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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Development and Validation of a Geant4 Radiation Shielding Simulation Framework

Geant4 (`Geometry and Tracking') is a C++ Monte- Carlo simulation toolkit developed by the Geant4 Collaboration under CERN and is used to describe inter- actions between particles and matter. It has been shown, through significant benchmarking against physical experiments and other software, to be remarkably accurate at simulating interactions between particles and matter. Currently, Geant4 is being used on world-wide projects such as ATLAS, CMS, Muon g-2, and ILC, largely due to being accurate, open-source, and written in a modern language. This openness and modernity is striking when compared to other Monte-Carlo simulation toolkits such as the Fortran toolkit MCNP (Monte-Carlo N-Particle code), which is restricted in acquisition and use by the US government. Despite barriers in the use of MCNP and having been written in an antiquated language, MCNP is still considered industry-standard for many fields, including radiation shielding. This paper details the development and validation of a radiation shielding simulation framework in Geant4 aimed at flexibility and ease of simulations with the goal of replacing MCNP scripts.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Evaluation of Building 23-117, Administration Building, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE) National Nuclear Security Administration Nevada Field Office (NNSA/NFO) plans to update the façade of Building 23-117, Administration Building (Nevada State Historic Preservation Office [SHPO] Resource No. B15256), at the Nevada National Security Site (NNSS) in Nye County, Nevada. The NNSA/NFO is implementing a long-term project to modernize the town of Mercury for future mission needs. The project is considered an undertaking subject to review under Title 54 of United States Code (USC) § 306108, commonly known as Section 106 of the National Historic Preservation Act, Title 54 USC § 300101, et seq., and its implementing regulations, Title 36 of the Code of Federal Regulations (36 CFR) Part 800.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ImpactX v0.1

ImpactX is the next generation of the IMPACT-Z code. It is a s-based simulation code for modeling intense beams in particle accelerators using symplectic tracking methods and includes collective effects. It is multi-node parallel and supports modern compute hardware such as GPUs, modern algorithms such as mesh-refinement and realistic geometries (embedded boundaries).

Huebl, Axel↗
DOE OSTI2015

Addressing fission product validation in MCNP burnup credit criticality calculations - 14508

The US Nuclear Regulatory Commission (NRC) Division of Spent Fuel Storage and Transportation issued Interim Staff Guidance (ISG) 8, Revision 3 in September 2012. This ISG provides guidance for NRC staff members' review of burnup credit (BUC) analyses supporting transport and dry storage of pressurized water reactor spent nuclear fuel (SNF) in casks. The ISG includes guidance for addressing validation of criticality (k{sub eff}) calculations crediting the presence of a limited set of fission products and minor actinides (FP and MAs). Based on previous work documented in NRC Regulatory Guide (NUREG) Contractor Report (CR)-7109, the ISG recommends that NRC staff members accept the use of either 1.5 or 3% of the FP and MA worth - in addition to bias and bias uncertainty resulting from validation of k{sub eff} calculations for the major actinides in SNF - to conservatively account for the bias and bias uncertainty associated with the specified unvalidated FP and MAs. The ISG recommends (1) use of 1.5% of the FP and MA worth if a modern version of SCALE and its nuclear data are used and (2) 3% of the FP and MA worth for well qualified, industry standard code systems other than SCALE with the Evaluated Nuclear Data Files, Part B (ENDF/B),-V, ENDF/B-VI, or ENDF/B-VII cross sections libraries. The work presented in this paper provides a basis for extending the use of the 1.5% of the FP and MA worth bias to BUC criticality calculations performed using the Monte Carlo N-Particle (MCNP) code. The extended use of the 1.5% FP and MA worth bias is shown to be acceptable by comparison of FP and MA worths calculated using SCALE and MCNP with ENDF/B-V, -VI, and -VII-based nuclear data. The comparison supports use of the 1.5% FP and MA worth bias when the MCNP code is used for criticality calculations, provided that the cask design is similar to the hypothetical generic BUC-32 cask model and that the credited FP and MA worth is no more than 0.1 Δk{sub eff} (ISG-8, Rev. 3, Recommendation 4). (authors)

42 ENGINEERING↗
DOE OSTI2022

Time- and Energy-Resolved Coupled Saturn Radiation Environments Simulations Using the Integrated Tiger Series (ITS) Code

Using a newly developed coupling of the ElectroMagnetic Plasma In Realistic Environments (EMPIRE) code with the Integrated Tiger Series (ITS) code, radiation environment calculations have been performed. The effort was completed as part of the Saturn Recapitalization (Recap) program that represents activities to upgrade and modernize the Saturn accelerator facility. The radiation environment calculations performed provide baseline results with current or planned hardware in the facility. As facility design changes are proposed and implemented as part of Saturn Recap, calculations of the radiation environment will be performed to understand how the changes impact the output of the Saturn accelerator.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Batched Sparse Linear Algebra (Final Report for Subcontract B648960)

This report finalizes design specifications for developing batched kernels for small tensor operations for unassembled matrix-free iterative solvers, batched solvers for partially assembled operators, and batched solvers with support for various sparse formats. The outcome of the project milestones is a set of interfaces to Batched Sparse LA solvers running on hardware accelerators for use in ECP Libraries and Applications. It is part of the development of sparse batched kernels, solvers/preconditioners as well as creating interoperability in xSDK libraries with sparse and dense batched functions to benefit ECP applications. The participants included representatives from ECP libraries (not limited to the xSDK project), applications, and vendors (AMD, Intel, and NVIDIA). Batched sparse linear algebra solvers form the new frontier for algorithmic development and performance engineering. Many applications (ECP and non-ECP alike) require simultaneous solutions of small linear systems of equations that are structurally sparse. To move towards high hardware utilization, it is important to provide these applications with appropriate interfaces to efficient batched sparse solvers running on modern hardware accelerators. We present interface designs in use by HPC software libraries supporting batched sparse linear algebra and the development of sparse batched kernel codes for solvers and preconditioners. We also address the potential interoperability opportunities to keep the software portable between the major hardware accelerators from AMD, Intel, and NVIDIA. The presented interface specifications includes batched band, sparse iterative, and sparse direct solvers. This report summarizes progress in Kokkos Kernels and the xSDK libraries MAGMA, Ginkgo, hypre, SUNDIALS, and SuperLU_dist.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Reassessing the MCNP Random Number Generator

Random number generators are integral components to Monte Carlo codes. They provide the pseudorandom number sequence used to actually sample the distributions of interest. As a result, they are one of the most important components to the software. The current recommended MCNP random number generator is a 63-bit linear congruential generator (LCG). This generator is quite fast, but it has some drawbacks. First, it only has a period of 2 63 . Due to the necessarily non-optimal usage of random numbers to ensure parallel reproducibility, this amount is too few to guarantee random number sequences are not reused in all configurations the code runs under. As simulation size increases, users will need to be aware of the limitations of the generator and tune configuration variables to best suit their simulations, or they will need to assume that reuse is not negatively affecting their answers. Neither of these are optimal. Second, small LCGs are fairly weak in bit generation quality, and this can have an unknown impact on the quality of the simulation. This paper is an investigation into whether or not more modern random number generators can supersede the current ones. The goal is to find a generator that is similar or superior in speed to the LCGs, has a state space large enough to make strong guarantees about random number reuse, and passes all modern random number test suites. If such a generator is found, it would eliminate the need for the user to even be aware of the limitations of the random number generator and would simplify the use of the code. This paper will be broken into several parts. Sec. 2 will discuss the evolution of the random number generator within the MCNP code. Sec. 3 will go over what a Monte Carlo code needs from a generator to be reproducible and portable and how the current generator behaves in that light. Sec. 4 goes through how each generator was tested. Finally, Sec. 5 will discuss improvements that could be made to the code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Sample problems for Section III, Division 5 design by inelastic analysis of Grade 91 components

This report works through two sample design analyses of representative Grade 91 components using the ASME Boiler & Pressure Vessel Code Section III, Division 5, Appendix HBB-T rules for the strain and deformation and creep-fatigue design criteria using a design by inelastic analysis approach. The component geometries and loadings were selected to span a wide variety of potential applications. The sample components are an endplate geometry, which is fairly representative of typical pressure vessel construction, and a core block, representing a critical component in a heat pipe microreactor, which has a complicated geometry and complex thermal stress history. These sample problems serve several functions. First, they are complete worked examples of how to apply the current design by inelastic analysis rules. Second, they serve as test cases to evaluate the current rules and provide feedback on how they could be optimized to deal with complicated component geometries or to be better automated in modern finite element analysis software. Finally, the analysis here uses an inelastic constitutive model for Grade 91 developed by Argonne National Laboratory and proposed for inclusion in the ASME Boiler & Pressure Vessel Code Section III, Division 5 rules as a reference constitutive model. Using this constitutive model in the sample problem analyses tests the model in the context of the ASME Code rules. The final conclusions of this report are that the material model and the current Code rules are satisfactory, but that the Code rules could be optimized to simplify automation and reduce over conservatism. Future work should address the specific optimization topics discussed in this report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas

Time-dependent, predictive simulations were performed with the 1.5D tokamak integrated modeling code TRANSP on a large set of well-analyzed, high performing discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate how well modern reduced transport models can reproduce experimentally observed temperature profiles in spherical tokamaks. Overall, it is found that simulations using the Multi-Mode Model (MMM) more consistently agree with the NSTX observations than those using the Trapped Gyro-Landau Fluid (TGLF) model, despite TGLF requiring orders of magnitude greater computational cost. When considering all examined discharges, MMM has median overpredictions of electron temperature (T e ) and ion temperature (T i ) profiles of 28% and 27%, respectively, relative to the experiment. TGLF overpredicts T e by 46%, with much larger variance than MMM, and underpredicts T i by 25%. As the ratio of kinetic to magnetic field pressure (β) is increased across NSTX discharges, TGLF predicts lower T e and significant flattening of the T i profile, conflicting with NSTX observations. When using an electrostatic version of TGLF, both T e and T i are substantially overpredicted, underscoring the importance of electromagnetic turbulence in the high β spherical tokamak regime. Additionally, calculations with neural net surrogate models for TGLF were performed outside of TRANSP with a time slice flux matching transport solver, finding better agreement with experiment than the TRANSP simulations, highlighting the impact of different transport solvers and simulation techniques. Furthermore, the reasonable agreement with experiment of temperature profiles predicted by MMM motivates a more detailed examination of the sensitivities of the TRANSP simulations with MMM to different NSTX plasma regimes in a companion paper (Lestz et al Plasma Phys. Control. Fusion 67 105030), in preparation for self-consistent, time-dependent predictive modeling of NSTX-U scenarios.

Lestz, J. B. [General Atomics, San Diego, CA (Unit↗
DOE OSTI2017

ORIGEN-based Nuclear Fuel Inventory Module for Fuel Cycle Assessment: Final Project Report

The goal of this project, “ORIGEN-based Nuclear Fuel Depletion Module for Fuel Cycle Assessment" is to create a physics-based reactor depletion and decay module for the Cyclus nuclear fuel cycle simulator in order to assess nuclear fuel inventories over a broad space of reactor operating conditions. The overall goal of this approach is to facilitate evaluations of nuclear fuel inventories for a broad space of scenarios, including extended used nuclear fuel storage and cascading impacts on fuel cycle options such as actinide recovery in used nuclear fuel, particularly for multiple recycle scenarios. The advantages of a physics-based approach (compared to a recipe-based approach which has been typically employed for fuel cycle simulators) is in its inherent flexibility; such an approach can more readily accommodate the broad space of potential isotopic vectors that may be encountered under advanced fuel cycle options. In order to develop this flexible reactor analysis capability, we are leveraging the Origen nuclear fuel depletion and decay module from SCALE to produce a standalone “depletion engine” which will serve as the kernel of a Cyclus-based reactor analysis module. The ORIGEN depletion module is a rigorously benchmarked and extensively validated tool for nuclear fuel analysis and thus its incorporation into the Cyclus framework can bring these capabilities to bear on the problem of evaluating long-term impacts of fuel cycle option choices on relevant metrics of interest, including materials inventories and availability (for multiple recycle scenarios), long-term waste management and repository impacts, etc. Developing this Origen-based analysis capability for Cyclus requires the refinement of the Origen analysis sequence to the point where it can reasonably be compiled as a standalone sequence outside of SCALE; i.e., wherein all of the computational aspects of Origen (including reactor cross-section library processing and interpolation, input and output processing, and depletion/decay solvers) can be self-contained into a single executable sequence. Further, to embed this capability into other software environments (such as the Cyclus fuel cycle simulator) requires that Origen’s capabilities be encapsulated into a portable, self-contained library which other codes can then call directly through function calls, thereby directly accessing the solver and data processing capabilities of Origen. Additional components relevant to this work include modernization of the reactor data libraries used by Origen for conducting nuclear fuel depletion calculations. This work has included the development of new fuel assembly lattices not previously available (such as for CANDU heavy-water reactor assemblies) as well as validation of updated lattices for light-water reactors updated to employ modern nuclear data evaluations. The CyBORG reactor analysis module as-developed under this workscope is fully capable of dynamic calculation of depleted fuel compositions from all commercial U.S. reactor assembly types as well as a number of international fuel types, including MOX, VVER, MAGNOX, and PHWR CANDU fuel assemblies. In addition, the Origen-based depletion engine allows for CyBORG to evaluate novel fuel assembly and reactor design types via creation of Origen reactor data libraries via SCALE. The establishment of this new modeling capability affords fuel cycle modelers a substantially improved ability to model dynamically-changing fuel cycle and reactor conditions, including recycled fuel compositions from fuel cycle scenarios involving material recycle into thermal-spectrum systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces

Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.

Tadimeti, Vijay [ORNL]↗
DOE OSTI2019

Ultrafast processing of pixel detector data with machine learning frameworks

Modern photon science performed at high repetition rate free-electron laser (FEL) facilities and beyond relies on 2D pixel detectors operating at increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly increasing amounts of data (towards TB/s). This data must be rapidly stored for offline analysis and summarized in real time for online feedback to the scientists. While at LCLS all raw data has been stored, at LCLS-II this would lead to a prohibitive cost; instead, enabling real time processing of pixel detector data (dark, gain, common mode, background, charge summing, subpixel position, photon counting, data summarization) allows reducing the size and cost of online processing, offline processing and storage by orders of magnitude while preserving full photon information. This could be achieved by taking advantage of the compressibility of sparse data typical for LCLS-II applications. Faced with a similar big data challenge a decade ago, computer vision stimulated revolutionary advances in machine learning hardware and software. We investigated if these developments are useful in the field of data processing for high speed pixel detectors and found that typical deep learning models and autoencoder architectures failed to yield useful noise reduction while preserving full photon information, presumably because of the very different statistics and feature sets in computer vision and radiation imaging. However, the raw performance of modern frameworks like Tensorflow inspired us to redesign in Tensorflow mathematically equivalent versions of the state-of-the-art, “classical” algorithms used at LCLS. The novel Tensorflow models resulted in elegant, compact and hardware agnostic code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive consumer GPU, reducing by 3 orders of magnitude the projected cost of online analysis and compression without photon loss at LCLS-II. The novel Tensorflow models also enabled ongoing development of a pipelined hardware system expected to yield an additional 3 to 4 orders of magnitude speedup, necessary for meeting the data acquisition and storage requirements at LCLS-II, potentially enabling acquiring every single FEL pulse at full speed. Computer vision a decade ago was dominated by hand-crafted filters; their structure inspired the deep learning revolution resulting in modern deep convolutional networks; similarly, our novel Tensorflow filters provide inspiration for designing future deep learning models for ultrafast and efficient processing and classification of pixel detector images at FEL facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

SU-F-J-72: A Clinical Usable Integrated Contouring Quality Evaluation Software for Radiotherapy

Purpose: To introduce the Auto Contour Evaluation (ACE) software, which is the clinical usable, user friendly, efficient and all-in-one toolbox for automatically identify common contouring errors in radiotherapy treatment planning using supervised machine learning techniques. Methods: ACE is developed with C# using Microsoft .Net framework and Windows Presentation Foundation (WPF) for elegant GUI design and smooth GUI transition animations through the integration of graphics engines and high dots per inch (DPI) settings on modern high resolution monitors. The industrial standard software design pattern, Model-View-ViewModel (MVVM) pattern, is chosen to be the major architecture of ACE for neat coding structure, deep modularization, easy maintainability and seamless communication with other clinical software. ACE consists of 1) a patient data importing module integrated with clinical patient database server, 2) a 2D DICOM image and RT structure simultaneously displaying module, 3) a 3D RT structure visualization module using Visualization Toolkit or VTK library and 4) a contour evaluation module using supervised pattern recognition algorithms to detect contouring errors and display detection results. ACE relies on supervised learning algorithms to handle all image processing and data processing jobs. Implementations of related algorithms are powered by Accord.Net scientific computing library for better efficiency and effectiveness. Results: ACE can take patient’s CT images and RT structures from commercial treatment planning software via direct user input or from patients’ database. All functionalities including 2D and 3D image visualization and RT contours error detection have been demonstrated with real clinical patient cases. Conclusion: ACE implements supervised learning algorithms and combines image processing and graphical visualization modules for RT contours verification. ACE has great potential for automated radiotherapy contouring quality verification. Structured with MVVM pattern, it is highly maintainable and extensible, and support smooth connections with other clinical software tools.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Loss-of-Flow and Loss-of-Pressure Simulations of the BR2 Research Reactor with HEU and LEU Fuel

Belgian Reactor 2 (BR2) is a research and test reactor located in Mol, Belgium and is primarily used for radioisotope production and materials testing. The Materials Management and Minimization (M3) Reactor Conversion Program of the National Nuclear Security Administration (NNSA) is supporting the conversion of the BR2 reactor from Highly Enriched Uranium (HEU) fuel to Low Enriched Uranium (LEU) fuel. The reactor core of BR2 is located inside a pressure vessel that contains 79 channels in a hyperboloid configuration. The core configuration is highly variable as each channel can contain a fuel assembly, a control or regulating rod, an experimental device, or a beryllium or aluminum plug. Because of this variability, a representative core configuration, based on current reactor use, has been defined for the fuel conversion analyses. The code RELAP5/Mod 3.3 was used to perform the transient thermal-hydraulic safety analyses of the BR2 reactor to support reactor conversion. The input model has been modernized relative to that historically used at BR2 taking into account the best modeling practices developed by Argonne National Laboratory (ANL) and BR2 engineers.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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↗