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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Wisconsin's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Wisconsin had 3,762,091 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗
DOE OSTI2022

Wyoming's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Wyoming had 367,435 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗
DOE OSTI2022

Washington DC's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Washington DC had 416,300 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗
DOE OSTI2024

Economic and Jobs Impacts of Point-Source Carbon Capture in Cement Industry – Case Study

The cement industry accounts for an estimated 8% of global CO2 emissions, which surpasses that of the entire aviation sector. In contrast with other industries, where CO2 emissions can be drastically reduced via electrification or fuels substitution, cement production releases CO2 as part of its process, during the calcination of carbonates to yield oxides. Thus, point-source carbon capture has become a key technology in the cement industry’s decarbonization. Apart from the expected environmental benefits, point-source carbon capture in the cement industry can yield important economic benefits and create jobs. The objective of this study was to perform a preliminary assessment of the economic and workforce impacts associated with the construction and operation of a point-source carbon capture retrofit of an existing cement production facility, using as a basis the data from a front-end engineering design (FEED) study to install a 3.9 million metric tons per year (Mtpy) CO2 capture facility at Holcim Ste Genevieve cement plant in Missouri, United States of America. The advanced carbon capture technology used in this FEED study was Air Liquide’s Cryocap™ FG carbon capture technology. The study evaluated the direct, indirect, and induced economic impacts of the construction, operation, and maintenance activities of the project over its lifespan. It also covered how the project will generate new jobs, their nature, and quantity, along with strategies to prepare the workforce. To perform this study, construction, operation, and maintenance cost estimates, as well as construction and operation staffing plans from the FEED study were input into IMPLAN version 7.5 software, licensed by IMPLAN Group LLC (Huntersville, VC), to predict the direct, indirect and induced economic impacts of the project using industry multipliers from the software. Additionally, recruitment strategies were developed for hiring individuals who belong to groups that are historically underserved or underrepresented, as well as anticipated recruitment of workers from the local community (whether training will be required or if the skills are associated with an existing labor force). The analysis estimated that the construction and operation of the carbon capture at Holcim Ste. Genevive will result in over 24 thousand work-years of job opportunities, close to USD 10 billion of economic impacts, including over USD 460 million of tax revenue. These results encompass the direct, indirect, and induced effects. A strategy to maximize hiring from the project and neighboring counties was developed, leveraging training agreements with local trade groups and universities. The result of this study can be used for a strategic preliminary assessment of the potential regional economic and job impacts of retrofitting existing cement plants with point source carbon systems, and its methodology can be replicated to individual projects to aid in planning and workforce development.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Flexible Pilot Jobs Framework for Distributed High Throughput Computing

Experimental particle physics has been at the forefront of analyzing the world’s largest datasets for decades. The high-energy physics (HEP) community was among the first to develop suitable software and computing tools for this purpose. GlideinWMS is a Glidein-based workload management system whose purpose is to provide experiments like CMS at CERN, DUNE at Fermilab, and others, a way to access and efficiently use vast amounts of computing resources. This system wants to provide a simple way to submit jobs to a set of computing resources, that will be provided to users behind the scenes. Glideins are the pilot jobs executed on the worker nodes at the grid sites, performing operations such as hardware detection, environment setup, and error handling. After all these operations, they will launch the actual user job. Many grid sites are supported, such as shared clusters, Google CE, and AWS. My internship aimed to design and code a flexible pilot jobs framework that will replace the one used by GlideinWMS, developing a modular and flexible skeleton of the Glidein and adding further functionalities. My project also focused on the application of machine learning techniques as support to this management system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1972

Job-mix modeling and system analysis of an aerospace multiprocessor.

An aerospace guidance computer organization, consisting of multiple processors and memory units attached to a central time-multiplexed data bus, is described. A job mix for this type of computer is obtained by analysis of Apollo mission programs. Multiprocessor performance is then analyzed using: 1) queuing theory, under certain 'limiting case' assumptions; 2) Markov process methods; and 3) system simulation. Results of the analyses indicate: 1) Markov process analysis is a useful and efficient predictor of simulation results; 2) efficient job execution is not seriously impaired even when the system is so overloaded that new jobs are inordinately delayed in starting; 3) job scheduling is significant in determining system performance; and 4) a system having many slow processors may or may not perform better than a system of equal power having few fast processors, but will not perform significantly worse.

Mallach, E. G.↗
NASA NTRS1974

An analysis of job placement patterns of black and non-black male and female undergraduates at the University of Virginia and Hampton Institute

Research questions were proposed to determine the relationship between independent variables (race, sex, and institution attended) and dependent variables (number of job offers received, salary received, and willingness to recommend source of employer contact). The control variables were academic major, grade point average, placement registration, nonemployment activity, employer, and source of employer contact. An analysis of the results revealed no statistical significance of the institution attended as a predictor of job offers or salary, although significant relationships were found between race and sex and number of job offers received. It was found that academic major, grade point average, and source of employer contact were more useful than race in the prediction of salary. Sex and nonemployment activity were found to be the most important variables in the model. The analysis also indicated that Black students received more job offers than non-Black students.

Anderson, A. F.↗
NASA NTRS2004

Job Scheduling in a Heterogeneous Grid Environment

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and three job migration algorithms. The architecture is scalable and does not assume control of local site resources. The job migration policies use the availability and performance of computer systems, the network bandwidth available between systems, and the volume of input and output data associated with each job. An extensive performance comparison is presented using real workloads from leading computational centers. The results, based on several key metrics, demonstrate that the performance of our distributed migration algorithms is significantly greater than that of a local scheduling framework and comparable to a non-scalable global scheduling approach.

Shan, Hong-Zhang↗
DOE OSTI2022

Ohio's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Ohio had 7,516,303 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state capture

clean energy↗
DOE OSTI2021

Regulators’ Energy Transition Primer: Economic Impacts of the Energy Transition on Energy Communities, Environmental Justice Considerations, and Implications on Clean Energy Jobs

Applications of new technology, such as horizontal drilling and hydraulic fracturing, enabled the United States to significantly increase its production of oil and natural gas during the last decade—the “Shale Gas Revolution.” As natural gas began to dominate the market with abundant supply and low prices, coal production and consumption have declined. Concurrently, the competitiveness of renewable energy and energy storage has climbed sharply, and analysts expect to see continued reductions in fossil fuel use in the coming decades. Many of these changes have been driven by market forces (i.e., low-cost natural gas and renewables), but current and future policy decisions aimed at tackling climate change concerns and reducing greenhouse gas emissions will also shape the future of the energy sector. This transition to low-carbon fuels has created both opportunities for clean energy technologies and challenges for communities traditionally dependent on fossil fuel-related industries. The power sector’s ongoing shift away from coal has left many coal miners and coal-fired power plant employees unemployed and often unprepared for jobs in other industries, including growing clean energy fields. This primer focuses on the declining coal industry, impacts on communities and workers, opportunities to transition workers who have lost their jobs to clean energy and other related sectors (including hydrogen-oriented jobs), recruitment and training strategies, and available programs and actions to make the shift to a low-carbon economy in a fair, just, and equitable manner by engaging the resources of federal and state governments, as well as the private sector.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

National Solar Jobs Accelerator (Final Technical Report (FTR))

The aptitudes and experiences gained through military service—such as dynamic leadership, teamwork and critical thinking skills, technical specialization, and a mission-completion work ethic—make veterans exceptional candidates for a wide range of solar energy careers. The solar industry offers a highly collaborative and purpose-driven work environment that resonates with service members and veterans looking to rise to their next challenge, and solar employers are eager to tap into this valuable talent pool. From October 2019 - February 2023, The National Solar Jobs Accelerator (publicly the Solar Ready Vets Network TM (SRV Network; SRVN)) enhanced and streamlined options for military service members and veterans to pursue solar training, certification, and employment, while advancing solar employers’ efforts and capacity to invest in military talent as part of a long-term workforce development strategy. The SRVN was led by the Interstate Renewable Energy Council (IREC) in partnership with the Solar Energy Industries Association (SEIA), the US Chamber of Commerce Foundation’s Hiring Our Heroes program (HOH) and the North American Board of Certified Energy Practitioners (NABCEP). Through several direct-impact and indirect, high-impact capacity building initiatives aligned with six key objectives, the SRV Network strengthened solar career pathways, and promoted increased representation of military talent across all levels and sectors of the solar workforce. A work-based learning Corporate Fellowship model connected transitioning service members with on-the-job experience in leadership roles with solar employers nationwide. The project advanced broader veteran recruitment and talent development by expanding GI Bill eligibility and streamlining veterans’ pathways for solar training and credentialing, supported direct connections to jobs with top solar employers, and led coordination among key education and industry partners to advance registered apprenticeships aligned with solar career pathways. To ensure that the project best served the needs of all stakeholders, an Advisory Committee of military-connected solar professionals, solar employers, and training providers met biannually to guide project activities and sustainability plans. The project team engaged the broader “SRV Network” (comprised of over 2,000 veterans, employers and training organizations) through regular newsletters and targeted outreach to share resources, hiring fairs, webinars, and other opportunities for engagement. The work done under this award builds on the previous iterations of the Department of Energy’s Solar Ready Vets ® program. As the solar industry continues to grow rapidly over the next decade, the military community will continue to be a highly valuable source of talent. The relationships established and work accomplished through this project will have an enduring positive impact well beyond the funding period.

14 SOLAR ENERGY↗
NASA NTRS1986

The honeymoon effect in job performance - Temporal increases in the predictive power of achievement motivation

Correlations between a job performance criterion and personality measures reflecting achievement motivation and an interpersonal orientation were examined at three points in time after completion of job training for a sample of airline reservations agents. Although correlations between the personality predictors and performance were small and nonsignificant for the 3-month period after beginning the job, by the end of six and eight months a number of significant relationships had emerged. Implications for the utility of personality measures in selection and performance prediction are discussed.

Helmreich, Robert L.↗
NASA NTRS1990

Scheduling periodic jobs that allow imprecise results

The problem of scheduling periodic jobs in hard real-time systems that support imprecise computations is discussed. Two workload models of imprecise computations are presented. These models differ from traditional models in that a task may be terminated any time after it has produced an acceptable result. Each task is logically decomposed into a mandatory part followed by an optional part. In a feasible schedule, the mandatory part of every task is completed before the deadline of the task. The optional part refines the result produced by the mandatory part to reduce the error in the result. Applications are classified as type N and type C, according to undesirable effects of errors. The two workload models characterize the two types of applications. The optional parts of the tasks in an N job need not ever be completed. The resulting quality of each type-N job is measured in terms of the average error in the results over several consecutive periods. A class of preemptive, priority-driven algorithms that leads to feasible schedules with small average error is described and evaluated.

Chung, Jen-Yao↗
NASA NTRS1996

Evaluation of Job Queuing/Scheduling Software: Phase I Report

The recent proliferation of high performance work stations and the increased reliability of parallel systems have illustrated the need for robust job management systems to support parallel applications. To address this issue, the national Aerodynamic Simulation (NAS) supercomputer facility compiled a requirements checklist for job queuing/scheduling software. Next, NAS began an evaluation of the leading job management system (JMS) software packages against the checklist. This report describes the three-phase evaluation process, and presents the results of Phase 1: Capabilities versus Requirements. We show that JMS support for running parallel applications on clusters of workstations and parallel systems is still insufficient, even in the leading JMS's. However, by ranking each JMS evaluated against the requirements, we provide data that will be useful to other sites in selecting a JMS.

Jones, James Patton↗
NASA NTRS1995

JSD: Parallel Job Accounting on the IBM SP2

The IBM SP2 is one of the most promising parallel computers for scientific supercomputing - it is fast and usually reliable. One of its biggest problems is a lack of robust and comprehensive system software. Among other things, this software allows a collection of Unix processes to be treated as a single parallel application. It does not, however, provide accounting for parallel jobs other than what is provided by AIX for the individual process components. Without parallel job accounting, it is not possible to monitor system use, measure the effectiveness of system administration strategies, or identify system bottlenecks. To address this problem, we have written jsd, a daemon that collects accounting data for parallel jobs. jsd records information in a format that is easily machine- and human-readable, allowing us to extract the most important accounting information with very little effort. jsd also notifies system administrators in certain cases of system failure.

Saphir, William↗
NASA NTRS1997

Second Evaluation of Job Queuing/Scheduling Software

The recent proliferation of high performance workstations and the increased reliability of parallel systems have illustrated the need for robust job management systems to support parallel applications. To address this issue, NAS compiled a requirements checklist for job queuing/scheduling software. Next, NAS evaluated the leading job management system (JMS) software packages against the checklist. A year has now elapsed since the first comparison was published, and NAS has repeated the evaluation. This report describes this second evaluation, and presents the results of Phase 1: Capabilities versus Requirements. We show that JMS support for running parallel applications on clusters of workstations and parallel systems is still lacking, however, definite progress has been made by the vendors to correct the deficiencies. This report is supplemented by a WWW interface to the data collected, to aid other sites in extracting the evaluation information on specific requirements of interest.

Jones, James Patton↗
DOE OSTI2024

Quantifying Uncertainty in HPC Job Queue Time Predictions

High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.

HPC↗
DOE OSTI2023

Jobs and Economic Development Impact (JEDI) Models

The Jobs and Economic Development Impact (JEDI) models (https://www.nrel.gov/analysis/jedi/) are publicly available, user-friendly tools designed to estimate the economic impacts of both construction and operation of power generation and biofuel plants at a local (usually state) level. Based on project-specific and default inputs (from techno-economic analysis data and NREL's expertise), these models estimate the number of jobs and economic benefits to a region that could reasonably be supported by a particular project. Underlying these calculations is an input-output framework that models the local economy as a network of sectors buying and selling to one-another, creating a multiplier effect. Over the last decade, JEDI has been widely used by both the academic and private sectors, serving as the foundations for multiple peer-reviewed publications and impact analysis reports.

economic impact analysis↗