SEARCH · Engineering Papers

Results for “Model based development”

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.

SourceResearch connectionFrom yearThrough year

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

At least 325 records · Page 18

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Development and formulation of physics based metallic fuel models and comparison to integral irradiation data

Metallic fuel has an important historical significance in the development of nuclear reactors and continues to be relevant to the progression of advanced test and power reactors. A number of models, ranging from empirical to mechanistic, have been developed and implemented in various fuel performance codes to describe U-Zr and U-Pu-Zr fuel and typical fast reactor cladding materials. One challenge of using these models to simulate fuel performance is the inevitable tangling of coupled phenomena that can cloud proper implementation, calibration, and eventual utilization of new models. Here in an effort to provide a baseline capability that will facilitate the use of advanced models, new capabilities have been implemented into the fuel performance code BISON specific to metallic fuel simulations, ranging from materials properties, fission gas release and swelling calculations, coolant channel models, and cladding correlations. These models have been applied to the X441/X441A EBR-II experimental assembly data, a set of irradiated metallic UPuZr fuel rods of varying pin designs. The models implemented in BISON are able to capture the general trend of the expected response of the fuel and cladding to irradiation in EBR-II, especially when considering the spread in experimental measurements and the uncertainties inherited from the historical material models. Ultimately, the models outlined here provide the baseline capabilities on which new models can build upon in order to improve the prediction of metallic fuel performance simulations in off-normal designs or operations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Quantitative x ray phase contrast imaging of oblique shock wave–interface interactions

Oblique shock wave–interface interactions of gases and liquids have been extensively studied in shock tubes using optical methods to measure equation-of-state (EOS) parameters. However, this is difficult with solids due to their opaqueness to visible light. X ray phase contrast imaging (XPCI) has the penetrative strength to probe solids while still being sensitive to mass density and enhancing the visibility of material boundaries. We investigate the accuracy and repeatability of measuring the mean value of the average mass density (areal density divided by thickness) over region S (B S ) and flow deflection angle (θ) from XPCI images of a sample. To that end, a Hough transform-based method for measuring θ is developed. To measure B S , the XPCI image intensity probability density function (PDF) is modeled accounting for the spatial distribution of x ray energy, scintillator response, and pulse-to-pulse variation in the x ray intensity. In addition, a Monte Carlo-based algorithm for computing the B S PDF is developed. Both methods are validated on an impact-generated oblique shock wave interacting at a solid polymer-to-polymer interface. Furthermore this is accomplished through a comparison to hydrodynamic simulations using well-established EOS. Under the modeling framework for the XPCI image intensity, B S is computed with an accuracy of < 0.1% and precision of 3%–5%, while θ has an uncertainty of 0.2°, respectively. This shows that the XPCI-based model that is developed here could be an invaluable tool for high-fidelity testing of hydrodynamic models in shock polar configurations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2012

Building Energy Model Development for Retrofit Homes

Based on previous research conducted by Pacific Northwest National Laboratory and Florida Solar Energy Center providing technical assistance to implement 23 deep energy retrofits across the nation, six homes were selected in Florida and Texas for detailed post-retrofit energy modeling to assess realized energy savings (Chandra et al, 2012). Of these homes, three homes in San Antonio were selected for detailed analysis of post-retrofit energy use and realized energy savings. The actual savings achieved in these homes post-retrofit, as determined by a utility bill comparison, is presented. For the three metered homes in Florida, no pre-retrofit utility bills are available, so a utility bill comparison to determine realized savings is not possible. Assessing realized savings can be difficult for some homes where pre-retrofit occupancy and energy performance are unknown. Initially, savings had been estimated using a HERS Index comparison for these homes. However, this does not account for confounding factors such as occupancy and weather. This research also addresses a method to more reliably assess energy savings achieved in deep energy retrofits for which pre-retrofit utility bills or occupancy information is not available.

energy efficiency↗

DOE OSTI2024

Predicting Damages to Remainder Parcels in Right-of-Way Acquisitions for Expanding Transportation Infrastructure: Using a Truncated Finite-Mixture Model

Right-of-way acquisition is a critical component of transportation infrastructure development. Transportation infrastructure projects cannot proceed without proper right-of-way acquisition or may face significant delays. State Departments of Transportation frequently acquire parcels of land for roadway expansion projects. A majority of these acquisitions can be partial takings, referring to a portion of a parcel that is acquired. The remainder of the property usually suffers economic changes due to the partial acquisition, which can be calculated as damage percentages. The damage percentage represents the extent to which the remaining land or property value has been diminished due to the acquisition. It reflects the remaining property value percentage that may have been lost or compromised due to the acquisition. Here, this study aims to provide a robust model to estimate damage percentages to the remainder parcels that may help state Departments of Transportation appraisers make early predictions about the damages in cases involving partial takings. The research uses 509 appraisal reports from the Tennessee Department of Transportation to identify the key parcel attributes that influence the percentage of damages. Three regression models are developed: a linear regression model, a finite-mixture model (FMM), and a truncated FMM with two latent classes. The modeling results show that the truncated FMM with two classes outperforms the other models. To validate the models, actual sales data is collected and analyzed for 59 properties, and the results suggest that the model predictions are fairly accurate. A predictive tool is developed based on the models to help appraisers anticipate right-of-way damages under different scenarios and can provide early predictions about the damages.

42 ENGINEERING↗

DOE OSTI2012

Human Initiated Cascading Failures in Societal Infrastructures

In this paper, we conduct a systematic study of human-initiated cascading failures in three critical inter-dependent societal infrastructures due to behavioral adaptations in response to a crisis. We focus on three closely coupled socio-technical networks here: (i) cellular and mesh networks, (ii) transportation networks and (iii) mobile call networks. In crises, changes in individual behaviors lead to altered travel, activity and calling patterns, which influence the transport network and the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. In contrast to system dynamics models for studying these interacting infrastructures, we develop interaction-based models in which individuals and infrastructure elements are represented in detail and are placed in a common geographic coordinate system. Using the detailed representation, we study the impact of a chemical plume that has been released in a densely populated urban region. Authorities order evacuation of the affected area, and this leads to individual behavioral adaptation wherein individuals drop their scheduled activities and drive to home or pre-specified evacuation shelters as appropriate. They also revise their calling behavior to communicate and coordinate among family members. These two behavioral adaptations cause flash-congestion in the urban transport network and the wireless network. The problem is exacerbated with a few, already occurring, road closures. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. A sensitivity analysis on the compliance rate of evacuees shows non-intuitive effect on the spatial distribution of people and on the loading of the base stations. For example, an evacuation compliance rate of 70% results in higher number of overloaded base stations than the evacuation compliance rate of 90%.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Characterization of Crystallographic Structures Using Bragg-Edge Neutron Imaging at the Spallation Neutron Source

Over the past decade, wavelength-dependent neutron radiography, also known as Bragg-edge imaging, has been employed as a non-destructive bulk characterization method due to its sensitivity to coherent elastic neutron scattering that is associated with crystalline structures. Several analysis approaches have been developed to quantitatively determine crystalline orientation, lattice strain, and phase distribution. In this study, we report a systematic investigation of the crystal structures of metallic materials (such as selected textureless powder samples and additively manufactured (AM) Inconel 718 samples), using Bragg-edge imaging at the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source (SNS). Firstly, we have implemented a phenomenological Gaussian-based fitting in a Python-based computer called iBeatles. Secondly, we have developed a model-based approach to analyze Bragg-edge transmission spectra, which allows quantitative determination of the crystallographic attributes. Moreover, neutron diffraction measurements were carried out to validate the Bragg-edge analytical methods. These results demonstrate that the microstructural complexity (in this case, texture) plays a key role in determining the crystallographic parameters (lattice constant or interplanar spacing), which implies that the Bragg-edge image analysis methods must be carefully selected based on the material structures.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

Preliminary Development of a Drift-Flux Model Based Pin-Resolved Core Thermal-Hydraulic Code for Dedicated Use in Efficient High-Fidelity Neutronics Coupled Calculations

As the high-fidelity multiphysics coupled core calculation becomes more practical with the advent of high-performance computing technology, efficient coupling of a neutronics code with a thermal-hydraulic (T/H) code at the pin level poses a new challenge. The COBRA-TF (CTF) code which is one of the well-known subchannel analysis codes based on the two-fluid, three-field model was coupled with many neutron transport codes such as MPACT, TORTTD, and nTRACER due to the easiness of coupling by its well-established coupling interface. Nevertheless, the calculation efficiency of such coupled codes is not satisfactory because of the heavy computational burden of CTF. The MATRA subchannel analysis code of KAERI which is based on the homogeneous equilibrium model (HEM) was coupled with neutronics codes as well. The choice of the HEM and the marching scheme in MATRA reduces the computational burden significantly. However, the applicability of the HEM for the two-phase flow conditions is limited to normal operating conditions of pressurized water reactors (PWRs) and therefore a better model which would allow wider applicability than HEM is necessary in order to deal with the core states involving nontrivial voiding. The drift-flux model (DFM) which uses 5 equations for two-fluid analyses by adopting the mixture velocity instead of two phasic velocities can be a good compromise between the two-fluid model and the HEM. With the DFM, the computational cost can be reduced and moreover the modeling of the interfacial area constitutive relations can be avoided. The phase separation for the mixture velocity can be incorporated by using the drift-flux parameters, C0 and Vgj, which can be obtained by using empirical formulas. Several subchannel codes such as WOSUB and ASSERT were developed based on the DFM. It was also adopted in some system analysis codes such as RETRAN3D. In the work here, the development of a DFM based pin-resolved T/H analysis code, ESCOT (Efficient Simulator of Core Thermal-hydraulics), is introduced for the use in efficient coupled calculations with a high-fidelity neutronics code. The field equations and the numerical methods are briefly described. Then the efficiency tests of various linear system solvers including the Krylov subspace methods (KSMs) are presented. A test problem is solved by ESCOT, MATRA and CTF for performance comparison and validation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2012

Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developed will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve these two formulations were developed and validated. For a given OSP problem the computation efficiency largely depends on the “size” of the problem. Initially a simplified 1-D gasifier model assuming axial and azimuthal symmetry was used to test out various OSP algorithms. Finally these algorithms were used to design the optimal sensor network for condition monitoring of IGCC gasifier refractory wear and RSC fouling. The sensors type and locations obtained as solution to the OSP problem were validated using model based sensing approach. The OSP algorithm has been developed in a modular form and has been packaged as a software tool for OSP design where a designer can explore various OSP design algorithm is a user friendly way. The OSP software tool is implemented in Matlab/Simulink© in-house. The tool also uses few optimization routines that are freely available on World Wide Web. In addition a modular Extended Kalman Filter (EKF) block has also been developed in Matlab/Simulink© which can be utilized for model based sensing of important process variables that are not directly measured through combining the online sensors with model based estimation once the hardware sensor and their locations has been finalized. The OSP algorithm details and the results of applying these algorithms to obtain optimal sensor location for condition monitoring of gasifier refractory wear and RSC fouling profile are summarized in this final report.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗

DOE OSTI2012

CRADA NFE-07-00912: Development of Model Fuels Experimental Engine Data Base & Kinetic Modeling Parameter Sets (Final Report)

The automotive and engine industries are in a period of very rapid change being driven by new emission standards, new types of after treatment, new combustion strategies, the introduction of new fuels, and drive for increased fuel economy and efficiency. The rapid pace of these changes has put more pressure on the need for modeling of engine combustion and performance, in order to shorten product design and introduction cycles. New combustion strategies include homogeneous charge compression ignition (HCCI), partial-premixed combustion compression ignition (PCCI), and dilute low temperature combustion which are being developed for lower emissions and improved fuel economy. New fuels include bio-fuels such as ethanol or bio-diesel, drop-in bio-derived fuels and those derived from new crude oil sources such as gas-to-liquids, coal-to-liquids, oil sands, oil shale, and wet natural gas. Kinetic modeling of the combustion process for these new combustion regimes and fuels is necessary in order to allow modeling and performance assessment for engine design purposes. In this research covered by this CRADA, ORNL developed and supplied experimental data related to engine performance with new fuels and new combustion strategies along with interpretation and analysis of such data and consulting to Reaction Design, Inc. (RD). RD performed additional analysis of this data in order to extract important parameters and to confirm engine and kinetic models. The data generated was generally published to make it available to the engine and automotive design communities and also to the Reaction Design Model Fuels Consortium (MFC).

computational fluid dynamics↗

DOE OSTI2022

The tracer-BHR4 model for subgrid mixing layers

A model for the passive evolution of turbulent subgrid-scale mixing layers is developed based on the model of tracer-BHR model of Braun & Gore. The new model, tracer-BHR4, mainly differs from the previous iteration in that it aims to replicate the behavior of BHR4, instead of BHR3.1. The coefficients from BHR4 are used and a new model for mixing layer growth is developed to allow the tracer model to reproduce the de-mixing behavior seen in BHR4. Additionally, an improved timestep size controller is introduced. The new model is tested on problems containing Rayleigh-Taylor or Richtmyer-Meshkov mixing layers, and is able to reproduce the behavior of well resolved BHR4 simulations with reasonable accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

BackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment's success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologiesone to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimizationcan provide a computational means for high-throughput hypothesis testing, and eventually, optimization.ResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.ConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.

Agent-based model↗

DOE OSTI2025

Machine learning models for segmentation and classification of cyanobacterial cells

Abstract Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.

Huffine, Clair A.↗

DOE OSTI2017

A Bottom's-Up Model for Siting and Developing Consent-Based Disposal Solutions for SNF and HLW in the USA - 17332

At the end of 2016, the licensing and continued development of the USA's only candidate HLW-repository since 1987 at the Yucca Mountain (YM) site in Nevada had been on hold since 2010 pending the enactment of enabling legislation for one or more of the following paths forward: 1. Re-start of the YM HLW repository. 2. Consent-based siting and development (S and D) of new HLW-repositories for commingled disposal of commercially- (CHLW) and defense-generated (DHLW) HLW. 3. Consent-based S and D of a repository and/or deep boreholes for DHLW only. Public trust in the implementing and regulating entities and a definitive say in an apolitical S and D process by the directly affected parties (DAPs) are historical imperatives for gaining public acceptance and maintaining sustained support of any or all of them. However, due to the widespread negative legacy currently attributed to past politically-controlled S and D organizations, an even more politically controlled organizational structure and an amorphous, 'consent-based', S and D model like that introduced twice in proposed Senate bills since June 2013 will exacerbate, rather than mitigate, the main root causes to past contentions, lawsuits, delays, and cost-increases. We therefore recommend: 1) Assigning the responsibility for safe disposal of CHLW to an independent, chartered public corporation with requisite subject-matter expertise and adequate financial resources. 2) Enacting a related law that: a. Defines 'consent-based' in measurable terms; b. Assigns irrevocable veto rights to facility host state(s) based upon the majority opinion of its legal residents at pre-determined S and D milestones. c. Requires Congressional approval before the facility can open and close. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2012

DEGRADATION ISSUES IN SOLID OXIDE CELLS DURING HIGH TEMPERATURE ELECTROLYSIS

Idaho National Laboratory (INL) is performing high-temperature electrolysis research to generate hydrogen using solid oxide electrolysis cells (SOECs). The project goals are to address the technical and degradation issues associated with the SOECs. This paper provides a summary of various ongoing INL and INL sponsored activities aimed at addressing SOEC degradation. These activities include stack testing, post-test examination, degradation modeling, and a list of issues that need to be addressed in future. Major degradation issues relating to solid oxide fuel cells (SOFC) are relatively better understood than those for SOECs. Some of the degradation mechanisms in SOFCs include contact problems between adjacent cell components, microstructural deterioration (coarsening) of the porous electrodes, and blocking of the reaction sites within the electrodes. Contact problems include delamination of an electrode from the electrolyte, growth of a poorly (electronically) conducting oxide layer between the metallic interconnect plates and the electrodes, and lack of contact between the interconnect and the electrode. INL's test results on high temperature electrolysis (HTE) using solid oxide cells do not provide a clear evidence whether different events lead to similar or drastically different electrochemical degradation mechanisms. Post-test examination of the solid oxide electrolysis cells showed that the hydrogen electrode and interconnect get partially oxidized and become non-conductive. This is most likely caused by the hydrogen stream composition and flow rate during cool down. The oxygen electrode side of the stacks seemed to be responsible for the observed degradation due to large areas of electrode delamination. Based on the oxygen electrode appearance, the degradation of these stacks was largely controlled by the oxygen electrode delamination rate. University of Utah (Virkar) has developed a SOEC model based on concepts in local thermodynamic equilibrium in systems otherwise in global thermodynamic non-equilibrium. This model is under continued development. It shows that electronic conduction through the electrolyte, however small, must be taken into account for determining local oxygen chemical potential, within the electrolyte. The chemical potential within the electrolyte may lie out of bounds in relation to values at the electrodes in the electrolyzer mode. Under certain conditions, high pressures can develop in the electrolyte just under the oxygen electrode (anode)/electrolyte interface, leading to electrode delamination. This theory is being further refined and tested by introducing some electronic conduction in the electrolyte.

08 HYDROGEN↗

DOE OSTI2021

A Data-Driven Global Sensitivity Analysis Framework for Three-Phase Distribution System with PVs

Global sensitivity analysis (GSA) of distribution systems with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution systems. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of PV and load power injections. The sampling-based method approximates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-node distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.

14 SOLAR ENERGY↗

DOE OSTI2016

Overview of lower length scale model development for accident tolerant fuels regarding U 3 Si 2 fuel and FeCrAl cladding

U 3 Si 2 and FeCrAl have been proposed as fuel and cladding concepts, respectively, for accident tolerance fuels with higher tolerance to accident scenarios compared to UO 2 . However, a lot of key physics and material properties regarding their in-pile performance are yet to be explored. To accelerate the understanding and reduce the cost of experimental studies, multiscale modeling and simulation are used to develop physics-based materials models to assist engineering scale fuel performance modeling. In this report, the lower-length-scale efforts in method and material model development supported by the Accident Tolerance Fuel (ATF) high-impact-problem (HIP) under the NEAMS program are summarized. Significant progresses have been made regarding interatomic potential, phase field models for phase decomposition and gas bubble formation, and thermal conductivity for U 3 Si 2 fuel, and precipitation in FeCrAl cladding. The accomplishments are very useful by providing atomistic and mesoscale tools, improving the current understanding, and delivering engineering scale models for these two ATF concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Development and Validation of Quantitative Model-Based Image Reconstruction for NDE of Reinforced Steel-Lined Concrete Structures: Kal-El FY 2020

This project studied the capabilities of Model-Based Image Reconstruction (MBIR) and Machine Learning (ML) algorithms in the imaging and estimation of rebar corrosion from ultrasound signals. The application was challenging due to the introduction of a steel liner between the sensor and the concrete containing the rebar. The study focused in a synthetic specimen with a 6.35 mm thick steel liner, and a 19.05 mm diameter rebar at a depth of 44.5 mm and with four levels of corrosion; 0%, 20%, 50%, and 100% corrosion level. The corrosion was applied uniformly around the rebar, generating a ring in the perimeter of the rebar with different acoustic density. In order to generate a realistic synthetic dataset, we added random texture to the concrete of the synthetic specimens. This texture will mimic the variations encountered in real concrete specimens.We adapted our MBIR algorithm for the application and enhanced the algorithm to integrate in the physical model known specimen features, such as the steel liner thickness. The new MBIR method was able to cancel out reverberation from the steel liner and properly image the rebar. In particular, corrosion for the 50% and 100% cases were easily visible and quantitative metrics showed a slight separation for the 0% and 20% levels. The quantitative results were in agreement with the qualitative assessment. We also developed a ML XGBoost model to estimate corrosion level from the ultrasound signals. By combining the ML method with MBIR, we can pinpoint in the ultrasound signals the section that corresponds to the echoes from the rebar. The extract signals are processed by the XGBoost model for prediction. From each system scan, we obtain 15 predictions. The median prediction value is used as the final prediction. The True Acceptance Rate for the method is over 99%.

36 MATERIALS SCIENCE↗