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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Helium Incorporation into Scandium Fluoride, a Model Negative Thermal Expansion Material

Scandium trifluoride is a model negative thermal expansion (NTE) material. Its simple structure can be described as an A-site vacant perovskite, and it shows isotropic NTE over a very wide temperature range (up to ~1100 K), due to transverse vibrational motion of the fluoride. Like many framework NTE materials, it undergoes a phase transition at low pressures, adopting a rhombohedral (R3̅c) structure at >0.7 GPa and 300 K in commonly used nonpenetrating pressure media, such as silicone oil. High pressure X-ray diffraction data and gas uptake/release measurements indicate that, on compression in helium above ~200 K, helium is inserted into ScF 3 to form the defect perovskite He x ScF 3 . The incorporation of helium stiffens the structure and changes its phase behavior. At room temperature, complete filling of the structure with helium does not occur until >1.5 GPa. On compression, a cubic perovskite structure is maintained until ~5 GPa. As the pressure was increased to ~9.5 GPa, a further transition occurred at ~7 GPa. The first transition at ~5 GPa is likely to a tetragonal (P4/mbm) perovskite, but the detailed structure of the perovskite phase formed on compression above ~7 GPa is unclear. Cooling down from 300 to 100 K in helium at ~0.4 GPa leads to an approximate composition of He 0.1 ScF 3 . High pressure neutron diffraction measurements, in the temperature range 15–150 K show that the incorporation of helium increases the pressure at which the cubic (Pm3̅m) to rhombohedral (R3̅c) putative quantum structural phase transition occurs from close to 0 GPa to ~0.2 GPa at 0 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Detecting hidden transient events in noisy nonlinear time-series

The information impulse function (IIF), running Variance, and local Hölder Exponent are three conceptually different time-series evaluation techniques. These techniques examine time-series for local changes in information content, statistical variation, and point-wise smoothness, respectively. Using simulated data emulating a randomly excited nonlinear dynamical system, this study interrogates the utility of each method to correctly differentiate a transient event from the background while simultaneously locating it in time. Computational experiments are designed and conducted to evaluate the efficacy of each technique by varying pulse size, time location, and noise level in time-series. Our findings reveal that, in most cases, the first instance of a transient event is more easily observed with the information-based approach of IIF than with the Variance and local Hölder Exponent methods. While our study highlights the unique strengths of each technique, the results suggest that very robust and reliable event detection for nonlinear systems producing noisy time-series data can be obtained by incorporating the IIF into the analysis.

42 ENGINEERING↗
DOE OSTI2023

Assessment of Potential Ergonomic Injury Risk in the Nuclear Material Processing Glovebox Environment [Capstone Project]

Gloveboxes are isolation barriers that are used within many different industries such as pharmaceuticals, electronic parts fabrication, nuclear, and biological. The research on glovebox ergonomics is currently limited with few ergonomic professionals that focusing exclusively on glovebox working environments. There is existing documentation on occupational injuries, such as musculoskeletal disorders (MSD), sustained as a direct result of working in gloveboxes. The main elements that pose ergonomic risks to glovebox workers are operational repetition, duration, force, vibration, lifting heavy (more than 15lbs with two hands) objects, and awkward postures. This paper examines a small sample of the potential causal or risk factors that lead to the ergonomic injuries. A meta-analysis utilizing a random effect model is used to examine data from several studies which focus on, dexterity and strength changes as result of glove thickness, and robotic assistive technology as a means to improve postural mechanics. With the meta-analysis technique, similar data sets from different research studies can be coalesced into a single weighted, statistically significant result for subsequent consideration. The results from the analysis show that increased glove thickness results in decreased dexterity for operators thus increasing ergonomic risk factors such as task duration. It is also shown that glove thickness decreases grip strength but a similar decrease in pinch strength is not definitively demonstrated. With decreased grip strength, operators will need to exert more force (a known ergonomic risk factor) on processing tools, etc. during operations thus increasing the risk of ergonomic injury. The use of robotic assistive technology as a means to improve operator posture (risk factor) was also examined. Although it may be intuitively assumed that human-robotic collaboration would be beneficial in reducing risk factors, the result from this study’s analysis was not statistically significant. It is inferred that with additional directed research on this topic another study/analysis could be statistically significant demonstrating the benefits of the technology.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Self-supervised and multi-fidelity learning for extended predictive soil spectroscopy

Infrared spectroscopy is a cost-effective, non-destructive, and environmentally benign technology that is increasingly recognized as an important solution for meeting the global demand for soil data. While both near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy enable rapid estimation of soil properties, they present a significant trade-off: NIR offers superior scalability and lower operational costs, whereas MIR provides higher analytical fidelity by capturing fundamental molecular vibrations. In this study, we propose a self-supervised, multi-fidelity learning framework designed to bridge this gap. Our approach leverages large-scale MIR spectral libraries to learn a compact, transferable latent representation, into which NIR spectra are subsequently aligned for downstream prediction. The workflow consists of pretraining a latent model on a large MIR library, adapting the representation using a smaller paired NIR–MIR dataset, and evaluating generalization on an independent external test set. Across a range of chemical and physical soil properties, we found that MIR-derived embeddings improved prediction accuracy relative to baseline models that used raw MIR inputs. Predictions derived from the spectrum conversion (NIR to MIR) task did not match the performance of the original MIR spectra but were similar or superior to predictive performance of NIR-only models, suggesting the unified spectral latent space can effectively leverage the larger and more diverse MIR dataset for prediction of soil properties not well represented in current NIR libraries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Heavy Versus Light Lanthanide Selectivity for Graphene Oxide Films is Concentration Dependent

Rare earths are important materials in various technologies such as catalysis and optoelectronics. Graphene oxide (GO) is a promising material for separation applications, including the isolation of lanthanides from complex mixtures. Previous works using fatty acid monolayers have demonstrated preferential heavy versus light lanthanide adsorption, which has been attributed to differences in lanthanide ion size. In this work, we used interfacial X-ray fluorescence measurements to reveal that GO thin films at the air/water interface have no lanthanide selectivity for dilute subphases. However, at high subphase concentrations, ~8 times more Lu is adsorbed than La. By comparing the GO results with an ideal monolayer with a carboxylic acid headgroup, arachidic acid (AA), we demonstrate that the number of Lu ions adsorbed to GO is significantly higher than the number expected to compensate for the surface charge. Vibrational sum frequency generation (SFG) spectroscopy results on both GO thin films and AA monolayers reveal a red-shifted SFG signal in the OH region, which we attribute to partial dehydration of the adsorbed ions and carboxylic acid headgroups. Liquid surface X-ray reflectivity data show that the GO thin film structure does not significantly change between the very dilute and concentrated subphases. We speculate that the functional groups of both GO and AA facilitate cation dehydration, which is essential for ion adsorption. Heavy lanthanide Lu has stronger ion–ion correlations that can overcome the electrostatic repulsion between cations at higher concentrations compared to light lanthanide La, meaning GO and AA can exhibit apparent overcharge with Lu. Lastly, the layered structure of the GO films and reactive chemical nature of GO itself can accommodate ion adsorption.

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

Benchmarking machine learning interatomic potentials via phonon anharmonicity

Abstract Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIPs have been extensively tested across various classes of materials and molecules, a clear characterization of the anharmonic terms encoded in the MLIPs is lacking. Here, we benchmark popular MLIPs using the anharmonic vibrational Hamiltonian of ThO 2 in the fluorite crystal structure, which was constructed from density functional theory (DFT) using our highly accurate and efficient irreducible derivative methods. The anharmonic Hamiltonian was used to generate molecular dynamics (MD) trajectories, which were used to train three classes of MLIPs: Gaussian approximation potentials, artificial neural networks (ANN), and graph neural networks (GNN). The results were assessed by directly comparing phonons and their interactions, as well as phonon linewidths, phonon lineshifts, and thermal conductivity. The models were also trained on a DFT MD dataset, demonstrating good agreement up to fifth-order for the ANN and GNN. Our analysis demonstrates that MLIPs have great potential for accurately characterizing anharmonicity in materials systems at a fraction of the cost of conventional first principles-based approaches.

interatomic potentials↗
DOE OSTI2021

Retro-Commissioning Sensor Suitcase for Energy Efficiency

This project will enable Pacific Northwest National Laboratory (PNNL) and Lawrence Berkeley National Laboratory (LBNL) to work with industry partner, GreenPath Energy Solutions, joint developers of the Suitcase, to enhance the capabilities and usability of the Retro-Commissioning Sensor Suitcase (hereinafter the Sensor Suitcase or Suitcase), assisting GreenPath to take the Suitcase to market and rapidly expand their market share. The proposed project will specifically focus on 1) adding sensors types to extend the data collection capability and support more building and equipment performance metrics and identification of even more energy saving opportunities, 2) developing algorithms to identify recommendations for the new energy savings opportunities from item 1 and to prototype software modifications implementing them, 3) validating the use of vibration sensors to detect the operating state of a broader set of packaged HVAC equipment (additional capacities, different numbers of stages, etc.) than tested in initial development and modifying the state algorithm and software code, as needed, 4) improving cost-effectiveness in manufacturing the Sensor Suitcase, and 5) additional field testing of the technology in real buildings to more comprehensively validate Suitcase performance and to guide refinement of its capabilities. Project results will position GreenPath, and potential future licensees, to implement the new capabilities developed in this project in GreenPath’s RCx Building Suitcase, increasing its functionality and the savings resulting from its use. These enhancements will increase the value of the Suitcase to users and increase the potential market for its use and impacts in that impact.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Radon kinetics in a natural indoor radon chamber

An unusual 180 m 3 storage room in the basement of a two-story laboratory building is unventilated, and separated from occupiable rooms by double steel doors. The space completely borders on soil through the concrete floor and two of its concrete walls. The room also contains a separate inner chamber with 1 m thick concrete walls designed to damp vibrations in the room above it. The space boasts a relatively high radon level, 1083 Bq m –3 , which varies with local outdoor environmental conditions. Measurements were made of radon concentrations at various locations and heights within the facility. More than a year of continuous radon concentration data corresponding to a single location are also available, along with measurements of indoor and outdoor pressure, temperature, and humidity. Data were also collected with as many as five fans placed in different locations and cycled on for variable time periods. First order linear kinetic models were created to explain the observed approaches to steady state due to changing conditions and wash-out resulting from intentional ventilation. Results demonstrate a good fit between changes in the radon concentration level and the developed compartmental models. Furthermore, no significant differences were observed between radon concentration at different locations or heights in the chamber.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A multi-chord, two-color interferometer using Hilbert transform phase detection for measuring electron density in spheromak plasmas

A new, four-chord, CO 2 /He–Ne heterodyne interferometer has been designed and built for measuring line-averaged plasma density in the HIT-SI3 and subsequent HIT-SIU sustained spheromak devices. The two-color system successfully eliminates vibration-induced errors caused by mirrors that are secured to the vacuum chamber and is able to resolve electron densities ne in the full operating range of 10 18 –10 20 m –3 in both experiments with an integrated error of 4.7 × 10 17 m –2 . Here, data are presented from high toroidal current plasma discharges, showing the time evolution of electron densities n e and j Φ /n e along multiple chords.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Observation of a single protein by ultrafast X-ray diffraction

The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes. This was first demonstrated on biological samples a decade ago on the giant mimivirus. Since then, a large collaboration has been pushing the limit of the smallest sample that can be imaged. The ability to capture snapshots on the timescale of atomic vibrations, while keeping the sample at room temperature, may allow probing the entire conformational phase space of macromolecules. Here we show the first observation of an X-ray diffraction pattern from a single protein, that of Escherichia coli GroEL which at 14 nm in diameter is the smallest biological sample ever imaged by X-rays, and demonstrate that the concept of diffraction before destruction extends to single proteins. From the pattern, it is possible to determine the approximate orientation of the protein. Our experiment demonstrates the feasibility of ultrafast imaging of single proteins, opening the way to single-molecule time-resolved studies on the femtosecond timescale.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Fibre-optic based DAS technology for Long-term Seismic Monitoring of Carbon Capture and Storage Projects

Long-term seismic monitoring of carbon capture and storage projects (CCS) is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring (PRM) technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. In this context, we have developed and trialed the use of distributed acoustic sensing (DAS) coupled to permanent rotary sources, called surface orbital vibrators (SOV), with the objective to build a continuous monitoring array for CCS projects.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Determination of excited state molecular structures from time-resolved gas-phase X-ray scattering

We present a comprehensive investigation of a recently introduced method to determine transient structures of molecules in excited electronic states with sub-ångstrom resolution from time-resolved gas-phase scattering signals. The method, which is examined using time-resolved X-ray scattering data measured on the molecule N-methylmorpholine (NMM) at the Linac Coherent Light Source (LCLS), compares the experimentally measured scattering patterns against the simulated patterns corresponding to a large pool of molecular structures to determine the full set of structural parameters. In addition, we examine the influence of vibrational state distributions and find the effect negligible within the current experimental detection limits, despite that the molecules have a comparatively high internal vibrational energy. Here, the excited state structures determined using three structure pools generated using three different computational methods are in good agreement, demonstrating that the procedure is largely independent of the computational chemistry method employed as long as the pool is sufficiently expansive in the vicinity of the sought structure and dense enough to yield good matches to the experimental patterns.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Detection and Association of Operational Events using DAS and Seismometers (FY 2025 Mid-Year Report)

This mid-year report summarizes ongoing work to identify anomalous vibration signals indicative of potential containment breaches. This work includes compiling continuous seismic datasets and testing and refining underground detection and geolocation techniques. In the first two quarters of FY25, we have completed two project work plan tasks: (1) creating a database of continuous waveforms and ground truth event data from multiple modalities and (2) refining and implementing a detection and association algorithm to create a catalog of anomalous underground activities. This report contains a summary of the seismic database including the continuous seismic data collected by a dense array of surface seismic stations above Pleasant Gap Mine, and continuous seismic data collected using subsurface distributed acoustic sensing (DAS) in the subsurface at Sanford Underground Research Facility (SURF) and the ground truth information gathered from both sites. This report also includes results from refining and applying a dynamic power spectral density detector to both continuous seismic datasets. Finally, the report provides an initial catalog of subsurface operational events from both sensing modalities.

58 GEOSCIENCES↗
DOE OSTI2022

Between Harmonic Crystal and Glass: Solids with Dimpled Potential-Energy Surfaces Having Multiple Local Energy Minima

Solids with dimpled potential-energy surfaces are ubiquitous in nature and, typically, exhibit structural (elastic or phonon) instabilities. Dimpled potentials are not harmonic; thus, the conventional quasiharmonic approximation at finite temperatures fails to describe anharmonic vibrations in such solids. At sufficiently high temperatures, their crystal structure is stabilized by entropy; in this phase, a diffraction pattern of a periodic crystal is combined with vibrational properties of a phonon glass. As temperature is lowered, the solid undergoes a symmetry-breaking transition and transforms into a lower-symmetry phase with lower lattice entropy. Here, we identify specific features in the potential-energy surface that lead to such polymorphic behavior; we establish reliable estimates for the relative energies and temperatures associated with the anharmonic vibrations and the solid–solid symmetry-breaking phase transitions. We show that computational phonon methods can be applied to address anharmonic vibrations in a polymorphic solid at fixed temperature. To illustrate the ubiquity of this class of materials, we present a range of examples (elemental metals, a shape-memory alloy, and a layered charge-density-wave system); we show that our theoretical predictions compare well with known experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Photoionization from the Ground and Excited Vibrational States of H$^+_2$ and Its Deuterated Isotopologues

Photoionization cross sections and rate coefficients have been calculated for all bound vibrational levels of the 1sσ g state of H$^+_2$, HD+, and D$^+_2$. The Born–Oppenheimer approximation is employed in our calculation of vibrationally resolved photoionization cross sections. Vibrationally resolved and local thermal equilibrium photoionization rate coefficients are presented for photon temperatures less than 50,000 K and are found to be several orders of magnitude larger than previous results in the literature. Analytic fits for the vibrationally resolved and local thermal equilibrium photoionization rate coefficients are provided. Near-threshold oscillations in the vibrationally resolved photoionization are observed. A benchmark set of photoionization cross sections are presented. Fixed-nuclei photoionization cross sections are calculated using two-center true continuum wave functions and are verified by comparison with previous calculations and are found to be in excellent agreement in all cases. Data files for our set of benchmark cross sections, rate coefficients, and fitting parameters for H$^+_2$, HD + , and D$^+_2$ are available on Zenodo under an open-source Creative Commons Attribution license at doi:10.5281/zenodo.8304061.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

BWR Spent Nuclear Fuel Acquisition and Testing to Support DOE-NE High Burnup Spent Fuel Data Project

The Office of Spent Fuel and Waste Disposition (SFWD) within the US Department of Energy (DOE) Office of Nuclear Energy (NE) established the Spent Fuel and Waste Science and Technology (SFWST) campaign to conduct research and development (R&D) activities related to the storage, transportation, and disposal of spent nuclear fuel (SNF) and high-level radioactive waste. The SFWST program was created within SFWD to address issues of extended or long-term SNF storage and transportation. Some near-term objectives of SFWST are to use a science-based, engineering-driven approach to: Support the enhancement of the technical bases to support the continued safe and secure dry storage of SNF for extended periods; Support the enhancement of the technical bases for retrieving SNF after extended dry storage; Support the enhancement of the technical bases for transporting high burnup (HBU) fuel and transporting low burnup fuel and HBU fuel after dry storage DOE-NE, in partnership with the Electric Power Research Institute, developed the High Burnup Spent Fuel Data Project to perform a large-scale demonstration and laboratory-scale testing of HBU pressurized water reactor (PWR) fuels (exceeding 45 gigawatt-days per metric ton of uranium [GWd/MTU]). Under this project, 25 sister rods—which are rods that have the same design, power histories, and other characteristics—were removed from assemblies at the North Anna Nuclear Power Station and sent to Oak Ridge National Laboratory (ORNL) in January 2016. ORNL performed detailed nondestructive examination (NDE) on all 25 rods. The NDE consisted of visual examinations, gamma and neutron scanning, profilometry and rod length measurements, and eddy current examinations. After completing the NDE, 10 of the sister rods were delivered to Pacific Northwest National Laboratory (PNNL) in a NAC International, Inc. legal-weight truck cask in September 2018 for destructive examination (DE). To date, SFWD work has focused on the PWR fuel that is part of the Sister Rod Test program. No boiling water reactor (BWR) fuel has been tested in the program, and the data needs that were identified for the PWR fuel have not been collected for BWR fuel. The goal to obtain six to nine BWR rods and test them at ORNL will support closing this important data gap. BWR fuel comprises approximately 56% of the total fuel assemblies currently in storage at nuclear power plants in the United States. BWR nuclear fuel and cladding designs and manufacturing are significantly different from PWRs. Differences include the following: BWR fuel pellets are larger than PWR pellets; Variations of Zircaloy-2 (including liners) are used instead of the Zircaloy-4 cladding materials used in PWRs; Clad manufacturing and stress-relief processes are different between PWRs and BWRs; The fuel rod dimensions are different because larger rod diameters and thicker cladding are used in BWRs; BWR fuel typically has lower internal rod pressures and sees vastly different operating conditions than PWR fuel (i.e., two-phase flow); BWR assemblies are “canned,” meaning each assembly is surrounded by a metal fuel channel; BWR cladding is often composed of an inner pure Zr liner that has widely different mechanical properties than the Zircaloy-2 alloy and exhibits a stronger affinity for hydrogen; The BWR SNF generally has more total hydrogen in the cladding/liner than typical PWR fuel; The construction of the PWR and BWR assemblies is vastly different; BWR rods are solidly attached to the assembly nozzles and experience a much different vibration and shock load than PWR rods, which are “floating” within a grid system attached to guide tubes, and the rods sit loosely on the bottom end plates. These numerous differences will affect the way the BWR SNF responds under dry storage preparation processes (e.g., vacuum drying) and during transportation. The results collected in the PWR experimental program must be compared with a subset of similar data collected on BWR SNF to establish a technical basis for whether the larger PWR database is sufficient to bound the BWR SNF end-of-life conditions as is currently assumed for several fuel/clad properties. Changes that occur in both fuel types at HBU could exacerbate any mechanical property differences. As the fuel burnup increases, several changes occur that might affect the performance of the fuel, cladding, and assembly hardware in storage and transportation. These changes include increased cladding corrosion layer thickness, increased cladding hydrogen content, increased cladding creep strains, increased fission gas release, and the formation of the HBU structure at the surface of the fuel pellets. The Nuclear Regulatory Commission (NRC) limits the current maximum rod-averaged burnup to 62 GWd/MTU due to these changes and the lack of data at higher burnups.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
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↗