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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-Burnup BWR LOCA Burst Analysis Framework Development and Demonstration

Nuclear power currently contributes approximately 20% of total electricity generation in the United States and more than 10% globally. Given the increasing reliance on nuclear energy to achieve our nation’s goal of reaching net-zero carbon emissions by 2050, there is significant pressure on the existing nuclear industry to extend plant operational licenses and improve efficiency. This is crucial as the existing nuclear fleet serves as a vital bridge until new light water and advanced reactors can be developed and deployed, bolstering the supply of carbon-free energy to meet domestic demands. Operational costs primarily consist of plant operation and maintenance and fuel costs, influenced by materials and reactor core designs. These factors, coupled with heavily subsidized renewable energy markets, create a challenging economic environment for the existing light water reactor fleet, as well as for new build projects. To address these economic challenges, the nuclear industry has developed a strategic blueprint aimed at enhancing nuclear power’s economic sustainability. Past initiatives, such as efforts to eliminate fuel failures by 2010 and reduce operating costs by 30% before 2020, have laid the groundwork. Optimizing core design parameters, including burnup limits and enrichment levels, can lengthen cycles, reduce outages, reduce batch reload batch fractions and spent fuel storage requirements, and lower maintenance and operating expenses, thereby enhancing economic viability. In the United States, boiling water reactors (BWRs) comprise approximately one-third of the fleet, although much of the research and development focus has traditionally been on pressurized water reactors (PWRs). Advances in modeling and simulation, particularly through the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, are crucial to the long-term viability of BWRs, just as they are for PWRs. A key research area of the high burnup/increased enriched fuel initiative is focused on addressing loss-of-coolant-accident (LOCA)-related issues. NEAMS has dedicated significant effort to enhancing tools to better support BWRs, with a current focus on showcasing the BWR framework for high-burnup LOCA analysis. This high-fidelity work will demonstrate a best estimate pin-by-pin high-burnup BWR LOCA analysis to assess full-core cladding rupture behavior. This modeling capability will help with better understanding and realistic evaluation of fuel fragmentation, relocation, and dispersal (FFRD) phenomena at BWRs, which then could be used to prevent FFRD at BWRs without penalizing operational parameters. In addition, the results of this work will help identify strategies to identify additional margins or to potentially limit cladding rupture through core design optimizations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Deformation behavior of crystalline/amorphous Al-Si nanocomposites with nanolaminate or nanofibrous microstructures

Deformation mechanisms in sputter-deposited crystalline Al/amorphous Si nanocomposites with nanolaminate or nanofibrous morphology are characterized by nanoindentation, micropillar compression testing and transmission electron microscopy (TEM). The nanofibrous composite having crystalline Al nanofibers with ~40–50 nm in length and 15–20 nm in diameter embedded in amorphous Si exhibits strain hardening to a maximum flow stress of 2.9 GPa and no shear band (SB) formation in compression up to plastic strain exceeding 24%. On the other hand, nanolaminate composite that is composed of 80 nm crystalline Al layers and 20 nm amorphous Si layers exhibits catastrophic SBs starting at plastic strains in the range of 5–10%. Cross-sectional TEM of the deformed samples reveals a high density of stacking faults and twin boundaries in Al nanofibers and no microshear bands in the nanofibrous composite, suggesting plastic deformation in amorphous Si phase and crystalline Al nanofibers. Further, molecular dynamics simulations revealed that the plastic deformation in amorphous Si phase in the cosputtered films could be favored by the decrease in flow strength of amorphous Si with increasing Al solute concentration trapped in Si.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Hierarchical memories: Simulating quantum LDPC codes with local gates

Constant-rate low-density parity-check (LDPC) codes are promising candidates for constructing efficient fault-tolerant quantum memories. However, if physical gates are subject to geometric-locality constraints, it becomes challenging to realize these codes. In this paper, we construct a new family of [[N,K,D]] codes, referred to as hierarchical codes, that encode a number of logical qubits K=Ω(N/log(N) 2 ). The N th element of this code family is obtained by concatenating a constant-rate quantum LDPC code with a surface code; nearest-neighbor gates in two dimensions are sufficient to implement the corresponding syndrome-extraction circuit and achieve a threshold. Below threshold the logical failure rate vanishes superpolynomially as a function of the distance D(N). We present a bilayer architecture for implementing the syndrome-extraction circuit, and estimate the logical failure rate for this architecture. Under conservative assumptions, we find that the hierarchical code outperforms the basic encoding where all logical qubits are encoded in the surface code.

Pattison, Christopher A. [California Institute of ↗
DOE OSTI2020

Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification

Here, we present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational Engineering of Human Apolipoprotein for Enhanced Interaction with Bacterial Toxins

Sepsis is caused by an over-activation of the immune system in response to the presence of bacterial endotoxins in the bloodstream. LPS exemplifies a potent immune stimulator which can lead to an excessive activation leading to an overly pro-inflammatory reaction and if left untreated results in multi-organ failure and death. Human lipoproteins (HLP) have been shown to neutralize LPS and facilitate its clearance from circulation via the liver. However, host-derived lipoproteins are inhomogeneous in content and size, presenting a challenge for therapeutic applications. In this work, we have used IC facilities in order to computationally engineer HLP for an enhanced interaction with LPS. This work is a continuation of our previous efforts to derive an optimal Nanodisc (ND) lipid mixture for enhanced association with the endotoxin. We have performed fully atomistic Molecular dynamic (MD) simulations in order to carefully identify the most optimal residues in HLP, which can potentially lead to a stronger association to LPS.

42 ENGINEERING↗
DOE OSTI2026

Wasserstein normalized autoencoder for anomaly detection

A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution—a Boltzmann distribution where the energy is the reconstruction error of the autoencoder (AE)—and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets—conical sprays of visible standard model (SM) particles and invisible dark matter states—with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated SM processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard AE fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
DOE OSTI2024

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Computer Science↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
DOE OSTI2025

SiC cladding R&D execution plan

To advance the development of accident-tolerant fuel cladding for light-water reactors, the Advanced Fuels Campaign actively addresses the R&D gap between the specialized technological progress in SiC-based composite materials, led by industrial partners, and the fundamental scientific understanding of material behavior under reactor operating conditions. The laboratory activities are systematically organized based on the Development Strategy document, which outlines critical barriers to the use of SiC composites in nuclear energy applications for the coming decade and beyond. To complement the strategy, this execution plan specifies a detailed year-by-year work package and establishes key milestones for assessing progress in technological advancement. This plan has been formulated in collaboration with principal investigators at national laboratories, stakeholders, and industry partners with the goal of closing technology gaps and readying SiC-based cladding technologies for full-scale commercial testing. The research conducted at the national laboratories is categorized into three primary subjects: (1) addressing challenges associated with the performance of SiC cladding under normal operating environments, (2) identifying the failure limits of SiC cladding under accident conditions, and (3) modeling fuel performance for SiC cladding systems. Comprehensive laboratory activities integrate the development of advanced modeling tools for SiC-based cladding systems with experimental validation via integrated testing of multiphysics phenomena. This document lays out the laboratory plan to support the development of SiC-based technologies, which have the potential for significant operational and safety benefits for the U.S. nuclear industry. This plan aligns with industry developer needs, relevant Nuclear Energy University Programs, and Nuclear Energy Advanced Modeling and Simulation Programs to ensure SiC cladding technologies are matured as efficiently as possible and will be maintained and updated to accommodate the dynamically evolving progress of development, capability needs, and resource availability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Computational determination of a primary diffusion mode in γ U-10Mo under irradiation

Low enriched uranium (< 20 % 235 U)-molybdenum (U-Mo) monolithic fuel is the primary candidate for high performance research and test reactors, and is in the process of being qualified to replace highly enriched uranium (≥ 20 % 23 5U) fuel. As part of the qualification process, it is critical to understand and predict the behavior of fission gas bubbles under irradiation, which affects fuel swelling and fuel failure. Mechanistic fuel models are being developed that can both reproduce the existing experimental data for fuel swelling, and be further applied to irradiation conditions beyond the experimental scope. Diffusion of species under irradiation conditions is an important parameter in the mechanistic fuel models; however, no temperature-relevant experimental diffusion data exists. In the present work, radiation-enhanced diffusion coefficients of U, Mo, and Xe in λU-10wt.%Mo were calculated in the temperature range between 300 K and 1400 K via rate-theory models and molecular dynamics simulations with an embedded-atom method interatomic potential for the U-Mo-Xe system. Accordingly, total diffusion coefficients under relevant irradiation conditions are determined using previously obtained intrinsic thermal diffusion and radiation-driven diffusion coefficients, as well as the newly calculated radiation-enhanced diffusion coefficients presented herein. Radiation-enhanced diffusion of U and Mo was dominant in the intermediate temperature range, whereas radiation-enhanced diffusion of Xe did not significantly contribute to total diffusion of Xe at the relevant fission rate densities. Radiation-enhanced diffusion of Xe became faster than both intrinsic thermal diffusion and radiation-driven diffusion at a fission rate density of 5 x 10 22 fissions/m 3 /s, which is higher than the typical fission rate density range in research reactors. The temperature regime that radiation-enhanced diffusion of each element dominated was dependent on the fission rate density. Finally, the total diffusion coefficients of U, Mo, and Xe, updated in this work, will be utilized as parameters in the mechanistic fuel models to help predict the behavior of fission gas bubbles under irradiation more accurately.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Thermo-Oxidation Analysis of Structural Adhesives: An Experimental Study

Abstract Cross-linked polymers play an important role in structural applications as adhesives. In this article, we performed an experimental study to understand the effects of high temperature i.e. thermo-oxidative aging on the mechanical behavior of polymers. Subsequently, the constitutive behavior, i.e. stress-strain relation of these adhesives before and after the accelerated aging tests were studied on samples aged at different temperature and different time durations at zero humidity. The aging conditions and periods selected for the tests have been specifically designed to simulate the effects of aging under the working conditions that the materials will be subjected to in their standard applications. The focus of this study is on the investigation of the effects of aging on the polymeric adhesives used in automotive applications. Consequently, four different polymeric adhesives (one silicone-based, two polyurethane-based, and one acrylic type), which are being used extensively in these applications, have been chosen. The quasi-static tensile loading was used on these four adhesives and the changes in the constitutive behavior were measured. The failure tensile tests as well as the cyclic tensile tests were performed in our lab using a Test-Resources tensile testing machine. The temperatures that were used in this work are 60, 80 and 95°C for varying aging periods of 24hrs, 240hrs, and 720hrs. Then, all the data was collected and consequently analyzed, and the resulting behavior was compared to that of the unaged material, showing varying results depending on the material, temperature, and aging period. Different behaviors have been observed after aging. The polymers’ behavior was expanding from no effect to significant damage caused by aging conditions. This work provides the core understanding required for modeling the material behavior during aging and lays down the groundwork for further data-infused model-free approaches.

Alazhary, Sharif↗
DOE OSTI2022

Safety Testing of AGR-5/6/7 Compacts 2-2-2 and 2-2-4

Compacts 2-2-2 and 2-2-4 from the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program’s final irradiation campaign (AGR-5/6/7) were each separately subjected to a thermal soak at 1600°C for 300 h under flowing helium to simulated conditions experienced during a postulated high-temperature gas-cooled reactor (HTGR) depressurization conduction cooldown event. The safety testing of the fuel compacts’ performance under accident conditions was conducted in the Oak Ridge National Laboratory (ORNL) Core Conduction Cooldown Test Facility (CCCTF), which utilizes a stand-alone hot cell at the Irradiated Fuels Examination Laboratory (IFEL). The CCCTF heats fuel to temperatures up to 1900°C in a non-oxidizing environment while continuously monitoring the sweep gas for radioactive 85 Kr to detect release levels associated with hermetic failure of the tristructural-isotropic (TRISO) coating surrounding each fuel kernel. In addition, certain metallic radionuclides that escape the fuel compact are collected on deposition cups that are periodically exchanged with a new cup to obtain information on the overall retention behavior of the TRISO coating layers. Because cesium can diffuse through intact pyrocarbon layers, abnormal degradation of the silicon carbide (SiC) layer in the absence of holistic TRISO coating failure is indicated by release of 134 Cs at levels equivalent to an individual particle inventory in the absence of significant 85 Kr release (Hunn et al. 2014). Additional description of the CCCTF system is provided in Appendix A.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Directed energy deposition of functionally graded V-4Cr-4Ti to Fe-9Cr transition for fusion power systems

This study proposes a graded structure via additive manufacturing for divertor and first wall blanket applications in fusion reactors. Materials were selected based on thermodynamic calculations to operate from 1100 °C at the plasma-facing level to 550 °C at the structural steel level. Conventional joining methods often lead to failures due to discrete reaction layers with significant mechanical property differences. Using laser beam-directed energy deposition (LB-DED), this study demonstrates the fabrication of a VCrTi-Gr91 steel functionally graded component through a novel process parameter optimization framework. A systematic approach included powder characterization, single-track depositions, and construction of printability maps. Near full-density specimens of each interlayer were additively manufactured, and a transition from V-based alloys to reduced activation ferritic martensitic steels was achieved. Computational material selection of interlayer alloys and thermodynamic/diffusion kinetics simulations prevented most interface incompatibilities. A brittle intermetallic formed at one interface, causing cracking, which was not predicted by current thermodynamic models. Transition alloy design approach was updated with a more recent database and a mitigation strategy has been proposed to eliminate the formation of deleterious intermetallic phases. Ultimately, LB-DED has proven effective for producing multi-material graded systems for fusion applications, with the demonstrated process parameter optimization framework applicable to various materials.

Additive manufacturing↗
DOE OSTI2023

Integration of sensors through additive manufacturing leading to increased efficiencies of gas turbines for power generation and propulsion

To realize the full capability of additively manufactured components in complex energy systems, it is imperative to minimize early component failures during development phases and during operation. Traditional field feedback timelines and offline inspection protocols significantly reduce the design-manufacturing iteration times. To address this specific question, the project developed and demonstrated a method for the integration of sensors into complex components through additive manufacturing. The team used gas turbine engines as a platform, which meets the need of both power generation and propulsion and offer opportunities for cost reductions and efficiency increases. The innovation of this intelligent integration of sensors into complex components uniquely customized to address questions of integrity and durability for additively manufactured components. With real-time sensing data from additively manufactured components, turbine manufacturers will realize higher efficiencies, reduced component failures, and a 30-50% acceleration in product deployment of high efficiency gas turbine components due to a faster reduction in component risk assessment under actual operating conditions. This is a transformative shift towards a data-driven design and qualification of additively manufactured gas turbine components. To directly integrate sensors into additively manufactured components with all the complexities of actual hardware, powder bed fusion (direct metal laser sintering) and laser metal deposition technologies was developed. Validation took take place in two university laboratories both of which contain actual engine hardware and closely simulate a gas turbine prior to demonstrating the technology in a turbine development test. Indeed, two major technologies from this research cold impact turbine systems in the near future: (1) higher efficiency materials and designs enabled by additive manufacturing with 50% faster design to manufacturing cycle time, to enable faster time-to-market targets; and (2) integration of sensors into additively manufactured components enabling broad health and condition based prognostics for faster component and engine risk reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Power Module Precursors and Prognostics

The ongoing work involves 1) the identification and evaluation of various electric signals as indicators to module failure and precursors to aging, and 2) the development of corresponding detection techniques. In the current phase, 1) the increase of on-state resistance is selected as the precursor to the power cycle aging of silicon carbide power MOSFET modules based on review and rest data. 2) A detection method leveraging the gate voltage fluctuations magnitude during commutation transients to inform the on-state resistance increase is investigated which has the benefit of being able to be integrated into the gate driver. A dedicated detection circuit for implementation is designed and simulated and is currently in fabrication.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Predicting Wind Loading and Instability in Solar Tracking PV Arrays

Wind loading and the fluctuating pressure loads it creates on PV panel surfaces are associated with multiple degradation mechanisms and failures. Modest wind speeds create reversing loads that can initiate cell cracks and weather cracked cells. Stronger wind speeds and extreme weather events can lead to larger scale forces and the aerodynamic instability known as torsional galloping. All these effects are dependent on the complex coupling between wind speed, panel orientation, and a myriad of other hardware and site-specific factors. In this work, we present the latest developments from our work to build an open-source, high-performance computing (HPC) fluid dynamics solver to predict and mitigate these effects. This simulation package allows users to easily specify different array layouts, solar-tracking angles, panel geometries, and weather conditions before automatically generating a refined computational mesh and solving for the unsteady loading on each panel surface. Small domains (e.g., a single panel row in isolation) can be solved on a modern laptop, while larger domains or very high-fidelity studies can be solved on distributed or HPC resources with minimal modifications to the underlying problem specification. We present preliminary case studies obtained using this simulation package and highlight how increased wind speeds combined with sub-optimal tracking angles can exacerbate degradation drivers.

aerodynamics↗
DOE OSTI2021

Quantifying Properties for a Mechanistic, Predictive Understanding of Aqueous Impact on Aging of Medium and Low Voltage AC and DC Cabling in Nuclear Power Plants

To address the gap in knowledge in understanding degradation in relevant service conditions, this project aimed to develop a mechanistic, predictive model of medium and low voltage cable failure based on the primary environmental degradation parameters of aqueous immersion time, temperature, and the oxidation extent. To do so, we took a two-fold approach toward evaluating degradation as related to the aqueous condition. First, we evaluated the chemical, mechanical, and electrical properties of polymers that comprise the cabling insulation under varied aqueous conditions at different temperatures. Secondly, we evaluated the performance of insulation materials under similar accelerated aging conditions in addition to the dielectric breakdown of the cabling under submersion conditions. Thermal oxidation and immersion of LDPE thin films were done using a Parr vessel and the extent of oxidation was monitored using the carbonyl index from ATR-FTIR spectrum, a ratio quantifying the carbonyl functionality produced on the LDPE film surface. Increasing the temperature, oxidation time, and oxygen pressure increased the water-vapor permeability and the carbonyl content. Thermogravimetric analysis of the films confirmed that there is indeed an initial weight loss of the smaller molecular weight molecules and volatiles. At higher temperatures, there is a secondary mass loss. Up to 70°C, the mass loss curves were similar until 80°C, when the aged films decreased at a much greater rate with respect to increasing temperature. This was seen in the 10, 50, 90% weight loss temperatures. The mechanical properties of sheet PP and HDPE as well as injection molded LDPE, HDPE, HDPP dog bones were plotted with respect to the time spent in the Parr vessel during thermal oxidation. For the sheet PP and HDPE, the UTS and the modulus of elasticity decreased while the elongation increased. The injection-molded LDPE, HDPE, and HDPP dog bones experienced a similar trend where the changes in the properties were within experimental uncertainty. This analysis of LDPE in dry and immersive oxygen-rich environments is an important baseline for future experiments looking at other degradation mechanisms. A predictive aging model for low-density polyethylene insulative cable housings was developed. This model revealed a combination of physical and chemical processes which lead to an increase in permeability and a decrease in strength of the insulator. In creating this predictive model, a gap of knowledge in the literature was revealed in two areas: understanding how the crystallinity of a polymer changes with time and visualizing the predicted pores formed through the insulator which leads to failure. Developing and using an adapted ATR-FTIR method, crystallinity was monitored and compared to bulk crystallinity found via DSC. Inhomogeneity in crystallinity changes were observed but the FTIR method but was found to be less reliable. Pore visualization was found utilizing electrical impedance spectroscopy saturated aged polyethylene films with synthesized citrate-capped gold nanoparticles, and chloroauric acid precursor. results indicate a decreasing impedance of the polyethylene films caused by the transport of ions through the film. SEM imaging was then performed for elemental analysis of the films and counter electrodes for the presence of ions. Chloride and gold ions were detected; however, no nanoparticles were found. This gives an estimated pore size of at least 0.3 nm through the aged film. Cyclic submergence of polyethylene and polypropylene in aqueous solutions of copper sulfate and Harrison’s solution were examined. It was discovered that aging in these mixed conditions and at 90°C did cause a small, yet significant increase to tensile strength for both unaged polyethylene and polypropylene. The PE and PP in cycled and submerged conditions did not have tensile strengths significantly different from dry-aged after 16 weeks. For both solutions, it was determined that capacitance increases with both water tree depth and cable temperature and is relatively independent of changing water tree AR. As for resistance, there is no apparent change between depth percentages of 10 and 80 for both solutions. This is because the water tree has not yet entered the conductor and thus there is no shorting current flowing through the tree yet. Between 80 and 100% water tree depth, it is observed that for both solutions there is a rapid decrease in resistance because the tree is now impacting the conductor shield and allowing shorting current to flow through it. The relationship between resistance and temperature was found to be inversed. As temperature increases, resistance decreases for both water tree solutions with distilled water having the most apparent difference across the range of temperatures simulated. With regards to water tree AR, resistance was found to be relatively independent at depth percentages of 10, 50, and 70, but there was some relationship at 100% depth for both solutions. At this depth, as water tree AR increased, resistance was found to also increase. Through the examination of voltage and electric field distribution plots, it was confirmed for EPR cables that increasing water tree depth leads to increasing distortion. The increase in shorting current at 100% depth for aqueous copper sulfate compared to distilled water was also visualized. Lastly, the relationship between shorting current and temperature for narrow water trees was visualized and validated using the distribution plots. This study has led to a better understanding of the effect of cable temperature, water tree solution, and geometry of the tree region on cable degradation. Furthermore, this study has documented simulation results of water tree degradation in EPR MV cables, which had been previously lacking in information. Future work in this area will add frequency into the mix and examine what effect it has on the rate of cable degradation as a result of water treeing. To compliment the work being carried by UMD on different polymer chemistries, harvested medium voltage (MV) cables were selected for accelerated aging study at ORNL. These medium voltage cables are representative of the vintages and materials that are currently in use at existing nuclear power plants (NPPs). These cables were immersed in water at 90°C and energized for a period of two years at elevated voltage. Partial discharge was measured during this period of time to track potential degradation. Unfortunately, due to the absence of failure, the extent of integration between the UMD techniques and the harvested MV insulation was limited. However, based on the findings and from the UMD research in the previous sections on polyethylene and polypropylene, follow-on characterization for harvested MV cable insulation should focus on sample preparation to take advantage of the UMD techniques to better understand mechanistic degradation in MV cable insulations. This would include: 1.) Utilization of solutions and impedance measurement insulation to track permeability in harvested and accelerated aging insulation samples, 2.) Adaptation of gold nanoparticle synthesis for electrical impedance spectroscopy with supporting SEM characterization to study pore formation in harvested and accelerated aged insulation samples, & 3.) ATR-FTIR crystallinity with multiple temperature dependent harvested MV cable insulation under different air and submerged accelerated aging. In addition, low voltage dielectric spectroscopy and high voltage dissipation factor, tan δ, could be implemented as a condition monitoring tool for harvested MV cable insulation in submerged environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗