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Results for “vibrational analysis”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multi-mode quasi-static excitation for systems with nonlinear joints

Finite element models can be used to model and predict the hysteresis and energy dissipation exhibited by nonlinear joints in structures. As a result of the nonlinearity, the frequency and damping of a mode is dependent on excitation amplitude, and when the modes remain uncoupled, quasi-static modal analysis has been shown to efficiently predict this behavior. However, in some cases the modes have been observed to couple such that the frequency and damping of one mode is dependent on the amplitude of other modes. To model the interactions between modes, one must integrate the dynamic equations in time, which is several orders of magnitude more expensive than quasi-static analysis. This work explores an alternative where quasi-static forces are applied in the shapes of two or more modes of vibration simultaneously, and the resulting load–displacement curves are used to deduce the effect of other modes on the effective frequency and damping of the mode in question. This methodology is demonstrated on a simple 2D cantilever beam structure with a single bolted joint which exhibits micro-slip nonlinearity over a range of vibration amplitudes. The predicted frequency and damping are compared with those extracted from a few expensive dynamic simulations of the structure, showing that the quasi-static approach produces reasonable albeit highly conservative bounds on the observed dynamics. This framework is also demonstrated on a 3D structure where dynamic simulations are infeasible.

42 ENGINEERING↗
DOE OSTI2024

Selective incorporation of antimony into gallium nitride

Dilute concentrations of antimony (Sb) incorporation into GaN induce strong bandgap bowing and tunable room-temperature photoluminescence from the UV to the green spectral regions. However, the atomistic details of the incorporation of Sb into the GaN host remain unclear. In this work, we use first-principles calculations to understand the thermodynamics of Sb substitution into GaN and its effect on the optical and Raman spectra. Although it is empirically considered that Sb is preferentially incorporated as an anion (Sb3−) into the N sublattice, we demonstrate that Sb can also be incorporated as a cation (Sb3+, Sb5+) into the metal sublattice. Our thermodynamic analysis demonstrates that SbN0, SbGa2+, and SbGa0 can co-exist under Ga-rich conditions in n-type samples. We further confirm the dual incorporation of Sb by calculating the vibrational frequencies of different anionic and cationic substitutions to explain the origins of experimentally observed additional Raman peaks of Sb-doped GaN. Moreover, the calculated band structures of different Sb substitutions into GaN explain the experimental photoluminescence and optical absorption spectra. Overall, our analysis suggests that the coexistence of Sb3−, Sb3+, and Sb5+ substitutions into GaN explains the totality of experimental measurements. Our results demonstrate that the selective incorporation of Sb into GaN (and potentially other group-V elements such as As, P, or Bi) by tuning the growth conditions can drastically modify the electronic properties, for applications in visible light emitters and photocatalysis.

Physics↗
DOE OSTI2024

The Role of Spin–Orbit Coupling in the Linear Absorption Spectrum and Intersystem Crossing Rate Coefficients of Ruthenium Polypyridyl Dyes

The successful use of molecular dyes for solar energy conversion requires efficient charge injection, which in turn requires the formation of states with sufficiently long lifetimes (e.g., triplets). The molecular structure elements that confer this property can be found empirically, however computational predictions using ab initio electronic structure methods are invaluable to identify structure-property relations for dye sensitizers. The primary challenge for simulations to elucidate the electronic and nuclear origins of these properties is a spin-orbit interaction which drives transitions between electronic states. Here, in this work, we present a computational analysis of the spin-orbit corrected linear absorption cross sections and intersystem crossing rate coefficients for a derivative set of phosphonated tris(2,2'-bipyridine)ruthenium(2+) dye molecules. After sampling the ground state vibrational distributions, the predicted linear absorption cross sections indicate that the mixture between singlet and triplet states plays a crucial role in defining the line shape of the metal-to-ligand charge transfer bands in these derivatives. Additionally, an analysis of the intersystem crossing rate coefficients suggests that transitions from the singlet into the triplet manifolds are ultrafast with rate coefficients on the order of 10 13 s -1 for each dye molecule.

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

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗
DOE OSTI2026

Average hydrodynamic radius analysis reveals critical solvation thresholds in high-concentration lithium electrolytes

Understanding the solvation structures of lithium salts in carbonate- and ether-based electrolytes is central to explaining the exceptional stability of high-concentration electrolytes (HCEs) and localized high-concentration electrolytes (LHCEs). Conventional techniques such as vibrational spectroscopy and one-dimensional NMR provide only limited information, typically restricted to coordination ratios and ion-pair distributions, without revealing the actual size and mass of solution complexes. Here, in this study, we introduce an average hydrodynamic radius (AHR) analysis based on internally referenced DOSY NMR, which enables direct estimation of the average volume and molecular weight of lithium–solvent complexes in solution. Using LiFSI–EMC and LiFSI–EMC–TTE electrolytes, we demonstrate that the onset of effective lithium metal stabilization and aluminum corrosion suppression coincides with the formation of very large complexes, whose average volume exceeds 300 times that of free EMC molecules. This finding supports a new “blocking mechanism,” wherein bulky solvated complexes impede direct solvent access to reactive surfaces. The AHR analysis thus not only clarifies the fundamental origin of HCE and LHCE effectiveness, but also provides a broadly applicable experimental framework for probing complex solvation structures and guiding rational electrolyte design.

Concentrated electrolytes↗
DOE OSTI2021

Cooperativity and Metal–Linker Dynamics in Spin Crossover Framework Fe(1,2,3-triazolate) 2

Cooperative interactions are responsible for the useful properties of spin crossover (SCO) materials–large hysteresis windows, critical temperatures near room temperature, and abrupt transitions–with hybrid framework materials exhibiting the greatest cooperativity and hysteresis of all SCO systems. However, little is known about the chemical origin of cooperativity in frameworks. Here, we present a combined experimental–computational approach for identifying the origin of cooperativity in the metal–organic framework (MOF) Fe(1,2,3-triazolate) 2 (Fe(TA) 2 ), which exhibits the largest known hysteresis window of all SCO materials and unusually high transition temperatures, as a roadmap for understanding the manipulation of SCO behavior in general. Variable-temperature vibrational spectroscopy provides evidence that “soft modes” associated with dynamic metal–linker bonding trigger the cooperative SCO transition. Thermodynamic analysis also confirms a cooperativity magnitude much larger than those of other SCO systems, while electron density calculations of Fe(TA) 2 support previous theoretical predictions that large cooperativity arises in materials where SCO produces considerable differences in metal–ligand bond polarities between different spin states. Taken together, this combined experimental–computational study provides a microscopic basis for understanding cooperative magnetism and highlights the important role of dynamic bonding in the functional behavior of framework materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Transferable Water Potentials Using Equivariant Neural Networks

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor–liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm 3 between 230 and 365 K), vapor–liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Superconductivity found in meteorites

Meteorites can contain a wide range of material phases due to the extreme environments found in space and are ideal candidates to search for natural superconductivity. However, meteorites are chemically inhomogeneous, and superconducting phases in them could potentially be minute, rendering detection of these phases difficult. To alleviate this difficulty, we have studied meteorite samples with the ultrasensitive magnetic field modulated microwave spectroscopy (MFMMS) technique [J. G. Ramírez, A. C. Basaran, J. de la Venta, J. Pereiro, I. K. Schuller, Rep. Prog. Phys. 77, 093902 (2014)]. Here, we report the identification of superconducting phases in two meteorites, Mundrabilla, a group IAB iron meteorite [R. Wilson, A. Cooney, Nature 213, 274–275 (1967)] and GRA 95205, a ureilite [J. N. Grossman, Meteorit. Planet. Sci. 33, A221–A239 (1998)]. MFMMS measurements detected superconducting transitions in samples from each, above 5 K. By subdividing and remeasuring individual samples, grains containing the largest superconducting fraction were isolated. The superconducting grains were then characterized with a series of complementary techniques, including vibrating-sample magnetometry (VSM), energy-dispersive X-ray spectroscopy (EDX), and numerical methods. Finally, these measurements and analysis identified the likely phases as alloys of lead, indium, and tin.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Hydroxyapatite/TiO 2 Nanomaterial with Defined Microstructural and Good Antimicrobial Properties

Due to the growing number of people infected with the new coronavirus globally, which weakens immunity, there has been an increase in bacterial infections. Hence, knowledge about simple and low-cost synthesis methods of materials with good structural and antimicrobial properties is of great importance. A material obtained through the combination of a nanoscale hydroxyapatite material (with good biocompatibility) and titanium dioxide (with good degradation properties of organic molecules) can absorb and decompose bacteria. In this investigation, three different synthesis routes used to prepare hydroxyapatite/titanium dioxide nanomaterials are examined. The morphology and semiquantitative chemical composition are characterized by scanning electron microscopy with energy dispersive X-ray analysis (SEM-EDX). The obtained materials’ phase and structural characterization are determined using the X-ray powder diffraction method (XRD). The crystallite sizes of the obtained materials are in the range of 8 nm to 15 nm. Based on XRD peak positions, the hexagonal hydroxyapatite phases are formed in all samples along with TiO 2 anatase and rutile phases. According to SEM and TEM analyses, the morphology of the prepared samples differs depending on the synthesis route. The EDX analysis confirmed the presence of Ti, Ca, P, and O in the obtained materials. The IR spectroscopy verified the vibration bands characteristic for HAp and titanium. The investigated materials show excellent antimicrobial and photocatalytic properties.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Structure, Bonding, and Vibrational Dynamics of a Triamine High Energy Density Material under Pressure

High energy density materials have complex intermolecular interactions which influence their stability and performance. We used a combination of synchrotron X-ray diffraction, synchrotron infrared spectroscopy, and Raman vibrational spectroscopy, supplemented by density functional theory calculations, to probe pressure-induced changes in structure and intermolecular interactions of 1H,4'H-[3,3'-bis(1,2,4-triazole)]-4',5,5'-triamine as a model high energy density material up to 40 GPa. We find that compression of the triamine is accompanied by increased intermolecular interactions that give rise to an interesting evolution of the structure and bonding with pressure. Analysis of the equation of state determined from the X-ray diffraction indicates a change in compression mechanism near 19 GPa consistent with changes in vibrational spectra that provide evidence for a structural rearrangement associated with changes in hydrogen bonding near that pressure. As a result, the overall compressional behavior calculated theoretically agrees with that observed experimentally though differences are found that indicate the need for improved treatment of the intermolecular interactions including hydrogen bonding under pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Distinct Kinetic Signatures of Photodesorption from Metal Nanoparticles

Visible photon fluxes can influence the rate and selectivity of heterogeneously catalyzed reactions on metal nanoparticle surfaces. Models describing the influence of photon fluxes have typically introduced photon flux dependent apparent thermal kinetic parameters (reaction orders, activation energies, binding energies, etc.). This has relied on empirical fitting of reaction rate data, making mechanistic interpretations of how photon fluxes influence elementary step rates challenging and inconsistent with fundamental descriptions of photochemistry on metal surfaces developed from surface science studies. Using the CO adsorption–desorption quasi-equilibrium reaction on Pt/Al 2 O 3 catalysts as a model system, we measured steady state adsorbed CO (CO*) coverages under isothermal and isobaric (1 mbar CO) conditions as a function of temperature (473–573 K) and of 440 nm photon flux ((0.1–5.2) × 10 3 # hv Pt site –1 s –1 ) using in situ IR spectroscopy. Steady state CO* coverage on Pt was photon flux dependent with increasing photon flux causing decreasing coverage, consistent with photons driving CO* desorption rates faster than thermal CO* desorption rates. However, photon flux dependent CO* coverages were essentially temperature independent, inconsistent with models that describe photon effects using perturbations to apparent thermal kinetic parameters. Instead, 120 steady state CO* coverages as a function of temperature and photon flux are quantitatively described by a kinetic model in which the overall desorption rate is a summation of independent thermal and photon induced CO* desorption rates. Site-resolved analysis reveals distinct kinetic parameters for photon driven desorption of CO* from well-coordinated, under-coordinated, and highly under-coordinated Pt sites, with temperature-dependent apparent quantum efficiencies (AQE) consistent with temperature dependence of vibrational quanta distribution of adsorbed CO. The rigorous kinetic rate laws for independent photon and thermal driven pathways allow for predictive modeling of the influence of photon fluxes on the rates of CO* desorption under catalytic conditions. Further, the analysis provides evidence that steady state continuous wave photon fluxes can drive desorption/adsorption reactions on metal surfaces out of thermal equilibrium, reconciling surface science observations of molecular photodesorption with applied catalysis. The work establishes a general kinetic framework to be considered for photon driven processes on metals, and defines catalyst, reaction, and photon flux characteristic design principles for breaking Sabatier limitations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Local and long-range atomic/magnetic structure of non-stoichiometric spinel iron oxide nanocrystallites

Spinel iron oxide nanoparticles of different mean sizes in the range 10–25 nm have been prepared by surfactant-free up-scalable near- and super-critical hydrothermal synthesis pathways and characterized using a wide range of advanced structural characterization methods to provide a highly detailed structural description. The atomic structure is examined by combined Rietveld analysis of synchrotron powder X-ray diffraction (PXRD) data and time-of-flight neutron powder-diffraction (NPD) data. The local atomic ordering is further analysed by pair distribution function (PDF) analysis of both X-ray and neutron total-scattering data. It is observed that a non-stoichiometric structural model based on a tetragonal γ-Fe 2 O 3 phase with vacancy ordering in the structure (space group P 4 3 2 1 2) yields the best fit to the PXRD and total-scattering data. Detailed peak-profile analysis reveals a shorter coherence length for the superstructure, which may be attributed to the vacancy-ordered domains being smaller than the size of the crystallites and/or the presence of anti-phase boundaries, faulting or other disorder effects. The intermediate stoichiometry between that of γ-Fe 2 O 3 and Fe 3 O 4 is confirmed by refinement of the Fe/O stoichiometry in the scattering data and quantitative analysis of Mössbauer spectra. The structural characterization is complemented by nano/micro-structural analysis using transmission electron microscopy (TEM), elemental mapping using scanning TEM, energy-dispersive X-ray spectroscopy and the measurement of macroscopic magnetic properties using vibrating sample magnetometry. Notably, no evidence is found of a Fe 3 O 4 /γ-Fe 2 O 3 core-shell nanostructure being present, which had previously been suggested for non-stoichiometric spinel iron oxide nanoparticles. Finally, the study is concluded using the magnetic PDF (mPDF) method to model the neutron total-scattering data and determine the local magnetic ordering and magnetic domain sizes in the iron oxide nanoparticles. The mPDF data analysis reveals ferrimagnetic collinear ordering of the spins in the structure and the magnetic domain sizes to be ∼60–70% of the total nanoparticle sizes. The present study is the first in which mPDF analysis has been applied to magnetic nanoparticles, establishing a successful precedent for future studies of magnetic nanoparticles using this technique.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Accurate prediction of short-range order and its effect on thermodynamic, structural, and electronic properties of disordered alloys: exemplified in archetypical Cu 3 Au

Electronic-structure methods based on density-functional theory (DFT) were used to quantify the effect of chemical short-range order (SRO) on thermodynamic, structural, and electronic properties of archetypal face-centered-cubic (fcc) Cu3Au alloy. We showed that SRO can be tuned to alter bonding and lattice dynamics (i.e., phonons) and detail how these properties are changed with SRO. Thermodynamically favorable SRO significantly improved the phase stability of fcc Cu3Au from -0.0343 eV-atom -1 to –0.0682 eV-atom -1 . We used our DFT-based linear-response theory to predict SRO and its electronic origin, and accurately estimate the observed transition temperature, ordering instability (L1 2 ), and Warren-Cowley SRO parameters, in agreement with experiments. The accurate prediction of real-space SRO gives an edge over computationally and resource intensive approaches such as monte-carlo methods or experiments, which will enable large scale molecular dynamic simulations by providing supercells with optimized SRO. Here we also analyzed phonon dispersion and estimated the vibrational entropy change (from 9kB at 300 K to 6kB at 100 K) in fcc Cu3Au. We established from SRO analysis that exclusion of chemical interactions may lead to a skewed view of true properties in chemically complex alloys. The first-principles methods described in this work are generally applicable to any arbitrary solid-solution alloys, including multi-principal-element alloys, therefore, holds promise for designing technologically useful materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Correlation between infrared spectra features and coverage of different adsorption sites for the NO/Pd(111) system

This study investigates the adsorption of nitric oxide (NO) on Pd(111) surfaces at 200 K using infrared reflection absorption spectroscopy (IRRAS). Peak positions and areas are used as proxies for tracking coverage of different species, serving as groundwork for in-situ experiments that need real-time tracking of surface species. Here, we derived a mathematical model correlating NO coverage with dose, consistent with the Molecular Langmuir Model. Distinct correlations are observed between coverage and spectral features (peak position and peak area for the NO vibration): at lower coverages (θ<0.6), the total integrated peak areas are linearly corelated to coverage; at higher coverages (θ>0.6), the peak center for a compressed-hollow site becomes the primary coverage indicator, showing a linear relationship between wavenumber and coverage. Note that this study addresses coverages below saturation. These findings refine our understanding of NO adsorption and establish a foundation for real-time in situ transient kinetics studies of chemical reactions involving NO on Pd(111).

25 ENERGY STORAGE↗
DOE OSTI2021

Optical vibrational spectra and proposed crystal structure of ε-UO 3

ε-UO 3 is an exotic polymorph in the uranium trioxide system with an undetermined crystal structure and limited optical vibrational spectroscopic data. To improve understanding of this compound, we synthesize and investigate the crystal structure and optical vibrational spectra of ε-UO 3 . Infrared spectra collected for ε-UO 3 are in good agreement with previously published results, and our studies extend the available data into the low-energy (600–100 cm –1 ) regime. For the first time, Raman spectra are presented for ε-UO 3 using both 785 and 532 nm excitation wavelengths. Previous reports suggest an impurity phase may be present in ε-UO 3 produced by calcination of U 3 O 8 ; however, spectral center-of-mass calculations, principal component analyses, and Raman spectroscopic mapping employed to investigate this possibility indicate that the product of U 3 O 8 calcined in O 3 (g) in this work is likely phase-pure. A possible novel structure solution for ε-UO 3 is determined via Rietveld refinement of powder X-ray diffraction data and is triclinic, P-1, with a = 4.01 Å, b = 3.85 Å, c = 4.18 Å, and α = 98.26°, β = 90.41°, γ = 120.46° (R wp = 8.30%). The asymmetric unit of ε-UO 3 consists of U(VI) in hexagonal bipyramidal coordination with displaced equatorial oxygen. Further analysis reveals that the structure of ε-UO3 is best described by a 2 × 1 × 2 supercell structure in P-1 with a = 8.03 Å, b = 3.86 Å, c = 8.37 Å with α = 98.26°, β = 90.41°, and γ = 120.46°, although a higher-symmetry structure is possible. Optical vibrational spectroscopic and structural measurements of ε-UO 3 presented here furthers our understanding of this complex uranium oxide and clarifies the origin of reported structural similarity to U 3 O 8 .

-UO3↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensor systems were tested to measure the key parameters, such as hoop strain, pipe pressure, surrounding soil temperature, and acoustic vibrations. The underground product pipeline’s outer diameter is 30 inches, the wall thickness is 1.28 inches, and 3 feet deep from the surface. The fiber deployment strategies and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety. These findings from pilot-scale testing offer valuable insights into advancing pipeline monitoring technologies and improving the reliability of underground pipeline systems.

fiber optic sensors↗
DOE OSTI2020

Uncertainty evaluation for twist drilling stability model

This paper describes the first uncertainty analysis for drilling stability using a frequency-domain drilling stability model. The stability model inputs include: the modal parameters for the torsional-axial vibration mode from the twist drill-holder-spindle axial frequency response function; and the mechanistic coefficients that relate the torque and thrust force to chip area for the selected drill-workpiece material combination. Furthermore, Monte Carlo simulation is applied to propagate the input uncertainties to output uncertainty in the predicted stability map, which separates stable from unstable (chatter) zones in the spindle speed-chip width parameter space. Additionally, the mean stability boundary and its 95% confidence intervals are determined for five cases: varying all four inputs simultaneously and varying them individually. This enables the individual sensitivities to be compared. Experimental results from drilling tests are included for comparison to the prediction. Additionally, Matlab code is provided to implement the stability model and Monte Carlo uncertainty analysis.

42 ENGINEERING↗