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At least 649 records · Page 36

Validation of Fast Reactor Depletion Tools Using EBR-II Measured Data

The validation of simulation tools for calculating fuel depletion and evolution in fast reactors is vital for design, licensing, deployment, operations, and material accountancy. The Physics Analysis Database (PADB) and Analytical Laboratory (AL) database contain measured data collected from Experimental Breeder Reactor II (EBR-II) and were used to validate the most recent versions of the Argonne Reactor Computation (ARC) tool suite and ORIGEN-S for calculating isotopic compositions in irradiated fast reactor fuel. The PADB contains important modeling and operational information about the EBR-II core design, fuel cycle, and analytical results from the legacy versions of the ARC tool suite. The AL database contains the measured isotopic compositions of irradiated samples taken from core subassemblies. A new procedure was developed for the ARC tool suite to perform the EBR-II depletion simulation, as well as to perform more detailed isotopic calculations using ORIGEN-S calculations by coupling it with the ARC suite. Both the ARC and ARC-ORIGEN results were compared with the AL measured data for all relevant samples and showed good agreement for the major actinides. Good agreement with measured data was also achieved using the ARC-ORIGEN approach for several fission products.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

An integrated computational materials engineering framework to analyze the failure behaviors of carbon fiber reinforced polymer composites for lightweight vehicle applications

A bottom-up multi-scale modeling approach is used to develop an Integrated Computational Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) composites, which has the potential to reduce development to deployment lead time for structural applications in lightweight vehicles. In this work, we develop and integrate computational models comprising of four size scales to fully describe and characterize three types of CFRP composites. In detail, the properties of the interphase region are determined by an analytical gradient model and molecular dynamics analysis at the nano-scale, which is then incorporated into micro-scale unidirectional (UD) representative volume element (RVE) models to characterize the failure strengths and envelopes of UD CFRP composites. Then, the results are leveraged to propose an elasto-plastic-damage constitutive law for UD composites to study the fiber tows of woven composites as well as the chips of sheet molding compound (SMC) composites. Subsequently, the failure mechanisms and failure strengths of woven and SMC composites are predicted by the meso-scale RVE models. Finally, building upon the models and results from lower scales, we show that a homogenized macro-scale model can capture the mechanical performance of a hat-section-shaped part under four-point bending. Along with the model integration, we will also demonstrate that the computational results are in good agreement with experiments conducted at different scales. The present work illustrates the potential and significance of integrated multi-scale computational modeling tools that can virtually evaluate the performance of CFRP composites and provide design guidance for CFRP composites used in structural applications.

36 MATERIALS SCIENCE↗

AI-Based Protein Interaction Screening and Identification (AISID)

In this study, we presented an AISID method extending AlphaFold-Multimer’s success in structure prediction towards identifying specific protein interactions with an optimized AISIDscore. The method was tested to identify the binding proteins in 18 human TNFSF (Tumor Necrosis Factor superfamily) members for each of 27 human TNFRSF (TNF receptor superfamily) members. For each TNFRSF member, we ranked the AISIDscore among the 18 TNFSF members. The correct pairing resulted in the highest AISIDscore for 13 out of 24 TNFRSF members which have known interactions with TNFSF members. Out of the 33 correct pairing between TNFSF and TNFRSF members, 28 pairs could be found in the top five (including 25 pairs in the top three) seats in the AISIDscore ranking. Surprisingly, the specific interactions between TNFSF10 (TNF-related apoptosis-inducing ligand, TRAIL) and its decoy receptors DcR1 and DcR2 gave the highest AISIDscore in the list, while the structures of DcR1 and DcR2 are unknown. The data strongly suggests that AlphaFold-Multimer might be a useful computational screening tool to find novel specific protein bindings. This AISID method may have broad applications in protein biochemistry, extending the application of AlphaFold far beyond structure predictions.

59 BASIC BIOLOGICAL SCIENCES↗

Algorithmic design for 3D printing at building scale

Here, this paper addresses the use of algorithmic design paired with additive manufacturing and their potential impact on architectural design and fabrication of a full-sized building, as demonstrated with the AMIE project. AMIE (Additive Manufacturing and Integrated Energy) was collaboration to 3d print a building and vehicle. Both the car and building were designed to generate, store and share energy in an effort to reduce or eliminate reliability on the power grid. This paper is intended to outline our methodology in successfully designing for these innovative strategies, with a focus on the use of computational design tools as a catalyst for design optimization, integrated project delivery, rapid prototyping and fabrication of building elements using additive manufacturing.

3D printing↗

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗

solveME: fast and reliable solution of nonlinear ME models

Background Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints. Results Here, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60× speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints. Conclusions Just as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields.

Biochemistry & Molecular Biology↗

Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts

The Advanced Materials and Manufacturing Technologies (AMMT) program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy However, the unique aspects of additive manufacturing (AM) materials in terms of their processing history, microstructure, and properties, are a major barrier for qualification and certification of nuclear components. Much of this challenge may be attributed to component scale variations in microstructure and properties that are driven by local influences of process conditions and geometry on thermal history, melt pool dynamics, and corresponding microstructure evolution. Computational modeling tools may be helpful in this regard to aid in predicting and controlling this level of variability. The purpose of this report is to review the current state-of-the-art for process modeling with regards to metal AM. For this purpose, we consider specifically the case study of laser powder bed fusion (LPBF) processing of SS316, a family of alloys that are both commonly used in nuclear energy applications and suitable for AM processing. The report first introduces the necessary components of a process modeling workflow, followed by a review of the current status of each. At the end, application of these modeling tools to understanding variability in AM process given their current state are considered, and recommendations for future development are proposed

36 MATERIALS SCIENCE↗

Choosing the Best Modeling Platform for Radiological Risk Assessment Models - 20468

Radiological risk assessments, in the form of performance or safety assessments, are often required under regulations or guidance for remediation of contaminated land, decommissioning of contaminated buildings or structures, and radioactive waste disposal. These risk assessments are usually supported by fate and transport models that address decay and ingrowth of radionuclides, as well as their movement through engineered systems and the natural environment. These models are often projected thousands, or more, years into the future, largely because the radioactive species change through decay and ingrowth, and hence the magnitude of the radioactive effect changes with time. There are many computer codes that are available to address this type of modeling. They range from addressing specific pathways or processes such as infiltration of water, groundwater, surface water, air, biota, diffusion and advection of water and gases, to those that try to couple all processes together to evaluate the impact of fate and transport through the entire system to places in space and time to which access is assumed. These different types of codes are sometimes separated with the monikers process-level and systems-level codes, although it is often not clear that this separation does justice to the capabilities of the many codes that are available to evaluate fate and transport of radionuclides. The focus of this paper is the latter group of modeling codes. Several systems level modeling codes exist and are used. There are differences between these codes in terms of utility, flexibility, complexity and cost. The purpose of this paper is to compare a few of these codes in the context of work currently being performed by the International Atomic Energy Agency (IAEA) Modeling and Data for Radiological Impact Assessments (MODARIA) II Working Group 1 (WG1). The MODARIA II WG1's main focus is how stakeholder engaged decision analysis can, or should, be applied to radiological contamination problems so that better, longstanding, sustainable, solutions are reached. However, the WG1 also recognizes the potential impact of the modeling tools that are chosen to address radiological risk, which is often a primary objective of decision making for radiological problems. Other objectives might also be important, such as constraining costs, obtaining financing, minimizing impact on ecosystems, saving cultural resources, saving jobs, farmland, environmental justice, etc., in a full decision analysis for a given radiological contamination problem, but none of these other objectives have the same types of complex modeling needs as minimize radiological dose. Consequently, a further focus of WG1 is to evaluate the potential impacts on decision making of the choice of fate and transport, and risk assessment, modeling codes that are used to support decision making. The WG1 will produce a report at the end of 2020 that will focus on an approach to effective decision making and stakeholder engagement. The report will also consider the role that performance assessment modeling should play in the decision-making process, including the impact of the choice of modeling tools or computer codes on risk-informed decision making. Several sites around the World have been made available by Member States for these model comparisons, and several modeling tools have been considered. However, the focus of this paper is on two of the sites, one in Belgium and one in Ukraine, and on three of the tools: NORMALYSA (NORM And Legacy Site Assessment); GoldSim{sup C}, and AMBER{sup C}. The final report from this working group will also cover other modeling tools, including RESRAD, and PC-Cream{sup R}. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

PyFaults: A Python Toolkit for Stacking Fault Screening

PyFaults is an open-source Python library designed to model stacking fault disorder in crystalline materials and qualitatively assess the characteristic selective broadening effects in powder X-ray diffraction (PXRD). Here, the main capabilities of PyFaults are presented, including unit cell and supercell model construction, PXRD pattern calculation, assessment against experimental PXRD, and methods for rapid screening of candidate models within a set of possible stacking vectors and fault occurrence probabilities. This program aims to serve as a computationally inexpensive tool for identifying and screening potential stacking fault models in materials with planar disorder. Three diverse case studies, involving GaN, Li2MnO3 and Li3YCl6, are presented to illustrate the program functionality across a range of structure types and stacking fault modalities.

MATHEMATICS AND COMPUTING↗

Graph-theoretical KMC Framework for Calculating Effective Diffusivity in TPBAR Components: Effective Diffusivity of Tritium in α-Zr Grain Boundaries

We report the development of a computer simulation tool based on the Graph-theoretical kinetic Monte Carlo (GT-KMC) approach that can be used to simulate diffusion and derive effective diffusivity of species in complex structures of TPBAR components. The GT-KMC framework was implemented in AKSOME, an on-lattice self-learning kinetic Monte Carlo tool developed at PNNL and was previously used to study solute diffusion in metal alloys. This newly developed tool will be helpful in simulating atom diffusion and extracting their effective diffusivities in grain boundaries and interfaces in various TPBAR components. Once benchmarked, the tool will be used to simulate the diffusion of tritium (hydrogen) along grain boundaries in α-Zr using the activation energy barrier data previously obtained from Density Functional Theory calculations.

36 MATERIALS SCIENCE↗

Testing and Simulation of an Updated Cartridge Loop Vehicle

The Versatile Test Reactor (VTR) is a sodium-cooled, fast-spectrum test reactor that is being developed in the United States and will support a variety of irradiation test vehicle configurations, including cartridge loops. This work includes out-of-pile experimental results from a single-phase, natural circulation cartridge loop vehicle with geometry relevant to VTR irradiation sites, as well as comparisons between the experimental results and results predicted using the TRAC/RELAP Advanced Computational Engine (TRACE) modeling tool. The experiments were conducted in the thermosyphon test loop (TSTL) facility at Oak Ridge National Laboratory. Comparisons are also made between the current experimental data and results from natural circulation experiments previously conducted in the TSTL in a cartridge vehicle that is similar in design but has smaller flow areas. This cartridge vehicle and the experimental program were developed to add to the single-phase, natural circulation data collected in the previous iteration of the cartridge loop design, which supports future irradiation experiments and adds to a database that is useful for validating computer models. Comparisons of experimental results to TRACE model predictions is a pertinent step in validating the computational tool for supporting future irradiation experiment design and safety calculations, and comparisons to previous cartridge loop results highlight the impact of the design changes made to the test vehicle. The experiments conducted include several steady state tests and transients, including power ramp, loss of offsite power, and loss of external flow scenarios. This work shows that TRACE can accurately predict temperatures and flow conditions in the cartridge loop and the updated vehicle design achieves higher mass flow rates at the same steady state power levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

High-performance implementation of Chebyshev filter diagonalization for interior eigenvalue computations

We study Chebyshev filter diagonalization as a tool for the computation of many interior eigenvalues of very large sparse symmetric matrices. In this technique the subspace projection onto the target space of wanted eigenvectors is approximated with filter polynomials obtained from Chebyshev expansions of window functions. After the discussion of the conceptual foundations of Chebyshev filter diagonalization we analyze the impact of the choice of the damping kernel, search space size, and filter polynomial degree on the computational accuracy and effort, before we describe the necessary steps towards a parallel high-performance implementation. Because Chebyshev filter diagonalization avoids the need for matrix inversion it can deal with matrices and problem sizes that are presently not accessible with rational function methods based on direct or iterative linear solvers. To demonstrate the potential of Chebyshev filter diagonalization for large-scale problems of this kind we include as an example the computation of the 10{sup 2} innermost eigenpairs of a topological insulator matrix with dimension 10{sup 9} derived from quantum physics applications.

97 MATHEMATICS AND COMPUTING↗

Measurement of a Neutrino-Induced Charged Current Single Neutral Pion Cross Section at MicroBooNE

Micro Booster Neutrino Experiment (MicroBooNE) is a Liquid Argon Time Projection Chamber (LArTPC) operating in the Booster Neutrino Beamline at Fermi National Accelerator Laboratory. MicroBooNE's physics goals include studying short basline $\nu$ oscillation and performing a suite of low energy $\nu$ cross section measurements. Of particular interest to MicroBooNE, and the broader LArTPC community, are electromagnetic showers; these showers are at the heart of searches for charged current $\nu_e$ interactions, including MicroBooNE's flagship search for a MiniBooNE-like low energy excess (LEE). Neutral current $\pi^0$'s, which decay into 2 electromagnetic showers ($\gamma$'s), are the dominant source of non-$\nu_e$ backgrounds in searches for $\nu_{\mu}\rightarrow\nu_e$ oscillations in LArTPCs, such as the LEE. While precise measurements of this neutral current channel will provide a tight constraint on our modeling uncertainties, such events are particularly difficult to iden tify in data with our current tools, as there is often little or no activity at the neutrino interaction point. Charged current interactions, on the other hand, have simpler topologies with a long $\mu$ track that anchors to the interaction vertex. With a vertex in hand, we can develop automated reconstruction tools for neutrino-induced shower topologies (like the $\gamma$'s from $\pi^0$ decay). Thus, in studying charged current $\pi^0$ interactions, we are developing tools that can potentially be used to reconstruct an important LEE background, while also studying the physics of neutrino interactions, of which data is sparse for argon. This thesis reports the world's first measurement of the absolute, flux-averaged cross section of $\nu_{\mu}$-charged current single $\pi^0$ production on argon. The analysis chain begins with the selection of inclusive $\nu_\mu$ charged current events, where a candidate $\mu$ and neutrino interaction vertex are identified. These events are t hen passed to a reconstruction framework where electromagnet! ic shower candidates are reconstructed using computer visualization tools. Finally, the cross section is calculated on two reconstructed topologies: those with at least two reconstructed showers and those with at least one. Additionally, this work describes the first fully-automated electromagnetic shower reconstruction process employed by a LArTPC to perform a cross section analysis. We measure the flux averaged cross section on argon at 824 MeV via the two and one shower selections respectively to be \par \noindent $\sigma_{\geq2 Shower}$ = (2.56 $\pm$ $0.50_{stat}$ $\pm$ $0.31_{genie}$ $\pm$ $0.37_{flux}$ $\pm$ $0.31_{det}$) $\times$ $10^{-38}$ $\frac{cm^2}{Ar}$, \par \noindent $\sigma_{\geq 1 Shower}$ = (2.64 $\pm$ $0.33_{stat}$ $\pm$ $0.36_{genie}$ $\pm$ $0.38_{flux}$ $\pm$ $0.35_{det}$) $\times$ $10^{-38}$ $\frac{cm^2}{Ar}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

The Safe Guards Analysis Toolbox for Non-Proliferation Modeling

As nuclear technology becomes more widely available outside of nuclear weapon armed nations, the importance of preventing the illicit development of nuclear weapons has increased dramatically. A system of nuclear safeguards prevents proliferation; stopping the diversion of nuclear material from civilian facilities for the construction of nuclear weapons. These safeguards function through a combination of accountancy measurements that confirm the locations and amounts of nuclear material, and physical security at every nuclear facility. Safeguards Analysis (SGA) is a computational tool-kit developed using the MATLAB Simulink software suite that simulates a system of nuclear safeguards. The code allows users to examine current safeguards systems for weaknesses and to pinpoint the best locations for improvement. Developed as a modular framework, SGA does not directly model material flows, and relies on an outside code for this information. Instead, it represents a series of safeguards measurements that represent the values inspectors would see. Furthermore, SGA is designed to be modular in design, allowing the user to construct as complex or simple a model as needed: simulating an entire fuel-cycle comprising multiple facilities to a single measurement. SGA's use of outside codes for its material flow information makes it applicable to any model available to the user, as opposed to previous work that applied only to a single scenario. This paper discusses the initial development of SGA and the testing of these modules. The tests described in this paper demonstrate that the different pieces of SGA currently work as intended. Future work on the toolbox will develop and test SGA against a more realistic and complicated scenario using the various tools available in SGA. SGA currently has the pieces to perform analysis on safeguards systems, but needs to be tested on large, multi-MBA, systems to ensure the code is capable of handling such large models, to see the effect large models have on calculation speed, and if further work is needed to improve code efficiency. (authors)

97 MATHEMATICS AND COMPUTING↗

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, in this paper, we describe and illustrate PeakQC’s functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

47 OTHER INSTRUMENTATION↗

Flux in a 1D-Reactor Cell by Response Matrix

Legacy computational reactor physics software tools and protocols currently used in support of the Advanced Test Reactor (ATR) core fuel management and safety assurance, and to some extent, experiment management, are inconsistent with the state of modern nuclear engineering practice. Thus, they are difficult, if not impossible, to verify and validate (V and V) according to modern standards. Furthermore, the legacy staff knowledge required for effective application of their application and protocols from the 1960's and 1970's is rapidly being lost due to staff turnover and retirements. In late 2009, the Idaho National Laboratory (INL) initiated a focused effort, the ATR Core Modeling Update Project, to address this situation through the introduction of modern high-fidelity transport theory based computational software and models to replace the legacy methods, with appropriate (V and V) protocols according to applicable national standards. The new computational tools include: - The 2-dimensional HELIOS deterministic transport code for integrated ATR core fuel and experiment management and Core Safety Analysis Package (CSAP) physics data generation. The MCNP5 3-dimensional stochastic simulation (Monte Carlo) code for verification and validation applications and for specialized ATR core analysis along with its longstanding traditional role in experiment support - The 2-dimensional deterministic NEWT transport code and the 3-dimensional KENO6 stochastic simulation code, with their SCALE-based support infrastructure for use in V and V applications and as supporting modules for sensitivity-uncertainty analysis via the TSUNAMI system. The ATTILA 3-dimensional discrete-ordinates transport code for specialized experiment support and possibly additional applications. Regardless of application, every neutron transport code, new or legacy, requires verification. The aim of this presentation is to provide a neutron transport benchmark for a simple 1D-reactor cell in plane geometry. The benchmark finds use as a standard of comparison for the codes listed above intended for the ATR upgrade. The method developed here is in contrast to the finite Fourier transform solution found in Ref. 1, which is limited to isotropic scattering and particularly difficult to numerically implement unless the total cross section is constant over the cell.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

The design space of E(3)-equivariant atom-centred interatomic potentials

Abstract Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.

Computer Science↗

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

59 BASIC BIOLOGICAL SCIENCES↗