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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.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Augmented Monitoring and Condition Assessment Program for SNF Wet Storage Life Extension - 20489

Approximately 27 MTHM of spent nuclear fuel (SNF) owned and managed by the U.S. Department of Energy, Office of Environmental Management is stored in the L Basin at the Savannah River Site (SRS). This 'DOE SNF' is comprised of approximately 12,000 aluminum-clad, aluminum-based fuel assemblies (∼7 MTHM), and approximately 2000 non-aluminum fuel assemblies (∼20 MTHM). A program is in progress to perform Non-Destructive Examination (NDE) of the fuel and their storage containers to characterize the materials' condition, and to evaluate the effects of service to enable continued safe wet storage of the SNF. The Augmented Monitoring and Condition Assessment Program (AMCAP), a two-part program to develop and implement remote underwater Non-Destructive Examination of the aluminum SNF, and of the containers of the non-aluminum SNF, respectively, is aimed at the characterization and evaluation of corrosion degradation of aluminum fuel and container materials. The predominant design of the aluminum SNF (ASNF) stored in L Basin is the Materials Test Reactor (MTR) equivalent design, a plate fuel design. These SNF are stored in 5'' diameter tubes or 5'' x 5'' squares (called bundles) that are nominally 12' or up to 14' long. The SNF is de-bundled and inspected using a custom-designed MTR Fuel Inspection Table. The inspection table provides for indexed fuel positioning for a video camera examination with controlled lighting. A total of 10 of the SNF originating from foreign research and test reactors were selected for examination based on burnup, enrichment, and prior damage caused by service/storage history. A special inspection campaign of these 10 assemblies is in progress. The observed corrosion damage included minor to moderate attack from general corrosion, pitting, crevice, end grain and galvanic corrosion. Example results from the completed inspections are shown and discussed. The focus is a comparison of the as-received condition versus the as-found current storage condition that will serve to validate Water Chemistry and Corrosion Monitoring Programs. The non-aluminum SNF (NASNF) stored in L Basin are of diverse design that includes various geometries with claddings of stainless steel, Zircaloy, and Hastelloy. The fuel core materials include uranium alloys, oxides/mixed oxides, and carbides. These SNF materials, originating from early experimental and test reactors, are in various physical forms including single fuel elements and cut pieces. The fuel is stored in L Basin in various configurations including in direct bundled storage in aluminum tubes and in isolation cans that are in the bundles or in a larger over-size storage container (OSC). Concern with inside-out corrosion and the potential loss of configuration control and ability to handle the storage containers prompted the development of remote NDE methods that include visual and UT technologies to assess the condition of the containers. Candidate stored materials were selected for a special inspection campaign; the development of the NDE methods for the inspection for galvanic, crevice and sediment-induced corrosion (inside-out) are discussed. A summary of the SNF storage in L Basin at the SRS, and an overview of the AMCAP to enable continued safe storage of DOE SNF are described. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Development and implementation of an in-situ cable condition monitoring method for nuclear power plants - 130

The sustainability of the existing fleet of nuclear power reactors depends on utilities' ability to manage the aging of key components that cannot easily or economically be replaced or upgraded. Cables, metals, and concrete structures are among the major and key components that the industry and government are working on to ensure the long term reliability and safety of nuclear plants. To ensure that these key components continue to meet their performance requirements, new testing and condition monitoring technologies must be developed that are capable of identifying and quantifying degradation in these materials. The work presented in this paper focuses on the development and implementation of an in-situ condition monitoring method for nuclear power plant cables. This cable condition monitoring method is based on the frequency domain reflectometry (FDR) technique, which is a non-destructive in-situ electrical test that uses the principle of transmission line theory to locate and quantify impedance changes in a cable circuit. Through extensive research, development, and validation, the FDR test technique has been adapted as a cable condition monitoring method that trends with increasing cable degradation. In addition, the FDR data has been correlated to the industry standard elongation at break (EAB) test to quantify the degree of localized degradation in nuclear plant cable insulation material caused by long term exposure to high temperatures and radiation. By correlating FDR to EAB, the aging condition of cables routed through harsh environments can be better understood in relation to its ability to perform its intended function. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Characteristics Analysis of SGTR accidents under the Shutdown Conditions

The Steam Generator Tube Rupture (SGTR) accident is one of design basis accidents that could result in undesirable radiation release as it bypasses the containment. According to Ref. [1], SGTRs have been experienced in several nuclear power plants and Ulchin 4 of Korea is one of them. Basically, SGTR analysis assumes that it occurs at full power, the SGTR at Ulchin 4 in 2002, was in the shutdown condition. Therefore, the transient behavior was different from the cases in full power conditions and especially, the effectiveness of Emergency Operating Procedure (EOP) needs to be checked if it is also applicable to the shutdown conditions, because EOP is prepared for full power conditions. In this paper, we set up a MARS-KS code model for Ulchin 4 unit and simulated the SGTR accident for benchmarking the model to the actual plant behavior during the accident. And then, several case studies were done to investigate the consequence of SGTR and the effectiveness of the EOP, focusing on the amount of the primary to secondary discharge flow which was influenced by operator actions. At the end, we'll discuss about the EOP for the shutdown conditions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

MECHANISTIC MULTIPHYSICS MODELING OF CLADDING RUPTURE IN NUCLEAR FUEL RODS DURING LOSS-OF-COOLANT ACCIDENT CONDITIONS

The Loss of Coolant Accident (LOCA) is a design basis accident that is included as part of the safety analysis of nuclear power plants. As the nuclear industry desires to increase the cycle length and discharge burnup of existing nuclear power plants they must demonstrate safe operation during a LOCA on high burnup fuel. During a LOCA transient on high burnup fuel rods, the rods may undergo a process known as fuel fragmentation, relocation, and dispersal (FFRD). To permit dispersal, the cladding encapsulating the fuel must rupture with an opening size large enough to allow the fragmented fuel particles to release. Current licensing tools used by industry and the United States Nuclear Regulatory Commission are limited in geometric fidelity and materials that can be analyzed. These simulation tools generally employ a quasi-two-dimensional (1.5D or Layered1D) or 2D-RZ axisymmetric geometric representations exclusively. While a valid approach under some instances, there are times when important physics have an asymmetric behavior in the fuel rod. Examples include fuel fragmentation, thermal-hydraulic boundary conditions, and cladding rupture, all of which are important for LOCA analysis. As industry pursues burnup extensions it must be demonstrated that fuel dispersal can be mitigated or eliminated. To do this, an understanding of the rupture opening after cladding failure is required. This work presents the development of a model for predicting the size and location of the rupture opening in failed fuel rods during LOCA conditions using advanced modeling and simulation tools. In order to supply the rupture model with appropriate boundary conditions, improvements to fuel fragmentation, axial relocation and oxidation modeling were required. First, the eXtended Finite Element Method (XFEM) is used to mechanistically predict the number of fuel fragments that form due to material strength randomization, criteria for strength randomization, mesh density, power ramping rates and irradiation effects. These predictions with associated uncertainty were compared to empirical correlations developed for UO2 verifying that they can be used with increased confidence in subsequent axial relocation analyses. Secondly, a new first-of-its-kind Layered2D computational framework was developed that provides the ability to apply azimuthally varying boundary conditions while providing discrete layers to track fuel movement during the LOCA. An existing fuel axial relocation model developed for Layered1D was extended to work within the Layered2D framework. A large sensitivity study was performed on the initial version of the model to identify modeling parameters of particular importance, with the emissivity used for radiation after blowdown being the primary source of uncertainty. Then, a simplistic approach to incorporate mechanical degradation of the cladding due to oxidation was also developed to investigate the impact of reduced cladding thickness on predictions of the time to failure of cladding tubes. It was found that the cladding will typically rupture prior to a reduction in thickness that is sufficient to impact the rupture behavior. A model was then developed for predicting cladding rupture that transfers the cladding surface temperatures, rod internal and external pressures, fast neutron fluence, and fast neutron flux from a more detailed Layered1D, Layered2D, or 2D-RZ analysis to a 3D cladding only analysis. Comparisons of the rupture model to a few experiments indicate reasonable predictions. The rupture model was then applied to two accident tolerant fuel concepts (FeCrAl and Cr-coated Zircaloy) where it predicted that both ATF concepts would have smaller rupture openings and delayed rupture times than the standard Zircaloy-4 cladding material under identical loading conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

An Alternative Frictional Boundary Condition for Computational Fluid Dynamics Simulation of Friction Stir Welding

For better application of numerical simulation in optimization and design of friction stir welding (FSW), this paper presents a new frictional boundary condition at the tool/workpiece interface for computational fluid dynamics (CFD) modeling of FSW. The proposed boundary condition is based on an implementation of the Coulomb friction model. Using the new boundary condition, the CFD simulation yields non-uniform distribution of contact state over the tool/workpiece interface, as validated by the experimental weld macrostructure. It is found that interfacial sticking state is present over large area at the tool-workpiece interface, while significant interfacial sliding occurs at the shoulder periphery, the lower part of pin side, and the periphery of pin bottom. Due to the interfacial sticking, a rotating flow zone is found under the shoulder, in which fast circular motion occurs. The diameter of the rotating flow zone is smaller than the shoulder diameter, which is attributed to the presence of the interfacial sliding at the shoulder periphery. For the simulated welding condition, the heat generation due to friction and plastic deformation makes up 54.4 and 45.6% of the total heat generation rate, respectively. In conclusion, the simulated temperature field is validated by the good agreement to the experimental measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Model validations for low-global warming potential refrigerants in mini-split air-conditioning units

To identify low GWP (global warming potential) refrigerants to replace R-22 and R-410A, extensive experimental evaluations were conducted for multiple candidates of refrigerant at the standard test conditions and at high-ambient conditions with outdoor temperature varying from 27.8 C to 55.0 C.. In the study, R-22 was compared to propane (R-290), DR-3, ARM-20B, N-20B and R-444B in a mini-split air conditioning unit originally designed for R-22; R-410A was compared to R-32, DR-55, ARM-71A, L41-2 (R-447A) in a mini-split unit designed for R-410A. To reveal physics behind the measured performance results, thermodynamic properties of the alternative refrigerants were analysed. In addition, the experimental data was used to calibrate a physics-based equipment model, i.e. ORNL Heat Pump Design Model (HPDM). The calibrated model translated the experimental results to key calculated parameters, i.e. compressor efficiencies, refrigerant side two-phase heat transfer coefficients, corresponding to each refrigerant. As a result, these calculated values provide scientific insights on the performance of the alternative refrigerants and are useful for other applications beyond mini-split air conditioning units.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Evaluation of wall boundary condition parameters for gas-solids fluidized bed simulations

Wall boundary conditions for the solids phase have significant effects on numerical predictions of various gas-solids fluidized beds. Several models for the granular flow wall boundary condition are available in the open literature for numerical modeling of gas-solids flow. In this study, a model for specularity coefficient used in Johnson and Jackson boundary conditions by Li and Benyahia (AIChE Journal, 2012, 58, 2058-2068) is implemented in the open-source CFD code-MFIX. The variable specularity coefficient model provides a physical way to calculate the specularity coefficient needed by the partial-slip boundary conditions for the solids phase. Through a series of 2-D numerical simulations of bubbling fluidized bed and circulating fluidized bed riser, the model predicts qualitatively consistent trends to the previous studies. Furthermore, a quantitative comparison is conducted between numerical results of variable and constant specularity coefficients to investigate the effect of spatial and temporal variations in specularity coefficient.

42 ENGINEERING↗
DOE OSTI2012

Principal Eigenvalue Minimization for an Elliptic Problem with Indefinite Weight and Robin Boundary Conditions

This paper focuses on the study of a linear eigenvalue problem with indefinite weight and Robin type boundary conditions. We investigate the minimization of the positive principal eigenvalue under the constraint that the absolute value of the weight is bounded and the total weight is a fixed negative constant. Biologically, this minimization problem is motivated by the question of determining the optimal spatial arrangement of favorable and unfavorable regions for a species to survive. For rectangular domains with Neumann boundary condition, it is known that there exists a threshold value such that if the total weight is below this threshold value then the optimal favorable region is like a section of a disk at one of the four corners; otherwise, the optimal favorable region is a strip attached to the shorter side of the rectangle. Here, we investigate the same problem with mixed Robin-Neumann type boundary conditions and study how this boundary condition affects the optimal spatial arrangement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Prediction of the Stress–Strain Behavior of Open-Cell Aluminum Foam under Compressive Loading and the Effects of Various RVE Boundary Conditions

The prediction of the mechanical behavior of metallic foams with realistic microstructure and the effects of various boundary conditions on the mechanical behavior is an important and challenging issue in modeling representative volume elements (RVEs). A numerical investigation is conducted to determine the effects of various boundary conditions and cell wall cross sections on the compressive mechanical properties of aluminum foam, including the stiffness, plateau stress and onset strain of densification. The open-cell AA6101-T6 aluminum foam Duocel is used in the analyses in this study. Geometrical characteristics including the cell size, foam relative density, and cross-sectional shape and thickness of the cell walls are extracted from images of the foam. Then, the obtained foam microstructure is analyzed as a 2D model. The ligaments are modeled as shear deformable beams with elastic-plastic material behavior. To prevent interpenetration of the nodes and walls inside the cells with large deformations, self-contact-type frictionless interaction is stipulated between the internal surfaces. Sensitivity analyses are performed using several boundary conditions and cells wall cross-sectional shapes. The predicted results from the finite element analyses are compared with the experimental results. Finally, the most appropriate boundary conditions, leading to more consistent results with the experimental data, are introduced.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Tribological properties of PDA + PTFE coating in oil-lubricated condition

In this work the tribological properties of 45 ± 2 μm-thick polytetrafluoroethylene (PTFE) and polydopamine (PDA) + PTFE coatings in oil-lubricated conditions were investigated under a normal load of 10 N and a linear reciprocating speed of 0.1 m/s. Both the PTFE and the PDA + PTFE coatings were deposited using an in-house spray-coating method. The PDA + PTFE coating was five times more durable compared to the PTFE coating in oil-lubricated conditions. Both PTFE and PDA + PTFE coatings showed lower COF in oil-lubricated conditions than in dry conditions. The nanomechanical properties measured by PeakForce Quantitative Nanomechanical (PFQNM) characterization method showed an increase in Young’s modulus and adhesion force for the PDA + PTFE coatings compared to the PTFE coatings. Chemical analysis showed that the cross-linking between PDA and PTFE polymer chains occurred during the high-temperature sintering procedure. Finally, the higher adhesion of PDA + PTFE coating to the cast iron substrate and the stronger cohesion due to the cross-linking between PDA and PTFE contributed to the higher durability of the PDA + PTFE composite coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A velocity/stress mixed stabilized nodal finite element for elastodynamics: Analysis and computations with strongly and weakly enforced boundary conditions

A new nodal mixed finite element is proposed for the simulation of linear elastodynamics and wave propagation problems in time domain. Our method is based on equal-order interpolation discrete spaces for both the velocity (or displacement) and stress (or strain) tensor variables. The mixed form is derived using either the velocity/stress or velocity/strain pair of unknowns, the latter being instrumental in extensions of the method to nonlinear mechanics. The proposed approach works equally well on hexahedral or tetrahedral grids and, for this reason, it is suitable for time-domain engineering applications in complex geometry. The peculiarity of the proposed approach is the use of the rate form of the stress update equation, which yields a set of governing equations with the structure of a non-dissipative space/time Friedrichs’ system. We complement standard traction boundary conditions for the stress with strongly and weakly enforced boundary conditions for the velocity (or displacement). Weakly enforced boundary conditions are particularly suitable when considering complex geometrical shapes, because they do not require dedicated data structures for the imposition of the boundary degrees of freedom, but, rather, they utilize the structure of the variational formulation. Here we also show how the framework of weakly enforced boundary conditions can be used to develop variational forms for multi-domain simulations of heterogeneous media. A complete analysis including stability and convergence proofs is included, in the case of a space–time variational approach. A series of computational tests are used to demonstrate and verify the performance of the proposed approach.

42 ENGINEERING↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Performance evaluation of an air conditioning system based on quasi isothermal dehumidifcation

This work presents a first study of novel air conditioning system based on quasi isothermal dehumidification combined with dew-point evaporative cooling through the Maisotsenko cycle (M-Cycle). The dehumidification process is realized in desiccant coated heat exchanger with heat rejection system. The key idea behind the solution is to maximize the cooling effectiveness by creating favourable conditions for dehumidification and evaporative cooling process. This is achieved by rejecting the heat of sorption in the desiccant system and providing cold and dry air for M-Cycle air cooler, which allows the system to be powered by using low-grade heat energy at 50 °C. The analysis is performed on the basis of experimentally validated numerical models of each component combined in a unified model of the system. The goal of the study is to determine the performance of the solution under different operational conditions. In addition two types of M-Cycle air coolers (i.e. cross-flow and regenerative unit) are compared to establish which unit is more suitable as the source of cooling energy for the system. It was established that proposed system is able to provide comfortable room parameters (supply air temperature equal 17 °C and humidity ratio equal 0.010 kg/kg-da) for outdoor air temperatures ranging from 28 to 36 °C and outdoor air humidity ranging from 0.010 to 0.018 kg/kg-da. This means that the proposed solution is able to provide comfortable conditions in temperate and humid climates. Results also indicate that the system consisting of the regenerative M-Cycle HMX allows better cooling load management, offers higher thermal Coefficient of Performance (COP th ) and uses less water compared to the system with the cross-flow HMX. However, the system with cross-flow heat and mass exchanger (HMX) has a relatively greater electrical Coefficient of Performance (COP el ) in most cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Low- and high-order accurate boundary conditions: From Stokes to Darcy porous flow modeled with standard and improved Brinkman lattice Boltzmann schemes

The present contribution focuses on the accuracy of reflection-type boundary conditions in the Stokes–Brinkman–Darcy modeling of porous flows solved with the lattice Boltzmann method (LBM), which we operate with the two-relaxation-time (TRT) collision and the Brinkman-force based scheme (BF), called BF-TRT scheme. In parallel, we compare it with the Stokes–Brinkman–Darcy linear finite element method (FEM) where the Dirichlet boundary conditions are enforced on grid vertices. In bulk, both BF-TRT and FEM share the same defect: in their discretization a correction to the modeled Brinkman equation appears, given by the discrete Laplacian of the velocity-proportional resistance force. This correction modifies the effective Brinkman viscosity, playing a crucial role in the triggering of spurious oscillations in the bulk solution. While the exact form of this defect is available in lattice-aligned, straight or diagonal, flows; in arbitrary flow/lattice orientations its approximation is constructed. At boundaries, we verify that such a Brinkman viscosity correction has an even more harmful impact. Already at the first order, it shifts the location of the no-slip wall condition supported by traditional LBM boundary schemes, such as the bounce-back rule. For that reason, this work develops a new class of boundary schemes to prescribe the Dirichlet velocity condition at an arbitrary wall/boundary-node distance and that supports a higher order accuracy in the accommodation of the TRT-Brinkman solutions. For their modeling, we consider the standard BF scheme and its improved version, called IBF; this latter is generalized in this work to suppress or to reduce the viscosity correction in arbitrarily oriented flows. Our framework extends the one- and two-point families of linear and parabolic link-wise boundary schemes, respectively called B-LI and B-MLI, which avoid the interference of the Brinkman viscosity correction in their closure relations. The performance of LBM and FEM is thoroughly evaluated in three benchmark tests, which are run throughout three distinctive permeability regimes. The first configuration is a horizontal porous channel, studied with a symbolic approach, where we construct the exact solutions of FEM and BF/IBF with different boundary schemes. The second problem refers to an inclined porous channel flow, which brings in as new challenge the formation of spurious boundary layers in LBM; that is, numerical artefacts that arise due to a deficient accommodation of the bulk solution by the low-accurate boundary scheme. The third problem considers a porous flow past a periodic square array of solid cylinders, which intensifies the previous two tests with the simulation of a more complex flow pattern. The ensemble of numerical tests provides guidelines on the effect of grid resolution and the TRT free collision parameter over the accuracy and the quality of the velocity field, spanning from Stokes to Darcy permeability regimes. It is shown that, with the use of the high-order accurate boundary schemes, the simple, uniform-mesh-based TRT-LBM formulation can even surpass the accuracy of FEM employing hardworking body-fitted meshes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗
DOE OSTI2022

Generalizability analysis of tool condition monitoring ensemble machine learning models

Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.

42 ENGINEERING↗
DOE OSTI2017

How do operating conditions affect As(III) removal by iron electrocoagulation?

Iron electrocoagulation (Fe-EC) has been shown to effectively remove arsenic from contaminated groundwater at low cost and has the potential to improve access to safe drinking water for millions of people. Understanding how operating conditions, such as the Fe dosage rate and the O 2 recharge rate, affect arsenic removal at different pH values is crucial to maximize the performance of Fe-EC under economic constraints. In this work, we improved upon an existing computational model to investigate the combined effects of pH, Fe dosage rate, and O 2 recharge rate on arsenic removal in Fe-EC. We showed that the impact of the Fe dosage rate strongly depends on pH and on the O 2 recharge rate, which has important practical implications. We identified the process limiting arsenic removal (As(III) oxidation versus As(V) adsorption) at different pH values, which allowed us to interpret the effect of operating conditions on Fe-EC performance. Finally, we assessed the robustness of the trends predicted by the model, which assumes a constant pH, against lab experiments reproducing more realistic conditions where pH is allowed to drift during treatment as a result of equilibration with atmospheric CO 2 . Furthermore, our results provide a nuanced understanding of how operating conditions impact arsenic removal by Fe-EC and can inform decisions regarding the operation of this technology in a range of groundwaters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗