Analytic prediction of baryonic effects from the EFT of large scale structures
We develop a method to describe analytically the effect of star formation physics on the clustering of galaxies at long distances.
79 ASTRONOMY AND ASTROPHYSICS↗
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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Gamma rays from dark matter subhalos revisited: Refining the predictions and constraints
Utilizing data from the ELVIS and Via Lactea-II simulations, we characterize the local dark matter subhalo population, and use this information to refine the predictions for the gamma-ray fluxes arising from annihilating dark matter in this class of objects. We find that the shapes of nearby subhalos are significantly altered by tidal effects, and are generally not well described by NFW density profiles, instead prefering power-law profiles with an exponential cutoff. From the subhalo candidates detected by the Fermi Gamma-Ray Space Telescope, we place limits on the dark matter annihilation cross section that are only modestly weaker than those based on observations of dwarf galaxies. Furthermore, we also calculate the fraction of observable subhalos that are predicted to be spatially extended at a level potentially discernible to Fermi.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits
Abstract The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein–protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.
Genetics & Heredity↗
Electronic and magnetic properties of T i 4 O 7 predicted by self-interaction-corrected density functional theory
Understanding electronic properties of substoichiometric phases of titanium oxide such as Magneli phase Ti 4 O 7 is crucial in designing and modeling resistive switching devices. Here we present our study on Magneli phase Ti 4 O 7 together with rutile TiO 2 and Ti 2 O 3 using density functional theory methods with atomic-orbital-based self-interaction correction (ASIC). We predict a new antiferromagnetic (AF) ground state in the low temperature (LT) phase, and we explain energy difference with a competing AF state using a Heisenberg model. The predicted energy ordering of these states in the LT phase is calculated to be robust in a wide range of modeled isotropic strain. We have also investigated the dependence of the electronic structures of the Ti-O phases on stoichiometry. The splitting of titanium t 2g orbitals is enhanced with increasing oxygen deficiency as Ti-O is reduced. Furthermore, the electronic properties of all these phases can be reasonably well described by applying ASIC with a "standard" value for transition metal oxides of the empirical parameter alpha of 0.5 representing the magnitude of the applied self-interaction correction.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control
Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Arbuscular mycorrhizal fungi as mediators of ecosystem responses to nitrogen deposition: A trait-based predictive framework
Not provided.
Environmental Sciences & Ecology↗
Viscosities and working region predictions for bismuth aluminoborosilicate glasses
Bismuth aluminoborosilicate glasses were prepared using a standard melt-quench technique, and the viscosity and thermal properties were measured and analyzed. Electron microprobe analysis was used to examine glass homogeneity. Fragility was calculated based on Mauro–Yue–Ellison–Gupta–Allan model fits to the viscosity curves. The system is characterized by an open structure with high fragility (44-59). Based on microanalysis and differential scanning calorimetry results, the addition of network intermediate Al2O3 appears to mitigate both phase separation and crystallization. From thermal property data, mixture models were created to predict the glass transition temperatures and softening temperatures as functions of composition. Based on K-fold validation findings, the first-order mixture model predictions of glass transition and softening appear to approximate actual values better than models with any additional second-order terms.
Gardner, Levi↗
HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling
We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024
36 MATERIALS SCIENCE↗
HydraGNN_OPF_GFM_2026 - Ensemble of predictive graph foundation models for power grid applications
This dataset supports research on graph foundation models for optimal power flow (OPF) on electric grids using HydraGNN. It contains heterogeneous graph representations of PGLib-OPF cases spanning systems from 14 to 13,659 buses, together with packed HDF5 datasets for pretraining, feasibility classification, and N-1 contingency analysis. The release includes OPF solution data, downstream fine-tuning datasets, pretrained HeteroSAGE and HeteroHEAT model checkpoints, hyperparameter-optimization summaries across multiple heterogeneous GNN architectures, and aggregated fine-tuning results for sample-efficiency studies. The dataset is designed to enable scalable training, evaluation, and transfer-learning studies for OPF surrogate modeling, including node-level AC-OPF solution prediction, graph-level prediction, feasibility classification, operating-condition generalization, and contingency-response tasks.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa
We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ordinary differential equation (ODE) based optimization approach based on Marcelin’s 1910 mass action equation is used to obtain the maximum entropy distribution; (2) the predicted metabolite concentrations are compared to those generally expected from experiments using a loss function from which post-translational regulation of enzymes is inferred; (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrations and reaction fluxes; and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1,6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.
Cannon, William R.↗
Synchrotron Emission from Dark Matter Annihilation: Predictions for Constraints from Non-detections of Galaxy Clusters with New Radio Surveys
The annihilation of dark matter particles is expected to yield a broad radiation spectrum via the production of Standard Model particles in astrophysical environments. In particular, electrons and positrons from dark matter annihilation produce synchrotron radiation in the presence of magnetic fields. Galaxy clusters are the most massive collapsed structures in the universe, and are known to host ∼μG-scale magnetic fields. They are therefore ideal targets to search for, or to constrain the synchrotron signal from dark matter annihilation. In this work, we use the expected sensitivities of several planned surveys from the next generation of radio telescopes to predict the constraints on dark matter annihilation models which will be achieved in the case of non-detections of diffuse radio emission from galaxy clusters. Specifically, we consider the Tier 1 survey planned for the Low Frequency Array (LOFAR) at 120 MHz, the Evolutionary Map of the Universe (EMU) survey planned for the Australian Square Kilometre Array Pathfinder (ASKAP) at 1.4 GHz, and planned surveys for Aperture Tile in Focus (APERTIF) at 1.4 GHz. We find that, for massive clusters and dark matter masses ≲100 GeV, the predicted limits on the annihilation cross section would rule out vanilla thermal relic models for even the shallow LOFAR Tier 1, ASKAP, and APERTIF surveys.
79 ASTRONOMY AND ASTROPHYSICS↗
Shuttle program. Solar activity prediction of sunspot numbers, predicted solar radio flux
A solar activity prediction technique for monthly mean sunspot numbers over a period of approximately ten years from February 1979 to January 1989 is presented. This includes the predicted maximum epoch of solar cycle 21, approximately January 1980, and the predicted minimum epoch of solar cycle 22, approximately March 1987. Additionally, the solar radio flux 10.7 centimeter smooth values are included for the same time frame using a smooth 13 month empirical relationship. The incentive for predicting solar activity values is the requirement of solar flux data as input to upper atmosphere density models utilized in mission planning satellite orbital lifetime studies.
Johnson, G. G.↗
Statistical prediction of dynamic distortion of inlet flow using minimum dynamic measurement. An application to the Melick statistical method and inlet flow dynamic distortion prediction without RMS measurements
The Melick method of inlet flow dynamic distortion prediction by statistical means is outlined. A hypothetic vortex model is used as the basis for the mathematical formulations. The main variables are identified by matching the theoretical total pressure rms ratio with the measured total pressure rms ratio. Data comparisons, using the HiMAT inlet test data set, indicate satisfactory prediction of the dynamic peak distortion for cases with boundary layer control device vortex generators. A method for the dynamic probe selection was developed. Validity of the probe selection criteria is demonstrated by comparing the reduced-probe predictions with the 40-probe predictions. It is indicated that the the number of dynamic probes can be reduced to as few as two and still retain good accuracy.
Schweikhard, W. G.↗
A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. 1: Theoretical development and application to yearly predictions for selected cities in the United States
A rain attenuation prediction model is described for use in calculating satellite communication link availability for any specific location in the world that is characterized by an extended record of rainfall. Such a formalism is necessary for the accurate assessment of such availability predictions in the case of the small user-terminal concept of the Advanced Communication Technology Satellite (ACTS) Project. The model employs the theory of extreme value statistics to generate the necessary statistical rainrate parameters from rain data in the form compiled by the National Weather Service. These location dependent rain statistics are then applied to a rain attenuation model to obtain a yearly prediction of the occurrence of attenuation on any satellite link at that location. The predictions of this model are compared to those of the Crane Two-Component Rain Model and some empirical data and found to be very good. The model is then used to calculate rain attenuation statistics at 59 locations in the United States (including Alaska and Hawaii) for the 20 GHz downlinks and 30 GHz uplinks of the proposed ACTS system. The flexibility of this modeling formalism is such that it allows a complete and unified treatment of the temporal aspects of rain attenuation that leads to the design of an optimum stochastic power control algorithm, the purpose of which is to efficiently counter such rain fades on a satellite link.
Manning, Robert M.↗
Aircraft noise prediction program theoretical manual: Rotorcraft System Noise Prediction System (ROTONET), part 4
This document describes the theoretical methods used in the rotorcraft noise prediction system (ROTONET), which is a part of the NASA Aircraft Noise Prediction Program (ANOPP). The ANOPP code consists of an executive, database manager, and prediction modules for jet engine, propeller, and rotor noise. The ROTONET subsystem contains modules for the prediction of rotor airloads and performance with momentum theory and prescribed wake aerodynamics, rotor tone noise with compact chordwise and full-surface solutions to the Ffowcs-Williams-Hawkings equations, semiempirical airfoil broadband noise, and turbulence ingestion broadband noise. Flight dynamics, atmosphere propagation, and noise metric calculations are covered in NASA TM-83199, Parts 1, 2, and 3.
Weir, Donald S.↗
Aircraft Noise Prediction Program (ANOPP) Fan Noise Prediction for Small Engines
The Fan Noise Module of ANOPP is used to predict the broadband noise and pure tones for axial flow compressors or fans. The module, based on the method developed by M. F. Heidmann, uses empirical functions to predict fan noise spectra as a function of frequency and polar directivity. Previous studies have determined the need to modify the module to better correlate measurements of fan noise from engines in the 3000- to 6000-pound thrust class. Additional measurements made by AlliedSignal have confirmed the need to revise the ANOPP fan noise method for smaller engines. This report describes the revisions to the fan noise method which have been verified with measured data from three separate AlliedSignal fan engines. Comparisons of the revised prediction show a significant improvement in overall and spectral noise predictions.
Hough, Joe W.↗
Comparison of NTF Experimental Data with CFD Predictions from the Third AIAA CFD Drag Prediction Workshop
Recently acquired experimental data for the DLR-F6 wing-body transonic transport con figuration from the National Transonic Facility (NTF) are compared with the database of computational fluid dynamics (CFD) predictions generated for the Third AIAA CFD Drag Prediction Workshop (DPW-III). The NTF data were collected after the DPW-III, which was conducted with blind test cases. These data include both absolute drag levels and increments associated with this wing-body geometry. The baseline DLR-F6 wing-body geometry is also augmented with a side-of-body fairing which eliminates the flow separation in this juncture region. A comparison between computed and experimentally observed sizes of the side-of-body flow-separation bubble is included. The CFD results for the drag polars and separation bubble sizes are computed on grids which represent current engineering best practices for drag predictions. In addition to these data, a more rigorous attempt to predict absolute drag at the design point is provided. Here, a series of three grid densities are utilized to establish an asymptotic trend of computed drag with respect to grid convergence. This trend is then extrapolated to estimate a grid-converged absolute drag level.
Vassberg, John C.↗
Predicting Drug Effects from High-dimensional Asymmetric Drug Data Sets using Graph Neural Networks: A Comprehensive Analysis of Multi-target Drug Effect Prediction
Graph neural networks (GNNs) have emerged as one of the most effective Machine learning (ML) techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using data sets that contain high dimensional asymmetrically co-occurrent drug effects as targets with complex correlations between them. Training individual learning models for each drug effect and incorporating every prediction result for a wide spectrum of drug effects is beyond practicality. Such an implication provides a testbed to address this challenge as multi-target prediction problems, aiming to predict all drug effects at a time. We develop standard and hybrid graph neural networks (GNNs)to perform two separate tasks that are multi-regression for continuous values and multi-label classification for categorical values contained in our data sets. Since this step makes the target data even more sparse and introduces asymmetric label co-occurrence, the learning of multi-label classification models becomes difficult and heavily impacts the GNN's performance. To address these challenges, we propose a new data oversampling technique to improve multi-label classification performances on all the given imbalanced molecular graph data sets. Using the technique, we improve the data imbalance ratio of the drug effects better than before while protecting the data set's integrity. Finally, we evaluate multi-label classification performance using the best-performant hybrid GNN model on all the oversampled data sets obtained from the proposed oversampling technique. These results outperform those of other ML models including GNN models when they are trained on the original data sets or oversampled data sets using MLSMOTE (a well-known oversampling technique) in all evaluation metrics precision, recall, and F1 score by a significant margin.
Bose, Avishek [ORNL]↗