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DOE OSTI2022

Reduction of ion backflow using a quadruple GEM detector with various gas mixtures

In gaseous tracking detectors with a large gaseous volume, like Time Projection Chambers (TPC), multiple layers of Gas Electron Multipliers (GEM) can be used to block positive ions from flowing back into the active volume, which is detrimental to the tracking performance. TPCs with multilayer GEM readout are used in the recently upgraded ALICE experiment, and in the upcoming sPHENIX experiment. In both cases, suppression of ion backflow (IBF) is important for achieving the desired detector performance. Here, we report on studies of effective gain, IBF, and energy resolution in quadruple GEM detectors, and on strategies for minimizing IBF by optimizing the operating voltages of the individual GEM layers and the potential differences between different layers. These studies are performed as baseline measurements intended to aid in the optimization of the operating conditions of the GEMs of the sPHENIX TPC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Thermal mechanical assessment of a SiC-SiC-composite clad fuel pin concept in a light water reactor environment

Accident Tolerant Fuels (ATFs) are designed to increase coping time following an accident scenario while preserving or improving current steady state reactor operational performance. A potential ATF concept is SiC-SiC composite claddings. Fuel performance simulations were conducted on a SiC-SiC based cladding concept utilizing a multilayered approach for improved performance. This cladding concept referred to as the Duplex concept is a duplex structure composed of a monolithic SiC layer placed on the outside of a SiC-SiC composite. A liquid metal is added to fuel-cladding gap for improved heat dissipation from the fuel. The monolithic SiC layer is used to improve the coolant corrosion characteristics and protect the SiC-SiC composite layer from exposure to the coolant. The fuel performance code BISON was used to conduct fuel performance simulations on the cladding concepts. Comparisons are made with a current prototypic fuel rod design (UO 2 fuel enclosed in Zircaloy-4 cladding). Representative steady-state cases were considered for normal power and two cycle power histories. Additionally, a PCI ramp case was simulated to analyze potential anticipated operational occurrences. Transient response during a Loss of Coolant Accident and a Reactivity Initiated Accident were also simulated. This computational study demonstrated that for normal operating conditions, the SiC concept cladding performed as well as the baseline for the standard power cases evaluated. The ramping evaluations indicate potential fracturing of the SiC-SiC composite of the composite cladding compared to the Zircaloy-4 cladding due to the temperature gradient and the subsequent differential thermal conductivity degradation and swelling across the composite thickness. In conclusion, the rod fails early at low enthalpy for RIA but survives a LOCA with minimal material loss due to high temperature steam corrosion.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Plant endomembranes and cytoskeleton: moving targets in immunity

Pathogens attack plant cells to divert resources toward pathogen proliferation. To resist pathogens, plant cells rely on multilayered signaling pathways that hinge upon the secretory pathway for the synthesis and trafficking of pathogen sensors and defense molecules. In recent years, significant strides have been made in the understanding of the functional relationship between pathogen response and membrane traffic. Furthermore, we discuss how the plant cytoskeleton and endomembranes are targeted by pathogen effectors and highlight an emerging role of membrane contact sites in biotic stress responses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Constraining Hamiltonians from chiral effective field theory with neutron-star data

Multi-messenger observations of neutron stars (NSs) and their mergers have placed strong constraints on the dense-matter equation of state (EOS). The EOS, in turn, depends on microscopic nuclear interactions that are described by nuclear Hamiltonians. These Hamiltonians are commonly derived within chiral effective field theory (EFT). Ideally, multi-messenger observations of NSs could be used to directly inform our understanding of EFT interactions, but such a direct inference necessitates millions of model evaluations. This is computationally prohibitive because each evaluation requires us to calculate the EOS from a Hamiltonian by solving the quantum many-body problem with methods such as auxiliary-field diffusion Monte Carlo (AFDMC), which provides very accurate and precise solutions but at a significant computational cost. Additionally, we need to solve the stellar structure equations for each EOS which further slows down each model evaluation by a few seconds. In this work, we combine emulators for AFDMC calculations of neutron matter, built using parametric matrix models, and for the stellar structure equations, built using multilayer perceptron neural networks, with the PyCBC data-analysis framework to enable a direct inference of coupling constants in an EFT Hamiltonian using multi-messenger observations of NSs. We find that astrophysical data can provide informative constraints on two-nucleon couplings despite the high densities probed in NS interiors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Particle resuspension: Challenges and perspectives for future models

Using what has become a celebrated catchphrase, Philip W. Anderson once wrote that “more is different” (Science, Vol. 177, Issue 4047, pp. 393–396, 1972). First formulated in the context of condensed matter, this statement carries far beyond the sole limits of solid-state physics. It emphasizes that collective behavior can be more than the mere sum of what happens for elementary constituents or the mere collation of the evolution of each degree of freedom. Said otherwise, complex phenomena can arise out of the interplay between multiple sub-phenomena each of which can be relatively simple. The process of particle resuspension, in which discrete particles adhering on a surface are pulled off and carried away by a fluid flow, is another example involving a web of phenomena pertaining to fluid mechanics, particle dynamics and interface chemistry whose cross-effects create an intricate topic. The purpose of this review is to analyze the physics at play in particle resuspension in order to bring insights into the rich complexity of this common but challenging concern. Following the more-is-different vision, this is performed by starting from a range of practical observations and experimental data. We then work our way through the investigation of the key mechanisms which play a role in the overall process. In turn, these mechanisms reveal an array of fundamental interactions, such as particle–fluid, particle–particle and particle–surface, whose combined effects create the tapestry of current applications. At the core of this analysis are descriptions of these physical phenomena and the different ways through which they are intertwined to build up various models used to provide quantitative assessment of particle resuspension. The physics of particle resuspension implies to hold together processes occurring at extremely different space and time scales and models are key in providing a single vehicle to lead us through such multiscale journeys. This raises questions on what makes up a model and one objective of the present work is to clarify the essence of a modeling approach. In spite of its ubiquitous nature, particle resuspension is still at the early stages of developments. Many extensions need to be worked out and revisiting the art of modeling is not a moot point. The need to consider more complex objects than small and spherical particles and, moreover, to come up with unified descriptions of mono- and multilayer resuspension put the emphasis on solid model foundations if we are to go beyond current limits. Further, this is very much modeling in the making and new ideas are proposed to stimulate interest into this everyday but challenging issue in physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Influence of Clay size on corrosion protection by Clay nanocomposite thin films

In this work, the influence of clay platelet size on the corrosion barrier performance of highly-aligned polymer clay nanocomposite (PCN) thin films was examined. Layer-by-layer (LbL) deposition of alternating branched polyethylenimine (PEI) and either laponite (LAP), montromorillonite (MMT) or vermiculite (VMT) clay platelets were assembled on mild steel plates to obtain 20 bilayer (BL) films and cross-linked using glutaraldehyde after deposition. The clay platelets were chosen based on their aspect ratio, approximately 30:1, 400:1, and 2000:1, respectively. Electrochemical impedance spectroscopy of the coated steel plates during immersion showed corrosion rates and coating permeability followed LAP > MMT > VMT for up to 7 days of exposure in 0.6 M NaCl. The PEI/VMT films, ~250 nm thick, slowed corrosion by a factor of >1000 compared to bare steel. The results support the premise that high aspect ratio clay platelets can improve the corrosion barrier efficacy of LbL PCN films by decreasing film permeability and provide exceptional protection to steel in saline environments compared to other thin multilayer coatings and pretreatments.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A novel probabilistic regression model for electrical peak demand estimate of commercial and manufacturing buildings

Due to the high cost of electricity in commercial and industrial sectors, demand forecast models have gained increasing attention. However, there are two unresolved issues: (1) Models are not adaptable when exposed to previously unknown data (2) The value of regression methods vs. state-of-the-art machine learning models has not been made apparent before. This study’s goal is to develop probabilistic demand estimation models. Herein, we propose a probabilistic Bayesian regression framework that can not only estimate future demands with high accuracy but also be updated once new information is available. By applying the proposed algorithm to two real-world case studies (commercial and manufacturing), we show a 40.3% and 30.8% improvement in terms of mean absolute error for the two cases. Moreover, the proposed technique outperforms powerful machine learning approaches, including support vector machine by 10.39%, random forest by 6.17%, and multilayer perceptron by 9.14% in terms of mean absolute percentage error.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

High-efficiency solar thermophotovoltaic system using a nanostructure-based selective emitter

Herein, we present the design, fabrication, optimization, and experimental results of a high-efficiency planar solar thermophotovoltaic (STPV) system utilizing a micro-textured absorber and a nanostructure multilayer metal-dielectric coated selective emitter fabricated on a tungsten (W) substrate. Light absorptance of more than 90% was achieved at visible and near-infrared wavelengths using the microtextured absorbing surface. The nanostructure selective emitter consists of two thin-film optical coatings of silicon nitride (Si3N4) and a layer of W in between to increase the surface emissivity in spectral regimes matching the quantum efficiency of the thermophotovoltaic (TPV) cells. Gallium antimonide (GaSb)-based TPV cells are used in our STPV design. The experiment was conducted at different operating temperatures using a high-power continuous wave laser diode stack as a simulated source of concentrated incident radiation. Our experimental setup measured a maximum electrical output power density of 1.71 W/cm 2 at 1676 K STPV temperature, and the overall power conversion efficiency of 8.4% after normalizing the output power density to the emitter area. This is the highest STPV system efficiency reported so far for any experimental STPV device. The incident optical laser power on the absorber side was 131 W. This is equivalent to a solar concentration factor of ~2100, which is within the practical limit and readily achievable with Fresnel lens setup.

14 SOLAR ENERGY↗

DOE OSTI2021

Thermomechanical and sequential stress performance of photovoltaic backsheets

Photovoltaic backsheet technology is utilized to protect the interior components of solar modules from environmental stress as well as provide electrical insulation to the module. The growing demand for solar energy and longer module service life requirements have brought backsheet performance into focus. Despite being a protective packaging material, many backsheets have exhibited failure in the field, limiting the performance and service life of entire modules. Most backsheets consist of multiple polymeric layers laminated together using adhesives. A new backsheet, with a high performance polyamide structure, has been developed as the solution to module failure. Primarily, it contains a unique polyamide-ionomer alloy targeted at improved weather resistance. In this study, several photovoltaic industry standard backsheets, and the novel polyamide compositions of multilayered backsheets fabricated from the coextrusion process, were examined using thermomechanical analysis (TMA). The coefficient of thermal expansion (CTE) for entire backsheets and their constituent layers was used to model the thermomechanical behavior of various backsheet constructions. Subsequently, thermal cycling was performed in the TMA to examine the intrinsic thermal behavior of the backsheets. Thermal cycling in the TMA indicates the resilience and dimensional stability in the coextruded structures as opposed to the laminated structures. Lastly, results from a Module Accelerated Sequential Testing (MAST), are included to show the mechanical integrity of these backsheets after sequential exposure to stressors including damp heat, ultra-violet (UV) light radiation, and thermal cycling. The laminate backsheets deteriorated extensively after sequential exposure to these stressors, while the novel coextruded structures retained their physical properties.

14 SOLAR ENERGY↗

DOE OSTI2021

Outdoor performance of a tandem InGaP/Si photovoltaic luminescent solar concentrator

Here, we report the design, fabrication and outdoor characterization of a tandem luminescent solar concentrator/Si multi-junction photovoltaic module. Our tandem LSC/Si device consists of an InGaP LSC functioning as a top cell and a passivated contact Si bottom cell. The LSC comprises of an InGaP microcell array coupled to a polymer waveguide, loaded with CdSe/CdS core-shell quantum dot luminophores. The light trapping efficiency of the LSC waveguide is enhanced by encapsulation with photoluminescence trapping mirrors consisting of dielectric multilayer thin films. We demonstrate the performance of the LSC/Si device through a series of outdoor tests under various irradiance conditions conducted at the National Renewable Energy Laboratory. We report the first outdoor testing data of an LSC/Si tandem module, displaying maintained performance across varied diffusivity conditions for the LSC component. Finally, we model the tandem module performance using a ray optic simulation-based multiphysics model and forecast a pathway for high efficiency tandem LSC/Si module performance.

14 SOLAR ENERGY↗

DOE OSTI2020

Superconducting magnesium diboride films for levitation of laser targets

Inertial confinement fusion targets would benefit from being levitated inside hohlraums to avoid capsule support-related implosion perturbations. Levitation inside a magnetic trap requires coating the capsule with a thin film that is superconducting at 20 K. Such non-epitaxial film growth on non-planar substrates is challenging. Here, we study Mg vapor annealing and solid-phase reactive inter-diffusion methods to form superconducting magnesium diboride (MgB 2 ) films on different planar and spherical carbon substrates, evaluating glassy carbon, polycrystalline diamond made by chemical vapor deposition, and carbon deposited by magnetron sputtering of graphite targets. Thin films of B and Mg are produced by magnetron sputtering onto stationary planar or rolling spherical substrates and annealed at either 600 or 850 °C in Mg vapor. The films are characterized by a combination of high-energy ion scattering, electron microscopy, and magnetometry. Results show that the critical superconducting temperature of resultant films depends on film microstructure and oxygen impurity content. The formation of MgB 2 films is also strongly substrate dependent, even in this case of non-epitaxial growth. Important factors to consider are oxygen outgassing of the substrate during thermal processing, substrate surface roughness, and the matching of thermal expansion coefficients of different layers in the multilayer structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Pulsed laser deposition and structural evolution of BaF 2 nanolayers in Eu-doped BaF 2 /Al 2 O 3 layered optical nanocomposite thin films

Here, we have developed a pulsed laser deposition (PLD) geometry for the growth of uniform BaF 2 nanoscale thin films, through control of the deposition conditions. Our goal is to use the BaF 2 layers with controllable structure as component layers in layered optical nanocomposites. The structure of the BaF 2 nanolayer evolves as a function of the layer thickness: BaF 2 grows via a layer-by-layer growth mode on Al 2 O 3 ; the layers are amorphous for a thickness < 3 nm, and then become nanocrystalline as the layer thickness increases. The BaF 2 nanocrystals have an FCC crystal structure with a weak < 111 > texture that becomes stronger for thicker films. We then demonstrate that our BaF2 films can be introduced into layered Al 2 O 3 /BaF 2 /EuO x nanocomposite films, which allows for control of the relative position of the Eu ions and the BaF 2 layer. Cross-section samples of the multilayered films show interfacial intermixing between the layers, which is related to implantation during the PLD process. This intermixing enables the incorporation of Eu ions into BaF 2 layers and form a thin Eu-doped BaF 2 nanolayer at the interface. The layered nanocomposite films show photoluminescence (PL) emission from Eu 3+ ions, and the PL intensity changes can be correlated with the crystallinity and crystal size changes in the BaF 2 layer. Our results provide guidance for achieving thin film nanocomposite materials with controllable structure and photoluminescence behavior for light emitting diodes, photovoltaics and other optical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Chemical recycling of post-consumer polyester wastes using a tertiary amine organocatalyst

Recycling diverse waste plastics poses challenges due to complex sorting and processing, resulting in high costs and inefficiency. To tackle this, we present a metal-free catalytic sorting method for targeted deconstruction of polyester from post-consumer plastic waste, encompassing textiles, plastic mixtures, and multilayer packaging materials. This method employs N-methylpiperidine, a tertiary amine catalyst in methanol, to depolymerize polyethylene terephthalate (PET). Operating under these conditions (160°C, 1 h), we achieve 100% yields of dimethyl terephthalate and ethylene glycol. This technique also effectively breaks down other polyesters, including polylactic acid, polycarbonate, and polybutylene terephthalate, yielding high-yield monomers at relatively low temperatures. Through comprehensive nuclear magnetic resonance (NMR) analysis, we propose that N-methylpiperidine’s role is in enhancing methanol nucleophilicity and activating PET’s ester bond. Our insights advance the chemical recycling of post-consumer plastic waste, offering a potentially simple and efficient path to closing the polyester production loop.

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↗

DOE OSTI2024

A structure-preserving machine learning framework for accurate prediction of structural dynamics for systems with isolated nonlinearities

The nonlinearities present in structural systems are often found in isolated regions within the structure, such as those containing joints or interfaces. However, despite the localized nature of these nonlinearities their presence serves to couple together the modes of the underlying linear system and significantly complicate the development of appropriate reduced-order models; the localized nonlinearities have a global effect on the dynamics of the system. Further, in the presence of evolving structural health the nonlinearities can arise from accumulating damage, with dynamics distinct from those observed in the healthy state. The present work develops a data-driven formulation to identify and include the contributions of the isolated nonlinearities on the dynamics of the underlying linear structure. A novel coordinate separation is developed that decomposes those nonlinearities restricted to the isolated subdomain from the known linear system defined over the entire domain, and the influence of the isolated nonlinearities is reintroduced as an appropriately identified traction at the boundary of the isolated subdomain, referred to as the deviatoric force. In the region exterior to the nonlinear subdomain the response of the ideal linear system recovers that of the original nonlinear system. In this work, the deviatoric force component is predicted using a structure-preserving multilayer perceptron, based only on measured responses at the boundary of the isolated subdomain. Therefore introduction of the perceptron is able to bypass the direct numerical simulation of the nonlinearities within the isolated subdomain. This approach is illustrated through a simple structural system in which an interior region contains cubic nonlinearities and hysteretic damping. Once trained, the machine learning system is able to accurately predict the deviatoric force so that the ideal system recovers the response of the original system in the region outside the isolated nonlinear subdomain. Moreover, the data-driven approach is able to accurately predict the response when the system is subject to differing initial conditions and external excitation without the need for retraining, so that the proposed approach provides a robust description of the structural dynamics of the overall system.

Machine learning↗

DOE OSTI2022

Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive

Three machine learning techniques (multilayer perceptron, random forest and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modelling and real-time control applications. The machine learning models use a database of more than 16 000 GENRAY/CQL3D simulations for training, validation and testing. Latin hypercube sampling methods ensure that the database covers the range of nine input parameters ( $n_{e0}$ , $T_{e0}$ , $I_p$ , $B_t$ , $R_0$ , $n_{\|}$ , $Z_{{\rm eff}}$ , $V_{{\rm loop}}$ and $P_{{\rm LHCD}}$ ) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to $\sim$ ms with high accuracy across the input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Narrow operator models of stellarator equilibria in Fourier Zernike basis

Numerical computation of the ideal magnetohydrodynamic (MHD) equilibrium magnetic field is at the base of stellarator optimisation and provides the starting point for solving more sophisticated partial differential equations like transport or turbulence models. Conventional approaches solve for a single stationary point of the ideal MHD equations, which is fully defined by three invariants and the numerical scheme employed by the solver. We present the first numerical approach that can solve for a continuous distribution of equilibria with fixed boundary and rotational transform, varying only the pressure invariant. This approach minimises the force residual by optimising parameters of multilayer perceptrons that map from a scalar pressure multiplier to the Fourier Zernike basis as implemented in the modern stellarator equilibrium solver DESC.

fusion plasma↗

DOE OSTI2022

Advanced Characterization of Fuel-cladding Chemical Interaction between U-10Zr Fuel and HT9 Cladding Tested in Fast Flux Test Facility

Fuel cladding chemical interaction (FCCI) can greatly accelerate the cladding failure. However, due to the limited space in a fuel cladding assembly, it is historically challenging to gain an mechanistical understanding of the formation mechanism of FCCI and its influence on fuel and cladding performance. With the imminent need to qualify U-10Zr based metallic fuel cladded by HT-9 for advanced reactors demonstration project, it is of vital importance to use advanced characterization method to study FCCI in a unprecedent detailed manner and gain better mechanism understanding of FCCI. Mechanistic Fuel Failure (MFF) series of prototypic fuel elements irradiated in FFTF [1, 2] provides the best samples to study FCCI since the MFF-series assemblies had an axial fuel height the same as proposed length by industry partners. Jason et al. [3] has performed preliminary post-irradiation examination on a MFF fuel pin sample, which was extracted from the HT9 cladded U-10at.%Zr MFF-3 pin MFF-3 pin (#193045) at an axial location of X/L = 0.98. This sample has a peak burnup of 5.7 at.% and peak inner cladding temperature (PICT) of around 615 °C during in-core testing. Scanning electron microscope examination has identified visible FCCI region on more than half of the HT9 circumference [3]. The most striking feature are grain boundary attacking by apparently lanthanides (Lns) rich phase. However, SEM cannot provide accurate assessment of phase and concentration of grain boundary phases and prevented a better understanding of the formation mechanism of such attach. This study, by pairing transmission electron microscope (TEM) characterization and atom probe tomography (APT) techniques with in-situ micro-tensile testing in scanning electron microscope (SEM), aims at gaining in-depth understanding on the formed FCCI region. The identified FCCI region roughly consists of multilayers as illustrated in Figure 1 (c). The main findings are: (1) layer-B shows observable lanthanides (Lns) infiltration along grain boundaries and mechanical softening due to FCCI- and irradiation-induced microstructural and microchemistry changes, particularly the recovery of martensitic lath structure and dissolution of pre-existing M23C6 together with the formation of coarsened Laves phases, (Fe, Cr)2(Mo, W); (2) layer-C is Fe depleted but Lns significantly enriched, becoming very brittle; (3) layer-D is mainly composed of UFe2 and Lns; (4) three FCCI-induced intermetallic U-Fe-Zr phases, ? (Fe0.5Zr0.32U0.18), e (Fe0.3Zr0.4U0.3), ? (Fe0.06Zr0.23U0.71), were identified near layer-E; (5) the ? (Fe0.5Zr0.32U0.18) phase was characterized to be a face centered cubic (FCC) crystal structure. These results will help to better understanding the governing mechanism of FCCI and facilitating the development of theoretical model for assessing the performance of metallic fuel and cladding integrity.

36 MATERIALS SCIENCE↗