Method of fission product beta spectra measurements for predicting reactor anti-neutrino emission
Not Available
Asner, D. M.↗
SEARCH · Engineering Papers
Results for “predicted”
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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Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning
A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.
Riihimaki, Laura↗
Prediction of Structures and Atomization Energies of Coinage Metals, (M)n, n < 20: Extrapolation of Normalized Clustering Energies to Predict the Cohesive Energy
The geometries of the group 11 coinage metals (n = 2-20) were optimized to determine the lowest energy isomers for each cluster size, singlets for even numbers and doublets for odd numbers. For copper and silver, 2-D (planar) geometries were favored up to n = 6. For gold, 2D (planar) geometries were favored up to n = 13. Normalized clustering energies were plotted as a function of cluster size (n-1/3, for n = 4-20) with various DFT functionals and the CCSD(T)-F12b method and were extrapolated to predict the bulk cohesive energy. In the case of copper and silver, there is excellent agreement between the cohesive energies predicted at the CCSD(T)-F12b level of theory and the experimental values. For gold, the CCSD(T)-F12b values needed to be corrected for spin-orbit relativistic effects to obtain good agreement with experiment. Electronic properties including the HOMO-LUMO gaps for the even clusters and the spin densities for the odd clusters were calculated. The lowest gap is predicted to occur for n = 16 where the HOMO and LUMO are very similar in shape.
Persuad, Rudradatt R.↗
Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores
Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2 ) of ~0.99 and a mean squared error of ~2.9 × 10 –5 . Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Rapid Prediction of Adsorption Isotherms of a Diverse Range of Molecules in Hyper-Cross-Linked Polymers
Hyper-cross-linked polymers (HCPs) are materials with permanent porosity that have potential applications in adsorption-based chemical separations. We use molecular simulations to predict the largest set of adsorption isotherms available to date for HCPs. Specifically, we predict room-temperature isotherms for 24 distinct molecules in 9 different HCPs that vary in both the monomer composition and cross-link density. This information allows us to develop efficient approximate models that predict adsorption isotherms in HCPs based only on the Henry’s constant for adsorption, textural information about the HCP, and bulk phase properties of the adsorbing molecule. This approximate approach will be useful in considering the adsorption of diverse groups of molecules in HCPs in the future. Our data also enables us to describe the selectivity of adsorption for the 276 binary mixtures that can be formed from the 24 molecules we simulated. Furthermore, this information greatly expands the information that is available about adsorption selectivity in HCPs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
How Accurately Can Extended X-ray Absorption Spectra Be Predicted from First Principles? Implications for Modeling the Oxygen-Evolving Complex in Photosystem II
Abstract not provided
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Enhanced predictability of Eastern North Pacific Tropical cyclone activity using the ENSO Longitude Index
Past studies have indicated that El Nino-Southern Oscillation (ENSO) plays a major role in the interannual variability of Eastern Pacific hurricane activity. The primary mechanism being the eastward displacement of the warm pool during an El Nino, which carries warm water into that basin thereby creating favorable oceanic conditions. Despite this, the question of whether an accurate knowledge of ENSO enhances seasonal predictabiity of Eastern Pacific hurricanes has not been addressed specifically. In this study, we show that unlike traditional indices of ENSO, the ENSO Longitude Index (ELI) is able to predict Eastern Pacific hurricane activity at significant lead times. By capturing changes in the location of deep convection and associated thermocline processes more accurately, ELI explains the most variability in the upper-ocean heat content in the main development region of the Eastern Pacific basin compared to other ENSO indices. These results have substantial implications for operational seasonal forecasts of Eastern Pacific hurricanes.
Balaguru, Karthik↗
Can Kohn–Sham density functional theory predict accurate charge distributions for both single-reference and multi-reference molecules?
Dipole moments are the first moment of electron density and are fundamental quantities that are often available from experiments. An exchange–correlation functional that leads to an accurate representation of the charge distribution of a molecule should accurately predict the dipole moments of the molecule. It is well known that Kohn–Sham density functional theory (DFT) is more accurate for the energetics of single-reference systems than for the energetics of multi-reference ones, but there has been less study of charge distributions. Here, in this work, we benchmark 48 density functionals chosen with various combinations of ingredients, against accurate experimental data for dipole moments of 78 molecules, in particular 55 single-reference molecules and 23 multi-reference ones. We chose both organic and inorganic molecules, and within the category of inorganic molecules there are both main-group and transition-metal-containing molecules, with some of them being multi-reference. As one would expect, the multi-reference molecules are not as well described by single-reference DFT, and the functionals tested in this work do show larger mean unsigned errors (MUEs) for the 23 multi-reference molecules than the single-reference ones. Five of the 78 molecules have relatively large experimental error bars and were therefore not included in calculating the overall MUEs. For the 73 molecules not excluded, we find that three of the hybrid functionals, B97-1, PBE0, and TPSSh (each with less than or equal to 25% Hartree–Fock (HF) exchange), the range-separated hybrid functional, HSE06 (with HF exchange decreasing from 25% to 0 as interelectronic distance increases), and the hybrid functional, PW6B95 (with 28% HF exchange) are the best performing functionals with each yielding an MUE of 0.18 D. Perhaps the most significant finding of this study is that there exists great similarity among the success rate of various functionals in predicting dipole moments. In particular, of 39 functionals designed as general-purpose functionals and that do not have a global value of 100% HF exchange, the average MUE is 0.23 D, with a standard deviation of only 0.04 D. Among gradient approximations, which are especially interesting because of their speed and portability, the best overall performance is by PBE, HCTH/407, OLYP, OreLYP, and GAM, each with MUE of 0.22 D.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
High-throughput computational prediction of cost of carbon capture using mixed matrix membranes
Presented by NETL at Nanoporous Materials and Their Applications, Gordon Research Conference, 08/19
Budhathoki, Samir↗
Relativistic effects for the reaction Sg + 6 CO → Sg(CO){sub 6}: Prediction of the mean bond energy, atomization energy, and existence of the first organometallic transactinide superheavy hexacarbonyl Sg(CO){sub 6}
Our ab initio all-electron fully relativistic Dirac–Fock (DF) and nonrelativistic (NR) Hartree-Fock calculations predict the DF relativistic and NR energies for the reaction: Sg + 6 CO → Sg(CO){sub 6} as −7.39 and −6.96 eV, respectively, i.e., our calculated ground state total DF relativistic and NR energies for the reaction product Sg(CO){sub 6} are lower by 7.39 and 6.96 eV than the total DF and NR ground state energies of the reactants, viz., one Sg atom plus six CO molecules, respectively. Our calculated DF relativistic and NR atomization energies (Ae) are 65.23 and 64.82 eV, respectively, and so the contribution of relativistic effects to the Ae of ∼0.40 eV is marginal. The Sg–C and C–O optimized bond distances for the octahedral geometry as calculated in our DF (NR) calculations are 2.151 (2.318 Å) and 1.119 (1.114 Å), respectively. The BSSE correction calculated using the DIRAC code ∼14 kcal/mol. The relativistic DF and NR mean energies predicted by us are 118.8 and 111.9 kJ/mol, respectively, and the contribution of ∼7 kJ/mol due to relativistic effects to the mean energy of Sg(CO){sub 6} is negligible. Ours are the first calculations of the relativistic effects for the atomization energy, mean bond energy, and energy of the reaction for possible formation of Sg(CO){sub 6}, and both our relativistic DF and the NR treatments clearly predict for the first time the existence of hexacarbonyl of the transactinide superheavy element seaborgium Sg. In conclusion, relativistic effects are not significant for Sg(CO){sub 6}.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Predicting images for the dynamics of stellar clusters ( π-DOC ): a deep learning framework to predict mass, distance, and age of globular clusters
ABSTRACT Dynamical mass estimates of simple systems such as globular clusters (GCs) still suffer from up to a factor of 2 uncertainty. This is primarily due to the oversimplifications of standard dynamical models that often neglect the effects of the long-term evolution of GCs. Here, we introduce a new approach to measure the dynamical properties of GCs, based on the combination of a deep-learning framework and the large amount of data from direct N-body simulations. Our algorithm, π-DOC (Predicting Images for the Dynamics Of stellar Clusters) is composed of two convolutional networks, trained to learn the non-trivial transformation between an observed GC luminosity map and its associated mass distribution, age, and distance. The training set is made of V-band luminosity and mass maps constructed as mock observations from N-body simulations. The tests on π-DOC demonstrate that we can predict the mass distribution with a mean error per pixel of 27 per cent, and the age and distance with an accuracy of 1.5 Gyr and 6 kpc, respectively. In turn, we recover the shape of the mass-to-light profile and its global value with a mean error of 12 per cent, which implies that we efficiently trace mass segregation. A preliminary comparison with observations indicates that our algorithm is able to predict the dynamical properties of GCs within the limits of the training set. These encouraging results demonstrate that our deep-learning framework and its forward modelling approach can offer a rapid and adaptable tool competitive with standard dynamical models.
Chardin, Jonathan↗
Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach
Explore the source record for details and available documents.
Computer Science↗
Soil carbon cycling proxies: Understanding their critical role in predicting climate change feedbacks
The complexity of processes and interactions that drive soil C dynamics necessitate the use of proxy variables to represent soil characteristics that cannot be directly measured (correlative proxies), or that aggregate information about multiple soil characteristics into one variable (integrative proxies). These proxies have proven useful for understanding the soil C cycle, which is highly variable in both space and time, and are now being used to make predictions of the C fate and persistence under future climate scenarios. As these proxies are used at increasingly larger scales, the C pools and processes that proxies represent must be thoughtfully considered in order to minimize uncertainties in empirical understanding, as well as in model parameters and in model outcomes. The importance of these uncertainties is further amplified by the current need to make predictions of the C cycle for the non steady state environmental conditions resulting from global climate change. To clarify the appropriate uses of proxy variables, we provide specific examples of proxy variables that could improve decision making, adaptation choices, and modeling skill, while not foreclosing on – and also encouraging – continued work on their mechanistic underpinnings. We explore the use of three common soil proxies used to study soil organic matter: metabolic quotient, clay content, and physical fractionation. We also consider emerging data types, specifically genome-sequence data, and how these serve as proxies for microbial community activities. We opine that the demand for increasing mechanistic detail, and the flood of data from new imaging and genetic techniques, does not replace the value of correlative and integrative proxies--variables that are simpler, easier, or cheaper to measure. By closely examining the current knowledge gaps and broad assumptions in soil C cycling with the proxies already in use, we can develop new hypotheses and specify criteria for new and needed proxies.
Bailey, Vanessa L.↗
Predicting the Dissolution Rate of Borosilicate Glasses using QSPR analysis based on molecular dynamics simulations
Quantitative Structure Property Relationship (QSPR) analysis based on molecular dynamics (MD) simulations is a promising approach for establishing the composition-property relationships of glass and other materials with complex structures. A series of 20 borosilicate and boroaluminosilicate glasses have been modeled by using MD simulations with recently developed effective potentials. Short- and medium-range structures of these glasses were analyzed and, based on these structural information, QSPR analysis of the initial dissolution rate (r0) was made and compared with measured r0 at 90°C and pH 9 using various structural descriptors such as percentage of bridging oxygen species, network connectivity and average ring size. The structural descriptors, Fnet, containing energetic information such as single bond strength and other structural information were also used. It was found that overall network connectivity, average ring size and Fnet give reasonable predictions of the r0 of studied glasses, given the conditions that the glasses are homogeneous and dissolve congruently. Modifying glass compositions to account preferential release of modifiers gives a better prediction for incongruently dissolving glasses. The results were compared with our recent work of predicting glass dissolution behavior from compositions using the topological-constraints-based models.
Du, Jincheng↗
Fukushima Daiichi Unit 1 ex-vessel prediction: Core melt spreading
Lower head failure and corium-concrete interaction were predicted to occur at Fukushima Daiichi Unit 1 (1F1) by several different system-level code analyses, including MELCOR v2.1 and MAAP5. Although these codes capture a wide range of accident phenomena, they do not contain detailed models for ex-vessel core melt behavior. However, specialized codes exist for analysis of ex-vessel melt spreading (e.g., MELTSPREAD) and long-term debris coolability (e.g., CORQUENCH). On this basis, an analysis has been carried out to further evaluate ex-vessel behavior for 1F1 using MELTSPREAD and CORQUENCH. Best-estimate melt pour conditions predicted by MELCOR v2.1 and MAAP5 were used as input. MELTSPREAD was then used to predict the spatially-dependent melt conditions and extent of spreading during relocation from the vessel. Lastly, this information was then used as input for the long-term debris coolability analysis with CORQUENCH that is reported in a companion paper.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A Modular System for Increasing Predictiveness for Extreme Climate Predictions
We know that climate change is poised to reshape our world, but we lack clear enough predictions about precisely how. The preponderance of these changes is associated with human activity, specifically the emission of CO 2 and other greenhouse gases. Problematically, projections of climate change continue to be marred by unacceptably large uncertainties which hamper informed decision-making and cost society a chance to adapt proactively and effectively. These uncertainties stem from deficiencies in predictions of future greenhouse gas emissions, but also from inaccuracies in the representation of the physical models used to predict the climate response to such emissions. The uncertainties in projections associated with the inaccurate representation of climate physics, chemistry and biology are similar to those that plagued the first global climate models developed fifty years ago, despite more than a factor 10 8 increase in computer performance. Our transformational question is then, how can the accuracy of climate projections be dramatically improved by applying recent advances in the computational and data sciences to train the models with the wealth of data being constantly collected about the ongoing changes in the climate system?
54 ENVIRONMENTAL SCIENCES↗
Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components: Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction
The long-term operation (LTO) of nuclear power plants (NPP) beyond their original design life of 40 years can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition, the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Requiring frequent NDE inspections may lead to frequent NPP shutdowns which can lead to power outages and additional NDE inspection cost-related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing a more digital approach such as using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. The DT framework is based on limited experimental data, Artificial-intelligence (AI)-Machine-Learning (ML) and multiphysics-computationalmechanics such as finite element- (FE) based models. Given the real-time thermal-hydraulic process measurements from several existing plant sensors, the overall goal of the DT framework is to predict the cumulative usages factors or equivalent fatigue lives in real time and at any random 3D location of the components. This includes inaccessible locations such as inside the thickness or location of a component. This prediction can be at thousands to millions of 3D point clouds or locations like conventional FE-based models, but without running an FE model in real time.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗