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DOE OSTI2023

The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Cladding Profilometry Analysis of Experimental Breeder Reactor-II Metallic Fuel Pins with HT9, D9, and SS316 Cladding

BISON finite element method fuel performance simulations were conducted using an existing automated process that couples the Fuels Irradiation & Physics Database (FIPD) and the Integral Fast Reactor Materials Information System database by writing input files and comparing the BISON output to post-irradiation fuel pin profilometry measurements contained within the databases. The importance of this work is to demonstrate the ability to benchmark fuel performance metallic fuel models within BISON using Experimental Breeder Reactor-II fuel pin data for a number of similar pins, while building off previous modeling efforts. Changes to the generic BISON input file include implementing pin specific axial power and flux profiles, pin specific fluences, frictional contact, and irradiation-induced volumetric swelling models for cladding. A statistical analysis of irradiation-induced volumetric swelling models for HT9, D9, and SS316 was performed for experiments X421/X421A, X441/X441A, and X486. Between these three experiments, there were 174 post-irradiation examination (PIE) profilometries used for validating the swelling models presented using a standard error of the estimate (SEE) method. Implementation of the volumetric swelling models for D9 and SS316 claddings was found to have a significant impact on the BISON profilometry simulated, where HT9 clad pins had an insignificant change due to low fluence values. BISON profilometry simulated for HT9, D9, and SS316 fuel pins agreed with PIE profilometry measurements, with assembly SEE values being 4.4 × 10−3 for X421A, 2.0 × 10−3 for X441A, and 2.8 × 10−3 for X486. D9 clad pins in X421/X421A had the highest SEE values, which is due to the BISON simulated profilometry being shifted axially. While this work accomplished its purpose to demonstrate the modeling of multiple fuel pins from the databases to help validate models, the results suggest that the continued development of metallic fuel models is necessary for qualifying new metallic fuel systems to better capture some physical performance phenomena, such as the hot pressing of U-Pu-Zr and the fuel cladding chemical interaction.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Portal and Repository for Information on Marine Renewable Energy (PRIMRE)

The Marine Renewable Energy (MRE) industry is in the early stages of development corresponding to low technology readiness levels (TRLs) where the ability of the MRE community (developers, researchers, academics, stakeholders, investors, and regulators) to work together to share knowledge, experience, and lessons learned is critical to the advancement of the entire MRE industry. Through collaboration on solving common problems, the MRE community has the potential to reduce cost and accelerate technology development. Currently, the US Department of Energy (US DOE) Water Power Technologies Office (WPTO) is addressing the challenge of storing, curating, and accessing MRE information by sponsoring development of MRE databases and information portals such as Tethys (https://tethys.pnnl.gov), OpenEI (https://openei.org), the MHK Data Repository (MHKDR, https://mhkdr.openei.org/), and a searchable MRE code catalog and open source code repository (MHKiT, currently under development), to name a few. These sites host scientific papers, news articles, reports, databases, open source codes, and stakeholder engagement information, but they are only a step towards facilitating global discovery and use. In short, there is an abundance of information available online, however it is located on many disparate sites and repositories that make the discovery of those data and information difficult. A DOE national laboratory team from the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Sandia National Laboratories (Sandia) is addressing the issues of data discoverability, shared knowledge, and interconnection of existing MRE databases and information portals. To meet the needs of the MRE community, as identified through multiple community outreach and engagement events, and described in this paper, the multi-lab team has developed an implementation plan for PRIMRE, the Portal and Repository for Information on Marine Renewable Energy. PRIMRE will provide broad access to information on engineering and technologies, resource characterization, device performance, and environmental effects of MRE projects. PRIMRE will facilitate the commercial development of the MRE industry by increasing the accessibility and discoverability of this information, integrating the databases and information portals described in this paper, and developing standards and guidelines. Providing consistent, easy access to information can help reduce duplication of effort and enable the MRE community to learn from past failures and build upon the successes of others to innovate and advance the commercialization of MRE technologies.

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Screening of Potential Biomass-Derived Streams as Fuel Blendstocks for Mixing Controlled Compression Ignition Combustion

Mixing controlled compression ignition or diesel engines are highly efficient and are likely to continue to be the primary means for movement of goods for many years to come. Low-carbon biofuels have the potential to significantly reduce the carbon footprint of diesel combustion and could potentially have advantageous properties for combustion such as high cetane number and reduced engine out particle emissions. In this study, we developed a list of potential biomass-derived diesel blendstocks. An online database of properties and characteristics of these bioblendstocks was populated with data. Fuel properties were determined by measurement, model prediction, or literature review. Screening criteria were developed to determine if a bioblendstock met the basic requirements for handling in the diesel distribution system and use as a blend with conventional diesel. Criteria included cetane number =40, flashpoint =52 degrees C, and boiling point or final boiling point <338 degrees C. Blendstocks needed to be soluble in diesel fuel, have a toxicity no worse than conventional diesel, and not be corrosive. Additionally, cloud point or freezing point below 0 degrees C was required. This screening produced a list of 27 potential bioblendstocks. Of these candidates, 13 were available commercially or could be synthesized by biofuels production researchers and included 11 nominally pure components and two mixtures. These 13 candidates were then subjected to further study based on how they impact fuel properties upon blending. Blend properties included cetane number, lubricity, conductivity, oxidation stability and viscosity. Results indicate that all thirteen candidates can meet the basic requirements for diesel fuel blending and are likely to reduce particle emissions from diesel combustion.

09 BIOMASS FUELS↗

DOE OSTI2021

The International Soil Moisture Network: serving Earth system science for over a decade

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

The Urban Energy-Water Nexus: Utility-Level Water Flows and Embedded Energy

There are limited open source data available for determining water production/treatment and required energy for cities across the United States. This database represents the culmination of a two-year effort to obtain data from cities across the United States via open records requests in order to determine the state of the U.S. urban energy-water nexus. Data were requested at the daily or monthly scale when available for 127 cities across the United States, represented by 253 distinct water and sewer districts. Data were requested from cities larger than 100,000 people and from each state. In the case of states that did not have cities that met these criteria, the largest cities in those states were selected. The resulting database represents a drinking water service population of 81.4 million and a wastewater service population of 86.2 million people. Average daily demands for the United States were calculated to be 560 liters per capita for drinking water and 500 liters per capita of wastewater. The embedded energy within each of these resources is 340 kWh/1000 m3 and 430 kWh/1000 m3, respectively. Drinking water data at the annual scale are available for production volume (89 cities) and for embedded energy (73 cities). Annual wastewater data are available for treated volume (104 cities) and embedded energy (90 cities). Monthly data are available for drinking water volume and embedded energy (73 and 56 cities) and wastewater volume and embedded energy (88 and 70 cities). Please see the two related papers for this metadata are included with this submission. Each folder name is a city that contributed data to the collection effort (City+State Abbreviation). Within each folder is a .csv file with drinking water and wastewater volume and energy data. A READ-ME file within each folder details the contents of the folder within any relevant information pertaining to data collection. Data are on the order of a monthly timescale when available, and yearly if not. Please cite the following papers when using the database: Chini, C.M. and Stillwell, A.S. (2017). The State of U.S. Urban Water: Data and the Energy-Water Nexus. Water Resources Research. 54(3). DOI: https://doi.org/10.1002/2017WR022265 Chini, C.M., and Stillwell, A. (2016). Where are all the data? The case for a comprehensive water and wastewater utility database. Journal of Water Resources Planning and Management. 143(3). DOI: 10.1061/(ASCE)WR.1943-5452.0000739

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Maximum demand charge rates for commercial and industrial electricity tariffs in the United States

NREL has assembled a list of U.S. retail electricity tariffs and their associated demand charge rates for the Commercial and Industrial sectors. The data was obtained from the Utility Rate Database. Keep the following information in mind when interpreting the data: (1) These data were interpreted and transcribed manually from utility tariff sheets, which are often complex. It is a certainty that these data contain errors, and therefore should only be used as a reference. Actual utility tariff sheets should be consulted if an action requires this type of data. (2) These data only contains tariffs that were entered into the Utility Rate Database. Since not all tariffs are designed in a format that can be entered into the Database, this list is incomplete - it does not contain all tariffs in the United States. (3) These data may have changed since this list was developed (4) Many of the underlying tariffs have additional restrictions or requirements that are not represented here. For example, they may only be available to the agricultural sector or closed to new customers. (5) If there are multiple demand charge elements in a given tariff, the maximum demand charge is the sum of each of the elements at any point in time. Where tiers were present, the highest rate tier was assumed. The value is a maximum for the year, and may be significantly different from demand charge rates at other times in the year. Utility Rate Database: https://openei.org/wiki/Utility_Rate_Database For updated dataset as of May 8, 2026 see https://data.openei.org/submissions/8685

/kW↗

DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

Development of Asset Fault Signatures for Prognostic and Health Management in the Nuclear Industry

Proactive online monitoring in the nuclear industry is being explored using the Electric Power Research Institute’s Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. The FW-PHM Suite is a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. The FW-PHM Suite has four main modules: Diagnostic Advisor, Asset Fault Signature (AFS) Database, Remaining Useful Life Advisor, and Remaining Useful Life Database. This paper focuses on development of asset fault signatures to assess the health status of generator step-up generators and emergency diesel generators in nuclear power plants. Asset fault signatures describe the distinctive features based on technical examinations that can be used to detect a specific fault type. At the most basic level, fault signatures are comprised of an asset type, a fault type, and a set of one or more fault features (symptoms) that are indicative of the specified fault. The AFS Database is populated with asset fault signatures via a content development exercise that is based on the results of intensive technical research and on the knowledge and experience of technical experts. The developed fault signatures capture this knowledge and implement it in a standardized approach, thereby streamlining the diagnostic and prognostic process. This will support the automation of proactive online monitoring techniques in nuclear power plants to diagnose incipient faults, perform proactive maintenance, and estimate the remaining useful life of assets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Implementation of Remaining Useful Lifetime Transformer Models in the Fleet-Wide Prognostic and Health Management Suite

Research and development efforts are required to address aging and reliability concerns of the existing fleet of nuclear power plants. As most plants continue to operate beyond the license life (i.e., towards 60 or 80 years), plant components are more likely to incur age-related degradation mechanisms. To assess and manage the health of aging plant assets across the nuclear industry, the Electric Power Research Institute has developed a web-based Fleet-Wide Prognostic and Health Management (FW-PHM) Suite for diagnosis and prognosis. FW-PHM is a set of web-based diagnostic and prognostic tools and databases, comprised of the Diagnostic Advisor, the Asset Fault Signature Database, the Remaining Useful Life Advisor, and the Remaining Useful Life Database, that serves as an integrated health monitoring architecture. The main focus of this paper is the implementation of prognostic models for generator step-up transformers in the FW-PHM Suite. One prognostic model discussed is based on the functional relationship between degree of polymerization, (the most commonly used metrics to assess the health of the winding insulation in a transformer) and furfural concentration in the insulating oil. The other model is based on thermal-induced degradation of the transformer insulation. By utilizing transformer loading information, established thermal models are used to estimate the hot spot temperature inside the transformer winding. Both models are implemented in the Remaining Useful Life Database of the FW-PHM Suite. The Remaining Useful Life Advisor utilizes the implemented prognostic models to estimate the remaining useful life of the paper winding insulation in the transformer based on actual oil testing and operational data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Supporting Building Portfolio Investment and Policy Decision Making through an Integrated Building Utility Data Platform

The American Recovery and Reinvestment Act stimulus funding of 2009 for smart grid projects resulted in the tripling of smart meters deployment. In 2012, the Green Button initiative provided utility customers with access to their real-time1 energy usage. The availability of finely granular data provides an enormous potential for energy data analytics and energy benchmarking. The sheer volume of time-series utility data from a large number of buildings also poses challenges in data collection, quality control, and database management for rigorous and meaningful analyses. In this paper, we will describe a building portfolio-level data analytics tool for operational optimization, business investment and policy assessment using 15-minute to monthly intervals utility data. The analytics tool is developed on top of the U.S. Department of Energy’s Standard Energy Efficiency Data (SEED) platform, an open source software application that manages energy performance data of large groups of buildings. To support the significantly large volume of granular interval data, we integrated a parallel time-series database to the existing relational database. The time-series database improves on the current utility data input, focusing on real-time data collection, storage, analytics and data quality control. The fully integrated data platform supports APIs for utility apps development by third party software developers. These apps will provide actionable intelligence for building owners and facilities managers. Unlike a commercial system, this platform is an open source platform funded by the U.S. Government, accessible to the public, researchers and other developers, to support initiatives in reducing building energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

EnergyIQ

EnergyIQ-the first "action-oriented" benchmarking tool for non-residential buildings-provides a standardized opportunity assessment based on benchmarking results. along with decision-support information to help refine action plans. EnergyIQ offers a wide array of benchmark metrics, with visuall as well as tabular display. These include energy, costs, greenhouse-gas emissions, and a large array of characteristics (e.g. building components or operational strategies). The tool supports cross-sectional benchmarking for comparing the user's building to it's peers at one point in time, as well as longitudinal benchmarking for tracking the performance of an individual building or enterprise portfolio over time. Based on user inputs, the tool generates a list of opportunities and recommended actions. Users can then explore the "Decision Support" module for helpful information on how to refine action plans, create design-intent documentation, and implement improvements. This includes information on best practices, links to other energy analysis tools and more. The variety of databases are available within EnergyIQ from which users can specify peer groups for comparison. Using the tool, this data can be visually browsed and used as a backdrop against which to view a variety of energy benchmarking metrics for the user's own building. User can save their project information and return at a later date to continue their exploration. The initial database is the CA Commercial End-Use Survey (CEUS), which provides details on energy use and characteristics for about 2800 buildings (and 62 building types). CEUS is likely the most thorough survey of its kind every conducted. The tool is built as a web service. The EnergyIQ web application is written in JSP with pervasive us of JavaScript and CSS2. EnergyIQ also supports a SOAP based web service to allow the flow of queries and data to occur with non-browser implementations. Data are stored in an Oracle 10g database. References: Mills, Mathew, Brook and Piette. 2008. "Action Oriented Benchmarking: Concepts and Tools." Energy Engineering, Vol.105, No. 4, pp 21-40. LBNL-358E; Mathew, Mills, Bourassa, Brook. 2008. "Action-Oriented Benchmarking: Using the CEUS Database to Benchmark Commercial Buildings in California." Energy Engineering, Vol 105, No. 5, pp 6-18. LBNL-502E.

MILLS, EVAN↗

DOE OSTI2017

Seventy-Five Years of Nuclear Criticality Safety Documents--A Bibliography

A collection of nuclear criticality documents was begun in the 1970s at the University of California’s Lawrence Livermore Laboratory (now Lawrence Livermore National Laboratory). Documents in this bibliography are dated as early as 1940. The early documents described experimental work that was of vital importance to the safe use of fissile materials at nuclear reactors, the facilities for preparing or transporting nuclear fuel, or its eventual storage. The experiments were of direct value to support safe operations with fissile materials, but they were also needed for validating early physics data and calculational methods and then for validating computational methods used in criticality safety evaluations. These documents provided information for our computerized bibliographic database. The bibliography was originally intended for use by Livermore staff members but criticality safety "pioneers” encouraged us to make the database available to the broader nuclear safety community and even shared copies of their bibliographies and reports. A publication of the bibliography is being prepared and a searchable database is available on the U.S. Department of Energy’s Nuclear Criticality Safety Program’s NCSP website. The NCSP website also includes links from the bibliography to many full-text documents. The database continues to expand--the information in this paper is based on a “snapshot” of its present status. The “vintage” hardcopy document collection is preserved and is being scanned into digital format.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

Calibration and Validation of NRC Fuel Performance Models

Pacific Northwest National Laboratory maintains steady-state and transient fuel performance codes for the U.S Nuclear Regulatory Commission (NRC). The NRC uses these codes in performing confirmatory calculations of licensee fuel performance codes and safety analysis methods during the review and approval of these codes and methods. The NRC also uses these codes to perform calculations to direct regulatory research and to assist in the development of new regulations and regulatory guides. Because of the safety-related uses for these codes, it is critically important that the codes are capable of accurately modeling modern fuel and operations and that the models and code predictions are well validated using data collected from conditions relevant to in-reactor conditions. FRAPCON and FRAPTRAN have an extensive database of separate effects data used to validate individual material property and fuel performance models and an integral effects database used to assess the overall code prediction within the full range of fuel operations. The key code predictions that are of regulatory interest as they relate to various specified acceptable design limits are; fuel and cladding temperature, rod internal pressure, cladding hoop strain, cladding corrosion and hydriding, and fission gas release. To assess the ability of the code to accurately predict these values, the integral assessment database consists of measurements of fuel centerline temperature, fission gas release, void volume, cladding corrosion, and cladding hoop strain. This paper will discuss the activities that are performed to ensure that FRAPCON and FRAPTRAN represent the state of the industry and will describe the separate effect and integral effect databases that are used to validate the models and are continually being expanded to ensure validation over the full range of fuel operations.

Geelhood, Kenneth J.↗

DOE OSTI2018

Knowledge catalysts

A computer implemented method integrates data from remote disparate data sources by processing a non-transitory media. The non-transitory media stores instructions for detecting data sets in different formats hosted in a plurality of heterogeneous databases that are accessible through a distributed network. The method extracts schema data from the plurality of heterogeneous databases and identifies related fields in two or more of the heterogeneous databases. The method links the related fields in the two or more of the plurality of heterogeneous databases and makes the data accessible through a virtual warehouse. As schemas change, as new data sources and analysis artifacts are created, the computer implemented method and system can act as a meta-data store, a provenance tracking device, and/or a knowledge management service.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Heliostat Consortium: Update on Resource, Training, and Education Development and Women+ in Concentrating Solar

One of the major barriers to Concentrating Solar Power (CSP) industry growth in the U.S. is the lack of accessibility of expert knowledge, a lack of a stable workforce pipeline, and little public awareness of the technology area. Improved Resource, Training, and Education (RTE) is needed to address these gaps, to draw workers into the field from diverse technology areas and backgrounds, and to reduce the learning curves of newcomers into the industry. As part of the RTE topic under the NREL-led Heliostat Consortium (HelioCon), a public online resource database has been created to support information sharing across the heliostat and CSP community. Here, information will be presented on the existing and planned database resources, including introductory information about CSP and heliostats, a reference library of texts and publications, educational videos, a CSP power tower plant database and case studies, a contact list of component suppliers and developers, and a list of available software/metrology tools. Women+ in Concentrating Solar is a newly created organization to promote the education, professional development, and advancement of underrepresented genders in the Concentrating Solar Power (CSP) community in a way that includes diverse backgrounds, attitudes, and perspectives. The main objectives of the groups are to provide the following opportunities for underrepresented genders: 1) organize events to provide networking opportunities and community building, 2) boost partnerships and scientific collaboration, 3) create resources to facilitate knowledge and experience sharing, 4) provide mentorship programs to support those new to the industry, 5) bring visibility to underrepresented genders and attract more to the field, and 6) promote Diversity, Equity, and Inclusion (DEI) and engage minorities and underrepresented groups. Initial activities being pursued by the group include creating an expert database to support event organizers in recruiting speakers with representation of all gender identities, hosting online networking and webinar events, organizing conference events including seminars and networking/social events, and establishing a mentorship program to connect members in the CSP community across the globe. The group is open to all genders and has grown to over 60 members in its first few months.

concentrating solar↗

DOE OSTI2016

Calibration and validation of NRC fuel performance models

FRAPCON and FRAPTRAN are the steady-state and transient fuel performance codes, respectively, that are used by the U.S. Nuclear Regulatory Commission (NRC) in performing confirmatory calculations of licensee fuel performance codes and safety analysis methods during the review and approval of these codes and methods for light-water reactors. The NRC also uses these codes to perform calculations to direct regulatory research and to assist in the development of new regulations and regulatory guides. FRAPCON and FRAPTRAN are well-validated fuel performance codes that are kept in a constant state of maintenance. FRAPCON and FRAPTRAN have an extensive database of separate effects data used to validate individual material property and fuel performance models and an integral effects database used to assess the overall code prediction within the full range of fuel operations. The key code predictions that are of regulatory interest as they relate to various specified acceptable design limits are: fuel and cladding temperature, rod internal pressure, cladding hoop strain, cladding corrosion and hydriding, and fission gas release. To assess the ability of the code to accurately predict these values, the integral assessment database consists of measurements of fuel centerline temperature, fission gas release, void volume, cladding corrosion, and cladding hoop strain. This paper will discuss the activities that are performed to ensure that FRAPCON and FRAPTRAN represent the state of the industry and will describe the separate effect and integral effect databases that are used to validate the models and are continually being expanded to ensure validation over the full range of fuel operations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Advances in Westinghouse fuel rod design technology (PAD5)

The Westinghouse Performance Analysis and Design (PAD) code for light-water reactors has been recently updated to version PAD5 and submitted to NRC for review. PAD5 incorporates extensive updates in fuel performance and material property models, including fuel thermal conductivity, pellet fragment relocation, fission gas release, fission gas swelling, and cladding creep and growth. Advances in thermal models are mainly in pellet fragment relocation, radial power distribution within the pellet, and the changes in fuel thermal conductivity due to changes in temperature, burnup and fuel porosity. The PAD5 thermal fission gas release model is a phenomenological model based on the irradiation dependence of the gas release from mechanistic processes for the diffusion of the fission gas generated in the fuel grains to the grain boundaries, formation of fission gas bubbles on the grain boundaries and release of the fission gas when these grain boundary bubbles inter-link. The PAD5 cladding creep model is predominantly a combination of the following mechanisms: thermal creep, irradiation induced creep, and irradiation enhanced creep. The fuel performance database used for the development, calibration and testing of the PAD5 code covers a broad range of both commercial irradiation data and test rod data and requires continuously additional data focusing on the latest development of the PAD5 code. Test programs are also included in the database. These data are checked and stored in a hierarchical database structure that supports the automation of model development and calibration. New data, once incorporated into the database, can be quickly evaluated with respect to key fuel and cladding performance models to confirm expected trends or to evaluate potential model improvements. This automated calibration system has been integrated with state-of-the-art optimization packages in DAKOTA Analysis Kit to provide for advanced statistical assessments and model coefficient optimization. Calibration results are automatically tabulated and plotted to provide easy assessment of trends or new behaviors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗