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At least 631 records · Page 35

MOOSE Web Server Interface: A Message-based External Interface for Multiphysics Simulations

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a C++ toolkit designed to streamline the development of finite element and finite volume applications. It offers an interface for input-based coupling of these applications to create multiscale, multiphysics models. We introduce a new capability that enables external applications to integrate with MOOSE-based applications in situ via a web server using HTTP requests. An example of this integration is provided, where a MOOSE thermal-fluids solve has a boundary condition that is driven by an external Python application. Additionally, the coupling of the Python-based OpenMC depletion solver with the Cardinal application is demonstrated. A multiphysics model of a pressurized water reactor, incorporating neutronics, heat conduction, thermal-fluids, and depletion, is presented to showcase this new Cardinal capability that is enabled by the MOOSE web server capability.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

MOOSE Web Server Interface: A Message-based External Interface for Multiphysics Simulations

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a C++ toolkit designed to streamline the development of finite element and finite volume applications. It offers an interface for input-based coupling of these applications to create multiscale, multiphysics models. We introduce a new capability that enables external applications to integrate with MOOSE-based applications in situ via a web server using HTTP requests. An example of this integration is provided, where a MOOSE thermal-fluids solve has a boundary condition that is driven by an external Python application. Additionally, the coupling of the Python-based OpenMC depletion solver with the Cardinal application is demonstrated. A multiphysics model of a pressurized water reactor, incorporating neutronics, heat conduction, thermal-fluids, and depletion, is presented to showcase this new Cardinal capability that is enabled by the MOOSE web server capability.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Irradiation induced void spheroidization, shrinkage and migration in Cu at elevated temperatures: An in situ study

We show understanding the void evolution in irradiation environment is of great interest and significance, as irradiation-induced voids typically lead to pronounced volumetric swelling and degradation of mechanical properties. In situ studies on the irradiation response of nanovoids at elevated temperature remain limited. In this work, we performed systematic in situ 1 MeV Kr ++ irradiations on Cu with nanovoids in a transmission electron microscope up to 350 °C. The in situ studies revealed intriguing void spheroidization, shrinkage and migration. Furthermore, the morphology evolution and migration of nanovoids showed a strong dependence on irradiation temperature and initial void size. Post-irradiation analyses identified defect clusters in the form of stacking fault tetrahedrons, and the remaining large faceted nanovoids. The underlying mechanisms of irradiation-induced void spheroidization and shrinkage were discussed based on phase-field modeling.

36 MATERIALS SCIENCE↗

Fabrication of a novel 3D-printed perfusion bioreactor for complex cell culture models

We introduce a novel fabrication method for developing a 3D-printed perfusion bioreactor (3D-PBR) to facilitate the in situ growth and differentiation of human bone marrow (BM)-derived mesenchymal stem cells (MSCs) while enabling coculture with vascular cells. To recapitulate human physiology, in vitro platforms must incorporate several key features of their native target organ. This often entails a supportive 3D architecture for growing and differentiating multiple human cell types in situ under perfusion. Other essential characteristics include reproducibility, ease of customization, and biocompatibility. Our 3D-PBR combines these features and was fabricated using a biocompatible resin-based polymer, which was 3D-printed, followed by the addition of a permeable membrane to create a coculture microenvironment. MSCs were encapsulated in a collagen-fibrin gel alongside human endothelium within the 3D-PBR. The physical cues that our 3D-PBR provided facilitated the differentiation of MSCs into specific lineages, such as adipocytes and osteoblasts. Immunohistochemistry images demonstrated that cells grown in the 3D-PBR exhibited more physiologically relevant BM perivascular niche markers compared to static culture models. Our method utilizes emerging 3D printing techniques and alternative materials, departing from traditional PDMS-based soft lithography. These advancements in fabrication further enhance in vitro platforms for diverse cell culture models and vascular permeability assays.

59 BASIC BIOLOGICAL SCIENCES↗

Investigating the Particle Growth in Bimodal Pt/C Catalysts by In-Situ Small-Angle X-ray Scattering: Challenges in the Evaluation of Stress Test Protocol-Dependent Degradation Mechanisms

The influence of different combinations of accelerated stress test (AST) protocols simulating load-cycle and start/stop conditions of a proton exchange membrane fuel cell (PEMFC) vehicle is investigated on a bimodal Pt/C catalyst. The bimodal Pt/C catalyst, prepared by mixing two commercial catalysts, serves as a model system and consists of two distinguishable size populations. The change in mean particle size was investigated by in situ small-angle X-ray scattering (SAXS). The comparison to the reference catalysts, i.e., the two single-size population catalysts, uncovers the presence of electrochemical Ostwald ripening as a degradation mechanism in the bimodal catalyst. Increasing the harshness of the applied AST protocol combinations by faster changing between load-cycle or start/stop conditions, the particle size of the larger population of the bimodal catalyst increases faster than expected. Surprisingly, the change in mean particle size of the smaller size population indicates a smaller increase for harsher AST protocols, which might be explained by a substantial electrochemical Ostwald ripening.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

In-Situ Measurement of Power Loss for Crystalline Silicon Modules Undergoing Thermal Cycling and Mechanical Loading Stress Testing

An in-situ method is proposed for monitoring and estimating the power degradation of mc-Si photovoltaic (PV) modules undergoing thermo-mechanical degradation tests that primarily manifest through cell cracking, such as mechanical load tests, thermal cycling and humidity freeze tests. The method is based on in-situ measurement of the module’s dark current-voltage (I-V) characteristic curve during the stress test, as well as initial and final module flash testing on a Sun simulator. The method uses superposition of the dark I-V curve with final flash test module short-circuit current to account for shunt and junction recombination losses, as well as series resistance estimation from the in-situ measured dark I-Vs and final flash test measurements. The method is developed based on mc-Si standard modules undergoing several stages of thermo-mechanical stress testing and degradation, for which we investigate the impact of the degradation on the modules light I-V curve parameters, and equivalent solar cell model parameters. Experimental validation of the method on the modules tested shows good agreement between the in-situ estimated power degradation and the flash test measured power loss of the modules, of up to 4.31 % error (RMSE), as the modules experience primarily junction defect recombination and increased series resistance losses. However, the application of the method will be limited for modules experiencing extensive photo-current degradation or delamination, which are not well reflected in the dark I-V characteristic of the PV module.

14 SOLAR ENERGY↗

Simulation and Emulation of X-Ray Diffraction from Dynamic Compression Experiments

Many important aspects of the dynamic thermo-mechanical response of materials occur at the mesoscale, i.e. a physical scale of interactions smaller than what can be adequately described by homogenous behaviors, yet larger than the scale of the atomic lattice. Concurrent advancements in computational power, continuum theory, and experimental diagnostics are enabling unprecedented understanding of such interactions. However, we cannot develop a sufficient level of confidence in such mesoscale capability until the constitutive description of the underlying constituents is reliably representative of their actual physical behavior. Therefore, there is a strong need to combine experimental, modeling, and data-science techniques to validate models of the thermomechanical response of individual single crystals. One experimental diagnostic with high potential impact to shock physics and materials science is in-situ x-ray diffraction. This paper is primarily focused on simulation of x-ray diffraction in shock physics, but with an aim toward quantifying parametric uncertainty of simulation models. Here, we develop and demonstrate a data-science and model-driven approach to constrain the parameterization of continuum models of crystal lattice deformation associated with the shock response of crystalline materials. The framework is built around the connection between continuum hydrodynamic simulations of lattice deformation and a new Bragg diffraction simulation code, BarberShop. The dynamic deformation of a crystal lattice is modeled using the DiscoFlux model within an arbitrary Lagrangian-Eulerian hydrodynamic code, FLAG. These detailed continuum simulations of lattice deformation can be computationally slow, thus a statistical model is used to emulate the evolution of lattice deformation fields in time and across the considered model parameter space. Emulated lattice deformation fields can then be generated rapidly for any combination of physics model parameters. In turn, these fields can be fed into BarberShop to realize a rapid prediction of Bragg diffraction patterns associated with particular values of physics model parameters. The framework enables parameterization of the single crystal model to obtain Bragg diffraction patterns that most closely resemble a corresponding measurement. Furthermore, the framework naturally provides sensitivities of the lattice deformation to the physics parameters. We highlight the utility of this framework through the application to a synthetic closed-loop inverse problem leading to the parameterization of a single crystal material model. As a model problem, we consider the dynamic response of the energetic molecular crystal, cyclotrimethylenetrinitramine (or RDX), under dynamic compression induced by simulated flyer plate impact experiments.

36 MATERIALS SCIENCE↗

Multimodal imaging of hemorrhagic transformation biomarkers in an ischemic stroke model.

Abstract Hemorrhagic transformation of ischemic stroke has devastating consequences, with high mortality and poor functional outcomes. Animal models of ischemic stroke also demonstrate the potential for hemorrhagic transformation, which complicates biochemical characterization, treatment studies, and hinders poststroke functional outcomes in affected subjects. The incidence of hemorrhagic transformation of ischemic stroke in animal model research is not commonly reported. The postmortem brain of such cases presents a complex milieu of biomarkers due to the presence of healthy cells, regions of varying degrees of ischemia, dead and dying cells, dysregulated metabolites, and blood components (especially reactive Fe species released from lysed erythrocytes). To improve the characterization of hemorrhage biomarkers on an ischemic stroke background, we have employed a combination of histology, X-ray fluorescence imaging (XFI), and Fourier transform infrared (FTIR) spectroscopic imaging to assess 122 photothrombotic (ischemic) stroke brains. Rapid freezing preserves brain biomarkers in situ and minimizes metabolic artifacts due to postmortem ischemia. Analysis revealed that 25% of the photothrombotic models had clear signs of hemorrhagic transformation. The XFI and FTIR metabolites provided a quantitative method to differentiate key metabolic regions in these models. Across all hemorrhage cases, it was possible to consistently differentiate otherwise healthy tissue from other metabolically distinct regions, including the ischemic infarct, the ischemic penumbra, blood vessels, sites of hemorrhage, and a region surrounding the hemorrhage core that contained elevated lipid oxidation. Chemical speciation of deposited Fe demonstrates the presence of heme-Fe and accumulation of ferritin.

Pushie, M. J. (ORCID:0000000174945427)↗

Phase Composition and Phase Transformation of Additively Manufactured Nickel Alloy 718 AM Bench Artifacts

Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. Here, we employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ ’ and γ ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.

36 MATERIALS SCIENCE↗

Elucidating Phosphate and Cadmium Cosorption Mechanisms on Mineral Surfaces with Direct Spectroscopic and Modeling Evidence

The simultaneous sorption of cations and anions at the mineral–water interface can substantially alter their individual sorption characteristics; however, this phenomenon lacks a mechanistic understanding. Our study provides direct spectroscopic and modeling evidence of the molecular cosorption mechanisms of the cadmium ion (Cd 2+ ) and phosphate (P) on goethite and layered manganese (Mn) oxide of birnessite, through in situ attenuated total reflection Fourier-transform infrared (ATR-FTIR), P K-edge X-ray absorption near-edge structure (XANES) spectroscopy, and surface complexation modeling. Phosphate synergistically cosorbed with Cd on goethite predominantly through P-bridged ternary complexes (≡Fe–P–Cd) and electrostatic interactions at wide pH conditions. Likewise, P and Cd exhibited synergistic cosorption on birnessite by forming P-bridged ternary complexes (≡Mn–P–Cd) and weak competitive sorption at the layer edge sites. As pH and Cd loading increased, the surface P species transitioned from a binary complex to a ternary complex and/or Cd 3 (PO 4 ) 2 precipitate for both goethite and birnessite. Compared to that in solution at pH 8, the formation of Cd 3 (PO 4 ) 2 was inhibited by the presence of goethite and birnessite, ascribed to the specific adsorption of P and Cd, more pronounced in birnessite due to the stronger sorption of Cd at its vacant sites. Finally, the discovered cosorption mechanisms of P and Cd have important implications for understanding and predicting their mobility and availability in Cd-contaminated settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site

Abstract The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables. Significance Statement A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.

54 ENVIRONMENTAL SCIENCES↗

A better understanding of the mechanics of borehole breakout utilizing a finite strain gradient-enhanced micropolar continuum model

Borehole breakout denotes the failure in rock mass subjected to drilling, caused by stress concentrations exceeding the material strength. Depending on the material properties, the preexisting in situ stress state, and the borehole dimensions, different types of borehole breakout, such as spiral-shaped breakout or v-shaped breakout, are distinguished in the literature. In the present work, we address the influence of the material properties on the predicted borehole breakout mode in a comprehensive finite element study. To this end, herein we employ a gradient-enhanced micropolar damage-plasticity model based on the Mohr–Coulomb strength criterion, formulated in the finite strain regime, which is calibrated based on experimental results from plane strain compression tests on Red Wildmoor sandstone. In the numerical study, the influence of the in situ stress state, the material friction angle, plastic dilation, post peak residual strength, and the inherent material length scale parameters are investigated. Thereby, we demonstrate that depending on the material parameters, substantially different failure modes, characterized by strongly localized shear bands or diffuse failure zones, are predicted. It is shown that in particular the brittleness of the material in the post peak regime has a major influence on the predicted breakout type. Moreover, a statistical validation of the results is obtained by considering different random field distributions of the initial material strength.

58 GEOSCIENCES↗

Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps

In situ computation of Lagrangian flow maps to enable post hoc time-varying vector field analysis has recently become an active area of research. However, the current literature is largely limited to theoretical settings and lacks a solution to address scalability of the technique in distributed memory. To improve scalability, we propose and evaluate the benefits and limitations of a simple, yet novel, performance optimization. Our proposed optimization is a communication-free model resulting in local Lagrangian flow maps, requiring no message passing or synchronization between processes, intrinsically improving scalability, and thereby reducing overall execution time and alleviating the encumbrance placed on simulation codes from communication overheads. To evaluate our approach, we computed Lagrangian flow maps for four time-varying simulation vector fields and investigated how execution time and reconstruction accuracy are impacted by the number of GPUs per compute node, the total number of compute nodes, particles per rank, and storage intervals. Our study consisted of experiments computing Lagrangian flow maps with up to 67M particle trajectories over 500 cycles and used as many as 2048 GPUs across 512 compute nodes. In all, our study contributes an evaluation of a communication-free model as well as a scalability study of computing distributed Lagrangian flow maps at scale using in situ infrastructure on a modern supercomputer.

Sane, Sudhanshu↗

Unveiling microstructure-property correlations in nuclear materials with high-energy synchrotron X-ray techniques

Understanding the microstructure-property correlation is critical for the performance evaluation of in-service materials and the development of advanced materials for nuclear reactor applications. Experimental studies are challenging for nuclear materials which are often hazardous. Recent developments in high-energy synchrotron X-ray (HEX) techniques offer the potential to address this challenge, by providing direct observations of internal responses to external stimuli in bulk-like materials, through in situ or 3D measurements. Here, in this review, developments in HEX techniques are introduced and recent applications in nuclear materials are presented. The results offer unprecedented insights into materials performance and provide unique input to computational models.

36 MATERIALS SCIENCE↗

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗

Ultra-Wideband Microwave Imaging System for Root Phenotyping

The roots are a vital organ for plant growth and health. The opaque surrounding environment of the roots and the complicated growth process means that in situ and non-destructive root phenotyping face great challenges, which thus spur great research interests. The existing methods for root phenotyping are either unable to provide high-precision and high accuracy in situ detection, or they change the surrounding root environment and are destructive to root growth and health. Thus,we propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively. To verify the method’s feasibility, we studied an electromagnetic numerical model that simulates the transmission signal of two ultra-wideband microwave antennas. The simulated signal of roots with different shapes shows the proposed system’s capability to measure the root size in the soil. Experimental validations were conducted considering three sets of measurements with different sizes, numbers and locations, and the experimental results indicate that the developed imaging system was able to differentiate root sizes and numbers with high contrast. The reconstruction from both simulations and experimental measurements provided accurate size estimation of the carrots in the soil, which indicates the system’s potential for root imaging.

59 BASIC BIOLOGICAL SCIENCES↗

Bridging New Observational Capabilities and Process-Level Simulation: Insights into Aerosol Roles in the Earth System

The spatial distribution of ambient aerosol particles significantly impacts aerosol–radiation–cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include three-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation–modeling integration, enabling an examination of how various assumptions about the organic–inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS–model–laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol–radiation–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction

Here, this work introduces an online greedy method for constructing quadratic manifolds from streaming data, designed to enable in situ analysis of numerical simulation data on the Petabyte scale. Unlike traditional batch methods, which require all data to be available upfront and take multiple passes over the data, the proposed online greedy method incrementally updates quadratic manifolds in one pass as data points are received, eliminating the need for expensive disk input/output operations as well as storing and loading data points once they have been processed. A range of numerical examples demonstrate that the online greedy method learns accurate quadratic manifold embeddings while being capable of processing data that far exceed common disk input/output capabilities and volumes as well as main-memory sizes.

97 MATHEMATICS AND COMPUTING↗