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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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NASA NTRS1990

Cost and quality planning for large NASA programs

The Software Cost and Quality Engineering methodology developed over the last two decades at IBM Federal Sector Div. is used to plan the NASA Space Station Data Management System (DMS). An ongoing project to capture this methodology, which is built on a foundation of experiences and lessons learned, has resulted in the development of a PC-based tool that integrates cost and quality forecasting methodologies and data in a consistent manner. This tool, Software Cost and Quality Engineering Starter Set (SCQESS), is being used to assist in the DMS costing exercises. At the same time, DMS planning serves as a forcing function and provides a platform for the continuing, iterative development, calibration, and validation and verification of SCQESS. The data that forms the cost and quality engineering data base is derived from more than 17 years of development of NASA Space Shuttle software, ranging from low criticality, low complexity support tools to highly complex and highly critical onboard software.

Rone, Kyle Y.↗

NASA NTRS2001

Role of Stratospheric Air in a Severe Weather Event: Analysis of Potential Vorticity and Total Ozone

The role of dry stratospheric air descending to low and middle tropospheric levels in a severe weather outbreak in the midwestern United States is examined using ACCEPT Eta model output, Rapid Update Cycle (RUC) analyses, and Earth probe Total Ozone Mapping Spectrometer (EP/TOMS) total ozone data. While stratospheric air was not found to play a direct role in the convection, backward trajectories show stratospheric air descended to 800 hPa just west of the convection. Damaging surface winds not associated with thunderstorms also occurred in the region of greatest stratospheric descent. Small-scale features in the high-resolution total ozone data compare favorably with geopotential heights and potential vorticity fields, supporting the notion that stratospheric air descended to near the surface. A detailed vertical structure in the potential vorticity appears to be captured by small-scale total ozone variations. The capability of the total ozone to identify mesoscale features assists model verification. The total ozone data suggest biases in the RUC analysis and Eta forecast of this event. The total ozone is also useful in determining whether potential vorticity is of stratospheric origin or is diabatically generated in the troposphere.

Goering, Melissa A.↗

DOE OSTI2022

A Knowledge-based Framework for Building Energy Model Performance Verification

Building energy modeling (BEM) has been widely used by researchers, regulators, and engineers to quantify building energy performance. Quality assurance (QA) and quality control (QC) of the model's performance are essential parts of such analysis. Currently, QA/QC is done in a manual and ad-hoc manner, which is tedious, error-prone, and time-consuming when QA/QC a large number of models. To solve these challenges, we propose a a dAta-driveN buIlding perforMance verificATion framEwork (ANIMATE), which conducts automated output-based verification of building operations requirements (especially for time-series output-based verification of control requirements). While this framework was developed for verifying energy model performance, it can be extended for other applications such as BEM software testing and performance verification of real buildings in the field.

Chen, Yan↗

DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1955

Aerodynamic Design of Axial-Flow Compressors. VII - Blade-Element Flow in Annular Cascades

Annular blade-element data obtained primarily from single-stage compressor installations are correlated over a range of inlet Mach numbers and cascade geometry. The correlation curves are presented in such a manner that they are related directly to the low-speed two-dimensional-cascade data of part VI of this series. Thus, the data serve as both an extension and a verification of the two-dimensional-cascade data. In addition, the correlation results are applied to compressor design.

Robbins, William H.↗

NASA NTRS2021

Comparing Cumulative Flight Thermal Fatigue to Ground Test Results for Memory Components on the Mars Curiosity Rover

This paper explores the methods used to examine Mars Curiosity Rover’s flight temperature data in relation to the Package Qualification and Verification (PQV) accelerated thermal cycle life test data in response to flight anomalies. The redundant Rover Compute Element (RCE-A, B) has experienced an inability to mount the flash memory several times. The leading cause of these anomalies is likely the loss of electrical connectivity, which is theorized to be due to cracks on the memory component solder joints by thermal cycle fatigue. To investigate this theory, the number of thermal cycles accumulated in flight was compared to PQV accelerated thermal cycle life test data. The goal of this study was to inform mission operations of the risk associated with thermal cycling damage on the RCE. However, since the RCE-B does not have continuous temperature data from the Platinum Resistance Thermometer (PRT) closest to the flash memory, a model was made to correlate continuous data from two other nearby sensors on the RCE-B to the noncontiguous sensor. Using the Rainflow Counting Algorithm, the reconstituted data was converted into the number of equivalent thermal cycles. The Coffin-Manson Equation was used to compare flight thermal cycles to the PQV test. The accelerated life test showed cracking on the memory component solder joints after 500 cycles of 80°C temperature excursions. This study found that the RCE-A and RCE-B have undergone an equivalent of 144 and 430 cycles of 80°C temperature excursions during flight, respectively.

Bell, Charles↗

DOE OSTI2022

Data assimilation for burnup distribution of PWR with three-dimensional variational algorithm and artificial neutral network

In this paper, a data-assimilation method has been proposed and applied for the burnup distribution of PWR. The burnup distribution is significant to the safety and economy of the reactor, as it is essential for the fuel-reloading design and optimization. Due to the burnup distribution cannot be measured directly during the reactor operation, the numerical simulation is widely applied to determine the burnup distribution. However, there is a deviation between the numerical simulation and the actual core due to some unavoidable factors, such as component manufacturing deviation, uneven flow distribution and so on. These differences would induce the errors to the simulation values of power distributions and hence to the burnup distribution. To address this problem, a data-assimilation method for the burnup distribution has been proposed with the application of power-distribution measurements. In our research, the three-dimensional variational (3DVAR) algorithm was applied for burnup-distribution calibration and the artificial neutral network (ANN) was applied to establish the relation between power distribution and corresponding burnup distribution. As engineering verification, the proposed data-assimilation method has been applied to the CNP1000 PWR operated in China. The numerical results indicated that the burnup-distribution errors can be reduced notably, as the maximum value of relative errors for power distribution can be reduced from 5.35% to 3.96%. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1986

A specification-based approach to concurrent structure verification in multiprocessor systems

A recently initiated research project concerned with the concurrent detection of software errors and errors due to physical failures in the hardware of multiprocessor systems is described in this paper. An approach to error detection is described, which is specification based and relies on the structural verification of program control flow and data structure integrity. The techniques discussed utilize the hardware redundancy inherent in parallel processing systems to provide verification of both program structure and data concurrently with program execution.

Fuchs, W. Kent↗

NASA NTRS2014

Architectures Toward Reusable Science Data Systems

Data Processing↗

NASA NTRS2015

Architectures Toward Reusable Science Data Systems

Science Data Systems (SDS) comprise an important class of data processing systems that support product generation from remote sensors and in-situ observations. These systems enable research into new science data products, replication of experiments and verification of results. NASA has been building systems for satellite data processing since the first Earth observing satellites launched and is continuing development of systems to support NASA science research and NOAAs Earth observing satellite operations. The basic data processing workflows and scenarios continue to be valid for remote sensor observations research as well as for the complex multi-instrument operational satellite data systems being built today. System functions such as ingest, product generation and distribution need to be configured and performed in a consistent and repeatable way with an emphasis on scalability. This paper will examine the key architectural elements of several NASA satellite data processing systems currently in operation and under development that make them suitable for scaling and reuse. Examples of architectural elements that have become attractive include virtual machine environments, standard data product formats, metadata content and file naming, workflow and job management frameworks, data acquisition, search, and distribution protocols. By highlighting key elements and implementation experience we expect to find architectures that will outlast their original application and be readily adaptable for new applications. Concepts and principles are explored that lead to sound guidance for SDS developers and strategists.

Data Processing↗

DOE OSTI2020

Putting Our Industry's Data to Work: A Case Study of Large-Scale Data Aggregation: Preprint

With increasing deployment of Advanced Metering Infrastructure (AMI), Building Automation System (BAS) controls, Internet of Things (IoT) network devices, and data-driven evaluation, measurement, and verification studies, the building sector is currently generating a staggering amount of energy-related data. In the right hands, these data sets can contribute to increased comfort and energy savings for building occupants and a more reliable electrical grid; however, due to a combination of factors, including significant privacy concerns, much of the data that are presently generated and stored are not used outside of basic operational applications. In the past year, our team has dedicated over 2,000 person-hours to accessing building energy data for a project funded by the U.S. Department of Energy (DOE) Building Technologies Office. We sought whole-building or end-use (e.g., lighting) timeseries data at the individual-building or equipment level where possible or aggregated information, such as timeseries averages and quartiles by building type (e.g., office, retail, hospital), where sharing individual building information was not an option. We are additionally working with IoT and BAS data sets to derive information important to the project. We have assembled an extensive data set that will enable the development of publicly available end-use load profiles to benefit the U.S. building and electricity industries. Here we present an overview of the data set that we have assembled to date, the motivators and approaches that got us here, and the lessons we learned through our efforts. We also discuss work underway that presents additional options for future data access.

building energy data↗

NASA NTRS2013

Performance Measurement of Advanced Stirling Convertors (ASC-E3)

NASA Glenn Research Center (GRC) has been supporting development of the Advanced Stirling Radioisotope Generator (ASRG) since 2006. A key element of the ASRG project is providing life, reliability, and performance testing data of the Advanced Stirling Convertor (ASC). The latest version of the ASC (ASC-E3, to represent the third cycle of engineering model test hardware) is of a design identical to the forthcoming flight convertors. For this generation of hardware, a joint Sunpower and GRC effort was initiated to improve and standardize the test support hardware. After this effort was completed, the first pair of ASC-E3 units was produced by Sunpower and then delivered to GRC in December 2012. GRC has begun operation of these units. This process included performance verification, which examined the data from various tests to validate the convertor performance to the product specification. Other tests included detailed performance mapping that encompassed the wide range of operating conditions that will exist during a mission. These convertors were then transferred to Lockheed Martin for controller checkout testing. The results of this latest convertor performance verification activity are summarized here.

Stirling Cycle↗

DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗

NASA NTRS2012

Synthetic and Enhanced Vision Systems for NextGen (SEVS) Simulation and Flight Test Performance Evaluation

The Synthetic and Enhanced Vision Systems for NextGen (SEVS) simulation and flight tests are jointly sponsored by NASA's Aviation Safety Program, Vehicle Systems Safety Technology project and the Federal Aviation Administration (FAA). The flight tests were conducted by a team of Honeywell, Gulfstream Aerospace Corporation and NASA personnel with the goal of obtaining pilot-in-the-loop test data for flight validation, verification, and demonstration of selected SEVS operational and system-level performance capabilities. Nine test flights (38 flight hours) were conducted over the summer and fall of 2011. The evaluations were flown in Gulfstream.s G450 flight test aircraft outfitted with the SEVS technology under very low visibility instrument meteorological conditions. Evaluation pilots flew 108 approaches in low visibility weather conditions (600 ft to 2400 ft visibility) into various airports from Louisiana to Maine. In-situ flight performance and subjective workload and acceptability data were collected in collaboration with ground simulation studies at LaRC.s Research Flight Deck simulator.

Shelton, Kevin J.↗

NASA NTRS1988

Knowledge-based assistance in costing the space station DMS

The Software Cost Engineering (SCE) methodology developed over the last two decades at IBM Systems Integration Division (SID) in Houston is utilized to cost the NASA Space Station Data Management System (DMS). An ongoing project to capture this methodology, which is built on a foundation of experiences and lessons learned, has resulted in the development of an internal-use-only, PC-based prototype that integrates algorithmic tools with knowledge-based decision support assistants. This prototype Software Cost Engineering Automation Tool (SCEAT) is being employed to assist in the DMS costing exercises. At the same time, DMS costing serves as a forcing function and provides a platform for the continuing, iterative development, calibration, and validation and verification of SCEAT. The data that forms the cost engineering database is derived from more than 15 years of development of NASA Space Shuttle software, ranging from low criticality, low complexity support tools to highly complex and highly critical onboard software.

Henson, Troy↗

DOE OSTI2021

Thermodynamic properties of the Nd-Bi system via emf measurements, $\mathrm{DFT}$ calculations, machine learning, and $\mathrm{CALPHAD}$ modeling

Thermodynamic properties of the Nd-Bi system were investigated using a combination of experimental measurements, first-principles calculations based on density functional theory (DFT), data mining and machine learning (DM + ML) predictions, and calculation of phase diagrams (CALPHAD) modeling. The electromotive force (emf) of Nd-Bi alloys in molten LiCl-KCl-NdCl 3 at 773–973 K was measured via coulometric titration of Nd into Bi for the determination of thermochemical properties such as activity coefficients and solubilities of Nd in Bi. A new peritectic reaction of [liquid + NdBi 2 = Nd 3 Bi 7 ] at 774 K was confirmed using differential scanning calorimetry, structural (X-ray diffraction), and microstructural (scanning electron microscopy) analyses. The unknown crystal structure of NdBi2 was suggested to be a mixture of the anti-La 2 Sb configuration and the La 2 Te-type configuration based on ML predictions for over 26,000 data-mined AB 2 -type configurations together with DFT-based verifications. Using the newly acquired experimental data and DFT-based calculations, the thermodynamic description of the Nd-Bi system was remodeled, and a more complete Nd-Bi phase diagram was calculated, including the Nd 3 Bi 7 compound, invariant transition reactions, and liquidus temperatures.

36 MATERIALS SCIENCE↗

NASA NTRS2007

AIRSAR Web-Based Data Processing

The AIRSAR automated, Web-based data processing and distribution system is an integrated, end-to-end synthetic aperture radar (SAR) processing system. Designed to function under limited resources and rigorous demands, AIRSAR eliminates operational errors and provides for paperless archiving. Also, it provides a yearly tune-up of the processor on flight missions, as well as quality assurance with new radar modes and anomalous data compensation. The software fully integrates a Web-based SAR data-user request subsystem, a data processing system to automatically generate co-registered multi-frequency images from both polarimetric and interferometric data collection modes in 80/40/20 MHz bandwidth, an automated verification quality assurance subsystem, and an automatic data distribution system for use in the remote-sensor community. Features include Survey Automation Processing in which the software can automatically generate a quick-look image from an entire 90-GB SAR raw data 32-MB/s tape overnight without operator intervention. Also, the software allows product ordering and distribution via a Web-based user request system. To make AIRSAR more user friendly, it has been designed to let users search by entering the desired mission flight line (Missions Searching), or to search for any mission flight line by entering the desired latitude and longitude (Map Searching). For precision image automation processing, the software generates the products according to each data processing request stored in the database via a Queue management system. Users are able to have automatic generation of coregistered multi-frequency images as the software generates polarimetric and/or interferometric SAR data processing in ground and/or slant projection according to user processing requests for one of the 12 radar modes.

Chu, Anhua↗

DOE OSTI2020

Linkages between Policy and Business Innovation in the Development of China’s Energy Performance Contracting Market

China has a large and growing energy performance contracting (EPC) market, which has played a significant role in driving energy efficiency retrofits and improvements This paper evaluates how policies and business innovations have worked synergistically to expand China’s EPC market. We analyze the interconnected roles of four key factors: incentive policies, China’s Five-Year Plans, and business innovations in diversifying contract models and strengthening measurement and verification. We use multiple data sources for the analysis, including detailed information on business choices in 21 pilot projects, industry-wide surveys, and policy information. Our study indicates that supportive policies were important for the initial market development. As the market was established and continued to grow, the private sector started to take initiatives to address the issues that are left out of the policies, which helped overcome certain market barriers and enabled the sustainable growth of the market. Future policies could incorporate the successful innovations from the businesses to ensure long-term development of China’s EPC market. Understanding these policy and business drivers in a holistic way is critical to understanding how China was able to leverage its relatively small policy investments into much larger transformation of its existing buildings and industrial facilities through energy efficiency retrofits. This in turn can be informative for other countries seeking to achieve large-scale energy efficiency improvements.

Zhou, Yuanrong↗