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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sensitivity analysis for characterizing the impact of HNGD model on the prediction of hydrogen redistribution in Zircaloy cladding using BISON code

Hydrogen in zirconium cladding is able to precipitate into zirconium hydrides which impacts cladding integrity. The Hydride Nucleation-Growth-Dissolution (HNGD) model in the BISON code accounts for the precipitation and dissolution kinetics of hydride in Zircaloy material. This paper presents global sensitivity analyses of the HNGD model aiming to enhance our understanding of the hydride precipitation phenomena by quantifying the variance that key parameters have on the prediction of hydrogen behavior under various environmental conditions. Here, model predictions are compared to experimental data obtained under two different conditions: 1) with uniformly precharged specimens subjected to a linear thermal gradient, and 2) specimens precharged with a cathodically applied hydride rim at one end of the sample and subjected to an asymmetric thermal gradient. The Sobol sensitivity analysis identifies the key parameters in the HNGD model for both types of specimens. For linear temperature cases, the heat of transport dominates the accuracy of predictions when no precipitation occurs at the cold end, while Terminal Solid Solubility for Dissolution (TSSD) is the most important parameter when precipitation occurs. A large variation in the predicted hydrogen concentration profiles is found in the range of high TSSD due to the occurrence of precipitation. For asymmetric temperature cases, the solubility coefficient gives the largest impact on the predicted hydrogen distribution, as it determines the amount of solute hydrogen dissolved from the initially applied hydride rim. A large discrepancy in hydrogen distribution between simulations and experiments exists with the asymmetric specimens because BISON simulations fail to predict the precipitation of hydride at the cooler end. Comparative studies using former and updated models verifies the significant impact of the hydride growth mechanism on predicted hydrogen concentration profiles. In particular, when hydride initially exists, changes in TSSD generate a large variation in the predicted amount of precipitation by hydride growth, giving large uncertainty in predicting the hydrogen distribution over the sample length. The outputs characterize the significant impact of the hydride growth mechanism in the HNGD model on predicting hydrogen behavior, and improve the understanding of the precipitation of hydride in Zircaloy cladding within a range of expected environmental conditions. The analyses indicate work is still needed to improve the hydride solvus models in the BISON code to accurately predict experimentally observed hydride concentrations and distributions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicted performance of a tangential viewing hard x-ray camera for the DIII-D high field side lower hybrid current drive experiment

High field side launch of lower hybrid current drive (LHCD) has improved accessibility and penetration over low field side launch on DIII-D. Simulations predict single pass absorption under a wide range of plasma conditions. Hard x-ray (HXR) measurement of LHCD generated fast electron bremsstrahlung (50–250 keV) will validate wave propagation and absorption. Emissivity profiles are recovered from one-dimensional inversion of HXR brightness to determine LH damping location, fast electron slowing down time, and some indication of the fast electron energy. The camera will be implemented by populating 32 tangential sightlines of the existing Gamma Ray Imager with Kromek SPEARTM Cadmium Zinc Telluride (CZT) detectors sensitive to 10–1000 keV photons with 10 keV energy resolution. Expected count rates allow for <0.5 ms time resolution. Pulses are processed using 50 ns shaping time Cremat CR-200 Gaussian shaping modules and are digitized by 25 MHz D-TACQ ACQ216 digitizers. The performance of the HXR camera is evaluated by comparing predicted fast electron density profiles and inverted synthetic brightnesses obtained from the ray-tracing/Fokker–Planck codes GENRAY/CQL3D. Inversions closely matched predicted fast electron profiles for a range of experimental parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy.

net radiation↗
DOE OSTI2025

Evaluation of Density-Functional Tight-Binding Methods for Simulation of Protic Molecular Ion Pairs

In this work, we benchmark the accuracy of the density-functional tight-binding (DFTB) method, namely the long-range corrected second-order (LC-DFTB2) and third-order (DFTB3) models, for predicting energetics of imidazolium-based ionic liquid (IL) ion pairs. We compare the DFTB models against popular density functionals such as LC-ωPBE and B3LYP, using ab initio domain-based local pair-natural orbital coupled cluster (DLPNO-CC) energies as reference. Calculations were carried out in the gas phase, as well as in aqueous solution using implicit solvent methods. We find that the LC-DFTB2 model shows excellent performance in the gas phase and agrees well with reference energies in implicit solvent, often outperforming DFTB3 predictions for complexation energetics. Our study identifies a range of opportunities for use of the LC-DFTB method and quantifies its sensitivity to protonation states and the types of chemical interactions between ion pairs.

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

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Automated theoretical chemical kinetics: Predicting the kinetics for the initial stages of pyrolysis

Large scale implementation of high level computational theoretical chemical kinetics offers the prospect for dramatically improving the fidelity of combustion chemical modeling. To facilitate such efforts, we have developed a suite of codes, collectively referred to as AutoMech, that allow for the automatic prediction of the kinetics for large sets of reactions via ab initio transition-state-theory based master-equation calculations. The primary input is simply the mechanism, a dictionary relating chemically identifiable species descriptors (e.g., SMILES or InChIs) to species labels in the mechanism, and a specification of the electronic structure and transition state theory models to be implemented. Here we illustrate the current utility of AutoMech through a study of the initial stages of pyrolysis for 3 sets of fuels: sequences of alkanes, alcohols, and aldehydes. For simplicity, the analysis focuses on abstractions from the fuel by H, CH 3 , and OH, and the decomposition of the resulting radicals. Altogether, there are a total of 166 input channels in these sets (more than 363 forward reactions when expanded to the full set of elementary reactions). The code successfully produces high quality rate estimates (with apparent uncertainties less than a factor of two in limited comparisons with experiment) for > 95% of these. For the radical decomposition reactions, the analysis includes predictions for the pressure dependence of the kinetics. This wide-ranging exploration illustrates (i) the effect of different levels of prediction on the expected accuracy, (ii) the branching between abstractions at different sites for different abstractors, (iii) the dependence of the rates on the chemical structure, and (iv) the variation in radical stabilities across chemical families. These results, as well as the demonstrated feasibility of the methodology, should find further utility in the development of accurate rate expressions for arbitrary fuels. (c) 2020 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Elliott, Sarah N.↗
DOE OSTI2022

Influence of Aerosol Embedded in Shallow Cumulus Cloud Fields on the Surface Solar Irradiance

Ubiquitous shallow cumulus clouds are associated with complex variability in surface solar irradiance (SSI). Aerosol embedded in the cloud field typically has a much smaller overall radiative effect, but can significantly perturb the shape of the SSI probability density function (PDF). These perturbations have important implications for several applications that utilize SSI, but are poorly quantified and are the subject of this study. Multiple cases of shallow cumulus cloud fields with embedded aerosol are simulated at the Southern Great Plains Atmospheric Observatory using large eddy simulation (LES). The LES-derived cloud and aerosol fields are then ingested into Monte Carlo three-dimensional (3D) radiative transfer to simulate SSI. We find a variety of perturbations to the SSI PDF that depend on aerosol presence and optical properties. The processes leading to these perturbations include extinction of the direct beam that often increases from the clear-sky region toward cloud edge due to aerosol hygroscopic growth, and scattering of radiation by aerosol into cloud shadows. The ability to predict the SSI PDF in the presence of aerosol is assessed by adding three representative aerosol optical properties into an existing machine learning framework. We show that machine learning accurately predicts the SSI PDF across a wide range of conditions with negligible computational expense. Importance metrics reveal the relatively high influence of aerosol optical properties in making the predictions. These new findings highlight the important role that aerosol plays in SSI variability for highly 3D cloud-aerosol environments and provides a computationally efficient route forward for its simulation.

3D radiative transfer↗
DOE OSTI2023

Dynamic subcanopy leaf traits drive resistance of net primary production across a disturbance severity gradient

Across the globe, the forest carbon sink is increasingly vulnerable to an expanding array of low- to moderate-severity disturbances. However, some forest ecosystems exhibit functional resistance (i.e., the capacity of ecosystems to continue functioning as usual) following disturbances such as extreme weather events and insect or fungal pathogen outbreaks. Unlike severe disturbances (e.g., stand-replacing wildfires), moderate severity disturbances do not always result in near-term declines in forest production because of the potential for compensatory growth, including enhanced subcanopy production. Community-wide shifts in subcanopy plant functional traits, prompted by disturbance-driven environmental change, may play a key mechanistic role in resisting declines in net primary production (NPP) up to thresholds of canopy loss. However, the temporal dynamics of these shifts, as well as the upper limits of disturbance for which subcanopy production can compensate, remain poorly characterized. In this study, we leverage a 4-year dataset from an experimental forest disturbance in northern Michigan to assess subcanopy community trait shifts as well as their utility in predicting ecosystem NPP resistance across a wide range of implemented disturbance severities. Through mechanical girdling of stems, we achieved a gradient of severity from 0% (i.e., control) to 45, 65, and 85% targeted gross canopy defoliation, replicated across four landscape ecosystems broadly representative of the Upper Great Lakes ecoregion. We found that three of four examined subcanopy community weighted mean (CWM) traits including leaf photosynthetic rate (p = 0.04), stomatal conductance (p = 0.07), and the red edge normalized difference vegetation index (p < 0.0001) shifted rapidly following disturbance but before widespread changes in subcanopy light environment triggered by canopy tree mortality. Surprisingly, stimulated subcanopy production fully compensated for upper canopy losses across our gradient of experimental severities, achieving complete resistance (i.e., no significant interannual differences from control) of whole ecosystem NPP even in the 85% disturbance treatment. Additionally, we identified a probable mechanistic switch from nutrient-driven to light-driven trait shifts as disturbance progressed. Our findings suggest that remotely sensed traits such as the red edge normalized difference vegetation index (reNDVI) could be particularly sensitive and robust predictors of production response to disturbance, even across compositionally diverse forests. The potential of leaf spectral indices to predict post-disturbance functional resistance is promising given the capabilities of airborne to satellite remote sensing. We conclude that dynamic functional trait shifts following disturbance can be used to predict production response across a wide range of disturbance severities.

resistance↗
DOE OSTI2021

The Atacama Cosmology Telescope: A Search for Planet 9

We use Atacama Cosmology Telescope (ACT) observations at 98 GHz (2015–2019), 150 GHz (2013–2019), and 229 GHz (2017–2019) to perform a blind shift-and-stack search for Planet 9. The search explores distances from 300 au to 2000 au and velocities up to 6farcm3 per year, depending on the distance (r). For a 5 Earth-mass Planet 9 the detection limit varies from 325 au to 625 au, depending on the sky location. For a 10 Earth-mass planet the corresponding range is 425 au to 775 au. The predicted aphelion and most likely location of the planet corresponds to the shallower end of these ranges. The search covers the whole 18,000 square degrees of the ACT survey. No significant detections are found, which is used to place limits on the millimeter-wave flux density of Planet 9 over much of its orbit. Overall we eliminate roughly 17% and 9% of the parameter space for a 5 and 10 Earth-mass Planet 9, respectively. These bounds approach those of a recent INPOP19a ephemeris-based analysis, but do not exceed it. We also provide a list of the 10 strongest candidates from the search for possible follow-up. More generally, we exclude (at 95% confidence) the presence of an unknown solar system object within our survey area brighter than 4–12 mJy (depending on position) at 150 GHz with current distance 300 au < r < 600 au and heliocentric angular velocityy <?CDATA $1\buildrel{\,\prime}\over{.} 5\,{\mathrm{yr}}^{-1}\lt v\cdot \tfrac{500\,\mathrm{au}}{r}\lt 2\buildrel{\prime\prime}\over{.} 3\,{\mathrm{yr}}^{-1}$?> 1 .' 5 yr – 1 < v · 500 au r < 2 ." 3 yr – 1 , corresponding to low-to-moderate eccentricities. These limits worsen gradually beyond 600 au, reaching 5–15 mJy by 1500 au.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems

Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation (E) and transpiration (T) would greatly enhance our understanding of the water cycle and its response to climate change related shifts in local-to-regional climate conditions and rising global levels of vapor pressure deficit (VPD). While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E. Here, we report a novel ET partitioning method using artificial neural networks (ANNs) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento-San Joaquin Delta, California, USA, as well as leaf-level T data for validation. The four wetlands vary in their vegetation make-up and structure, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R 2 across all sites. The order of variable inclusion (and importance) was: VPD > gap-filled sensible heat flux (H_gf) > air temperature (T air ) > friction velocity (u * ) > other variables. The model using VPD, H_gf, T air , and u * showed the best performance during validation with independent data and had a mean testing R 2 value of 0.853 (averaged across all sites, range from 0.728 to 0.910). In comparison to other methods, our ANN method generated T/ET partitioning results which were more consistent with CO 2 exchange data especially for more heterogeneous sites with large E contributions. Further, our method improves the understanding of T/ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T/ET partitioning in other systems, increasing our knowledge of the global water cycle and ecosystem functioning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Extended Search for the Invisible Axion with the Axion Dark Matter Experiment

This paper reports on a cavity haloscope search for dark matter axions in the galactic halo in the mass range 2.81–3.31 µeV . This search excludes the full range of axion-photon coupling values predicted in benchmark models of the invisible axion that solve the strong CP problem of quantum chromodynamics. Unprecedented sensitivity in this higher mass range is achieved by deploying an ultra low-noise Josephson parametric amplifier as the first-stage signal amplifier.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Intense infrared lasers for strong-field science

The advent of chirped-pulse amplification in the 1980s and femtosecond Ti:sapphire lasers in the 1990s enabled transformative advances in intense laser–matter interaction physics. Whereas most of experiments have been conducted in the limited near-infrared range of 0.8–1 μm, theories predict that many physical phenomena such as high harmonic generation in gases favor long laser wavelengths in terms of extending the high-energy cutoff. Significant progress has been made in developing few-cycle, carrier-envelope phase-stabilized, high-peak-power lasers in the 1.6–2 μm range that has laid the foundation for attosecond X ray sources in the water window. Even longer wavelength lasers are becoming available that are suitable to study light filamentation, high harmonic generation, and laser–plasma interaction in the relativistic regime. Long-wavelength lasers are suitable for sub-bandgap strong-field excitation of a wide range of solid materials, including semiconductors. In the strong-field limit, bulk crystals also produce high-order harmonics. In this review, we first introduce several important wavelength scaling laws in strong-field physics, then describe recent breakthroughs in short- (1.4–3 μm), mid- (3–8 μm), and long-wave (8–15 μm) infrared laser technology, and finally provide examples of strong-field applications of these novel lasers. Furthermore, some of the broadband ultrafast infrared lasers will have profound effects on medicine, environmental protection, and national defense, because their wavelengths cover the water absorption band, the molecular fingerprint region, as well as the atmospheric infrared transparent window.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2025

Lattice Structure and Dynamics of Sparse Molecular Crystals: OsO 4 and RuO 4

OsO 4 and RuO 4 are molecular oxides with unique tetrameric structures and rare +8 oxidation states. Accurately modeling their properties remains challenging for density functional theory (DFT) due to weak intertetramer interactions, which standard functionals fail to capture. Here, in this work, we show that the van der Waals (vdW)-corrected density functional (vdW-DF-optB86b) provides structural parameters that are much closer to experimental values than the standard generalized gradient approximation, with volume predictions that fall within the experimentally observed range. Phonon band structure analysis shows that the inclusion of vdW interactions stabilizes soft phonon modes, highlighting the importance of dispersion corrections for accurate predictions of lattice dynamics. Experimental measurements of the phonon density of states for OsO 4 , obtained via inelastic neutron scattering, demonstrate good agreement with our vdW-DF-optB86b calculations. These results validate OsO 4 and RuO 4 as valuable benchmarks for structural and vibrational calculations via vdW-corrected DFT methods and offer insights for studying the broader class of sparse molecular materials.

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

Experimental study for critical heat flux in 2x2 rod bundles at high pressure conditions

Critical heat flux (CHF) phenomena has been a topic of study for over fifty years yet remains a serious concern across industries and especially in water-cooled nuclear reactor design. CHF data in the high-pressure range is limited, and the effects of bundle geometry and non-uniform heat flux profile are challenging to quantify. In this study, CHF experiments were performed in upward flowing water in a 2 × 2 rod bundle with a cosine profile heat flux. Data were collected between 16.5 and 18 MPa at two mass flux conditions with three inlet subcooling conditions. Under these conditions, average CHF was shown to decrease with increasing pressure. However, pressure had less of an effect than decreasing the inlet subcooling or the mass flux, both of which reduce the CHF value considerably. Of interest, correlations that have been developed for lower pressure continued to predict CHF occurrences with moderate accuracy outside their ranges of validity. Nearly all predicted values were within 20% of experimental values.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Testing in Support of the Development of EPP Plus SMT Design Method at ORNL (FY2021)

Experiments in support of the development of the integrated Elastic–Perfectly Plastic (EPP) plus Simplified Model Test (SMT) design methodology, referred to as the EPP+SMT method, continued in FY21. This report focuses on the methodology for developing the EPP+SMT creep-fatigue (CF) design curves at low strain ranges. The creep damage-based method and dissipated work-based method were used to evaluate the available CF data at the low strain range region. A set of failure criteria were determined, and a simple extrapolation method was developed to predict the CF life cycles at low strain ranges that are not accessible by experiments due to the extraordinarily long failure times at the low strain region (thousands to hundreds of thousands of years) and the inability of the test machine to control these small strain ranges due to the signal to noise issues. An experimental method with the concept of block-strain range CF testing was proposed to generate the information needed to extrapolate the CF design curves to low strain ranges. Based on this new testing approach, a preliminary EPP+SMT CF design curve was developed for Alloy 617 at 950°C with tension hold time of 100 s. The analysis in this report shows the potential of generating a set of EPP+SMT CF design curves with different hold times within a reasonable amount of time and testing effort. Based on such a progress, a hold time extrapolation procedure for low strain ranges will be developed in FY22 and critical testing will be carried out to complete the development of the EPP+SMT CF design curves for Alloy 617.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗
DOE OSTI2023

Prediction of the structures and heats of formation of MO 2 , MO 3 , and M 2 O 5 for M = V, Nb, Ta, Pa

Structures for the mono-, di-, and tri-bridge isomers of M 2 O 5 as well as those for the MO 2 and MO 3 fragments for M = V, Nb, Ta, and Pa were optimized at the density functional theory (DFT) level. Single point CCSD(T) calculations extrapolated to the complete basis set (CBS) limit at the DFT geometries were used to predict the energetics. Here, the lowest energy dimer isomer was the di-bridge for M = V and Nb and the tri-bridge for M = Ta and Pa. The di-bridge isomers were predicted to be composed of MO 2 + and MO 3 - fragments, whereas the mono- and tri-bridge are two MO 2 + fragments linked by an O 2 - . The heats of formation of M 2 O 5 dimers, as well as MO 2 and MO 3 neutral and ionic species were predicted using the Feller–Peterson–Dixon (FPD) approach. The heats of formation of the MF 5 species were calculated to provide additional benchmarks. Dimerization energies to form the M 2 O 5 dimers are predicted to become more negative going down group 5 and range from -29 to -45 kcal mol -1 . The ionization energies (IEs) for VO 2 and TaO 2 are essentially the same at 8.75 eV whereas the IEs for NbO 2 and PaO 2 are 8.10 and 6.25 eV, respectively. The predicted adiabatic electron affinities (AEAs) range from 3.75 eV to 4.45 eV for the MO 3 species and vertical detachment energies from 4.21 to 4.59 eV for MO 3 - . The calculated M[double bond, length as m-dash]O bond dissociation energies increase from 143 kcal mol -1 for M = V to ~170 kcal mol -1 for M = Nb and Ta to ~200 kcal mol -1 for M = Pa. The M–O bond dissociation energies are all similar ranging from 97 to 107 kcal mol -1 . Natural bond analysis provided insights into the types of chemical bonds in terms of their ionic character. Pa 2 O 5 is predicted to behave like an actinyl species dominated by the interactions of approximately linear PaO 2 + groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗