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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Materials Data on Pr(BOs)4 by Materials Project

Pr(OsB)4 crystallizes in the tetragonal P4_2/n space group. The structure is three-dimensional. Pr3+ is bonded in a 8-coordinate geometry to eight equivalent Os+0.75- atoms. There are four shorter (3.22 Å) and four longer (3.23 Å) Pr–Os bond lengths. Os+0.75- is bonded in a 4-coordinate geometry to two equivalent Pr3+ and four equivalent B atoms. There are a spread of Os–B bond distances ranging from 2.14–2.18 Å. B is bonded in a 5-coordinate geometry to four equivalent Os+0.75- and one B atom. The B–B bond length is 1.78 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on Nd(BOs)2 by Materials Project

Nd(OsB)2 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Nd3+ is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms. All Nd–Os bond lengths are 3.05 Å. Os+1.50- is bonded in a 4-coordinate geometry to two equivalent Nd3+ and four equivalent B atoms. There are two shorter (2.10 Å) and two longer (2.18 Å) Os–B bond lengths. B is bonded to four equivalent Os+1.50- atoms to form a mixture of distorted edge and corner-sharing BOs4 trigonal pyramids.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on Gd(BOs)2 by Materials Project

Gd(OsB)2 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Gd3+ is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms. All Gd–Os bond lengths are 3.02 Å. Os+1.50- is bonded in a 4-coordinate geometry to two equivalent Gd3+ and four equivalent B atoms. There are two shorter (2.08 Å) and two longer (2.18 Å) Os–B bond lengths. B is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2026

BOS Gas Detection Pipeline (Integrated System for Optical Hydrogen Detection Using Background Oriented Schlieren and Machine Learning) [SWR-26-007]

This software is the world's first integrated background oriented schlieren and machine learning-based leak detection system. The system provides real time visualization of gas leaks and machine learning interpenetration of leak severity. The software is supplemented by SWR-25-177, "gpu_piv (Graphics Processing Unit Accelerated Background Oriented Schlieren Algorithm", also developed by the National Laboratory of the Rockies. SEE DOECODE ID 182832.

Palin, Ian [National Laboratory of the Rockies (NL↗
DOE OSTI2012

Offshore Wind Plant Balance-of-Station Cost Drivers and Sensitivities (Poster)

With Balance of System (BOS) costs contributing up to 70% of the installed capital cost, it is fundamental to understanding the BOS costs for offshore wind projects as well as potential cost trends for larger offshore turbines. NREL developed a BOS model using project cost estimates developed by GL Garrad Hassan. Aspects of BOS covered include engineering and permitting, ports and staging, transportation and installation, vessels, foundations, and electrical. The data introduce new scaling relationships for each BOS component to estimate cost as a function of turbine parameters and size, project parameters and size, and soil type. Based on the new BOS model, an analysis to understand the non-turbine costs associated with offshore turbine sizes ranging from 3 MW to 6 MW and offshore wind plant sizes ranging from 100 MW to 1000 MW has been conducted. This analysis establishes a more robust baseline cost estimate, identifies the largest cost components of offshore wind project BOS, and explores the sensitivity of the levelized cost of energy to permutations in each BOS cost element. This presentation shows results from the model that illustrates the potential impact of turbine size and project size on the cost of energy from US offshore wind plants.

17 WIND ENERGY↗
DOE OSTI2012

U.S. Balance-of-Station Cost Drivers and Sensitivities (Presentation)

With balance-of-system (BOS) costs contributing up to 70% of the installed capital cost, it is fundamental to understanding the BOS costs for offshore wind projects as well as potential cost trends for larger offshore turbines. NREL developed a BOS model using project cost estimates developed by GL Garrad Hassan. Aspects of BOS covered include engineering and permitting, ports and staging, transportation and installation, vessels, foundations, and electrical. The data introduce new scaling relationships for each BOS component to estimate cost as a function of turbine parameters and size, project parameters and size, and soil type. Based on the new BOS model, an analysis to understand the non‐turbine costs has been conducted. This analysis establishes a more robust baseline cost estimate, identifies the largest cost components of offshore wind project BOS, and explores the sensitivity of the levelized cost of energy to permutations in each BOS cost element. This presentation shows results from the model that illustrates the potential impact of turbine size and project size on the cost of energy from U.S. offshore wind plants.

17 WIND ENERGY↗
DOE OSTI2014

Land-Based Wind Plant Balance-of-System Cost Drivers and Sensitivities (Poster)

With Balance of System (BOS) costs contributing up to 30% of the installed capital cost, it is fundamental to understand the BOS costs for wind projects as well as potential cost trends for larger turbines. NREL developed a BOS model using project cost estimates developed by industry partners. Aspects of BOS covered include engineering and permitting, foundations for various wind turbines, transportation, civil work, and electrical arrays. The data introduce new scaling relationships for each BOS component to estimate cost as a function of turbine parameters and size, project parameters and size, and geographic characteristics. Based on the new BOS model, an analysis to understand the non‐turbine wind plant costs associated with turbine sizes ranging from 1-6 MW and wind plant sizes ranging from 100-1000 MW has been conducted. This analysis establishes a more robust baseline cost estimate, identifies the largest cost components of wind project BOS, and explores the sensitivity of the capital investment cost and the levelized cost of energy to permutations in each BOS cost element. This presentation shows results from the model that illustrate the potential impact of turbine size and project size on the cost of energy from US wind plants.

17 WIND ENERGY↗
DOE OSTI2015

Solar, Install, Mount, Production, Labor, Equipment Balance of Systems

The GTRI led project team in partnership with the DOE, universities, and numerous industry leaders, have advanced the mission of the DOE EERE, the Solar Energy Technologies Program, and the SunShot Initiative by accelerating the research, development, and demonstration of solar PV technologies that provide Extreme Balance of Systems Cost Reductions (BOS-X). The research produced 132 design concepts, resulting in 19 invention disclosures, five patent applications, four 90% pre-commercial designs, and three licensed technologies. Technology practice rights were obtained by an industry partner, and a new solar commercial start-up company was launched in Atlanta as a result of this project. Innovations in residential, commercial, and utility scale balance of systems technologies were realized through an unprecedented multi-disciplinary university/industry partnership with over 50 students and 24 faculty members that produced 18 technical publications, a PhD thesis, and two commercially deployed operating prototypes. The technical effectiveness and economic feasibility of the multidisciplinary systems based approach executed by the project team was realized through 1) a comprehensive evaluation of industry, regulatory, and public stakeholder requirements; 2) numerous industry/student/faculty engagements in design studios, technical conferences, and at solar PV installation sites; 3) time and motion studies with domain experts that provided technical data and costs for each phase and component of the solar PV installation processes; 4) extensive wind tunnel and systems engineering modeling; and 5) design, construction, and demonstration of the selected technologies in the field at high profile sites in Atlanta. The SIMPLE BOS project has benefitted the public in the following ways: • Workforce development: The launch of a start-up company to commercialize the DOE funded SIMPLE BoS designs has directly created 9 new jobs in the State of Georgia. As of November 2014, the Georgia solar industry employs 2,890 solar workers, representing a 12.8% growth in employment over 2013 (Solar Jobs Census, 2014). • Growth of the solar industry: The DOE SIMPLE BoS SunShot Award to GTARC accelerated the growth of the solar industry in Georgia, due to the national publicity of the award and the engagement of numerous solar PV manufacturers, designers, and installers on the SIMPLE BoS project. In 2011 less than 50 megawatts of solar PV capacity existed in Georgia; by 2016 Georgia may reach nearly 800 MW of total approved solar capacity with the 2012 Georgia Power Advanced Solar Initiative. • Technical outreach and publications: Georgia Tech has participated in numerous technical symposiums, technology demonstrations, campus solar PV tours, and produced 18 publications for the solar industry and general public. • Cost reductions for consumers: The SIMPLE BoS pre-commercial systems and discoveries enable cost reductions of 50% or more in labor and materials for residential, commercial and utility scale PV installations.

14 SOLAR ENERGY↗
DOE OSTI2020

ORBIT: Offshore Renewables Balance-of-System and Installation Tool

This report describes the Offshore Renewables Balance-of-system Installation Tool ("ORBIT"), a new model developed by the National Renewable Energy Laboratory (NREL) to evaluate the balance-of-system (BOS) costs associated with offshore wind projects. In the context of wind energy projects BOS costs encompass all expenses required to construct a project other than the capital expenditures (CapEx) of the turbines and towers, including the procurement costs for all other components (such as substructures, cables, and electrical infrastructure), offshore and land-based construction costs, port costs, site surveying fees, permitting fees, and leasing fees are all categorized as BOS costs. BOS costs significantly contribute to the levelized cost of energy (LCOE) for offshore wind, typically comprising over 50\% of the CapEx for a fixed-bottom offshore wind project and 60\% for a floating project. In addition, technology solutions and installation methods vary drastically between projects as they are impacted by factors such as vessel availability, geographic considerations and site geotechnical conditions. These effects require a model with appropriate fidelity to understand how these costs scale as turbine rating increases and the offshore wind supply chain, particularly offshore construction vessels, is expanded. For offshore wind, cost savings attributed to increased turbine rating are primarily realized through BOS procurement and project installation as fewer substructures and less cable are required. BOS costs represent both a modeling challenge, as well as an opportunity, for project developers to optimize solutions to reduce costs. It is critical to understand how these costs are affected by novel technologies, innovative installation processes, and operational constraints in order identify meaningful cost reductions for offshore wind energy.

17 WIND ENERGY↗
DOE OSTI2021

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. Furthermore, the results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

NREL's Balance-of-System Cost Model for Land-Based Wind

NREL's Land-based Balance of System Systems Engineering (LandBOSSE) model is a tool for modeling the balance of system (BOS) costs of land-based wind plants. BOS costs currently account for approximately 30% of the capital expenditures needed to install a land-based wind plant and include all costs associated with installing a wind plant, such as permitting, labor, material, and equipment costs associated with site preparation, foundation construction, electrical infrastructure, and tower installation. NREL developed LandBOSSE after identifying a need for a hybrid of process-based and empirically derived model that can provide flexibility for assessing wind plant BOS costs at a system level. NREL's prior BOS models have relied on empirical fits of legacy industry data, which limit their predictive ability. LandBOSSE, however, was designed to help users explore tradeoffs between innovative design scenarios while balancing the level of detail and speed required for model execution. The model was developed using a hybrid of process-based and empirically derived methods to create a modular model design that will allow for updates as wind energy technology evolves. The goal of LandBOSSE is to allow researchers, analysts, wind power developers, government agencies, and the public to explore how BOS may vary for different wind plant designs.

17 WIND ENERGY↗
DOE OSTI2013

Bonded Bracket Assmebly for Frameless Solar Panels

In February 2011 the US Department of Energy announced their new Sunshot Initiative. The Sunshot goal is to reduce the total cost of solar energy systems by about 75 percent before the end of the decade. The DOE estimated that a total installed cost of $$1 per watt for photovoltaic systems would be equivalent to 5-6¢/kilowatt hour (kWh) for energy available from the grid. The DOE also estimated that to meet the $1 per watt goal, PV module costs would need to be reduced to $ .50 per watt, balance of systems costs would need to be reduced to $.40 per watt, and power electronic costs would need to reach $.10 per watt. To address the BOS balance of systems cost component of the $1 per watt goal, the DOE announced a funding opportunity called (BOS-X) Extreme Balance of System Hardware Cost Reductions. The DOE identified eight areas within the total BOS costs: 1) installation labor, 2) installation materials, 3) installation overhead and profit, 4) tracker, 5) permitting and commissioning, 6) site preparation, 7) land acquisition, 8) sales tax. The BOS-X funding announcement requested applications in four specific topics;Topic 1: Transformational Building Integrated Photovoltaic (BIPV) Modules; Topic 2: Roof and Ground Mount Innovations; Topic 3: Transformational Photovoltaic System Designs; and Topic 4: Development of New Wind Load Codes for PV Systems.The application submitted by ARaymond Tinnerman reflected the requirements listed in Topic #2, Roof and Ground Mount Innovations. The goal of topic #2 was to develop technologies that would result in the extreme reduction of material and labor costs associated with applications that require physical connections and attachments to roof and ground mount structures. The topics researched in this project included component cost reduction, labor reduction, weight reduction, wiring innovations, and alternative material utilization. The project objectives included; 1) The development of an innovative quick snap bracket assembly that would be bonded to frameless PV modules for commercial rooftop installations; 2) The development of a composite pultruded rail to replace traditional racking materials; 3) In partnership with a roofing company, pilot the certification of a commercial roof to be solar panel compliant, eliminating the need for structural analysis and government oversight resulting in significantly decreased permitting costs; and 4) Reduce the sum of all cost impacts in topic #2 from a baseline total of $2.05/watt to $.34/watt.

14 SOLAR ENERGY↗
DOE OSTI2016

On the Path to SunShot. The Role of Advancements in Solar Photovoltaic Efficiency, Reliability, and Costs

This report examines the remaining challenges to achieving the competitive photovoltaic (PV) costs and large-scale deployment envisioned under the U.S. Department of Energy's SunShot Initiative. Solar-energy cost reductions can be realized through lower PV module and balance-of-system (BOS) costs as well as improved system efficiency and reliability. Numerous combinations of PV improvements could help achieve the levelized cost of electricity (LCOE) goals because of the tradeoffs among key metrics like module price, efficiency, and degradation rate as well as system price and lifetime. Using LCOE modeling based on bottom-up cost analysis, two specific pathways are mapped to exemplify the many possible approaches to module cost reductions of 29%-38% between 2015 and 2020. BOS hardware and soft cost reductions, ranging from 54%-77% of total cost reductions, are also modeled. The residential sector's high supply-chain costs, labor requirements, and customer-acquisition costs give it the greatest BOS cost-reduction opportunities, followed by the commercial sector, although opportunities are available to the utility-scale sector as well. Finally, a future scenario is considered in which very high PV penetration requires additional costs to facilitate grid integration and increased power-system flexibility--which might necessitate even lower solar LCOEs. The analysis of a pathway to 3-5 cents/kWh PV systems underscores the importance of combining robust improvements in PV module and BOS costs as well as PV system efficiency and reliability if such aggressive long-term targets are to be achieved.

14 SOLAR ENERGY↗
DOE OSTI2019

PIV/BOS synthetic image generation in variable density environments for error analysis and experiment design

In this work, we present an image generation methodology based on ray tracing that can be used to render realistic images of Particle Image Velocimetry (PIV) and Background Oriented Schlieren (BOS) experiments in the presence of density/refractive index gradients. This methodology enables the simulation of aero-thermodynamics experiments for experiment design, error, and uncertainty analysis. Images are generated by emanating light rays from the particles or dot pattern, and propagating them through the density gradient field and the optical elements, up to the camera sensor. The rendered images are realistic, and can replicate the features of a given experimental setup, like optical aberrations and perspective effects, which can be deliberately introduced for error analysis. We demonstrate this methodology by simulating a BOS experiment with a known density field obtained from direct numerical simulations (DNS) of homogeneous buoyancy driven turbulence, and comparing the light ray displacements from ray tracing to results from BOS theory. The light ray displacements show good agreement with the reference data. This methodology provides a framework for further development of simulation tools for use in experiment design and development of image analysis tools for PIV and BOS applications. Finally, an implementation of the proposed methodology in a Python-CUDA program is made available as an open source software for researchers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Scaling trends for balance-of-system costs at land-based wind power plants: Opportunities for innovations in foundation and erection

Wind power plant sizes, hub heights, and turbine ratings have increased since 2008 to optimize the cost and performance of wind power; however, the limits of these economies of scale remain unclear. Here, we explore how the costs incurred to install turbines at a wind power plant—the balance-of-system (BOS) costs—scale with turbine rating, hub height, and plant size. We also investigate how these changes in BOS costs influence the levelized cost of energy (LCOE). We show that increasing the plant size from 150 to 400 MW could reduce the BOS costs by 21%. We also show that if the foundation costs decreased by 50%, building a wind power plant with 5-MW turbines (having rotor diameters of 166 m and hub heights of 120 m) could decrease the LCOE by 5%. These results could help inform future BOS cost-reduction opportunities and thereby reduce future capital costs for land-based wind power.

17 WIND ENERGY↗