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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Chapter 7: Sun Photometers

Sun photometry is an extended technique for monitoring the atmospheric composition and support satellite product validation. By measuring the direct solar irradiance, the amount and characteristics of the atmospheric aerosol particles, precipitable water vapor column, and ozone columns can be derived. Global operational networks such as the Aerosol Robotic Network (AERONET), Global Atmosphere Watch-Precision Filter Radiometer Network (GAW-PFR), Maritime Aerosol Network (MAN), or the Brewer spectrophotometer networks constitute sustained efforts for long-term monitoring of the atmosphere, with invaluable data production using cost-effective and relatively simple instrumentation. The data quality relies on the standardization of these networks, with special emphasis on the calibration as the key element in Sun photometry.

Sun photometry↗
NASA NTRS1978

The Array Automated Assembly Task for the Low Cost Solar Array Project, Phase 2

During the program a process sequence was proposed and tested for the fabrication of dendritic welb silicon into solar modules. This sequence was analyzed as to yield and cost and these data suggest that the price goals of 1986 are attainable. Specifically, it was shown that a low cost POCL3 is a suitable replacement for the semiconductor grade, and that a suitable CVD oxide can be deposited from a silane/air mixture using a Silox reactor. A dip coating method was developed for depositing an antireflection coating from a metalorganic precursor. Application of photoresist to define contact grids was made cost effective through use of a dip coating technique. Electroplating of both Ag and Cu was shown feasible and cost effective for producing the conductive metal grids on the solar cells. Laser scribing was used to separate the cells from the dendrites without degradation. Ultrasonic welding methods were shown to be feasible for interconnecting the cells. A study of suitable low cost materials for encapsulation suggest that soda lime glass and phenolic filled board are preferred.

Campbell, R. B.↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS2013

TPSAS-NF1676L-17243-DND

This presentation captures ERA Project's effort in using integrated cost-schedule-risk analysis tool, identifying and capturing explicit relationships between program funds, schedule, and discrete risks. The project team merged a myriad of cost and schedule data for timely, objective, uncertainty analysis of the ITD's budget and schedule. The project team conducted detailed schedule uncertainty analysis showing schedule activities and how their interrelationships were impacted, representing an impressive advance beyond routine Gantt charts. Furthermore, the ERA Project collected discrete risk events including their impacts to costs and/or schedule and interwove these risks into cost and schedule logic networks, providing the foundation to conduct cost and schedule analyses. The team integrated this data into the analysis model, providing probabilistic results that were then used by the ERA Project's senior management for decision-making purposes. The ERA Project management team, through technical and pathfinder expertise in integrating cost, schedule, risk, and technical content, consistently provided risk-informed recommendations to Implementing Centers and ISRP senior management which ultimately ensured the successful ARMD Key Decision Point (KDP) review of the ERA Phase 2 ITD Portfolio and increased the likelihood of achieving technical objectives within cost and schedule constraints.

Gaudy M Bezos-O'Connor↗
DOE OSTI2025

Impacts of PV Module Connector Failures on Cost and Performance of Utility Scale Photovoltaic Systems

The reliability, cost and performance of electrical connectors are a concern in all types of electrical systems, and demands on connectors used on photovoltaic (PV) systems include that connectors maintain electrical conductivity and physical strength, endure ultraviolet sunlight and high ambient temperature, and resist moisture and chemical intrusion over a very long (>25 year) performance period. Connector failures increase operation and maintenance (O&M) costs and reduce plant production, but connector failure can also cause safety and liability problems, which are of greater concern. This work results from a three-year collaboration between Sandia National Laboratories (SNL), the Electric Power Research Institute (EPRI), and the National Renewable Energy Laboratory (NREL) and funded by the U.S. Department of Energy (DOE) Solar Energy Technology Office (SETO) under Agreements #39035 and #38531 "Connector Reliability Across the US Solar Sector." a multi-pronged investigation of PV connector health across the US (see https://energy.sandia.gov/pvconnectors/). This report presents derivation of a Techno-Economic Analysis (TEA) that models failure modes and frequencies (how often failure occurs), estimates O&M costs and lost production associated with connector failures, and then calculates the effect that PV module connectors can have on Levelized Cost of Energy (LCOE). The model is informed with initial data from quantitative assessment of failure rates, root causes and mechanisms, in-situ diagnostics and data collection, lab-based forensics, and interviews with PV connector manufacturers and plant operators. SNL conducted site inspections at multiple utility-scale sites in different climates and subjected field samples of new, used, and degraded connectors to visual and electrical characterization. EPRI conducted metallurgical analysis of the pin and sleeve conductors to study failure-induced morphological and compositional changes. There is in general a shortage of statistically valid data, but data from PVROM database maintained by SNL was sufficient to ascertain failure rates and lost production as well as provide qualitative insight in its curated maintenance records. This report details the structure of the mathematical model but the sources of data to inform the model will continue to evolve. Analysis of a 100 MW PV plant is provided as an example of the use of the model, with results indicating that connectors are responsible for Annualized O&M Costs of $\$$71,933/year; Annualized Unit O&M Costs of $\$$0.72/kW/year; that a Reserve Account of $\$$187,220 should be available to fund repairs related to connectors; that connectors add $\$$1,494,004 to the Net Present Value of the O&M Costs (project life); and that O&M related to connectors adds about $\$$0.00088/kWh to the Levelized Cost of Energy. The impact of this model is to provide a tool to make the US solar sector more robust by quantifying and monetizing the reliability risks to utility-scale PV systems posed by poorly installed, mismatched and/or poorly designed and manufactured connectors. The TEA provides a model incorporating failure statistics, O&M cost data, and lost production into a single figure of merit, informing decisions and enabling practitioners to optimize cost and performance trade-offs. Stakeholders include connector manufacturers, system designers and equipment specifiers, standards bodies, installers and O&M providers, investors and insurance underwriters. This report supports continued growth of PV predicated on assurances that properly installed and maintained PV system connectors are safe and reliable. The project team is proposing future work including accelerated testing of connectors and expanding the approach taken here to other PV system components, such as TEA for rapid shut-down devices.

14 SOLAR ENERGY↗
DOE OSTI2015

Confirming the Strength of Residential Roof Structures for Solar Installations

Researchers at the U.S. Department of Energy’s Sandia National Laboratories conducted a first-ofits- kind study to help dispel misperceptions that many existing rooftops cannot carry the actual load created by rooftop solar photovoltaic (PV) installations. Test data on rooftop structure load capacity wasn’t previously available to the industry, and research to collect such data was made costly by the need to test each scaled structure to the point of failure. The Energy Department funded this study to provide scientific data to the industry that can decrease uncertainty, help eliminate misperceptions, and ultimately alleviate extraneous solar PV permitting costs.

Dwyer, Stephen F.↗
DOE OSTI2014

Solar Avoided Cost Solution SunShot 6 Final Report

The core objectives of this project were two separate but integrated products, collectively providing game-changing Avoided Cost capabilities. The first was a kit of avoided cost tools and data that any solar provider can use a-lacarte or as a whole. It’s open and easily accessible nature allows the rapid and accurate calculation of avoided cost in whatever context and software that make sense (“Typical and Avoided Cost Tools”). This kit includes a dataset of typical energy rates, costs and usage that can be used for solar prospecting, lead generation and any situation where data about an opportunity is missing or imperfect. The second is a web application and related APIs specifically built for solar providers to radically streamline their lead-to-sale process (“Solar Provider Module”). The typical and Avoided Cost tools are built directly into this, and allow for solar providers to track their opportunities, collaborate with their installers and financiers, and close more sales faster.

14 SOLAR ENERGY↗
NASA NTRS1999

Computing with Beowulf

Parallel computers built out of mass-market parts are cost-effectively performing data processing and simulation tasks. The Supercomputing (now known as "SC") series of conferences celebrated its 10th anniversary last November. While vendors have come and gone, the dominant paradigm for tackling big problems still is a shared-resource, commercial supercomputer. Growing numbers of users needing a cheaper or dedicated-access alternative are building their own supercomputers out of mass-market parts. Such machines are generally called Beowulf-class systems after the 11th century epic. This modern-day Beowulf story began in 1994 at NASA's Goddard Space Flight Center. A laboratory for the Earth and space sciences, computing managers there threw down a gauntlet to develop a $50,000 gigaFLOPS workstation for processing satellite data sets. Soon, Thomas Sterling and Don Becker were working on the Beowulf concept at the University Space Research Association (USRA)-run Center of Excellence in Space Data and Information Sciences (CESDIS). Beowulf clusters mix three primary ingredients: commodity personal computers or workstations, low-cost Ethernet networks, and the open-source Linux operating system. One of the larger Beowulfs is Goddard's Highly-parallel Integrated Virtual Environment, or HIVE for short.

Cohen, Jarrett↗
NASA NTRS1980

Lightweight diesel aircraft engines for general aviation

A methodical design study was conducted to arrive at new diesel engine configurations and applicable advanced technologies. Two engines are discussed and the description of each engine includes concept drawings. A performance analysis, stress and weight prediction, and a cost study were also conducted. This information was then applied to two airplane concepts, a six-place twin and a four-place single engine aircraft. The aircraft study consisted of installation drawings, computer generated performance data, aircraft operating costs and drawings of the resulting airplanes. The performance data shows a vast improvement over current gasoline-powered aircraft. At the completion of this basic study, the program was expanded to evaluate a third engine configuration. This third engine incorporates the best features of the original two, and its design is currently in progress. Preliminary information on this engine is presented.

Berenyi, S. G.↗
DOE OSTI2020

The Cost of Floating Offshore Wind Energy in California Between 2019 and 2032

California’s energy planning is centered around meeting the emissions reduction and renewable energy requirements of Senate Bill 350 by 2030. However, state power system planning is expected to eventually address California’s requirement to achieve 100% of total retail electricity sales from renewable energy and zero-carbon resources by 2045, as mandated by Senate Bill 100. To comply with these directives, California needs to investigate the further development of energy efficiency, storage, and a diverse range of renewable energy, zero-carbon emission, and transmission resources, including offshore wind. Wind resources off the coast of California have the potential to generate a significant portion of the state’s electric energy as it moves toward a zero-carbon economy and can help diversify its energy mix. Floating offshore wind technology, which is suitable for the deep waters along the California coast, is currently in a precommercial phase, with approximately 84 megawatts (MW) installed worldwide at the end of 2019. Globally there are over 7,000 MW in planning and permitting phases of development, with the first commercial-scale projects expected to be operational in 2024. This study provides site-specific cost and performance data for floating offshore wind to inform California’s long-term energy planning. The identification of new resources to meet California’s policy goals at least cost is part of the Integrated Resource Planning (IRP) process, which is coordinated by the California Public Utilities Commission (CPUC). In 2019–2020 IRP modeling, offshore wind was included for the first time as a candidate resource in some sensitivity cases (CPUC 2019). The data and information presented in this report can be used to update offshore wind inputs in future IRP cycles. The authors conducted a geospatial cost analysis over portions of the offshore wind resource area of California. The analyzed spatial domain includes sites with a mean wind speed of at least 7 meters per second and water depths between 40 meters (m) and 1,300 m. Costs and energy production vary across this analysis domain. We calculated these parameters on a grid layout with over 750 sites, with each site representing a 1,000-MW commercial offshore wind power plant. Levelized cost of energy (LCOE) was calculated at each site over the analysis domain. The resulting variation in LCOE across the analysis domain is illustrated through heat maps in this report. Five study areas were selected within the analysis domain where more detailed cost analysis was conducted and cost parameters, such as annual energy production, capital cost expenditures (CapEx), operational cost expenditures (OpEx), and net capacity factors are reported. These five study areas include Morro Bay, Diablo Canyon, Humboldt, Cape Mendocino and Del Norte.

17 WIND ENERGY↗
DOE OSTI2012

Data Center Energy Efficiency Measurement Assessment Kit Guide and Specification

A portable and temporary wireless mesh assessment kit can be used to speed up and reduce the costs of a data center energy use assessment and overcome the issues with respect to shutdowns. The assessment kit is comprised of temperature, relative humidity, and pressure sensors. Also included are power meters that can be installed on computer room air conditioners (CRACs) without intrusive interruption of data center operations. The assessment kit produces data required for a detailed energy assessment of the data center.

Data, Center, Energy, Efficiency, Measurement, Ass↗
NASA NTRS1977

A procedure for merging land cover/use data from Landsat, aerial photography, and map sources - Compatibility, accuracy and cost

A method is developed to merge land cover/use data from Landsat, aerial photography and map sources into a grid-based geographic information system. The method basically involves computer-assisted categorization of Landsat data to provide certain user-specified land cover categories; manual interpretation of aerial photography to identify other selected land cover/use categories that cannot be obtained from Landsat data; identification of special features from aerial photography or map sources; merging of the interpreted data from all the sources into a computer compatible file under a standardized coding structure; and the production of land cover/use maps, thematic maps, and tabular data. The specific tasks accomplished in producing the merged land cover/use data file and subsequent output products are identified and discussed. It is shown that effective implementation of the merging method is critically dependent on selecting the 'best' data source for each user-specified category in terms of accuracy and time/cost tradeoffs.

Enslin, W. R.↗
DOE OSTI2023

A Task Based Approach for Co-Scheduling Ensemble Workloads on Heterogeneous Nodes

Scientific workflows consist of multiple, connected applications, with data and results flowing from one to another in a pipeline. Traditionally, such workflows are executed in sequential order, storing intermediate data in storage disks. Co-scheduling application workflows concurrently on the same compute nodes would greatly reduce the cost of moving data to/from storage and allow real-time analysis of intermediate results. Nevertheless, most parallel programming runtimes do not allow seamless integration of various applications in a scientific workflow, in part due to the complexity of managing data and resources. The situation is even more complicated for heterogeneous systems. In this work we extend the Minos Computing Library (MCL) runtime to accelerate pipe-lined and parallel workloads where multiple applications are running in the same system. MCL’s asynchronous task library and runtime dynamically manages resources to allow co-scheduling of multiple processes sharing heterogeneous resources. In addition, we design a custom ex- tension of the Open Compute Language (OpenCL) to enable multiple processes to share device memory. We enable MCL to coordinate these shared buffers to allow for easy, fast data sharing between applications. Using malleable micro-benchmarks and two application workflows that combine scientific simulation and AI-based analysis, we show that our method outperforms traditional approaches.

Index Terms—Parallel systems, Scheduling and Task ↗
DOE OSTI2016

In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms, a State-of-the-art (STAR) Report

The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed /visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU’s and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Space Station racks weight and CG measurement using the rack insertion end-effector

The objective was to design a method to measure weight and center of gravity (C.G.) location for Space Station Modules by adding sensors to the existing Rack Insertion End Effector (RIEE). Accomplishments included alternative sensor placement schemes organized into categories. Vendors were queried for suitable sensor equipment recommendations. Inverse mathematical models for each category determine expected maximum sensor loads. Sensors are selected using these computations, yielding cost and accuracy data. Accuracy data for individual sensors are inserted into forward mathematical models to estimate the accuracy of an overall sensor scheme. Cost of the schemes can be estimated. Ease of implementation and operation are discussed.

Brewer, William V.↗
NASA NTRS2014

Assimilation of MODIS Snow Cover Through the Data Assimilation Research Testbed and the Community Land Model Version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (2345N). Only minimal modifications are made in the higher-middle (4566N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snowmove poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

data assimilation↗
NASA NTRS1977

Estimating costs and performance of systems for machine processing of remotely sensed data

This paper outlines a method for estimating computer processing times and costs incurred in producing information products from digital remotely sensed data. The method accounts for both computation and overhead, and may be applied to any serial computer. The method is applied to estimate the cost and computer time involved in producing Level II Land Use and Vegetative Cover Maps for a five-state midwestern region. The results show that the amount of data to be processed overloads some example computer systems, but that the processing is feasible on others.

Ballard, R. J.↗
NASA NTRS1995

Aeronautical Satellite Data Link System (SDLS) for high-density air-traffic areas

The European Space Agency has recently commissioned a study to investigate the feasibility of a low-cost aeronautical Satellite Data Link System (SDLS) to provide for the needs of Air Traffic Services, i.e. safety related communications over continental areas with high air-traffic density. This study is placed in today's context which sees the first generation of Aeronautical Mobile Satellite System (AMSS) being gradually but restrictively put into service in oceanic airspaces with low air-traffic density. This paper first discusses the case of ATS dedicated versus mixed (ATS and commercial) Comms service provision and identifies the specific ATS comms requirements context. Specific emphasis is put on the ICAO (International Civil Aviation Organization) standardization framework for both the ATN (Aeronautical Telecommunication Network) and the SSR (Secondary Surveillance Radar) Mode S specific services. An architectural system and network design for a future SDLS is then proposed, such as to meet the ATS comms requirements within the realm of existing technologies. To minimize development risk and cost, consideration is given to re-use the ESA-developed Land Mobile Communication Technology, known as MSBN (Mobile Satellite Business Network) featuring distinct subnetworks. It is particularly suited to an ATM (Air Traffic Management) decentralized architecture made of independent ATC (Air Traffic Control) Centers. Finally the study follow-on phase is introduced, which is intended to cover system design and development leading to a demonstration program, as a first step towards proposals for international standardization and acceptance.

Alain Delrieu↗