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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reinforcement learning pulses for transmon qubit entangling gates

The utility of a quantum computer is highly dependent on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor—in contrast to many established gate implementation strategies. In this work, we utilize a continuous control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly, with little to no additional optimization. Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

wastewater_virus

This repo contains software used to clean and assemble high-throughput sequencing data containing viruses. The input is raw illumina sequencing reads and the output is a database of high-quality viral genomes. The specific application is to wastewater viral concentrates but it is not restricted to that sample type. The software is composed of Nextflow workflows and a set of custom Python and bash scripts that call publicly available bioinformatics tools to accomplish obvious tasks in data analysis in a high performance computing environment. For detailed information, please see the repo's README file.

Kantor, Rose [Lawrence Livermore National Laborato↗
DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗
DOE OSTI2025

input4MIPs.CMIP7.DRES.DRES-CMIP-BB4CMIP7-2-0

CMIP7 Forcing Datasets (input4MIPs). The dataset is available at: https://esgf-node.llnl.gov/search/input4mips/?mip_era=CMIP7&target_mip=CMIP&activity_id=input4MIPs&institution_id=DRES&source_id=DRES-CMIP-BB4CMIP7-2-0 This dataset Source ID: DRES-CMIP-BB4CMIP7-2-0 is part of input4MIPs under " ['emissions'] " category. More information about the dataset can be found at the following links: https://www.globalfiredata.org https://input4mips-controlled-vocabularies-cvs.readthedocs.io/en/stable/database-views/input4MIPs_source-id_CMIP7.html https://input4mips-controlled-vocabularies-cvs.readthedocs.io/en/stable/dataset-overviews/

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Addressing Parental Vaccine Hesitancy and Other Barriers to Childhood/Adolescent Vaccination Uptake During the Coronavirus (COVID-19) Pandemic

Routine childhood immunizations are proven to be one of the most effective public health interventions at controlling numerous deadly diseases. Therefore, the CDC recommends routine immunizations for children and adolescent populations against vaccine-preventable diseases e.g., tetanus, pertussis, diphtheria, etc. This current review sought to examine barriers to pediatric vaccine uptake behaviors during the COVID-19 pandemic. We also explored the implications for parental vaccine hesitancy/delay during an ongoing health crisis and proposed recommendations for increasing vaccine confidence and compliance. Our review determined that the receipt for vaccinations steadily improved in the last decade for both the United States and Tennessee. However, this incremental progress has been forestalled by the COVID-19 pandemic and other barriers i.e. parental vaccine hesitancy, social determinants of health (SDoH) inequalities, etc. which further exacerbate vaccination disparities. Moreover, non-compliance to routine vaccinations could cause an outbreak of diseases, thereby, worsening the ongoing health crisis and already strained health care system. Healthcare providers are uniquely positioned to offer effective recommendations with presumptive languaging to increase vaccination rates, as well as, address parental vaccine hesitancy. Best practices that incorporate healthcare providers’ quality improvement coaching, vaccination reminder recall systems, adherence to standardized safety protocols (physical distancing, hand hygiene practices, etc.), as well as, offer telehealth and outdoor/drive-through/curbside vaccination services, etc. are warranted. Additionally, a concerted effort should be made to utilize public health surveillance systems to collect, analyze, and interpret data, thereby, ensuring the dissemination of timely, accurate health information for effective health policy decision-making e.g., vaccine distribution, etc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Multi-modal dynamic radiography using short-pulse laser-generated probe beams

Radiography is an important tool for the interrogation of dynamic experiments in the fields of dynamic properties of materials, and in condensed matter, high explosive, and high-energy-density physics. Multi-modal radiography advances the hypothesis that combining the information delivered by multiple radiographic modalities can lead to more constrained (improved) “reconstruction” of the scene than can be obtained from a single probe. We identify four modalities: multi-probe, time sequence, multi-view, and multi-messenger. Multi-probe radiography is a promising candidate for a next-generation dynamic radiographic facility. High-energy X-rays are the most frequently used probe for dynamic radiography, although recent developments show the utility of proton (pRad), electron (eRad), and neutron probe beams. Because each probing species interacts with material in the radiographic scene through quantitatively different mechanisms, each returns independent information about the scene, which can add extra constraints to the reconstruction process. How to conduct detailed, quantitative “co-analysis” of multiple data streams remains an area of active research. Multi-beam, short-pulse, laser-generated probes offer sufficient dose, an appropriate spectrum, and appropriate spatio-temporal resolution to produce high-quality dynamic radiographs. This paper reports on technology development to advance the state of the art of multi-modal/multi-probe radiography and the pursuit of both deterministic and inferential (AI/ML assisted) co-analysis methodologies to produce more constrained reconstructions from multi-modal data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Environmental, Health and Safety Assessment Application of a Heat Integrated, Post-combustion CO2 Capture System Using Monoethanolamine and Hitachi H3-1 Solvent at the E.W. Brown Coal-fired Power Plant

An Environmental Health and Safety (EH&S) Assessment was completed for operation of a pilot scale carbon dioxide (CO2) capture system (CCS) at the Louisville Gas & Electric (LG&E) and Kentucky Utilities (KU) Energy, LLC (LG&E-KU) E. W. Brown Generating Station in Harrodsburg, Mercer County, Kentucky. The pilot system was evaluated using two CO2 capture solvents, monoethanolamine (MEA) and Hitachi’s H3-1 solvent. The purpose of the EH&S Assessment was to determine if there were any unacceptable environmental, health or safety concerns that may prevent operation or environmental permitting of a larger scale pilot scale plant. The assessment included review of process flow diagrams, input and output flow rates for primary materials, literature describing similar processes and chemicals used or created during the process, actual air emission and wastewater test data and air emission calculations. The evaluation included identification of risks related to hazardous chemicals, air emissions, wastewater discharges, solid wastes generated and employee hazards. Most potential EH&S issues that were identified are commonly found and successfully managed at large industrial facilities. No risks were identified that could not be successfully managed or would likely prevent implementation or environmental permitting of the pilot or larger scale plant. Potential exposures and resulting health risks from exposure to low concentrations of nitrosamines generated from degradation of the Hitachi H3-1 and possibly MEA solvents may be a health concern for site workers, but additional investigation is needed to accurately assess this risk. Applying the information gained from operating the pilot plant, performing air emission testing and used solvent testing to a large-scale facility at a commercial coal-fired electric generating plant suggests additional air emission control measures may be necessary to reduce potential site worker health risks and possibly community risks. Depending upon the location and regional air quality, considerable effort may be required to obtain the necessary air permit for construction and operation of a similarly configured large-scale PCCCS facility.

gas separation↗
DOE OSTI2020

Environmental, Health and Safety Assessment Application of a Heat Integrated, Post-combustion CO 2 Capture System Using Monoethanolamine and Hitachi H3-1 Solvent at the E.W. Brown Coal-Fired Power Plant

An Environmental Health and Safety (EH&S) Assessment was completed for operation of a pilot scale carbon dioxide (CO 2 ) capture system (CCS) at the Louisville Gas & Electric (LG&E) and Kentucky Utilities (KU) Energy, LLC (LG&E-KU) E. W. Brown Generating Station in Harrodsburg, Mercer County, Kentucky. The pilot system was evaluated using two CO 2 capture solvents, monoethanolamine (MEA) and Hitachi’s H3-1 solvent. The purpose of the EH&S Assessment was to determine if there were any unacceptable environmental, health or safety concerns that may prevent operation or environmental permitting of a larger scale pilot scale plant. The assessment included review of process flow diagrams, input and output flow rates for primary materials, literature describing similar processes and chemicals used or created during the process, actual air emission and wastewater test data and air emission calculations. The evaluation included identification of risks related to hazardous chemicals, air emissions, wastewater discharges, solid wastes generated and employee hazards. Most potential EH&S issues that were identified are commonly found and successfully managed at large industrial facilities. No risks were identified that could not be successfully managed or would likely prevent implementation or environmental permitting of the pilot or larger scale plant. Potential exposures and resulting health risks from exposure to low concentrations of nitrosamines generated from degradation of the Hitachi H3-1 and possibly MEA solvents may be a health concern for site workers, but additional investigation is needed to accurately assess this risk. Applying the information gained from operating the pilot plant, performing air emission testing and used solvent testing to a large-scale facility at a commercial coal-fired electric generating plant suggests additional air emission control measures may be necessary to reduce potential site worker health risks and possibly community risks. Depending upon the location and regional air quality, considerable effort may be required to obtain the necessary air permit for construction and operation of a similarly configured large-scale PCCCS facility.

gas separation↗
DOE OSTI2020

Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation

In this work we address the problem of accelerating complex power-grid simulation through machine learning ( ML). Specifically, we develop a framework, Smart-PGSim,which generates multitask-learning (MTL) neural network (NN)models to predict the initial values of variables critical to the problem convergence. MTL models allow information sharing when predicting multiple dependent variables while including customized layers to predict individual variables. We show that,to achieve the required accuracy, it is paramount to embed domain-specific constraints derived from the specific power-grid components in the MTL model. Smart-PGSim then employs the predicted initial values as a high-quality initial condition for the power-grid numerical solver (warm start), resulting in both higher performance compared to state-of-the-art solutions while maintaining the required accuracy. Smart-PGSim brings 2.60×speedup on average (up to 3.28×) computed over 10,000 problems, without losing solution optimality.

machine learning, neural networks↗
DOE OSTI2022

Oak Ridge National Laboratory FY 2023 Site Sustainability Plan With FY 2022 Performance Data

At the close of each fiscal year, the US Department of Energy (DOE) Sustainability Performance Division (SPD) issues guidance documents and technical resource aids/tools necessary for DOE sites and national laboratories to complete sustainability reporting requirements. SPD is part of the DOE Office of Asset Management. As required by DOE Order 436.1, Departmental Sustainability, “each site will develop and commit to an annual Site Sustainability Plan (SSP) that identifies its respective contribution toward meeting the DOE’s sustainability goals.” SPD collects and compiles information reported by each site to develop an agency-wide Sustainability Report and Implementation Plan, which is used to report DOE sustainability progress to the federal government as required by all major federal agencies. DOE launched a formal Sustainability Office and annual SSP process in 2011. Each year, Oak Ridge National Laboratory (ORNL), in concert with the Office of Science (SC), provides the resources essential to fulfill its commitment to deliver a complete and accurate SSP report and quality performance data for entry into the DOE Sustainability Dashboard as managed by SPD. The performance data entered by each DOE site are then combined to disclose the progress of each DOE Program Office and are further combined to show comprehensive progress for the agency. The Office of Asset Management provides assistance to program offices in sustaining their missions, freeing up resources by reducing waste, avoiding excess expenditure on utilities, maximizing productivity, and improving the efficiency of facilities and processes. By focusing on mission needs, programs and associated DOE sites can help the agency meet its sustainability goals, as outlined in federal statutory and regulatory requirements. In FY 2022, the SSP guidance was updated to capture requirements from Executive Order (EO) 14008, Tackling the Climate Crisis at Home and Abroad, the Energy Act of 2020 (EAct 20), actions outlined in DOE’s Climate Adaptation & Resilience Plan and Sustainability Plan, and EO 14057, Catalyzing Clean Energy Industries and Jobs Through Federal Sustainability. Updates in SSP guidance help to minimize and streamline reporting while simultaneously addressing updated federal requirements. Per DOE, each SSP report should provide an overview of the site’s planned actions, as well as an overview of efforts and accomplishments during the reporting period. SPD collects and compiles information reported by each site to develop DOE’s Annual Sustainability Report, Climate Adaptation & Resilience Plan, and Annual Energy Management Report to Congress. The agency goal has been to lower the reporting burden for sites and increase and improve the consistency of information available to decision makers, allowing them to better identify projects and potential for increased efficiency, as well as to reduce waste, lower emissions, and enhance operational resilience. Sites may elect to produce a more polished publication for their leadership and stakeholders, but this step is no longer required. The ORNL SSP narrative report (this document) and the reporting of DOE SPD Sustainability Dashboard performance data is a collaborative effort of approximately 30 subject matter experts (SMEs) from ORNL facility management and research divisions. Annually, these associates come together to provide a report that can be used by DOE to demonstrate continued agency progress in energy efficiency and sustainable federal operations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Photo- and Electrocouplings of Nucleon Resonances

Advances in the exploration of the spectrum and structure of the excited states of the nucleon from experiments with electromagnetic probes on proton targets are presented here. Impressive progress has been achieved in the studies of exclusive meson photoproduction in experiments with continuous electron beams and with detectors of almost 4π acceptance. The high-quality data, coupled with the advances in the amplitude analyses of exclusive photo- and hadroproduction data, allow for the observation of several long-awaited new baryon states known previously as the “missing” resonances. Studies of exclusive meson electroproduction in the resonance region with the CLAS detector at JLab have provided the dominant part of the available world information on exclusive meson electroproduction observables. These data offer unique information on the structure of most well-established excited nucleon states in the mass region up to 1.8 GeV in terms of the evolution of their electroexcitation amplitudes with momentum transfer of the virtual photon. We discuss the impact of these results on the insight into the strong interaction dynamics that underlie the generation of the full spectrum of nucleon resonances of distinctively different structure. These results shed light on the emergence of hadron mass, which is one of the most important and still open problems in the Standard Model. The extension of the nucleon resonance stud

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Optomechanical ring resonator for efficient microwave-optical frequency conversion

Phonons traveling in solid-state devices are emerging as a universal excitation for coupling different physical systems. Phonons at microwave frequencies have a similar wavelength to optical photons in solids, enabling optomechanical microwave-optical transduction of classical and quantum signals. It becomes conceivable to build optomechanical integrated circuits (OMIC) that guide both photons and phonons and interconnect photonic and phononic devices. Here, we demonstrate an OMIC including an optomechanical ring resonator (OMR), where co-resonant infrared photons and GHz phonons induce significantly enhanced interconversion. The platform is hybrid, using wide bandgap semiconductor gallium phosphide (GaP) for waveguiding and piezoelectric zinc oxide (ZnO) for phonon generation. The OMR features photonic and phononic quality factors of >1 × 10 5 and 3.2 × 10 3 , respectively. The optomechanical interconversion between photonic modes achieved an internal conversion efficiency $η_i$ = (2.1 ± 0.1)% and a total device efficiency $η_{tot}$ = 0.57 x 10 -6 at a low acoustic pump power of 1.6 mW. The efficient conversion in OMICs enables microwave-optical transduction for quantum information and microwave photonics applications.

42 ENGINEERING↗
DOE OSTI2025

EMPDF : inferring the Milky Way mass with data-driven distribution function in phase space

We introduce the emPDF (empirical distribution function), a novel dynamical modelling method that infers the gravitational potential from kinematic tracers with optimal statistical efficiency under the minimal assumption of steady state. emPDF determines the best-fitting potential by maximizing the similarity between instantaneous kinematics and the time-averaged phase-space distribution function (DF), which is empirically constructed from observation upon the theoretical foundation of oPDF (Han et al. 2016). This approach eliminates the need for presumed functional forms of DFs or orbit libraries required by conventional DF- or orbit-based methods. emPDF stands out for its flexibility, efficiency, and capability in handling observational effects, making it preferable to the popular Jeans equation or other minimal assumption methods, especially for the Milky Way (MW) outer halo where tracers often have limited sample size and poor data quality. We apply emPDF to infer the MW mass profile using Gaia DR3 data of satellite galaxies and globular clusters, obtaining enclosed masses of M (,r) = 26±8, 46±8, 90±13⁠, and 149±40 x 10 10 M ⊙ at r = 30, 50, 100⁠, and 200 kpc, respectively. These are consistent with the updated constraints from simulation-informed DF fitting (Li et al. 2020). While the simulation-informed DF offers superior precision owing to the additional information extracted from simulations, emPDF is independent of such supplementary knowledge and applicable to general tracer populations. emPDF is currently implemented for tracers with complete 6D kinematics within spherical potentials, but it can potentially be extended to address more general problems.

Astrophysics of Galaxies (astro-ph.GA)↗
DOE OSTI2026

From Bricks to Clicks: Mapping the White Space in Building Innovation

It is a critical national imperative to transform the buildings sector, yet innovation is impeded by deployment failures that leave promising technologies stranded. Conventional market reports and techno-economic analysis provide an insufficient understanding of markets and resource allocation for emerging building technologies. They omit crucial commercialization factors such as ecosystem maturity and adoption friction, where the coordinated participation of a network of suppliers, contractors, financiers, regulators, and integrators is required to scale solutions. This study addresses these gaps by introducing an evaluation framework grounded in front-line data from six years of the DOE's IMPEL incubator, comprising experience from 300 building-sector innovators and the adjacent, complex ecosystem. Our methodology synthesizes top-down market analysis with bottom-up, practitioner-level data across five megatrends: (M1) Affordable materials and industrialized construction; (M2) Healthy and efficient mechanical systems; (M3) Intelligent building operations; (M4) Buildings as grid assets; and (M5) High-density power and cooling for data centers and therein identify twelve "white space" technology opportunities. Next, we develop a multi-criteria scoring rubric to rank these opportunities based on parameters, i.e., Affordability, Quality of Life, Reliability, and Security, yielding composite ‘Demand’ and ‘Maturity’ indices. Our results indicate that the most significant white spaces may not be incremental products but a new class of ‘Ecosystem Enablers’, such as logistics platforms, orchestration layers, and automated compliance software that solve structural deployment gaps. This paper summarizes this transparent, evidence-based, practitioner-informed evaluation framework for policymakers and investors to re-evaluate policy and resource allocation and unlock scalable market transformation.

Singh, Reshma↗
DOE OSTI2020

Montana Community-Scale Solar Strategy Project (Final Technical Report)

Grid-tied solar photovoltaic (PV) development began gradually in Montana, initially taking root in the early 2000s, largely in the form of small residential systems, rarely larger than two kilowatts. Utility support for solar technology through public benefit funding triggered the installation of the state’s first solar PV installations on community-owned facilities including schools, libraries, fire stations and local government buildings in the mid-2000s. By 2016, solar PV installations totaled approximately 28 megawatts of installed capacity, generating less than 0.05% of electricity sold in Montana. Montana’s solar fleet in 2016 included 17 megawatts of utility-scale solar farms, and three shared solar projects developed by rural electric cooperatives that ranged from 25 to 50 kilowatts in size. However, with only 3,000 households utilizing solar PV, either through an on-site installation or participation in a shared solar project, the potential benefits and reach of solar technology was limited. The Montana Solar Community Project (MSCP) was developed by the Montana Energy Office (MEO) at the Department of Environmental Quality in order to implement a “community-scale” solar energy strategy for the state that would cost-effectively expand Montanans’ access to solar PV. This project defines three types of community-scale solar projects: shared solar, community-sited solar, and group purchasing programs. The MSCP project conducted research and stakeholder engagement that helped to inform and develop model community-scale initiatives and an overarching community-scale solar strategy for Montana. Specifically, the project included a solar market assessment, stakeholder meetings across Montana, development of a menu of community-scale solar options, a study of solar potential on schools, community solar market research, and community-scale project implementation support. MSCP activities were successful in implementing solar projects and supporting solar development in communities across Montana. Project activities realized 280 kW of newly installed solar from two solarize programs, a shared solar array at a rural electric cooperative, and a solar array atop a Bozeman high school. The community-scale project implementation support and Solar on Schools components of the project resulted directly in the analysis and design of nearly 2 MW of distributed solar systems across the state. The Solar on Schools analysis alone produced solar designs for 25 schools, representing 920.8 kW of solar potential. Many of the deliverables from this project provide guidance on future development of community-scale solar in Montana, and resulted in a number of important conclusions about community-scale solar in Montana. There is strong interest in community-scale solar in Montana, as noted during stakeholder meetings, in response to solar market research surveys, and as seen by applications for implementation support grants provided by MEO. Project activities also suggest that Montanans demonstrated a desire and need for more information about solar energy technology, solar financing options, and the state’s policy and regulatory framework. Additionally, the project found that schools will be key partners for future community-scale solar programs. In addition to being excellent hosts for solar arrays, schools have the opportunity to use the installation of a solar array to develop educational materials and curricula to educate Montana’s future leaders about these technologies, which can be shared and replicated across the state. Project activities laid a clear path forward for solar energy in Montana. Certain activities have concrete next steps. For example, numerous solar arrays were designed for schools and other public buildings around the state. Many of those designs will be used as soon as funding becomes available. Other activities have less developed next steps, though lay the foundation for future work. The solar market assessment, solar market research, and community stakeholder meetings are good examples. These documents can be used as stepping stones to increase renewable energy education, increase stakeholder outreach, and support communities as they seek to develop community-scale solar projects.

14 SOLAR ENERGY↗
DOE OSTI2021

Evaluating China's Road to Cyber Super Power

This report examines open source, non-classified qualitative analysis to evaluate China’s current cyber maturity. Evidence for this document draws on materials from academia, private cybersecurity companies, and national security research institutions. Private sector threat intelligence firms produce high quality analysis on Chinese APTs tactics, techniques, and procedures (TTPs), and investigation from companies such as FireEye illuminate China’s ability to wield cyber means for its security ends. None of the materials cited in this assessment originate from classified United States or foreign government sources. Any references to United States government sources are sourced entirely to unclassified information.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Análisis de Tareas del Auditor Energético [Energy Auditor Job Task Analysis (Spanish Translation)]

El Laboratorio Nacional de Energía Renovable (NREL), bajo el contrato con el Programa de Asistencia para la Climatización (WAP) del Departamento de Energía de EE.UU. (DOE), desarrolla y mantiene los recursos en el marco del proyecto Directrices para Profesionales de la Energía Doméstica (GHEP). El objetivo del proyecto GHEP es aumentar la calidad del trabajo de remodelación energética residencial realizado por WAP y otros programas de remodelación residencial en Estados Unidos. Para cumplir el objetivo de “Establecer certificaciones nacionales de la mano de obra y normas de formación”, el DOE encargó al NREL el desarrollo de recursos GHEP, incluido un conjunto de certificaciones avanzadas, basadas en aptitud, para el personal de los profesionales de la energía doméstica (HEP). Desde 2010, el NREL ha reclutado voluntarios expertos en la materia de WAP y de la industria del mantenimiento del hogar para que formen parte de comités que desarrollen y actualicen programas de certificación y sus análisis de tareas (JTA) necesarios como base de programas de certificación y formación estandarizados. Las certificaciones HEP apoyan al WAP y al sector del mantenimiento de viviendas residenciales en general mediante el proceso de acreditación y el desarrollo de JTA definidos para auditores energéticos (EA) e inspectores de control de calidad (QCI). Este informe es un resumen de las actualizaciones más recientes (2022) del JTA del EA. [The National Renewable Energy Laboratory (NREL) is contracted by the U.S. Department of Energy (DOE) Weatherization Assistance Program (WAP) to develop and maintain the resources under the Guidelines for Home Energy Professionals (GHEP) project. The purpose of the GHEP project is to increase the quality of work conducted for residential energy retrofits in the United States through the WAP network and other residential retrofit programs. This is the Spanish translation of NREL/TP-7A40-85300.]

29 ENERGY PLANNING, POLICY, AND ECONOMY↗