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All sources NASA NTRS DOE OSTI

DOE OSTI2021

AI to Predict Glass Compositions Satisfying Property and Cooling Rate Criteria

This project aimed to develop a predictive, artificial intelligence/machine learning-based model to identify glass compositions satisfying specified property requirements. Such a model would provide a systematic approach for narrowing down the nearly infinite range of possible compositions for glasses and minimize unnecessary experimental trial and error. A large empirical data set for training and testing the algorithm was obtained from the SciGlass database. It contains glass compositions and corresponding property data from a wide range of literature sources. However, the currently available form of this data, recently released under an open database license, is not conducive to easy querying and use. The data structure was deciphered and a customized parsing code developed to make this data more usable for the current and future work. Neural network models were developed and trained on viscosity data from the database and demonstrated potential for improving prediction accuracy over a traditional regression model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Energetics of oxidation and formation of uranium mononitride

Uranium mononitride (UN) is an advanced nuclear fuel currently being considered for use in several generation IV fast and thermal neutron spectrum core designs, with additional applications to thermal and electric nuclear propulsion reactors. Here, to better understand the thermal behavior and thermodynamic stability of UN, we investigated the bulk thermal oxidation process and thermochemical reactions, including the enthalpy of oxidation and standard enthalpy of formation, by conducting thermalgravimetric analysis – differential scanning calorimetry coupled with mass spectrometry (TGA-DSC-MS), and high temperature transposed temperature drop and oxide melt drop solution calorimetry. The bulk oxidation of UN (containing a small amount of α-UN 1.5+x ) in air was found to follow a step-wise process characterized by consecutive oxidative reactions UN-UN 1.5+x -UO 2 → UO 2 -UO 3 —N k → UO 3 —N k → UO 3 → U 3 O 8 . TGA results support that the UO 2 – U 2 N 3+x passivating layer delays the onset of rapid bulk oxidation of UN in air up to 662 K. Synchrotron X-ray diffraction (XRD) and extended X-ray absorption fine structure (EXAFS) analyses were performed to characterize UN and its final oxidized product. The standard enthalpy of formation (ΔH° f ) of UN was determined to be –144.4 ± 5.9 kJ/mol·atom, in good agreement with previously determined values from Pt encapsulation and bomb calorimetric experiments. Lastly, a negative linear correlation between ΔH° f and the N/U molar ratio was established based on the thermochemical data obtained in this work and previously reported enthalpies of formations of β-UN 1.5-x and α-UN 1.5+x .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

First-Principles Cost Estimation of a Sodium Fast Reactor Nuclear Plant

A multi-tiered cost analysis is performed to estimate full costs of a nuclear power plant (NPP) based on sodium-cooled fast reactor (SFR) technology. To address the lack of fully transparent cost estimations from past undertakings for NPPs, we have developed a detailed and first-principles-based cost estimate for a generalized SFR NPP. Our intent is to achieve a high degree of transparency with our cost assumptions and develop a cost model that is flexible and easily extendable to variations in NPP design and other nuclear reactor types. Furthermore, we strive to achieve a clear organization of costs and complete identification of key cost drivers based on first principles. To this end, the cost results of our analysis as given in Table 24 and Table 25 are organized and categorized into a code of accounts (COA) under development at Idaho National Laboratory (INL). Varying degrees of first-principles methods are employed, such as design for manufacture and assembly® (DFMA® ), to elucidate costs in all process levels of the plant equipment, buildings and site structures, personnel, and other miscellaneous but significant cost elements. These approaches have been successfully applied in past cost analysis projects and are designed to enable rapid and flexible cost estimation. Application of these techniques for evaluating NPP costs is similar in concept to the full, detailed estimation of construction and fabrication costs determined in a later stage of NPP development. Note that our approach tries to avoid use of other past analysis results and data such as those from the legacy Energy Economic Data Base (EEDB) Program, as these resources are based on historical NPP costs and thus may not be indicative of new reactor technologies or construction and fabrication/manufacturing techniques. However, we provide a comparison of our SFR NPP cost results in Table 90 against those included in the EEDB for a representative pressurized water reactor (PWR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Thermodynamics of Plutonium Monocarbide from Anharmonic and Relativistic Theory

Thermodynamics of plutonium monocarbide is studied from first-principles theory that includes relativistic electronic structure and anharmonic lattice vibrations. Density-functional theory (DFT) is expanded to include orbital-orbital coupling in addition to the relativistic spin-orbit interaction for the electronic structure and it is combined with anharmonic, temperature dependent, lattice dynamics derived from the self-consistent ab initio lattice dynamics (SCAILD) method. The obtained thermodynamics are compared to results from simpler quasi-harmonic theory and experimental data. Formation enthalpy, specific heat, and Gibbs energy calculated from the anharmonic model are validated by direct comparison with a calculation of phase diagram (CALPHAD) assessment of PuC and sub-stochiometric PuC0.896. Overall, the theory reproduces CALPHAD results and measured data for PuC rather well, but the comparison is hampered by the sub-stoichiometric nature of plutonium monocarbide. It was found that a bare theoretical approach that ignores spin-orbit and orbital-orbital coupling (orbital polarization) of the plutonium 5f electrons promotes too soft phonons and Gibbs energies that are incompatible with that of the CALPHAD assessment of the experimental data. The investigation of PuC suggests that the electronic structure is accurately described by plutonium 5f electrons as “band like” and delocalized, but correlate through spin polarization, orbital polarization, and spin-orbit coupling, in analogy to previous findings for plutonium metal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Ultra-rapid, physics-based development pathway for reactor-relevant RF antenna materials

This paper presents a rapid, atomistically-informed, experimental development pathway for fusion reactor-relevant radio frequency (RF) antenna materials in the Cu-Cr-(Nb,Al,Zr) composition system, with the goal of improving upon GRCop-84. RF antennas in a tokamak fusion reactor will face a unique set of challenges as both structural and functional materials. The desired material must simultaneously achieve and maintain high electrical conductivity, high strength, high thermal conductivity, resist high temperatures, possess low nuclear activation, and incur low damage due to neutron bombardment. The GRCop-84 alloy serves as a starting point for iterative improvement, with the desire to reduce or eliminate Nb from the material to minimize nuclear activation. The rapid development pathway makes use of a multi-target combinatorial thick film sputtering process to produce full ternary phase diagrams on a Si wafer substrate. Transient grating spectroscopy (TGS), a laser-ultrasonic method, will determine spatially-varying thermo-elastic properties, while four terminal electrical conductivity measurements will map out the best per- forming regions of the sample for in-depth study at larger length scales. High energy proton and self-ion irradiation emulates the effects of neutron damage on the thermal/electric properties. With rapid turnaround time (∼days) in terms of mapping radiation damage-induced material property changes in the full ternary system, these techniques allow rapid iteration towards an optimal material, testing hundreds of nearby compositions in the time it took to test one. Focused testing of larger, single composition samples (produced in an arc furnace or by laser sintering) provides data on structural and high power RF properties, and validates our thick-film based workflow.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2005

Nuclear Radiation Fields on the Mars Surface: Risk Analysis for Long-term Living Environment

Mars, our nearest planet outward from the sun, has been targeted for several decades as a prospective site for expanded human habitation. Background space radiation exposures on Mars are expected to be orders of magnitude higher than on Earth. Recent risk analysis procedures based on detailed dosimetric techniques applicable to sensitive human organs have been developed along with experimental data regarding cell mutation rates resulting from exposures to a broad range of particle types and energy spectra. In this context, simulated exposure and subsequent risk for humans in residence on Mars are examined. A conceptual habitat structure, CAD-modeled with duly considered inherent shielding properties, has been implemented. Body self-shielding is evaluated using NASA standard computerized male and female models. The background environment is taken to consist not only of exposure from incident cosmic ray ions and their secondaries, but also include the contribution from secondary neutron fields produced in the tenuous atmosphere and the underlying regolith.

Brooke M Anderson↗

NASA NTRS2014

Investigation of HZETRN 2010 as a Tool for Single Event Effect Qualification of Avionics Systems

NASA's future missions are focused on long-duration deep space missions for human exploration which offers no options for a quick emergency return to Earth. The combination of long mission duration with no quick emergency return option leads to unprecedented spacecraft system safety and reliability requirements. It is important that spacecraft avionics systems for human deep space missions are not susceptible to Single Event Effect (SEE) failures caused by space radiation (primarily the continuous galactic cosmic ray background and the occasional solar particle event) interactions with electronic components and systems. SEE effects are typically managed during the design, development, and test (DD&T) phase of spacecraft development by using heritage hardware (if possible) and through extensive component level testing, followed by system level failure analysis tasks that are both time consuming and costly. The ultimate product of the SEE DD&T program is a prediction of spacecraft avionics reliability in the flight environment produced using various nuclear reaction and transport codes in combination with the component and subsystem level radiation test data. Previous work by Koontz, et al.1 utilized FLUKA, a Monte Carlo nuclear reaction and transport code, to calculate SEE and single event upset (SEU) rates. This code was then validated against in-flight data for a variety of spacecraft and space flight environments. However, FLUKA has a long run-time (on the order of days). CREME962, an easy to use deterministic code offering short run times, was also compared with FLUKA predictions and in-flight data. CREME96, though fast and easy to use, has not been updated in several years and underestimates secondary particle shower effects in spacecraft structural shielding mass. Thus, this paper will investigate the use of HZETRN 20103, a fast and easy to use deterministic transport code, similar to CREME96, that was developed at NASA Langley Research Center primarily for flight crew ionizing radiation dose assessments. HZETRN 2010 includes updates to address secondary particle shower effects more accurately, and might be used as another tool to verify spacecraft avionics system reliability in space flight SEE environments.

Rojdev, Kristina↗

DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components: Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction

The long-term operation (LTO) of nuclear power plants (NPP) beyond their original design life of 40 years can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition, the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Requiring frequent NDE inspections may lead to frequent NPP shutdowns which can lead to power outages and additional NDE inspection cost-related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing a more digital approach such as using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. The DT framework is based on limited experimental data, Artificial-intelligence (AI)-Machine-Learning (ML) and multiphysics-computationalmechanics such as finite element- (FE) based models. Given the real-time thermal-hydraulic process measurements from several existing plant sensors, the overall goal of the DT framework is to predict the cumulative usages factors or equivalent fatigue lives in real time and at any random 3D location of the components. This includes inaccessible locations such as inside the thickness or location of a component. This prediction can be at thousands to millions of 3D point clouds or locations like conventional FE-based models, but without running an FE model in real time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Majorana parameters of the interacting boson model of nuclear structure and their implication for 0 ν β β decay

Here, the well-known spherical-deformed-transitional nucleus and potential 0$\textit{νββ}$ emitter 150 Nd and its daughter 150 Sm were investigated in nuclear resonance fluorescence experiments using quasimonoenergetic, linearly polarized γ-ray beams. For both nuclei transitions from the 1 + scissors mode to the $0^+_2$ and $2^+_2$ states were observed for the first time and their respective $\textit{M}$ 1 transition strengths were determined. Through a systematic investigation, a sensitivity of these transition strengths to the three Majorana parameters of the interacting boson model-2 (IBM-2) was established. In combination with the novel experimental data, this poses strong constraints to the Majorana parameters in improved IBM-2 representations of both nuclei. A subsequent recalculation of the nuclear matrix elements (NMEs) for the 150 Nd → 150 Sm 0$\textit{νββ}$ decay in the IBM-2 with these improved representations results in $M^{(0νββ)}_{\text{IBM-2}}[0^+_1]$ = 3.35 for the NME for 0$\textit{νββ}$ decay into the ground state of 150 Sm and $M^{(0νββ)}_{\text{IBM-2}}[0^+_2]$ = 1.30 for 0$\textit{νββ}$ decay to its $0^+_2$ state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel

The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.

Laser powder bed fusion↗

NASA NTRS1995

Issues in protection from galactic cosmic rays

Radiation risks to astronauts depend on the microscopic fluctuations of energy absorption events in specific tissues. These fluctuations depend not only on the space environment but also on the modifications of that environment by the shielding provided by structures surrounding the astronauts and the attenuation characteristics of the astronaut's body. The effects of attenuation within the shield and body depends on the tissue biological response to these microscopic fluctuations. In the absence of an accepted method for estimating astronaut risk, we examined the attenuation characteristics using conventional linear energy transfer (LET)-dependent quality factors (as one means of representing relative biological effectiveness, RBE) and a track-structure repair model to fit cell transformation (and inactivation) data in the C3H10 T1/2 mouse cell system obtained for various ion beams. Although the usual aluminum spacecraft shield is effective in reducing dose equivalent with increasing shield thickness, cell transformation rates are increased for thin aluminum shields. Clearly, the exact nature of the biological response to LET and track width is critical to evaluation of biological protection factors provided by a shield design. A significant fraction of biological injury results from the LET region above 100 keV/mu m. Uncertainty in nuclear cross-sections results in a factor of 2-3 in the transmitted LET spectrum beyond depths of 15 g/cm2, but even greater uncertainty is due to the combined effects of uncertainty in biological response and nuclear parameters. Clearly, these uncertainties must be reduced before the shield design can be finalised.

NASA Discipline Number 45-10↗

DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Analysis of spin frustration in an Fe III 7 cluster using a combination of computational, experimental, and magnetostructural correlation methods

The synthesis, structure, and magnetic properties are reported for [Fe 7 O 3 (O 2 C t Bu) 9 (mda) 3 (H 2 O) 3 ] ( 1 ), where mdaH 2 is N -methyldiethanolamine. 1 was prepared from the reaction of [Fe 3 O(O 2 C t Bu) 6 (H 2 O) 3 ](NO 3 ) with mdaH 2 in a 1:~3 ratio in MeCN. The core of 1 consists of a central octahedral Fe III ion held within a non-planar Fe 6 loop by three μ 3 -O 2- and three μ 2 -RO - arms from the three mda 2- chelates. Variable-temperature dc and ac magnetic susceptibility studies revealed dominant antiferromagnetic coupling, leading to a ground state spin of S = 5 / 2 . The ground state was confirmed by a fit of magnetization data collected in the 0.1–7.0 T and 1.8–10.0 K ranges. The four Fe 2 pairwise exchange parameters ( J 1 - J 4 ) were estimated by independent methods: theoretical calculations using either broken symmetry energy differences (-46.3, -16.2, -3.9, and - 28.1 cm -1 , respectively) or Green’s function approximation methods (-41.4, -14.8, -13.2, and - 24.7 cm -1 ), and a magnetostructural correlation (MSC) previously developed for high nuclearity Fe III /O complexes (-39.5, -13.8, -6.7, and - 23.5 cm -1 ). Additionally, the J 1 - J 4 obtained from the MSC and theoretical methods were used with the program PHI to both simulate χ M T vs T as well as to serve as reasonable input values to fit the experimental data (-41.0, -11.4, -5.0, and - 27.3 cm -1 ). Analysis of the J ij led to identification of the spin frustration effects operative and the resultant spin vector alignments at each Fe III ion, thus allowing for the rationalization of the experimental ground state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2003

Benchmark studies of the effectiveness of structural and internal materials as radiation shielding for the international space station

Accelerator-based measurements and model calculations have been used to study the heavy-ion radiation transport properties of materials in use on the International Space Station (ISS). Samples of the ISS aluminum outer hull were augmented with various configurations of internal wall material and polyethylene. The materials were bombarded with high-energy iron ions characteristic of a significant part of the galactic cosmic-ray (GCR) heavy-ion spectrum. Transmitted primary ions and charged fragments produced in nuclear collisions in the materials were measured near the beam axis, and a model was used to extrapolate from the data to lower beam energies and to a lighter ion. For the materials and ions studied, at incident particle energies from 1037 MeV/nucleon down to at least 600 MeV/nucleon, nuclear fragmentation reduces the average dose and dose equivalent per incident ion. At energies below 400 MeV/nucleon, the calculation predicts that as material is added, increased ionization energy loss produces increases in some dosimetric quantities. These limited results suggest that the addition of modest amounts of polyethylene or similar material to the interior of the ISS will reduce the dose to ISS crews from space radiation; however, the radiation transport properties of ISS materials should be evaluated with a realistic space radiation field. Copyright 2003 by Radiation Research Society.

NASA Discipline Radiation Health↗

DOE OSTI2024

Search for axion-like particles through nuclear Primakoff production using the GlueX detector

We report on the results of the first search for the production of axion-like particles (ALPs) via Primakoff production on nuclear targets, γA → aA, in the “SRC-CT” experiment using the GlueX detector at Jefferson Lab. This search uses an integrated luminosity of 100 pb -1 nucleon on a 12 C target with a real photon beam of energies 6 < E γ < 10.8 GeV, and explores the mass region of 200 < m a < 450 MeV via the decay a → γγ. This mass range is between the π 0 and η meson masses, which enables the use of the measured η meson production rate to obtain absolute bounds on the ALP production with reduced sensitivity to experimental luminosity and detection efficiency. We find no evidence for an ALP, consistent with previous searches in the quoted mass range, and present limits on the effective photon coupling scale of $\mathscr{O}$(1 TeV -1 ). We further find that the ALP production limit we obtain is hindered by the peaking structure of the non-target-related dominant background the in GlueX spectrometer, which we treat by using data on 4 He to estimate and subtract it. We comment on how this search can be improved in a future higher-statistics dedicated measurement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Boosting efficiency and reducing graph reliance: Basis adaptation integration in Bayesian multi-fidelity networks

The computational cost of high-fidelity numerical models makes outer-loop analysis, which requires repeated interrogation of the model such as uncertainty quantification, computationally demanding. Multi-fidelity methods, which construct a surrogate model using data from an ensemble of models of varying cost and accuracy, can substantially reduce the cost of outer-loop analysis. However, these methods can be difficult to apply when the model ensemble does not admit a clear hierarchy a priori and the correlations between models are low. Consequently, in this paper, we present a multi-fidelity method that leverages dimension reduction to enhance the correlation between models, thereby reducing the amount of data needed to train a surrogate from an unordered ensemble of models. Our method utilizes basis adaptation to build low-dimensional polynomial chaos expansions of each model and employs Multi-fidelity Networks to encode the relationships among models. We show that the resulting method exhibit two notable advantages over its counterpart: (1) enhanced accuracy (both reduced bias and variance); and (2) reduced dependency on the graph structure encoding relationships among models. We demonstrate the approach on an analytical test problem and a challenging finite element model for a spent nuclear fuel. Our method produces a surrogate model that is significantly more accurate than either a single-fidelity surrogate or a multi-fidelity surrogate constructed without basis adaptation.

42 ENGINEERING↗

DOE OSTI2022

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗