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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Original images, ground truth annotations, and precited masks from U-NET for a fungal species x nitrogen experiment

This document describes the “MicroVision-MV003” dataset. This folder contains three subfolders: “originals”, “predicted_masks”, and “gt_masks”. The “originals” folder contains 720 original images acquired using two imaging modalities: 360 images from overhead imaging and 360 images from transmission (raw) imaging. Naming scheme:YYYY-MM-dd__plate_strain_nitrogen-level_replication_experiment_imaging-modalityYYYY-MM-dd: 2024-12-20, 2024-12-21, 2024-12-22,2024-12-23,2024-12-24,2024-12-25,2024-12-26,2024-12-27,2024-12-28,2024-12-29,2024-12-30,2024-12-31.plate: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30.strain: FG, LE.nitrogen-level: N-1, N-10, N-100.replication: 1, 2, 3, 4, 5.experiment: MV.003.imaging-modality: overhead, transmission (raw). For example, the image2024-12-20__1_FG_N-10_3_MV.003_overheadwas taken on 2024-12-20. The plate number is 1, the fungal strain is FG, the nitrogen treatment is N-10, and the replication number is 3 for experiment MV.003, acquired using the overhead imaging modality. The “predicted_masks” folder contains masks generated by a trained U-Net model for the transmission imaging modality.The “gt_masks” folder contains 326 human hand-traced masks that serve as ground truth.

09 BIOMASS FUELS↗
DOE OSTI2023

Dynamic integration of GCAM-Demeter and CLUBS-Select: Datasets on urban extensification effects on land cover, crop yields, net primary production, and CO2 emissions

The datasets provide background data from McManamay et al. (2024) Nature Communications Earth and Environment. We integrate projected dynamic non-urban lands from GCAM-Demeter with projected dynamic urban lands from SELECT from 2015 to 2100 at 1-km resolution to examine stress to the land system, compromised crop production, and losses in Net Primary Production (NPP), that can arise from the competition over land resources. These datasets include changes to non-urban land use and land cover, estimated losses in crop yields, losses in NPP, and increases in CO2 emissions.

Earth Systems↗
DOE OSTI2012

Net Zero Energy Installations (Presentation)

A net zero energy installation (NZEI) is one that produces as much energy from on-site renewable sources as it consumes. NZEI assessment provides a systematic approach to energy projects.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

Net-charge probability distributions in heavyion collisions at chemical freeze-out

We compute net-proton probability distributions in heavy ion collisions within the hadron resonance gas model. The model results are compared with data taken by the STAR Collaboration in Au-Au collisions at {radical}s{sub NN} = 200 GeV for different centralities and for the central energy bin at {radical}s{sub NN} = 39 GeV. We show that, in central Au-Au collisions at {radical}s{sub NN} = 39 GeV, the measured distribution is consistent with the hadron resonance gas model but differs from the predictions of the model at {radical}s{sub NN} = 200 GeV. At the highest energy, deviations from model results are smaller for peripheral collisions. We argue that such properties of probability distributions are expected if the freeze-out conditions probed by fluctuations are located close to the QCD crossover transition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Army Reserve Expands Net Zero Energy, Water, Waste

In 2012, the Army initiated a Net Zero (NZ) program to establish NZ energy, water, and/or waste goals at installations across the U.S. In 2013, the U.S. Army Reserve expanded this program to cover all three categories at different types of Reserve Centers (RCs) across 5 regions. Projects identified at 10 pilot sites resulted in an average savings potential from recommended measures of 90% for energy, 60% for water, and 83% for waste. This article provides results of these efforts.

Solana, Amy E.↗
DOE OSTI2014

DOE Zero Energy Ready Home Case Study: One Sky Homes — Cottle Zero Net Energy Home, San Jose, CA

This builder took home the Grand Winner prize in the Custom Builder category in the 2014 Housing Innovation Awards for its high performance building science approach. The builder used insulated concrete form blocks to create the insulated crawlspace foundation for its first DOE Zero Energy Ready Home, the first net zero energy new home certified in the state of California.

2014 housing innovation awards↗
DOE OSTI2013

New Whole-House Case Study: Transformations, Inc. Net Zero Energy Communities, Devens, Easthampton, Townsend, Massachusetts

In 2009, Transformations, Inc. partnered with Building America team Building Science Corporation (BSC) to build new net zero energy houses in three developments in Massachusetts. The company has been developing strategies for cost-effective super-insulated homes in the New England market since 2006. After years of using various construction techniques, it has developed a specific set of assemblies and specifications that achieve a 44.9% reduction in energy use compared with a home built to the 2009 International Residential Code, qualifying the houses for the DOE’s Challenge Home. The super-insulated houses provide data for several research topics in a cold climate. BSC studied the moisture risks in double stud walls insulated with open cell spray foam and cellulose. The mini-split air source heat pump (ASHP) research focused on the range of temperatures experienced in bedrooms as well as the homeowners’ perceptions of equipment performance. BSC also examined the developer’s financing options for the photovoltaic (PV) systems, which take advantage of Solar Renewable Energy Certificates, local incentives, and state and federal tax credits.

air source heat pump↗
DOE OSTI2016

Measured Zero Net Energy Performance: Results, Lessons, and Surprises

As more and more zero net energy (ZNE) buildings are built and monitored, we can learn from both careful case studies of individual projects as well as a broader perspective of trends over time. In a forum sponsored by Pacific Gas and Electric Company (PG&E), eight expert speakers discussed: results and lessons from monitoring occupied ZNE buildings; best practices for setting performance targets and getting actionable performance information, and; things that have surprised them about monitored ZNE buildings. This paper distills the content of the forum by laying out the most common hurdles that are encountered in setting up monitoring projects, frequent performance issues that the monitoring uncovers, and lessons learned that can be applied to future projects.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Net Zero Pilot Program Lights the Path to Big Savings in Guam

Case study describes how the Army Reserve 9th Mission Support Command (MSC) reduced lighting energy consumption by 62% for a total savings of 125,000 kWh and more than $50,000 per year by replacing over 400 fluorescent troffers with 36 W LED troffers. This project was part of the Army Reserve Net Zero Pilot Program, initiated in 2013, to reduce energy and water consumption, waste generation, and utility costs.

doe/ee-1492↗
DOE OSTI2016

URBAN-NET: A Network-based Infrastructure Monitoring and Analysis System for Emergency Management and Public Safety

Abstract Critical Infrastructures (CIs) such as energy, water, and transportation are complex networks that are crucial for sustaining day-to-day commodity flows vital to national security, economic stability, and public safety. The nature of these CIs is such that failures caused by an extreme weather event or a man-made incident can trigger widespread cascading failures, sending ripple effects at regional or even national scales. To minimize such effects, it is critical for emergency responders to identify existing or potential vulnerabilities within CIs during such stressor events in a systematic and quantifiable manner and take appropriate mitigating actions. We present here a novel critical infrastructure monitoring and analysis system named URBAN-NET. The system includes a software stack and tools for monitoring CIs, pre-processing data, interconnecting multiple CI datasets as a heterogeneous network, identifying vulnerabilities through graph-based topological analysis, and predicting consequences based on what-if simulations along with visualization. As a proof-of-concept, we present several case studies to show the capabilities of our system. We also discuss remaining challenges and future work.

Lee, Sangkeun↗
DOE OSTI2017

URBAN-NET-TOOLKIT

Strengthening and maintaining secure, functioning, and resilient Critical Infrastructure Systems (CISs) is one of our nation's primary goals, and it requires proactive and coo0rdinated efforts among federal, state, local, public and private owners and operators of CISs. Recent worldwide extreme events have shown how interdependencies across CISs (electric grid, telecommunications, transportation, water networks, etc.) can drastically increase system vulnerability. To understand these complex interdependencies, ORNL has instantiated Urban-NET, a novel decision support system for CIS operators who need to identify vulnerabilities for minimizing the cascading impacts - saving utilities, the federal and state government, and private sector companies potentially billions by avoiding a complete CI failure and/or shutdown. The developed system includes a stack of software tools for preprocessing, modeling, interconnecting multiple CIS datasets as an integrated heterogeneous graph (i.e. network), identifying vulnerabilities through topological analysis, and predicting consequences based on simulations along with visualization.

Chinthavali, Supriya↗
DOE OSTI2019

GMLS-Nets: Scientific Machine Learning Methods for Unstructured Data

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a nonparametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

GMLS-Nets: A Framework for Learning from Unstructured Data

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a non-parametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Community-Scale Energy Efficiency Assessment for Zero Net Energy Using the URBANopt Simulation Platform: Preprint

This project explored an innovative modeling approach for assessing the energy efficiency potential of the existing low-income Oceano neighborhood, in unincorporated San Luis Obispo County, and its effects on renewable generation requirements for achieving zero net energy (ZNE). One particular challenge faced was ascertaining the technical energy savings potential across a large neighborhood in a timely manner. The project overcame this challenge through the use of the URBANopt simulation platform, developed at the National Renewable Energy Laboratory. URBANopt manages the automated model creation, simulation, and result aggregation for different design scenarios of existing or candidate buildings in a large neighborhood. Customized energy efficiency packages considered for the residential and commercial sector in the neighborhood included lighting, HVAC, plug load, water heating, envelope, and energy education measures. As a result of the analysis for the Oceano community, energy efficiency measures that are available through incentive programs may provide a 7.4% reduction in the required distributed energy resource (DER) size, corresponding to a 25,000 ft2 decrease in PV array area, to achieve ZNE. Through this work, a computational tool was developed and is being leveraged by the County of San Luis Obispo (CoSLO). The tool has enabled the CoSLO staff to perform similar analysis of the technical potential for energy efficiency, and an understanding of the DER asset requirements and potential savings in other communities within the county.

building energy modeling↗
DOE OSTI2021

Sustainable, Net-Zero Carbon Steelmaking Utilizing Nuclear and Renewable-based Integrated Energy Systems

Decarbonizing Steelmaking: Emissions Background Industrial processes (including power) account for 28% of global CO2 emissions dominated by steel, cement, NH3, ethylene. Steel accounts for 7% of global CO2 emissions (25% of Industrial), 2-3 Gigatonnes annually. Given long asset life and large investments in steel industry, recommend: Detailed analysis, materials R&D and pre-pilot technology integration required by 2030 to position U.S. industry for an investment decision for building first net-zero integrated steel plan Symposium site: https://www.aiche.org/cei/conferences/competitive-energy-systems/2021

08 HYDROGEN↗
DOE OSTI2020

Paving the Way for Net Zero Energy Codes through Performance Based Approaches

The prescriptive path is the most widely used approach for commercial code compliance in the United States. However, to achieve near-term net zero energy performance that many stakeholders in the buildings industry desire, it is clear that energy codes will need to transition from prescriptive to performance-based approaches. However, that transition is not without potential pitfalls. Among the concerns expressed by stakeholders are added complexity and cost, lack of confidence in energy modeling results, gamesmanship on the part of applicants, lack or qualified reviewers, challenges in promoting de-carbonization, and the inequity of trading long lived envelope efficiency for short lived measures such as building controls. This paper will discuss multiple approaches being evaluated and implemented by Pacific Northwest National Laboratory to improve the usability of the performance-based approach for both code compliance and beyond code programs to meet aggressive energy savings targets. A 'system performance' approach provides a simpler approach compared to whole building performance, while keeping tradeoffs limited to specific building systems. A simplified whole-building performance-based approach for small commercial buildings can cost effectively achieve deeper savings for buildings that typically follow the prescriptive compliance approach. Improved reporting and verification processes applied to the traditional performance path can provide greater confidence in simulation results and facilitate adoption of performance-based approaches. Prescriptive packages provide prescriptive solutions while targeting specific performance thresholds. This paper discusses the progress being made with each of these approaches and provides examples of their implementation in energy codes and beyond code programs.

Performance Based Codes, Appendix G, PRM, TSPR, Sy↗