Physics-based Code Research Articles

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (SS316L) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M23C6, γ’, and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate for periods of up to 3000 h. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25 % volume fraction and mechanical properties within 6 % relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ∼1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that microstructural evolution under variable conditions can be predicted using a statistics-based model informed by a practicably obtainable dataset. The potential applications of this type of modeling framework are discussed.

Enabling particle transport on CAD-based geometries for radiation simulations with penRed

Geometry construction is a fundamental aspect of any radiation transport simulation, regardless of the Monte Carlo code being used. Typically, this process is tedious, time-consuming, and error-prone. The conventional approach involves defining geometries using mathematical objects or surfaces. However, this method comes with several limitations, especially when dealing with complex models, particularly those with organic shapes. Furthermore, since each code employs its own format and methodology for defining geometries, sharing and reproducing simulations among researchers becomes a challenging task. Consequently, many codes have implemented support for simulating over geometries constructed via Computer-Aided Design (CAD) tools. Unfortunately, this feature is lacking in penRed and other PENELOPE physics-based codes. Therefore, the objective of this work is to implement such support within the penRed framework. New version program summaryProgram Title: Parallel Engine for Radiation Energy Deposition (penRed)CPC Library link to program files:https://doi.org/10.17632/rkw6tvtngy.2Developer's repository link:https://github.com/PenRed/PenRedCode Ocean capsule:https://codeocean.com/capsule/1041417/treeLicensing provisions: GNU Affero General Public License v3Programming language: C++ standard 2011.Journal reference of previous version: V. Giménez-Alventosa, V. Giménez Gómez, S. Oliver, PenRed: An extensible and parallel Monte-Carlo framework for radiation transport based on PENELOPE, Computer Physics Communications 267 (2021) 108065. doi:https://doi.org/10.1016/j.cpc.2021.108065.Does the new version supersede the previous version?: YesReasons for the new version: Implements the capability to simulate on CAD constructed geometries, among many other features and fixes.Summary of revisions: All changes applied through the code versions are summarized in the file CHANGELOG.md in the repository package.Nature of problem: While Monte Carlo codes have proven valuable in simulating complex radiation scenarios, they rely heavily on accurate geometrical representations. In the same way as many other Monte Carlo codes, penRed employs simple geometric quadric surfaces like planes, spheres and cylinders to define geometries. However, since these geometric models offer a certain level of flexibility, these representations have limitations when it comes to simulating highly intricate and irregular shapes. Anatomic structures, for example, require detailed representations of organs, tissues and bones, which are difficult to achieve using basic geometric objects. Similarly, complex devices or intricate mechanical systems may have designs that cannot be accurately represented within the constraints of such geometric models. Moreover, when the complexity of the model increases, geometry construction process becomes more difficult, tedious, time-consuming and error-prone [2]. Also, as each Monte Carlo geometry library uses its own format and construction method, reproducing the same geometry among different codes is a challenging task.Solution method: To face the problems stated above, the objective of this work is to implement the capability to simulate using irregular and adaptable meshed geometries in the penRed framework. This kind of meshes can be constructed using Computer-Aided Design (CAD) tools, the use of which is very widespread and streamline the design process. This feature has been implemented in a new geometry module named “MESH_BODY” specific for this kind of geometries. This one is freely available and usable within the official penRed package1. It can be used since penRed version 1.9.3b and above.

A layered 2D computational framework: Theory and applications to nuclear fuel behavior

Nuclear fuel performance computer codes have been developed over the last 50 years to analyze fuel behavior under various operating conditions. Traditionally, these codes have used quasi-two-dimensional (also commonly known as 1.5D) representations of the fuel rod, which model the rod using a set of one-dimensional axisymmetric models that represent the behavior at specific axial positions on the rod. Modern fuel performance codes have the ability to investigate full three-dimensional (3D) effects and couple to other physics-based codes for true multiphysics simulations. However, with increasing complexity comes increasing computational costs. Many phenomena of interest involve azimuthally-varying behavior that cannot be represented using the aforementioned quasi-two-dimensional approach, but do not require the use of a full 3D model. To efficiently address these problems, there is a need for a computational framework that provides a compromise between the quasi-two-dimensional and full 3D models. This paper presents a new quasi-three-dimensional approach that represents the fuel rod as a set of 2D planar models that represent the behavior of the fuel cross-section at various axial positions. Presented here are the theory behind the methodology, test cases to illustrate proper implementation, and practical applications of its use in the BISON fuel performance code for a variety of cases in nuclear fuel analysis, such as fuel fracture, axial fuel relocation, and cladding distension and oxidation.

The dangers of sparse sampling for the quantification of margin and uncertainty

Activities such as global sensitivity analysis, statistical effect screening, uncertainty propagation, or model calibration have become integral to the Verification and Validation (V&V) of numerical models and computer simulations. One of the goals of V&V is to assess prediction accuracy and uncertainty, which feeds directly into reliability analysis or the Quantification of Margin and Uncertainty (QMU) of engineered systems. Because these analyses involve multiple runs of a computer code, they can rapidly become computationally expensive. An alternative to Monte Carlo-like sampling is to combine a design of computer experiments to meta-modeling, and replace the potentially expensive computer simulation by a fast-running emulator. The surrogate can then be used to estimate sensitivities, propagate uncertainty, and calibrate model parameters at a fraction of the cost it would take to wrap a sampling algorithm or optimization solver around the physics-based code. Doing so, however, offers the risk to develop an incorrect emulator that erroneously approximates the “true-but-unknown” sensitivities of the physics-based code. We demonstrate the extent to which this occurs when Gaussian Process Modeling (GPM) emulators are trained in high-dimensional spaces using too-sparsely populated designs-of-experiments. Our illustration analyzes a variant of the Rosenbrock function in which several effects are made statistically insignificant while others are strongly coupled, therefore, mimicking a situation that is often encountered in practice. In this example, using a combination of GPM emulator and design-of-experiments leads to an incorrect approximation of the function. A mathematical proof of the origin of the problem is proposed. The adverse effects that too-sparsely populated designs may produce are discussed for the coverage of the design space, estimation of sensitivities, and calibration of parameters. This work attempts to raise awareness to the potential dangers of not allocating enough resources when exploring a design space to develop fast-running emulators.

Space Weather Model Descriptions: Which Should Be Published?

Since the launch of Space Weather, a number of space weather models of all types have been reported in the journal. One of the more uncertain (and at times controversial) aspects of numerical simulations of the entire Sun-to-Earth environment is for authors (and editors) to determine when such “global” models and their results are deserving of consideration for publication. Models of solar-terrestrial processes are essential for successful predictions of these processes and of their effects on technological systems. Models can take many forms, from simple empirical expressions of the relationships among two or more physical variables to more detailed physics-based codes of subsets of the solar-terrestrial environment. Numerical simulations of the entire environment—from the Sun to Earth's ionosphere and surface—now exist. Such simulations generally incorporate modules for the several subsystems of the environment, with each module containing the relevant physics (and chemistry in appropriate cases) that is required. Many codes are in various states of testing, validation, and use in the joint U.S. National Science Foundation/NASA Community Coordinated Modeling Center. Numerical simulations must be considered and evaluated in the context of actual physical measurements. Models can be essential in deriving physical understanding from obtained data. At the same time, data are essential in validating and calibrating models. Therefore, computational simulations considered for publication should ideally report their success, or lack of success, in explaining observations. In particular, reports of failures permit a modeling group and interested outside research groups to better understand limitations of a numerical code and its modules, and thereby spur new modeling insights and developments. Often it appears that modeling teams concentrate on studying some of the largest solar-terrestrial events, especially those that involve the release of coronal mass ejections and their transit to Earth. This concentration is natural in that large events are those that can most affect technological systems. While models often do not fully explain all of the data in such large-event studies, these attempts nevertheless illuminate important limitations in the codes and their modules, and therefore point the way to new directions. But which models deserve consideration for publication? The perception of some modelers that Space Weather and its applications-oriented readers favor modeling papers that address extreme events is not in actuality the case. The journal is quite interested in publishing modeling papers that address less severely disturbed solar-terrestrial conditions, but ones that still involve perturbations of the Earth's environment that can produce effects on technical systems. One condition that requires more modeling analysis under numerical simulation is the case where enhanced relativistic electrons are produced at geosynchronous orbit when Earth is hit by an interplanetary corotating interaction region. In such a case, the enhanced relativistic electrons can produce charging of dielectric materials such as shielded electronics and coaxial cables deep inside spacecraft bodies. Modeling, beginning at the Sun and continuing to geosynchronous orbit, is of prime interest for this problem. I invite modelers to consider possible papers on this and other less disturbed but important solar-terrestrial space weather topics. Louis J. Lanzerotti is editor of Space Weather, a distinguished research professor at the New Jersey Institute of Technology, and a consultant at Alcatel-Lucent Technologies’ Bell Laboratories.

Spatial surface prior information reflectance estimation (spire) algorithms

A new set of algorithms has been developed to estimate spectral reflectance in remote sensing imagery. These algorithms are called surface prior information reflectance estimation (SPIRE) algorithms and estimate surface spectral reflectance using prior spatial and spectral information about the surface reflectance. This paper describes SPIRE algorithms that employ spatial processing of single-channel data to estimate local changes in spectral reflectance under spatially and spectrally varying multiplicative and additive noise caused by variations in illumination and atmospheric effects. Rather than modeling the physics of the atmosphere and illumination (using a physics-based code such as the Atmospheric Removal (ATREM) program), or using ground truth spectra at known locations to compensate for these effects [using the empirical line method (ELM)], prior information about the low spatial frequency content of the scene in each spectral channel is used instead. HYDICE visible near-infrared shortwave infrared (VNIR-SWIR) hyperspectral data were used to compare the performance of SPIRE, ATREM, and ELM atmospheric compensation algorithms. The spatial SPIRE algorithm performance was found to be nearly identical to the ELM ground-truth-based results, while spatial SPIRE performed better than ATREM overall and significantly better under high clouds and haze.

Vacuum electronics for the 21st century

This article explores the history and diversity of this remarkable technology, with emphasis on advances in vacuum-electronic amplifiers, including the microwave power module (MPM), that have been enabled by the ongoing development of modeling and simulation tools. These physics-based codes, enabled by rapid advances in computational hardware, allow simulation-based design and optimization of complex vacuum-electronic circuits. The growth of satellite-based digital communications technology over the past decade has opened lucrative commercial opportunities for vacuum-electronic amplifiers. This exciting new area requires the efficient production of high-frequency power and the ability to handle spectrally efficient modulations within stringent packaging constraints at affordable cost.