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The (wind) turbine-models GitHub repo hosts an archive/database of turbine models already in the public domain. This includes mainly tabular power and thrust (when available) curve data in .csv files, other technical data, as well as documentation and references built with Sphinx/GitHub Pages. There is a script called "curve_parser.py" to assist with the parsing of power and thrust curves, but this repo is mostly a central location for turbine model data.
The reaction rate of gas-solid non-catalytic reactions is typically investigated using reactant conversion data over time and ex-situ measurements of the porous solid reactant textural properties; these data enable the experimental estimation of the initial intrinsic reaction rate, whereas the evolution of the textural properties and the reaction rate over time are evaluated theoretically using reaction models. In this work, a different methodology is presented, based on: a) in-situ time-resolved USAXS-SAXS-WAXS measurements of the solid sample, and b) a multi-region modeling approach; this methodology allows for the estimation of the textural properties and the intrinsic reaction rate at any time during the reaction. Referring to a reaction in which a solid product is obtained from a solid reactant and a gaseous reactant, the porous particle is described as consisting of several distinct regions with different microstructural properties, and both a gas-solid reaction model and a SAXS model are derived and applied to the carbonation of porous CaO. The proposed reaction model highlights the role of the inaccessible reactant and accurately predicts the solid reactant conversion versus time profile. The SAXS model accurately predicts the measured linear trends of the Porod invariant and of the pre-factor of the power law scattering profile versus CaO mass fraction; in the absence of chemical reactions, the proposed equations extend classical SAXS theory to porous materials containing macropores and nonporous solid phases in addition to standard nanoscale inhomogeneous regions.
In this project, the code for the fast-running Freitas wildfire plume model was successfully ported into the NARAC system, creating an operations-ready capability for predicting the transport of hazardous smoke from large wildfires. “Porting” refers to copying a section of code from one software package to another while accounting for unique dependencies in each software package. In this case, the code was ported from the Weather Research and Forecasting (WRF) Model, which is used extensively by the project team. To verify correct implementation, model results from the updated NARAC system were compared to those from WRF and the High-Resolution Rapid Refresh (HRRR) forecast model, which also employs the Freitas scheme. The NARAC User Interface (UI) and code documentation were also updated to include the new wildfire plume option, and a wildfire case was added to the NARAC test suite. The code was used in a real-time, semi-operational setup to predict smoke transport during the Cerro Pelado fire near Los Alamos National Laboratory in May 2022. The Freitas plume model was also tested for LLNL climate applications. Although the code was not ported into the Energy Exascale Earth System Model (E3SM), it was tested offline using E3SM atmospheric inputs, and results were compared to existing high-resolution plume predictions from large-eddy simulations in WRF. The code implementation and testing completed during this project will enable future work predicting smoke transport from large wildfires in LLNL national security and climate mission spaces.
Due to a lack of observations, it is a big challenge to quantify the momentum flux transported from the troposphere into the lower stratosphere by gravity waves generated by convection. This limits our understanding of the dynamics of general circulation in the stratosphere and the realism of simulations of the Quasi Biennial Oscillation. In the past decade, some general circulation models have linked the momentum flux to the latent heating from subgrid scale convective precipitation with some success. However, there is still a large uncertainty in the sources of subgrid scale convection in these models. This study applies the links between the momentum flux and convective latent heating directly to the convective precipitation derived from 16 years of TRMM precipitation radar observations in the tropics and subtropics. The total and directional momentum flux at 100 hPa are derived at individual convective pixels by using the TRMM Spectrum Latent Heating product and large-scale wind profiles from the ERA-Interim dataset. For the first time, we are able to estimate the geographical distribution of momentum flux at 100 hPa in tropics and subtropics from the observed convective sources. The diurnal, seasonal, and interannual variations of the derived momentum flux are presented and discussed. These results could provide a reference to validate the dynamic coupling between the tropical troposphere and the stratosphere in the general circulation models.
Abstract Thermoelastic properties of mantle candidate minerals are essential to our understanding of geophysical phenomena, geochemistry, and geodynamic evolutions of the silicate Earth. However, the lower-mantle mineralogy remains much debated due to the lack of single-crystal elastic moduli (Cij) and aggregate sound velocities of (Al,Fe)-bearing bridgmanite, the most abundant mineral of the planet, at the lower mantle pressure-temperature (P-T) conditions. Here we report single-crystal Cij of (Al,Fe)-bearing bridgmanite, Mg0.88Fe0.1Al0.14Si0.90O3 (Fe10-Al14-Bgm) with Fe3+/ΣFe = ~0.65, up to ~82 GPa using X-ray diffraction (XRD), Brillouin light scattering (BLS), and impulsive stimulated light scattering (ISLS) measurements in diamond-anvil cells (DACs). Two crystal platelets with orientations of (–0.50, 0.05, –0.86) and (0.65, –0.59, 0.48), that are sensitive to deriving all nine Cij, are used for compressional and shear wave velocity (νP and νS) measurements as a function of azimuthal angles over 200° at each experimental pressure. Our results show that all Cij of singe-crystal Fe10-Al14-Bgm increase monotonically with pressure with small uncertainties of 1–2% (±1σ), except C55 and C23, which have uncertainties of 3–4%. Using the third-order Eulerian finite-strain equations to model the elasticity data yields the aggregate adiabatic bulk and shear moduli and respective pressure derivatives at the reference pressure of 25 GPa: KS = 326 ± 4 GPa, µ = 211 ± 2 GPa, KS′ = 3.32 ± 0.04, and µ′ = 1.66 ± 0.02 GPa. The high-pressure aggregate νS and νP of Fe10-Al14-Bgm are 2.6–3.5% and 3.1–4.7% lower than those of MgSiO3 bridgmanite end-member, respectively. These data are used with literature reports on bridgmanite with different Fe and Al contents to quantitatively evaluate pressure and compositional effects on their elastic properties. Comparing with one-dimensional seismic profiles, our modeled velocity profiles of major lower-mantle mineral assemblages at relevant P-T suggest that the lower mantle could likely consist of about 89 vol% (Al,Fe)-bearing bridgmanite. After considering uncertainties, our best-fit model is still indistinguishable from pyrolitic or chondritic models.
Abstract The long-lived radiotoxicity of the high-level radioactive waste generated by nuclear power plants requires safe isolation from the biosphere for many hundreds of thousands of years. An international consensus has emerged that such isolation can best be provided by disposal in mined geologic repositories, a strategy that today is pursued by most countries dealing with radioactive waste. However, the need to predict the performance of such repositories over very long time periods generates large uncertainties that have to be accounted for in safety assessments. The findings from such safety assessments need to be conveyed to all stakeholders in a clear way, such that public confidence in geologic disposal solutions can be achieved. It is suggested here that close international collaboration on the technical aspects of geologic waste disposal has helped, and will continue to help, building trust and increasing confidence. This paper discusses a particular international collaboration initiative referred to as DECOVALEX, which brings together multiple teams and disciplines to collectively tackle complex experimental and modeling challenges related to geologic disposal. By describing how DECOVALEX works and by providing joint research examples, a case is made that such international collaboration contributes to knowledge transfer and confidence building in radioactive waste disposal science.
Laser-Induced Breakdown Spectroscopy or LIBS is a rapid, in-situ analytical technique where a laser of known energy is pulsed at the surface of an analyte. The laser pulse rapidly heats a localized area to many thousand degrees Kelvin, ablating part of the analyte and turning it into a plasma. As the plasma cools, excited atoms return to a ground state with known emission energies. This emitted energy is captured by various spectrometers and provides a spectrum of the emitted energies and intensities. This spectrum can be analyzed to provide the elemental composition of a sample by using known emission lines and relative abundance. ElementLIBS was developed for the SciAps hand-held LIBS model Z300 but will work with any model that provides a LIBS spectrum of similar resolution. The Z300 has an integrated resolution of 1/30 nm with a range of approximately 180nm – 960nm, providing an output spectrum of 23431 pixels across three spectrometers. These criteria are only provided as a reference, as the software was designed to work with any size spectrum, provided the input file is of the correct format and the models were developed using the same framework. If spectra of varying dimensions are used, the software will fail without warning and unusual events could occur.
Improving the design of hydrogen evolution reaction (HER) catalysts requires a detailed understanding of the catalyst's structure and environment. Here wwe have used multifrequency electron paramagnetic resonance (EPR) spectroscopy (X-band, Q-band, and D-band) in combination with density functional theory (DFT) modeling to elucidate the electronic structure of three Co(II) molecular catalysts for H 2 evolution to determine correlations of electronic structure parameters with catalytic activity and stability. The Co(II) coordination complexes investigated here are representative of two types of H 2 -evolving molecular catalysts: cobaloximes, formed from cobalt coordination to two glyoxime ligands, and cobalt poly(pyridyl) complexes, which commonly have either four or five pyridyl ligands coordinating a single Co(II) ion. These Co(II) complexes were studied in a variety of solvents with a range of polarities and stoichiometric amounts of potential ligands to the cobalt ion to assess the strength and nature of ligand- cobalt interactions. Using magnetic resonance parameters obtained by EPR spectroscopy as reference data, we reconstructed the electronic structure of the Co(II) complexes with the help of DFT modeling. For all three catalysts, we uncovered a linear correlation between the Co(II) A z value and the g x value that is dependent on the strength of the axial ligands. The correlations that we have established between the electronic structure and metal coordination environment provide insight into the structural and electronic factors underpinning the observed trends in HER catalysis activity.
Savannah River Site (SRS) stores plutonium materials within model 9975 shipping packages in the K-Area Complex (KAC). The 9975 shipping package consists of a 35 gallon stainless steel drum, Celotex fiberboard insulation, lead shield, and primary and secondary containment vessels. The 9975 shipping package design, performance, and analysis for safe transport of radioactive material are described in the Safety Analysis Report for Packaging (SARP). Celotex fiberboard provides three safety functions: thermal insulation to limit internal temperature during a fire, criticality control, and resistance to package crushing. The fiberboard material must retain its dimensions and density within certain ranges to provide the required impact resistance, criticality control, and fire resistance. The SRS Surveillance Program monitors material performance to establish a basis for service life and ensures the continued integrity of 9975 packages. Fiberboard samples, taken from multiple fiberboard assemblies fabricated from cane and softwood fiberboard, have been conditioning in elevated temperature environments since 2005. The samples are periodically examined to monitor thermal, mechanical, and physical properties, and assess degradation trends. Fiberboard properties of interest that are evaluated to demonstrate acceptable long-term performance include dimensional stability, density, compressive strength, thermal conductivity, and specific heat capacity. Duplicate samples from multiple package sources have been tested to identify the range of variability in fiberboard properties and degradation rates. Baseline and long-term testing of fiberboard material properties have been reported previously; reference 6 summarized experimental results of cane and softwood fiberboard through May 2019 and presented degradation models for the measured properties. This report presents the cumulative data collected through June 2020 and the corresponding updated aging models.
The cycle-to-cycle variation in the knock intensity is commonly encountered under abnormal combustion conditions. The severity of these abnormal combustion events can vary significantly, and the efficiency of engines at high loads is limited in practice by heavy knocking phenomena. Since, a thorough analysis of such recurrent but non-cyclic phenomena via experiments alone becomes highly cumbersome, in the present work, a multi-cycle large-eddy simulation study was performed to quantitatively predict cyclic variability in the combustion process and cyclic knock intensity variability in a direct injection spark-ignition engine. To account for the turbulence-chemistry interaction effects on flame propagation, the G-equation combustion model was used. Detailed chemistry was solved outside the flame front with a toluene primary reference fuel skeletal kinetic mechanism. For both the mild knock and heavy knock conditions, the numerical results were validated against experimental measurements. Based on the simulation results, a correlation analysis was performed considering combustion phasing, peak cylinder pressure and maximum amplitude of pressure oscillation. Furthermore, a detailed three-dimensional spatial analysis illustrated the evolution of auto-ignition kernel development and propagation of pressure waves during knocking combustion for three typical cycles with different knock intensities. In this process, it was found that an early occurrence of auto-ignition in the end gas was prone to high knock intensity. Although multiple auto-ignition kernels were observed in different cycles, the degree of coupling between chemical heat release and pressure waves varied, thereby leading to different maximum amplitude of pressure oscillation values.
In this article, bubble point pressures of three binary mixtures at two compositions each have been measured utilizing a static method. The mixtures studied were difluoromethane + 2,3,3,3-tetrafluoroprop-1-ene (R-32/1234yf), difluoromethane + (E)-1,3,3,3-tetrafluoroprop-1-ene (R-32/1234ze(E)), and 1,1-difluoroethene + 2,3,3,3-tetrafluoroprop-1-ene (R-1132a/1234yf). For all of the mixtures, the minimum temperature measured was 270 K. The maximum temperatures measured for the R-32/1234ze(E), R-32/1234yf, and R-1132a/1234yf mixtures were 360, 345, and 325 K, respectively. A total of 100 bubble point pressures are reported and compared to available literature data relative to the REFPROP database. Lemmon, E. W. [ NIST Standard Reference Database 23: Reference Fluid Thermodynamic and Transport Properties-REFPROP, 10.0; National Institute of Standards and Technology, Standard Reference Data Program: Gaithersburg, 2018]. Additionally, the bubble point pressures obtained in this study are modeled using the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state. The PC-SAFT model parameters, as well as deviations of this work and literature data from the model, are reported.
In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. With O(N 3 ) scaling with the number of electrons N, DMC has the potential to be a reference method for larger systems that are not accessible to more traditional methods such as CCSD(T). Assessing the accuracy of DMC for smaller molecules becomes the stepping stone in making the method a reference for larger systems. We show that when coupled with quantum machine learning (QML)-based surrogate methods, the computational burden can be alleviated such that quantum Monte Carlo (QMC) shows clear potential to undergird the formation of high-quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: the fixed-node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons-set-based QML (AQML) models. Numerical evidence presented includes converged DMC results for over 1000 small organic molecules with up to five heavy atoms used as amons and 50 medium-sized organic molecules with nine heavy atoms to validate the AQML predictions. Finally, numerical evidence collected for Δ-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space.
Optical circular dichroism (OCD) is a well-established technique for probing molecular chirality typically in the UV-visible window. Although molecular chirality is usually related to a chiral center, it can alternatively describe a global structure. The electronic transitions are then delocalized and OCD is said to probe the global chirality. Recent advances controlling the polarization and spatial structure of X-ray beams have led to novel spectroscopic techniques such as X-ray circular dichroism (XCD), which can exploit the localized nature of the X-ray electronic transitions. XCD is thus sensitive to local structures and the relevant chirality probed with it can be referred as local. Here, we use the racemization mechanism of the [12]Helicene as a model to assess the capabilities of OCD and XCD as probes of global and local chiralities. In conclusion, our simulations suggest that XCD is a reliable spectroscopic technique for probing the local chirality of molecules, suggesting the further development of this experimental technique.
Perovskite oxides (ternary chemical formula ABO 3 ) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula $A_xA^{'}_{1-x}B_yB^{'}_{1-y}O_3$) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed $A_xA^{'}_{1-x}B_yB^{'}_{1-y}O_3$ compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407 $A_{0.5}A^{'}_{0.5}B_{0.5}B^{'}_{0.5}O_3$ compositions were generated in the a - b + a - Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔH f ) and decomposition enthalpies (ΔH d ) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated $A_{0.5}A^{'}_{0.5}B_{0.5}B^{'}_{0.5}O_3$ perovskite structures using MP-compatible DFT calculations.
Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.
Here, microscopic calculations of neutrino-nucleus scattering cross sections are critical for the success of the neutrino-oscillation program. In addition to retaining nuclear correlations in the initial and final state of the reaction, they are based on consistent nuclear interactions and transition current operators, thereby enabling robust uncertainty quantification. In this work, we address a significant limitation of these microscopic methods, which arises from their nonrelativistic nature. By performing the calculations in a reference frame that minimizes nucleon momenta and utilizing the so-called two-fragment model, we extend the applicability of Green's function Monte Carlo calculations of neutrino-nucleus scattering to higher momenta than currently possible. To validate this approach, we compare our theoretical predictions against inclusive data measured by the MiniBooNE, T2K, and MINER ν A experiments.
Sensitivity and uncertainty analyses of spent nuclear fuel (SNF) observables - decay heat, neutron and γ-ray emission rate - to operational and model parameters have been performed. A 2D model representing a typical PWR 17x17 UO{sub 2} fuel assembly has been taken as reference. The Serpent code and ENDF/B-VII.1 evaluated nuclear data library have been used for the analyses. Relative uncertainty of decay heat, neutron and γ-ray emission rates due to Monte Carlo counting statistics, as well as the number of fuel pin radial regions required for an accurate characterization of SNF neutron emission, have been estimated. A parametric study has been carried out to investigate the impact of the homogenization to an average value of the fuel and moderator temperature, boron concentration power and burnup on the prediction of the selected observables. In addition, linear sensitivity coefficients have been calculated. (authors)
Perovskite oxides (ternary chemical formula ABO3) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, the AA'BB'O6 compositions were generated in the a-b+a- Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with DFT using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔHf) and decomposition enthalpies (ΔHd) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated AA'BB'O6 perovskite structures using MP-compatible DFT calculations