forming inverters and to dispatch grid-following inverters and deferrable loads in microgrid systems to enhance grid resilience and reliability. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm with two sequential contingency events of lost generation units. The experimental results indicate that the NLM engine can maintain system stability, achieve the targeted system voltage and frequency, and balance load and generation to serve the critical facility with improved system resilience and reliability.

grid-following inverter↗
DOE OSTI2022

Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation

This presentation discusses the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., the loss of a large generation unit). This NLM engine is particularly important for microgrid systems because it provides a high-speed, cost-optimal control solution to coordinate grid-forming inverters and to dispatch grid-following inverters and deferrable loads in microgrid systems to enhance grid resilience and reliability. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm with two sequential contingency events of lost generation units. The experimental results indicate that the NLM engine can maintain system stability, achieve the targeted system voltage and frequency, and balance load and generation to serve the critical facility with improved system resilience and reliability.

droop control↗
DOE OSTI2023

Accelerating Random Forest Classification on GPU and FPGA

Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that for high accuracy targets, our GPU implementation yields 5-9x speedup over CSR, and up to a 2x speedup over cuML.

FPGA, Xilinx FPGA, GPU, Random Forest classificati↗
DOE OSTI2022

Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation: Preprint

This paper presents the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., lost a large generation unit). The NLM engine is a central dispatch control system that provides high-speed cost-optimal coordination of a set of net loads (combination of generation and deferrable loads) connected to the same electrical network. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm. The experimental results indicate that the NLM engine can achieve the targeted system voltage and frequency and balance load and generation to serve the critical facility.

droop control↗