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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Surrogate Models for TREAT Transient Calculations

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) is currently preparing for resumption of operation after more than 20 years in a long-term standby state. In parallel with preparation for operations and a new transient testing program, the DOENE Advanced Modeling and Simulation program (NEAMS) has been supporting the development of three-dimensional transient multi-physics modeling capabilities within the reactor physics application MAMMOTH. Recently, the MAMMOTH reactor physics team has begun development of uncertainty and sensitivity analysis for the TREAT core. This research will be used to support the analysis of reactor operation and experiment planning. Traditional approaches, i.e., Monte Carlo simulations, for such tasks require a large number of model evaluations under both steady state and transient conditions. However, exhaustive sets of sampling runs are simply not practical for TREAT since evaluations of each model can take a substantial amount of time. A solution to this problem is to build a surrogate model employing a smaller number of model evaluations. The surrogate model is designed to provide an accurate and fast approximation to the detailed TREAT model. In this manner, the surrogate model can be used in place of the TREAT transient model for uncertainty and sensitivity analysis at a fraction of the computational cost. The high dimensional model representation (HDMR) method is employed here for approximating the responses of interest in terms of functions of lower dimensions. This method has been implemented in RAVEN and all TREAT transient calculations are performed using the multiphysics code MAMMOTH. In addition, a code interface has also been developed in order to couple RAVEN with MAMMOTH. The surrogate model is then tested and compared for TREAT startup testing program Transient Test no. 15 core, and its accuracy is measured by its ability to estimate the mean, coefficient of variance, skewness, excess kurtosis and probability density function of the outputs of Transient Test no. 15 model, as well as root mean square error metric.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

MLCommons Science Benchmarks

Benchmarks are a cornerstone of modern machine learning practice, providing standardized eval- uations that enable reproducibility, comparison, and scientific progress. Yet, as AI systems particularly deep learning models become increasingly dynamic, traditional static benchmarking approaches are losing their relevance. Models rapidly evolve in architecture, scale, and capability; datasets shift; and deployment contexts continuously change, creating a moving target for evaluation. Without adaptive benchmarking frame- works, both scientific assessment and real-world de- ployment risk becoming misaligned with actual system behavior. Drawing on our experience from MLCommons, educa- tional initiatives, and government programs such as the DOE s Million Parameter Consortium, we identify key barriers that hinder the broader adoption and utility of benchmarking in AI. These include substantial resource demands, limited access to specialized hardware, lack of expertise in benchmark design, and uncertainty among practitioners about how to relate benchmark results to their own application domains. Moreover, current benchmarks often emphasize peak performance on leadership-class hardware, offering limited guidance for more diverse, real-world deployment scenarios. We argue that benchmarking itself must become dy- namic in order to incorporate evolving models, updated data, and heterogeneous computational platforms while maintaining transparency, reproducibility, and inter- pretability. Democratizing this process requires not only technical innovation, but also systematic educational efforts spanning undergraduate to professional levels to develop sustained expertise in benchmark design and use. Finally, benchmarks should be framed and com- municated to support application-relevant comparisons, enabling both developers and users to make informed, context-sensitive decisions. Advancing dynamic and inclusive benchmarking practices will be essential to ensure that evaluation keeps pace with the evolving AI landscape and supports responsible, reproducible, and accessible AI deployment.

Hawks, Benjamin G. [Fermilab]↗
DOE OSTI2014

Beyond the Standard Curriculum: A Review of Available Opportunities for Medical Students to Prepare for a Career in Radiation Oncology

Purpose: To review currently available opportunities for medical students to supplement their standard medical education to prepare for a career in radiation oncology. Methods and Materials: Google and PubMed were used to identify existing clinical, health policy, and research programs for medical students in radiation oncology. In addition, results publicly available by the National Resident Matching Program were used to explore opportunities that successful radiation oncology applicants pursued during their medical education, including obtaining additional graduate degrees. Results: Medical students can pursue a wide variety of opportunities before entering radiation oncology. Several national specialty societies, such as the American Society for Radiation Oncology and the Radiological Society of North America, offer summer internships for medical students interested in radiation oncology. In 2011, 30% of allopathic senior medical students in the United States who matched into radiation oncology had an additional graduate degree, including PhD, MPH, MBA, and MA degrees. Some medical schools are beginning to further integrate dedicated education in radiation oncology into the standard 4-year medical curriculum. Conclusions: To the authors' knowledge, this is the first comprehensive review of available opportunities for medical students interested in radiation oncology. Early exposure to radiation oncology and additional educational training beyond the standard medical curriculum have the potential to create more successful radiation oncology applicants and practicing radiation oncologists while also promoting the growth of the field. We hope this review can serve as guide to radiation oncology applicants and mentors as well as encourage discussion regarding initiatives in radiation oncology opportunities for medical students.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

A practical guide to replica-exchange Wang—Landau simulations

This paper is based on a series of tutorial lectures about the replica-exchange Wang-Landau (REWL) method given at the IX Brazilian Meeting on Simulational Physics (BMSP 2017). It provides a practical guide for the implementation of the method. A complete example code for a model system is available online. In this paper, we discuss the main parallel features of this code after a brief introduction to the REWL algorithm. The tutorial section is mainly directed at users who have written a single-walker Wang–Landau program already but might have just taken their first steps in parallel programming using the Message Passing Interface (MPI). In the last section, we answer "frequently asked questions" from users about the implementation of REWL for different scientific problems.

Vogel, Thomas↗
DOE OSTI2017

Python in the NERSC Exascale Science Applications Program for Data

We describe a new effort at the National Energy Re- search Scientific Computing Center (NERSC) in performance analysis and optimization of scientific Python applications targeting the Intel Xeon Phi (Knights Landing, KNL) many- core architecture. The Python-centered work outlined here is part of a larger effort called the NERSC Exascale Science Applications Program (NESAP) for Data. NESAP for Data focuses on applications that process and analyze high-volume, high-velocity data sets from experimental/observational science (EOS) facilities supported by the US Department of Energy Office of Science. We present three case study applications from NESAP for Data that use Python. These codes vary in terms of “Python purity” from applications developed in pure Python to ones that use Python mainly as a convenience layer for scientists without expertise in lower level programming lan- guages like C, C++ or Fortran. The science case, requirements, constraints, algorithms, and initial performance optimizations for each code are discussed. Our goal with this paper is to contribute to the larger conversation around the role of Python in high-performance computing today and tomorrow, highlighting areas for future work and emerging best practices

Ronaghi, Zahra↗
DOE OSTI2014

Review of Prior Commercial Building Energy Efficiency Retrofit Evaluation: A Report to Snohomish Public Utilities District

Snohomish County Public Utilities District (the District or Snohomish PUD) provides electricity to about 325,000 customers in Snohomish County, Washington. The District has an incentive programs to encourage commercial customers to improve energy efficiency: the District partially reimburses the cost of approved retrofits if they provide a level of energy performance improvement that is specified by contract. In 2013 the District contracted with Lawrence Berkeley National Laboratory to provide a third-party review of the Monitoring and Verification (M&V) practices the District uses to evaluate whether companies are meeting their contractual obligations. This work helps LBNL understand the challenges faced by real-world practitioners of M&V of energy savings, and builds on a body of related work such as Price et al. (2013). The District selected a typical project for which they had already performed an evaluation. The present report includes the District's original evaluation as well as LBNL's review of their approach. The review is based on the document itself; on investigation of the load data and outdoor air temperature data from the building evaluated in the document; and on phone discussions with Bill Harris of the Snohomish County Public Utilities District. We will call the building studied in the document the subject building, the original Snohomish PUD report will be referred to as the Evaluation, and this discussion by LBNL is called the Review.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Simulative Prediction of Solar Illuminance and Application of the Du-Sharples Model in Estimating Adapted Daylighting Metrics for an Urban Environment

The practice of daylighting in indoor spaces can significantly reduce electricity consumption and carbon emissions, improve human productivity, and enhance mood and cognitive perception. This work discussed the recent developments in daylighting science and practice, computed the periodic variations in average diurnal daylight levels for each month, quantified in terms of global horizontal illuminance and diffuse horizontal illuminance, for Kolkata, India, a city with tropical wet and dry climate, with two empirical luminous efficacy models of estimating solar illuminance, and assessed daylighting metrics with the Du-Sharples model. A program was formulated that could compute and generate daylight data with monthly-hourly solar irradiation data and the Du-Sharples model was utilized to predict dirt-corrected daylighting metrics for three glazing transmittance values and five elemental carbon deposition levels on glazing material. The highest monthly average global horizontal illuminance is recorded in April (64.05 klx for Littlefair model and 66.82 klx for Muneer-Kinghorn model) and the highest monthly average diffuse horizontal illuminance is recorded in July (33.23 klx for Littlefair model and 30.63 klx for Muneer-Kinghorn model). Further, the computed yearly average global and diffuse horizontal illuminance levels agree well with a previous study that applied the Perez model. Yearly average horizontal work surface illuminance level remained >1.5 klx for window-towall area ratio >30 %. The approach adopted in this work and the temporal variation charts of computed exterior daylight level data may assist building service engineers, architects, and indoor lighting practitioners in making informed policy decisions at different stages of building planning.

Engineering↗
DOE OSTI2018

Piloting the Use of Energy Policy Data to Drive Market Action

Information is only useful when put to use. Across the U.S., an ever-growing number of state and local jurisdictions are implementing building performance reporting laws regarding building energy and water use in the commercial and multifamily sectors. This wealth of performance information is not yet being deployed to its full potential to drive smarter business decisions and increase energy savings. Recognizing that this information can help advance energy efficiency in cities, in 2015 the Institute for Market Transformation (IMT) partnered with New York City and the District of Columbia and local groups, NYCEEC and the District of Columbia Sustainable Energy Utility, on a three-year project to understand and document how, exactly, this data can and should be integrated to reap an array of benefits for cities, energy efficiency service providers, utilities, and building owners. This paper examines how local governments, utilities, and program implementers can replicate the success of DC and NYC. It gathers the findings from the Cities’ and their partners’ efforts to fully understand the datasets, overcome integration challenges, and apply the data to visual engagement, strategic outreach, evidence-based policymaking, and retrofit project identification and deployment. The paper goes beyond theoretical discussion by outlining practical application of data using specific examples from NYC and DC, summarizing challenges and findings, and highlighting next steps for other jurisdictions to learn from and build off the work done in those jurisdictions.

Cities↗
DOE OSTI2023

Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities

Although deep learning has made great progress in recent years, the exploding economic and environmental costs of training neural networks are becoming unsustainable. To address this problem, there has been a great deal of research on algorithmically-efficient deep learning, which seeks to reduce training costs not at the hardware or implementation level, but through changes in the semantics of the training program. In this paper, we present a structured and comprehensive overview of the research in this field. First, we formalize the algorithmic speedup problem, then we use fundamental building blocks of algorithmically efficient training to develop a taxonomy. Our taxonomy highlights commonalities of seemingly disparate methods and reveals current research gaps. Next, we present evaluation best practices to enable comprehensive, fair, and reliable comparisons of speedup techniques. To further aid research and applications, we discuss common bottlenecks in the training pipeline (illustrated via experiments) and offer taxonomic mitigation strategies for them. Finally, we highlight some unsolved research challenges and present promising future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Surrogate Models for TREAT Transient Calculations

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) is currently preparing for resumption of operation after more than 20 years in a long-term standby state. In parallel with preparation for operations and a new transient testing program, the DOE-NE Advanced Modeling and Simulation program (NEAMS) has been supporting the development of three-dimensional transient multi-physics modeling capabilities within the reactor physics application MAMMOTH [1]. Recently, the MAMMOTH reactor physics team has begun development of uncertainty and sensitivity analysis for the TREAT core. This research will be used to support the analysis of reactor operation and experiment planning. Traditional approaches, i.e., Monte Carlo simulations, for such tasks require a large number of model evaluations under both steady state and transient conditions. However, exhaustive sets of sampling runs are simply not practical here since evaluations of each model can take a substantial amount of time. A solution to this problem is to build a surrogate model employing a smaller number of model evaluations. The surrogate model is designed to provide an accurate and fast approximation to the detailed TREAT model. In this manner, the surrogate model can be used in place of the TREAT transient model for uncertainty and sensitivity analysis at a fraction of the computational cost. The high dimensional model representation (HDMR) [2] method is employed here for approximating the responses of interest in terms of functions of lower dimensions. This method has been implemented in RAVEN [3] and all TREAT transient calculations are performed using the multiphysics code MAMMOTH. In addition, a code interface has also been developed in order to couple RAVEN with MAMMOTH. The surrogate model is then tested and compared for the MAMMOTH model of the TREAT startup testing program Transient Test #15 core [4], and its accuracy is measured by its ability to estimate the mean, coefficient of variance, skewness, excess kurtosis and probability density function of the outputs of Transient Test #15 model, as well as root mean square error metric.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

How to implement security controls for an information security program at CBRN facilities

This document was prepared by PNNL within the framework of Project 19 of the European Union Chemical Biological Radiological and Nuclear Risk Mitigation Centres of Excellence Initiative entitled, ''Development of procedures and guidelines to create and improve secure information management systems and data exchange mechanisms for CBRN materials under regulatory control.'' It provides management and workers at CBRN facilities, parent organization managers responsible for those facilities, and regulatory agencies (governmental and nongovernmental) with guidance on the best practices for protecting information security. The security mitigation approaches presented in this document were chosen because they present generally accepted guidance in an easy-to-understand manner, making it easier for facility personnel to grasp key concepts and envision how security controls could be implemented by the facility. This guidance is presented from a risk management perspective.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Energy Efficiency and Infrastructure Upgrades on Nuclear Clean-up Sites: The Hanford Experience - 18641

The Hanford site, located in southeastern Washington, produced much of our nation's weapons-grade plutonium for the Defense nuclear weapons program. This production lasted the better part of four decades with production ending in 1987. Since 1987, the focus of the Hanford site has turned to clean-up activities. Mission Support Alliance (MSA) is the integrator of the multi-contractor effort to clean up the Hanford site. MSA collaborates with the U.S. Department of Energy (DOE) and all Hanford site contractors in finding and implementing new, cost-saving, safe and practical solutions to cleanup challenges with a continual focus on innovative processes and technologies that reduce costs, energy use and bring safety improvements to the site. Support Services include environmental integration and land management; fleet and road maintenance; water/electric and utilities; cyber security and information management as well as the preservation of the site's cultural artifacts. This paper describes the process, challenges, and focus on infrastructure upgrade and energy efficiency projects in the context of a multi-contractor nuclear clean-up site. Project development and new technology deployment at Hanford has long been plagued with economic challenges. For example, despite extensive auditing, analysis, and project development, most of the energy-related projects on the Hanford site have gone unfunded due to the low cost of electricity, the relatively high cost of labor, and costs associated with detailed safety and cultural reviews. - The Hanford site is a direct-service customer of the Bonneville Power Administration (BPA) and has a marginal electricity rate of $0.033/kWh. - Labor costs on the Hanford site track high because of the requirements associated with the safety and security necessary to operate a legacy nuclear weapons complex. - In partnership with three federally recognized and other affected Native American tribes (Yakama Nation, Confederated Tribes of the Umatilla Indian Reservation, the Nez Perce Tribe, and Wanapum People), most activities on the Hanford site are afforded in-depth cultural reviews. To work within these constraints, the MSA Energy Management team has embarked on a three-pronged approach to energy project development: Program Visibility: The MSA Energy Management team has promoted both project opportunities and requirements across the Hanford site. This information comes in the form of site-wide newsletters, group meetings, e-mails, success stories, and recognition of staff. Single-point Analysis: To by-pass the inevitable duplications of effort, learning curve issues, and to improve project consistency/delivery, the MSA Energy Management team serves as the single resource to the site for energy project analysis. This resource includes initial assessment, engineering analysis, efficiency calculation and project recommendations. Contractor/utility Interface: In addition to the infrastructure under its own purview, MSA provides all support services for the contractor/utility interface. These activities include formulation and submission of incentive-based efficiency projects, project tracking, and incentive awards. All incentive awards are forwarded to the relevant contractor and then highlighted in the visibility campaign. Successful energy efficiency projects at Hanford have included high efficiency heating, ventilation, air conditioning (HVAC) systems, interior and exterior lighting/lighting control systems, pump and motor systems, and the right-sizing of transformers and area-wide electrical distribution systems. Prior to the initiation of this program and structure (roughly 3 years ago) the average annual energy efficiency incentive applied for and received was under $10,000, with an annual energy savings of approximately 36,000 kilowatt hours (kWh). In fiscal year 2017, the annual incentive received was over $419,000, with identified energy savings of over 1.3 million kWh. Details and case studies of the major project accomplishments are included. The success of this program can be traced to an understanding of the constraints, the development of a program to address these constraints, and good communication/coordination across DOE and the multiple contractors serving the Hanford site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

The Radiation Oncology Job Market: The Economics and Policy of Workforce Regulation

Examinations of the US radiation oncology workforce offer inconsistent conclusions, but recent data raise significant concerns about an oversupply of physicians. Despite these concerns, residency slots continue to expand at an unprecedented pace. Employed radiation oncologists and professional corporations with weak contracts or loose ties to hospital administrators would be expected to suffer the greatest harm from an oversupply. The reduced cost of labor, however, would be expected to increase profitability for equipment owners, technology vendors, and entrenched professional groups. Policymakers must recognize that the number of practicing radiation oncologists is a poor surrogate for clinical capacity. There is likely to be significant opportunity to augment capacity without increasing the number of radiation oncologists by improving clinic efficiency and offering targeted incentives for geographic redistribution. Payment policy changes significantly threaten radiation oncologists' income, which may encourage physicians to care for greater patient loads, thereby obviating more personnel. Furthermore, the implementation of alternative payment models such as Medicare's Oncology Care Model threatens to decrease both the utilization and price of radiation therapy by turning referring providers into cost-conscious consumers. Medicare funds the vast majority of graduate medical education, but the extent to which the expansion in radiation oncology residency slots has been externally funded is unclear. Excess physician capacity carries a significant risk of harm to society by suboptimally allocating intellectual resources and creating comparative shortages in other, more needed disciplines. There are practical concerns associated with a market-based solution in which medical students self-regulate according to job availability, but antitrust law would likely forbid collaborative self-regulation that purports to restrict supply. Because Congress is unlikely to create one central body to govern residency controls for all specialties, we recommend better reporting of program-specific employment metrics and careful, intellectually honest re-evaluation of existing Accreditation Council for Graduate Medical Education accreditation standards.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

TeraChem protocol buffers ( TCPB ): Accelerating QM and QM/MM simulations with a client–server model

The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. Here, we describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google’s protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ~250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI - )—a model chromophore in green fluorescent proteins—on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

User Guide to the Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software (V.1.0)

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the initial software release as ADDER v1.0.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

User Guide to the Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software (V.1.01)

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.0.1. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software for Depletion and Fuel Management

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.1.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software for Depletion and Fuel Management

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.1.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗