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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probabilistic data fusion and physics-informed machine learning: A new paradigm for modeling under uncertainty, and its application to accelerating the discovery of new materials

In this report we summarize the work conducted by PI Perdikaris and his group under this Early Career project DE–SC0019116 during the period of 09/01/2018 – 08/31/2023. The central aim of the work was to introduce a new paradigm for scientific data analysis that can seamlessly synthesize rigorous mathematical modeling with data of variable fidelity (e.g., measurements at multiple scales/resolutions or predictions of variable fidelity models) and multiple modalities (e.g., images, time–series, or scattered measurements). The setting we are interested in involves complex systems that are partially observed and whose dynamical behavior could be hard to model or totally unknown. The inherent uncertainty associated with this setting necessitates a departure from the classical deterministic realm of modeling and scientific computation, and, consequently, our main building blocks can no longer be crisp deterministic numbers and governing laws, but instead we must operate with probabilistic models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Analyzing the Effects of Atmospheric Turbulent Fluctuations on the Wake Structure of Wind Turbines and Their Blade Vibrational Dynamics

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions.

Farrell, Alayna (ORCID:000000023555720X)↗
DOE OSTI2024

Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SIGNATURES OF STEADY HEATING IN TIME LAG ANALYSIS OF CORONAL EMISSION

Among the multitude of methods used to investigate coronal heating, the time lag method of Viall and Klimchuk is becoming increasingly prevalent as an analysis technique that is complementary to those that are traditionally used. The time lag method cross correlates light curves at a given spatial location obtained in spectral bands that sample different temperature plasmas. It has been used most extensively with data from the Atmospheric Imaging Assembly on the Solar Dynamics Observatory . We have previously applied the time lag method to entire active regions and surrounding the quiet Sun and created maps of the results. We find that the majority of time lags are consistent with the cooling of coronal plasma that has been impulsively heated. Additionally, a significant fraction of the map area has a time lag of zero. This does not indicate a lack of variability. Rather, strong variability must be present, and it must occur in phase between the different channels. We have previously shown that these zero time lags are consistent with the transition region response to coronal nanoflares, although other explanations are possible. A common misconception is that the zero time lag indicates steady emission resulting from steady heating. Using simulated and observed light curves, we demonstrate here that highly correlated light curves at zero time lag are not compatible with equilibrium solutions. Such light curves can only be created by evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Electron Emission Spectroscopy of III-N Semiconductor Devices

Gallium nitride (GaN) has proven to be a semiconductor material system that is ideal for the development of lasers and light-emitting diodes (LEDs) with emission wavelengths spanning the visible spectrum. In particular, the prevalence of white LEDs has been made possible by pairing GaN-based optoelectronic devices emitting at 450 nm (blue) with phosphors for down-conversion. The efficiency of these devices, although better than alternatives, is limited at high current densities (efficiency droop), elevated temperatures (thermal droop) and is markedly worse at longer wavelengths. Understanding the physical origins of these efficiency problems can provide a pathway for engineering solutions to these long-standing issues. Despite extensive efforts to improve and understand these limitations, the unambiguous identification of these efficiency loss mechanisms has remained an open topic of research. Most studies of the efficiency loss mechanisms in GaN LEDs rely on an indirect measurement of carrier dynamics by analysis of the emitted light. The inadequacy of these indirect measurements has motivated the development of an electron spectroscopy technique, capable of directly probing several electron transport and recombination mechanisms in GaN-based optoelectronic devices. In 2013, electron emission spectroscopy (EES) was used for the first time to measure high energy electrons generated via Auger recombination in the active region of an LED. This result brought an end to the longstanding controversy over the causes for efficiency droop at high carrier densities. Our analysis of the kinetic energy of vacuum emitted electrons from GaN-based LEDs xiii has unambiguously identified carrier overshoot as the dominant mechanism responsible for thermal droop. Control experiments from p-n diodes show evidence for a previously unknown hot carrier generating process, likely trap-assisted Auger recombination (TAAR) occurring in AlGaN/GaN barrier heterostrucutres. We also discuss the possibility of TAAR occurring in InGaN quantum wells through examination of low-efficiency LEDs grown by molecular beam epitaxy. Finally, we discuss preliminary EES results regarding long wavelength GaN LEDs as well as the first EES measurements from semipolar ($20\bar2\bar1$) GaN LEDs and p-n diodes.

light emitting diodes, solid state lighting↗
DOE OSTI2012

Multi-physics nuclear reactor simulator for advanced nuclear engineering education

Multi-physics nuclear reactor simulator, which aims to utilize for advanced nuclear engineering education, is being introduced to Nagoya Univ.. The simulator consists of the 'macroscopic' physics simulator and the 'microscopic' physics simulator. The former performs real time simulation of a whole nuclear power plant. The latter is responsible to more detail numerical simulations based on the sophisticated and precise numerical models, while taking into account the plant conditions obtained in the macroscopic physics simulator. Steady-state and kinetics core analyses, fuel mechanical analysis, fluid dynamics analysis, and sub-channel analysis can be carried out in the microscopic physics simulator. Simulation calculations are carried out through dedicated graphical user interface and the simulation results, i.e., spatial and temporal behaviors of major plant parameters are graphically shown. The simulator will provide a bridge between the 'theories' studied with textbooks and the 'physical behaviors' of actual nuclear power plants. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Investigating Low Temperature Properties of Rubber Seals - 13020

To achieve the required tightness levels of containers for low and intermediate level radioactive wastes rubbers are widely applied as main sealing materials. The save encapsulation of the radioactive container contents has to be guaranteed according to legislation and appropriate guidelines for long storage periods as well as down to temperatures of -40 deg. C during transportation. Therefore the understanding of failure mechanisms that lead to leakage at low temperatures is of high importance. It is known that the material properties of rubbers are strongly influenced by temperature. At low temperatures this is caused by the rubber-glass transition (abbr. glass transition). During continuous cooling the material changes from rubber-like entropy-elastic to stiff energy-elastic behaviour, that allows nearly no strain or retraction. Therefore, rubbers are normally used above their glass transition but the minimum working temperature limit is not defined precisely, what can cause problems during application. The temperature range where full functionality is possible is strongly dependent on the application conditions and the material. For this investigation mainly ethylene propylene diene (EPDM) and fluorocarbon rubbers (FKM) were selected as they are often used for radioactive waste containers. Differential Scanning Calorimetry (DSC) and Dynamic Mechanical Analysis (DMA) are typically used for the determination of the temperature range of the glass transition process. The standardized compression set measurement according to ISO 815 is common for investigation of rubber sealing materials as the test simulates the seal behaviour after release. To reduce the test time of the standard tests a faster technique giving the same information was developed. Additionally, the breakdown temperature of the sealing function of complete O-ring seals is measured in a component test setup to compare it with the results of the other tests. The experimental setup is capable of measuring the leakage rate at low temperatures by the pressure rise method. A model was developed that allows calculating the minimum working temperature limit of a seal by combining the results of the applied methods. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

A Predictor-Corrector Strategy for Adaptivity in Dynamical Low-Rank Approximations

Here, in this paper, we present a predictor-corrector strategy for constructing rank-adaptive, dynamical low-rank approximations (DLRAs) of matrix-valued ODE systems. The strategy is a compromise between (i) low-rank step-truncation approaches that alternately evolve and compress solutions and (ii) strict DLRA approaches that augment the low-rank manifold using subspaces generated locally in time by the DLRA integrator. The strategy is based on an analysis of the error between a forward temporal update into the ambient full-rank space, which is typically computed in a step-truncation approach before recompressing, and the standard DLRA update, which is forced to live in a low-rank manifold. We use this error, without requiring its full-rank representation, to correct the DLRA solution. A key ingredient for maintaining a low-rank representation of the error is a randomized SVD, which introduces some degree of stochastic variability into the implementation. The strategy is formulated and implemented in the context of discontinuous Galerkin spatial discretizations of PDEs and applied to several versions of DLRA methods found in the literature as well as a new variant. Numerical experiments comparing the predictor-corrector strategy to other methods demonstrate robustness to overcome shortcomings of step truncation or strict DLRA approaches: The former may require more memory than is strictly needed, while the latter may miss transients solution features that cannot be recovered. The effect of randomization, tolerances, and other implementation parameters is also explored.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Geospatial analysis of freight accessibility and job attraction: The role of interstate ramps, airports, ports, and rail

The number of jobs within an industry is significantly influenced by geographical location, with transportation infrastructure playing a key role. While previous research has largely focused on how access to jobs affects employment, less attention has been given to how transportation infrastructure impacts business operations and job attraction. Here, this study addresses this gap by examining how the ease of transporting products to key transportation facilities affects job numbers in freight-intensive industries. Using job data from the Longitudinal Employment Household Dynamics dataset at the Census Tract level, we applied a non-parametric model to assess the impact of proximity to interstate ramps, rail intermodals, ports, and airports. Our analysis revealed that closer transportation infrastructure generally has a greater impact on employment. Specifically, interstate ramps are crucial for attracting jobs, particularly in rural areas, while airport proximity is essential for industries dealing with high-value, time-sensitive goods, as seen notably in Massachusetts. The importance of transportation facilities varies considerably across states and industries. The findings and method in this study can be used by transportation agencies for freight planning.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

NuScale Power Module Instrumentation

The safe and efficient operation of any nuclear power plant (NPP) depends on accurate and timely measurement of the primary system temperature, pressure, level, flow, and neutron flux. Integral pressurized water reactor (iPWR) or small modular reactors (SMRs) present unique challenges to instrumentation and control (I&C) sensors and their maintenance strategies. More specifically, the calibration and response time of these sensors may have to be verified periodically to ensure that they maintain their required degree of accuracy and speed of response. A review of technical reports and safety analysis documentation of the NuScale Power Module (NPM) by Analysis and Measurement Services Corporation (AMS), together with NuScale engineers, confirms that the I&C sensors within the NPM will need to be testable as installed to verify their static and dynamic performance at plant operating conditions. Sensor placement and installation as well as the process conditions expected in natural circulation integral SMRs like the NPM are very different from those in large scale NPPs with conventional primary system loop piping. In particular, reactor coolant system (RCS) flows are more complex and flow rates much lower, the average containment temperature is higher, nuclear radiation levels in some areas are greater, and the accessibility of sensors for hands-on maintenance is very limited. These challenges among others related to I&C sensors can be overcome by adapting existing I&C sensor test methods, developing new techniques for non-conventional I&C sensors, and incorporating online monitoring (OLM) technologies into the I&C architecture of the plant to verify the calibration and response time of sensors within the NPM prior to startup, during operation, and during subsequent refueling outages. These methods and technologies will be demonstrated and validated through a research and development (R&D) grant recently awarded to AMS by the U.S. Department of Energy (DOE) to facilitate timely deployment of the first NuScale SMR in the United States and to enable efficient I&C maintenance strategies during the life of the plant. Lastly, this paper presents the R&D plan for the DOE project with a focus on temperature measurement needs of NuScale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Simulation of high-frequency dissolved oxygen dynamics in a shallow estuary, the Corsica River, Chesapeake Bay

Understanding shallow water biogeochemical dynamics is a challenge in coastal regions, due to the presence of highly variable land-water interface fluxes, tight coupling with sediment processes, tidal dynamics, and diurnal variability in biogeochemical processes. While the deployment of continuous monitoring devices has improved our understanding of high-frequency (12 - 24 hours) variability and spatial heterogeneity in shallow regions, mechanistic modeling of these dynamics has lagged behind conceptual and empirical models. The inherent complexity of shallow water systems is represented in the Corsica River estuary, a small basin within the Chesapeake Bay ecosystem, where abundant monitoring data have been collected from long-term monitoring stations, continuous monitoring sensors, synoptic sensor surveys, and measurements of sediment-water fluxes. A state-of-the-art modeling system, the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), was applied to the Corsica domain with a high-resolution grid and nutrient loads from the most recent version of the Chesapeake Bay watershed model. The Corsica SCHISM model reproduced observed high-frequency variability in dissolved oxygen, as well as seasonal variability in chlorophyll-a and sediment-water fluxes. Time-series signal analyses using Empirical Model Decomposition and spectral analysis revealed that the diurnal and M2 tide frequencies are the dominant high-frequency modes and physical transport contributes a larger share to dissolved oxygen budgets than biogeochemical processes on an hourly time scale. Heterogeneity and patchiness in dissolved oxygen resulting from phytoplankton distributions and geometry-driven eddies amplify the physical transport effect, and on longer time scales oxygen is controlled more by photosynthesis and respiration. Our simulation demonstrates that interactions among physical and biological dynamics generate complex high-frequency variability in water quality and non-linear reposes to nutrient loading and environmental forcing in shallow water systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Thermal Shape Stability of fcc Metal Nanocrystals Synthesized with Faceted Nonequilibrium Shapes

Highly refined capabilities of the shape-controlled solution-phase synthesis of metal nanocrystals (NCs) allow the generation of NCs with faceted nonequilibrium shapes, which optimize properties for target applications such as catalysis and plasmonics. Often, for such applications and also for TEM analysis, the NCs are removed from the solution-phase environment. We explore the postsynthesis evolution of these metastable NCs in a high-vacuum TEM environment. Specifically, here we analyze their reshaping toward the equilibrium Wulff shapes mediated by surface diffusion, where such reshaping degrades the above-mentioned optimized properties. Typical sizes for these NCs range from 5 to 30 nm or 10 3 –10 6 atoms, and reshaping often occurs on the time scale of minutes for temperatures around, say, 400 °C. We discuss the development of predictive stochastic atomistic-level models for NC evolution with a realistic description of surface diffusion. These models, in contrast to Molecular Dynamics, can naturally address the relevant time and length scales for these systems. KMC simulation results for the stochastic models are described, focusing on the reshaping of slightly elongated nanorods and of mildly truncated octahedra and nanocubes. In addition, we review appropriate theoretical formulations for reshaping, which involves the nucleation and growth on 2D islands or layers on outer facets of the NC. We note the limitations of classical nucleation theory in some scenarios and demonstrate the successes of a more fundamental and general master equation-based analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Hot-spot induced mild ignition: Numerical simulation and scaling analysis

Mild ignition is a 'non-ideal' autoignition process observed at some conditions in rapid compression machine, shock tube and flow reactor experiments where flames and/or reaction fronts initiate due to non-uniformities within the test mixture. These flames/fronts can consume the entire mixture, or compression-heat the unburned gas, causing bulk ignition at times earlier than in homogeneous environments. This study numerically investigates mild ignition features using computational fluid dynamics techniques under a scenario where a small hot-spot is employed to initiate the process in a spherically-symmetric geometry. Only subsonic regimes are considered. The intent is to develop greater fundamental understandings of competing phenomena, and formulate a robust framework to quantify the perturbative behavior. Syngas blends (CO/H 2 = 80/20) are employed as the fuel, mixed with air at lean fuel loadings of phi = 0.2 and 0.5, with the initial temperatures and pressures covering 900-1120K, and 1.5-15 bar, respectively. Hot-spots ranging from 50 to 2,000 mu m in diameter are utilized with temperatures elevated from the surroundings by 1 to 90%, i.e., T hot /T 0 =1.01-1.90. Scaling arguments are derived to analyze competing processes of: (a) quenching vs. ignition, (b) extinction vs. propagation, and (c) flame consumption/forced autoignition vs. homogeneous ignition. Here, this work provides new insight into non-uniform autoignition phenomena, and discusses opportunities and challenges associated with mitigating it. In particular, scaling arguments indicate that experimental parameters could have potential to either suppress hot-spot initiated flame formation (e.g., via bath gases with high thermal diffusivity), or avert flame compression of the end gas (e.g., via lean/diluted mixtures, or bath gases with high heat capacity).

42 ENGINEERING↗
DOE OSTI2021

Hole formation effect on surface morphological response of plasma-facing tungsten

We report simulation results on the effect of helium (He) bubble bursting-mediated surface hole formation on the surface morphological response of tungsten plasma-facing components (PFCs) in nuclear fusion devices. Our analysis is based on an atomistically informed, continuum-scale model, which is capable of accessing the spatiotemporal scales relevant to the fuzz nanostructure formation process on the surface of PFC tungsten. Our simulations account, in an empirical fashion, for two types of subsurface bubble dynamical phenomena in the nanobubble region of PFC tungsten during He plasma irradiation, involving bubble bursting and surface crater formation. We demonstrate that the hole formation effect on the PFC tungsten surface accelerates the growth rate of nanotendrils and the onset of fuzz formation. As a result, the predicted incubation time for surface nanotendril growth is shortened, in agreement with experimental data of incubation fluence at comparable plasma exposure conditions. We also explore systematically the dependence of the PFC surface morphological response on the areal density of holes introduced at regular time intervals onto the He-implanted tungsten surface, a parameter in our analysis that serves as a proxy for the rate of He bubble bursting. More importantly, our simulations capture fine surface features in the PFC tungsten surface morphology and predict that the average spacing between nanotendrils is on the order of 100 nm, consistent with the experimental findings.

Chen, Chao-Shou (ORCID:0000000307910042)↗
DOE OSTI2016

Dynamic deformation and fracture of single crystal silicon: Fracture modes, damage laws, and anisotropy

Impact fracture of single-crystal Si is critical to long-term reliability of electronic devices and solar cells for its wide use as components or substrates in semiconductor industry. Single-crystal Si is loaded along two different crystallographic directions with a split Hopkinson pressure bar integrated with an in situ x-ray imaging and diffraction system. Bulk stress histories are measured, simultaneously with x-ray phase contrast imaging (XPCI) and Laue diffraction. Damage evolution is quantified with grayscale maps from XPCI. Single-crystal Si exhibits pronounced anisotropy in fracture modes, and thus fracture strengths and damage evolution. For loading along [11¯ 0] and viewing along [001], (1¯1¯0)[11¯ 0] cleavage is activated and induces horizontal primary cracks followed by perpendicular wing cracks. However, for loading along [011¯] and viewing along [111], random nucleation and growth of shear and tensile-splitting crack networks lead to catastrophic failure of materials with no cleavage. The primary-wing crack mode leads to a lower characteristic fracture strength due to predamage, but a more concentrated strength distribution, i.e., a higher Weibull modulus, compared to the second loading case. Furthermore, the sequential primary cracking, wing cracking and wing-crack coalescence processes result in a gradual increase of damage with time, deviating from theoretical predictions. Particle size and aspect ratios of fragments are discussed with postmortem fragment analysis, which verifies fracture modes observed in XPCI.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Analysis of Niobium Electropolishing Using a Generalized Distribution of Relaxation Times Method

Using electrochemical impedance spectroscopy, we have devised a method of sensing the microscopic surface conditions on the surface of niobium as it is undergoing an electrochemical polishing (EP) treatment. The method uses electrochemical impedance spectroscopy (EIS) to gather information on the surface state of the electrode without disrupting the polishing reaction. The EIS data is analyzed using a so-called distribution of relaxation times (DRT) method. Using DRT, the EIS data can be deconvolved into discrete relaxation time peaks without any a priori knowledge of the electrode dynamics. By analyzing the relaxation time peaks, we are able to distinguish two distinct modes of the EP reaction. As the polishing voltage is increased, the electrode transitions from the low voltage EP mode, characterized by a single relaxation time peaks, to the high voltage EP mode, characterized by two relaxation time peaks. We theorize that this second peak is caused by the formation of an oxide layer on the electrode. We also find that this oxide induced peak transitions from to a negative relaxation time, which is indicative of a blocking electrode process. By analyzing EPed samples, we show that samples polished in the low voltage mode have significantly higher surface roughness due to grain etching and faceting. We find that the surface roughness of the samples only improves when the oxide film peak is present and in the negative relaxation time region. This shows that EIS combined with DRT analysis can be used to predict etching on EPed Nb. This method can also be performed before or during the EP, which could allow for adjustment of polishing parameters to guarantee a smooth cavity surface finish.

43 PARTICLE ACCELERATORS↗