Search NASA⌕ Search

SEARCH · Search NASA

Results for “Reactor Physics Method”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Assessment and Improvement of Fission Product Transport Predictions of Particle Fuel in BISON

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. This study was motivated by the need to incorporate more physics-based models in BISON in order to foster tri-structural isotropic (TRISO) applications. This document details the integration of new modeling capabilities in BISON, including (1) development of pyrolytic carbon (PyC) and silicon carbide (SiC) layer anisotropic thermal and mass transport capabilities, (2) verification of the mass diffusion solution in TRISO modeling, (3) calibration of fission product diffusivity using Advanced Gas Reactor (AGR) experiments, (4) improved fission product release modeling by developing compact diffusion modeling capabilities, and (5) documentation of accelerated failure analysis on the BISON website. Improvements made to the diffusion models and parameters were documented and validated against AGR-1 and -2 experiment data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Multi physics simulation on Reactor Pressure Vessel (RPV) subjected to Pressurized Thermal Shock (PTS) by using Cardinal

The structural components that comprise nuclear reactors and their supporting structures are subjected to harsh operating environments that can challenge their integrity, especially after exposure for extended durations or under accident condition. As one of the most significant components of a Reactor, the Reactor Pressure Vessel (RPV) is exposed to an aggressive environment during the operation time (e.g. more than 40 years). Ageing degradation mechanisms (e.g. thermo-fatigue) could grow initial defects up to a critical size, increasing the susceptibility to failure in the RPV. The conventional methods are mostly based on simple crack and structure geometries. Very limited studies consider the real conditions of the RPV subjected to a thermal shock due to a Loss of Coolant Accident (LOCA). During a LOCA event, the most severe conditions take place when the emergency core cooling (ECC) water is injected inside the cold legs filled initially with hotter water and/or steam. The rapid cooling of the down-comer and the internal RPV surface followed probably by re-pressurization of the RPV causes large temperature gradients and variation of pressure which induces thermal-mechanical stresses. In order to develop the model for integrity assessment of a reactor pressure vessel (RPV) subjected to pressurized thermal shock (PTS), a multi-physics simulation, which includes the thermo-hydraulic, thermo-mechanical and fracture mechanics analyses is necessary.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

HIGH-FIDELITY SIMULATION-DRIVEN MODEL DEVELOPMENT FOR COARSE-GRAINED COMPUTATIONAL FLUID DYNAMICS

Nuclear reactor safety analysis requires identifying various credible accident scenarios and determining their consequences. For a full-scale nuclear power plant system behavior, it is impossible to obtain sufficient experimental data for a broad range of risk-significant accident scenarios. In single-phase flow convective problems, Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) can provide us with high fidelity results when physical data are unavailable. However, these methods are computationally expensive and cannot be afforded for simulation of long transient scenarios in nuclear accidents despite extraordinary advances in high performance scientific computing over the past decades. The major issue is the inability to make the transient computation parallel, thus making number of time steps required in high-fidelity methods unaffordable for long transients. In this work, we propose to apply a high fidelity simulation-driven approach to model sub-grid scale (SGS) effect in Coarse Grained Computational Fluid Dynamics CG-CFD. This approach aims to develop a statistical surrogate model instead of the deterministic SGS model. We chose to start with a turbulent natural convection case with volumetric heating in a horizontal fluid layer with a rigid, insulated lower boundary and isothermal (cold) upper boundary. This scenario of unstable stratification is relevant to turbulent natural convection in a molten corium pool during a severe nuclear reactor accident, as well as in containment mixing and passive cooling. The presented approach demonstrates how to create a correction for the CG-CFD solution by modifying the energy balance equation. A global correction for the temperature equation proves to achieve a significant improvement to the prediction of steady state temperature distribution through the fluid layer.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Modeling In-Reactor Temperature Gradients in Lithium Aluminate Ceramic Pellets

In reactor thermal performance of lithium aluminate ceramic pellets are calculated to determine operating temperatures if internal components shift during irradiation. A 2D conduction model was setup to model this scenario with additional physics, including thermal expansion of components to reactor temperatures, creep down of the cladding due to differential pressure between the inside of the rod and reactor coolant system, and net radiation exchange between components. The non-linear coupling of the governing equations presented convergence challenges due to the very different effects from a small temperature change on the radiation vs radial and circumferential conduction equations. Initial temperature guesses for the multidimensional solution were provided from conservative 1D models simulating bounding scenarios. Convergence was greatly improved by taking small steps towards the root solutions and averaging with initial guesses. The final method robustly converged all 132 points at an axial plane after a few dozen iterations. The temperature results show that the additional physics have a secondary effect as the dominant heat transfer mechanism is radial conduction across the gas gap followed by circumferential conduction around components. Circumferential conduction in the lithium aluminate pellets is enhanced by the relatively high thermal conductivity of this ceramic.

Carstens, Nathan A.↗
DOE OSTI2021

A novel implicit hybrid machine learning model and its application for reinforcement learning

A novel methodology to develop implicit hybrid models is presented. PyTorch is used to integrate physics-based equations with machine learning models. Automatic differentiation of the hybrid model is leveraged to solve the implicit equations. Iterative solving enables gradient based updates to the machine learning model. The novel methodology is compared to an explicit hybrid approach on a continuously stirred tank reactor (CSTR). The novel method results in a lower modelling error. Both hybrid models effectively train with noisy data. To test the implicit hybrid model, it is employed as a reinforcement learning (RL) training model. The RL algorithm trained on the hybrid model outperforms real time optimization of the CSTR and performs nearly as well as RL trained directly on the CSTR and a traditional gradient based approach. Training RL directly on the CSTR requires over 60,000 system interactions compared to 6000 historical data points for hybrid model development.

42 ENGINEERING↗
DOE OSTI2025

Extending the Nuclide Inventory Validation Basis for High-Burnup Fuel with New Radiochemical Assay Data

Efforts are underway at Oak Ridge National Laboratory to improve the nuclide inventory validation basis for spent nuclear fuel at high burnups. Recently conducted radiochemical assay experiments provided new measurement data for nine samples of fuel irradiated in a pressurized water reactor, with estimated sample burnups in the 30 to 70 GWd/t range. This type of destructive assay data is essential for validating computational methods, tools, and nuclear data applied in nuclear safety analyses and for improving our understanding of the bias and uncertainty in code predictions. The measurement data include key actinides and fission products that span a gamut of needs and interests for nuclear science and engineering applications in criticality safety, reactor physics, nuclide inventory, decay heat, and radiation shielding. The SCALE 6.3 code system with ENDF/B-VII.1 cross-section libraries was used to simulate the irradiation histories of the measured fuel samples. The calculated nuclide concentrations are compared to corresponding measurement data. The significance of the comparisons is discussed, emphasizing how the addition of the new measurement data fills gaps in the validation basis at high burnups and contributes to the decrease in bias and uncertainty for predicted nuclide concentrations. The discussion addresses the effect of the sample burnup used in the simulation—which is based on reactor operator records or on calibration to measured data for burnup indicator fission products—on the validation results.

Nuclide inventory↗
DOE OSTI2023

Advanced Reactor Cyber Analysis and Development Environment (ARCADE) for System-Level Design Analysis

Cybersecurity is a persistent concern to the safety and security of Nuclear Power Plants (NPPs), but has lacked data-driven, evidence-based research. Rigorous cybersecurity analysis is critical for the licensing of advanced reactors using a performance-based approach. One tool that enables cybersecurity analysis is modeling and simulation. The nuclear industry makes extensive use of modeling and simulation throughout the decision process but lacks a method to incorporate cybersecurity analysis with existing models. To meet this need, the Advanced Reactor Cyber Analysis and Development Environment (ARCADE) was developed. ARCADE is a suite of publicly available tools that can be used to develop emulations of industrial control system devices and networks and integrate those emulations with physics simulators. This integration of cyber emulations and physics models enables rigorous cyber-physical analysis of cyber-attacks on NPP systems. This report provides an overview of key considerations for using ARCADE with existing physics models and demonstrates ARCADE’s capabilities for cybersecurity analysis. Using a model of the Small Modular Advanced High Temperature Reactor (SmAHTR), ARCADE was able to determine the sensitivity of the primary heat exchangers (PHX) to coordinated cyber-attacks. The analysis determined that while the PHX’s failures cause disruption to the reactor, they did not cause any safety limits to be exceeded because of the plant design, including passive safety features. Further development of ARCADE will enable rigorous, repeatable, and automated cyber-physical analysis of advanced reactor control systems. These efforts will also help reduce regulatory uncertainty by presenting similar types of cybersecurity analyses in a common format, driving standard approaches and reporting.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

High-burnup isotopics bias and uncertainty estimations for LANCR02

Calculated nuclide concentrations are compared against destructive radiochemical assays of modern, high-burnup BWR fuel to validate the LANCR02 lattice physics code. Good agreement is observed between different lattice physics methodologies - including the LANCR02 implementation of Method of Characteristics neutron and gamma transport solver and the TGBLA06 methodology - and against measurements. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Neutron activation of natural materials in a PWR spectrum: feedback on {sup 116m}In relative γ emission intensities and half-life

During the MAESTRO program, carried out between 2011 and 2014 in MINERVE zero power reactor, common Gen-II and Gen-III light water reactor materials were irradiated. For some of these materials, the decay of their activation products was also measured by γ spectrometry. Initially devoted to the measurement of the integral capture cross section by activation and reactivity-oscillation method, these results can also provide useful information on decay data of various radionuclides. This approach of this experiment led to a common roadmap shared by the Experimental Physics Section and the Henri Becquerel National Laboratory to improve decay data in nuclear data libraries. Results discussed in this paper concern the relative emission intensities of the main γ rays of {sup 116m}In. Six irradiations of samples with various physical forms of {sup nat}In were carried out. Measurements were analyzed using decay data from several evaluations and it is shown that γ ray activities are not consistent. Analyses were carried out to provide new relative γ emission intensities from these measurements. The {sup 116m}In half-life has also been measured and shows a good agreement with existing values. Finally, an overview of the foreseen results on additional decay data from the MAESTRO program is given. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Development of a hybrid deterministic/stochastic method for 1D nuclear reactor kinetics

A new method has been implemented for solving the time-dependent neutron transport equation efficiently and accurately. This is accomplished by coupling the hybrid stochastic-deterministic steady-state coarse-mesh radiation transport (COMET) method [1,2] with the new predictor-corrector quasi-static method (PCQM) developed at Politecnico di Torino [3]. In this paper, the coupled method is implemented and tested in 1D slab geometry.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

MOOSE Framework Speed and Memory Optimizations

The Multiphysics Object Oriented Simulation Environment (MOOSE) framework has been developed to solve a variety of complex simulations ranging from groundwater flows to full reactor simulations. MOOSE excels at solving fully-implicit, fully-coupled, highly nonlinear models using the Jacobian Free Newton-Krylov (JFNK) method relying on years of expertise in several dependent packages including PETSc, libMesh, Hypre and many others. The modular design of MOOSE enables the rapid development of new physics models that can be coupled with relative ease within the framework for building up simulations of different levels of complexity. With the ongoing adoption of MOOSE to new application areas along with streamlining of existing simulations, the priorities must be adjusted to include code optimizations. This includes both execution speed and the memory footprint of MOOSE-based simulations. These two categories can be further broken down into ''micro-optimizations'' or source code optimizations and algorithmic optimizations where source code is restructured to accomplish the same tasks through a more efficient means. However, before any code optimizations can occur, one must have robust way of measuring these critical simulation properties to identify the best candidates for optimizations along with having a way to identify the effectiveness of proposed optimizations to the framework. This report discusses the preliminary work towards the implementation of a robust execution baseline of the framework and derivative applications under a variety of different simulations. In addition, several potential optimization target areas in both the MOOSE framework and dependent libraries have been identified. An implementation strategy and potential benefits are discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Annular Core Research Reactor (ACRR) Pulse Curve Characterization

Reactor pulse characterization at the Annular Core Research Reactor (ACRR) at Sandia Technical Area V (TA-V) is commonly done through photo conductive detection (PCD) and calorimeter detectors. Each of these offer a mode of analyzing a digital signal with different advantages/methods for determination of integrated dose or temporal based metrology. This report outlines a method and code that takes the millions of data points from such detectors and delivers a characteristic pulse trendline through two main methods: digital signal filtration and machine learning, in particular, Support Vector Machines (SVMs). Each method's endpoint is to deliver a characteristic curve for the many bucket environments of ACRR while considering other points of interest including delayed gamma removal for prompt dose metrics. This work draws and adds on previous work detailing the delayed gamma fraction contributions from CINDER simulations of the ACRR. Results from this project show a method to determine characteristic curves in a way that has previously been limited by data set size.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Developing ML/AI Methods for High-Throughput Characterization of Multiple-Sensor Streams of Tokamak Dynamics for High-Speed Control (Final Report)

This project evaluated and developed new mathematical and algorithmic techniques capable of handling (in real-time) the growing amounts of data generated by modern fusion research. While existing numerical linear algebra (NLA) methods provide the backbone to classical data analysis and algorithms, these methods fundamentally do not port to distributed architectures nor do they allow low-latency data reduction for control. Motivated by the needs for modern fusion reactors, this project explored and implemented new numerical methods to characterize plasma dynamics, respond in real-time to discharge evolution, and to process massive-scale data accurately and rapidly more fully. This project links expertise in multiple-sensor diagnostics of tokamak plasma dynamics from Columbia University’s Plasma Physics Laboratory with expertise in massive-scale data reduction and extreme data control algorithms at Columbia University’s Data Science Institute. This interdisciplinary project (i) applied machine learning methods, (ii) implemented a properly-trained neural-network for very fast processing of high-speed plasma videography, and (ii) developed the applied mathematical methods, based on randomized-NLA (rNLA) routines, for data analysis, reduction, and real-time control. The Columbia University High Beta Tokamak-Extended Pulse (HBT-EP) facility provided data to test new algorithms and partnership with Columbia University's Data Sciences Institute evaluated the broader use of new algorithms for many challenging control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Developing a digital twin framework for remotely monitoring nuclear reactor facilities

A digital twin must seek to represent all applicable functional components of the system of interest. Different expertise is required for understanding the physical system being modeled than the skills needed for transforming those models into a functional digital twin through physics modeling, machine learning analysis, and visualization. The diversity of knowledge requires a multi-disciplinary team to ensure all system details are captured. Team members also need a method to verify that the data they generate within their domain can be effectively communicated to professionals in other fields. To address this challenge, this work provides an approach for developing a digital twin framework to remotely monitoring nuclear facilities. Through this, general knowledge of the framework is presented along with two examples to solidify the process. The AGN-201 digital twin and microreactor digital twins provide varying levels of complexity in a potential nuclear facility, where common threads are identified and lessons learned are provided. The goal of this research is to aid future researchers by providing a formula for a successful digital twin and in turn reducing the development time of nuclear system digital twins, specifically for remote monitoring.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Passive Safety System Reliability Analysis: Methodology, Application, and Results

Many advanced reactor designs rely on passive systems to provide assurance that certain safety functions, such as reactivity control and reactor heat removal, are maintained during transient scenarios. The performance of these systems is sensitive to boundary and environmental conditions and can lead to functional failure, which is the inability of the system to perform as intended even without any physical failures. Additionally, conditions can cause the passive systems to operate in an intermediate or degraded state. These operational modes of passive systems and their overall reliability can be difficult to capture in traditional probabilistic frameworks where discrete operating modes are assumed or time-dependent boundary conditions are not accounted for. Novel strategies and methods are needed to be able to capture the reliability of these passive systems and their impact on facility safety. The current abstract provides a summary of such a methodology and examples of its use in a case study, the GE Hitachi (GEH) PRISM probabilistic safety assessment (PSA), and the U.S. Department of Energy (USDOE) Versatile Test Reactor (VTR) PSA.

Bucknor, M.↗
DOE OSTI2022

$χ$-$MeRA$: Computationally efficient adaptive mesh refinement of Monte Carlo mesh based tallies

Here, the reactor physics community is always focused on reducing the computational time and memory required for simulations. $χ$-$MeRA$, which stands for flux-based-($χ$)-Mesh tally Refinement Adaptively, was built to reduce the computational time and memory required to solve the neutronics side of a multiphysics problem when compared to traditional methods for mesh based tallies in Monte Carlo (MC) simulations. $χ$-$MeRA$ couples a MC code with an adaptive mesh refinement (AMR) algorithm to take advantage of the accuracy of a MC code and the efficiency of an AMR algorithm. Also developed within $χ$-$MeRA$ was a set of metrics to assess the effects of the refinement on various parameters in the simulation space. For a plutonium sphere, $χ$-$MeRA$ shows a reduction in memory usage and computation time when compared to a fully refined mesh by a factor of 14.7 and 6.7, respectively. When compared to an unstructured mesh, improvement of 1.3 and 4.8 was achieved for memory usage and computation time. The development of $χ$-$MeRA$ helps solve the neutronics side of a multiphysics problem in a faster, more computationally efficient manner than traditional methods, and the final mesh created contains accurate results that can be passed onto the next physics code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗