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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Cosmological structure formation in Decaying Dark Matter models

The standard cold dark matter (CDM) model predicts too many and too dense small structures. We consider an alternative model that the dark matter undergoes two-body decays with cosmological lifetime τ into only one type of massive daughters with non-relativistic recoil velocity V{sub k}. This decaying dark matter model (DDM) can suppress the structure formation below its free-streaming scale at time scale comparable to τ. Comparing with warm dark matter (WDM), DDM can better reduce the small structures while being consistent with high redshfit observations. We study the cosmological structure formation in DDM by performing self-consistent N-body simulations and point out that cosmological simulations are necessary to understand the DDM structures especially on non-linear scales. We propose empirical fitting functions for the DDM suppression of the mass function and the concentration-mass relation, which depend on the decay parameters lifetime τ, recoil velocity V{sub k} and redshift. The fitting functions lead to accurate reconstruction of the the non-linear power transfer function of DDM to CDM in the framework of halo model. Using these results, we set constraints on the DDM parameter space by demanding that DDM does not induce larger suppression than the Lyman-α constrained WDM models. We further generalize and constrain the DDM models to initial conditions with non-trivial mother fractions and show that the halo model predictions are still valid after considering a global decayed fraction. Finally, we point out that the DDM is unlikely to resolve the disagreement on cluster numbers between the Planck primary CMB prediction and the Sunyaev-Zeldovich (SZ) effect number count for τ ∼ H{sub 0}{sup −1}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Building and Running TDAAPS Models: WRF Predictions

This document serves to guide a researcher through the process of predicting atmospheric conditions in a region of interest utilizing the Weather Research and Forecasting (WRF) model. This documentation is specific to WRF and WRF Preprocessing System (WPS) version 3.8.1. WRF is an atmospheric prediction system designed for meteorological research and numerical atmospheric prediction. In WRF, simulations may be generated utilizing real data or idealized atmospheric conditions. Output from WRF serves as input into the Time-Domain Atmospheric Acoustic Propagation Suite (TDAAPS) which performs staggered-grid finite difference modeling of the acoustic velocity pressure system to produce Green's functions through these atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Generation-Storage Coordination Dispatch Strategy for Power System Based on Causal Reinforcement Learning

In the backdrop of global energy transformation, power systems integrating high proportions of renewable energy sources are facing unprecedented challenges in operational stability and dispatch efficiency. To address these challenges, this study introduces a generation-storage coordination real-time dispatch strategy based on Causal Power System Dynamic Reinforcement Learning (CPSDRL). Diverging from traditional reinforcement learning approaches, CPSDRL innovatively incorporates causal inference within the state prediction model - the crux of model-based reinforcement learning - thereby establishing the Power Causal Dynamic Model (PCDM). Assisted by the prior knowledge of power systems, the model significantly enhances prediction accuracy and reliability through a two-stage training process. Utilizing PCDM, this study further applies a direct policy search algorithm to optimize the real-time dispatch strategy. Experimental results indicate that the proposed method improves the stability of generation-storage coordination real-time dispatch and exhibits competitive advantages in sample efficiency and computational speed, compared to traditional model-based and model-free reinforcement learning algorithms. This method is expected to enhance the practicality and adaptability of causal reinforcement learning techniques in power system scheduling and control.

causal reinforcement learning↗
DOE OSTI2025

Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations

This dataset contains the code and data files needed for implementation of a Multivariate Bayesian Regression model, described in Jin et al. (2025), for the historical prediction of the chemical composition of disposed coal ash at U.S. coal fired power plants as a function of annualized coal purchase data. The integrated coal supply data file (CoalSupplyDataset.csv) represents a compilation of monthly fuel purchase records for the period 1973-2022 at major U.S. power stations. These records were obtained from the U.S. Energy Information Administration. The CSV file also contains, for each coal purchase record, the coal region of the mine as defined by the U.S. Geological Survey. Data entry errors and data gaps in the EIA records were corrected as described in Jin et al. This CSV file represents the integrated coal supply data after corrections were made. The model structure and fitting parameters are encoded in pickle file format (Bayesian.pkl). The model was developed with the coal supply data and coal ash composition data, apportioned according to the Stratified Shuffle Split for training and testing subsets. The model was built using Python and the PyMC library. Reference Publication: Jin, Z.; Huang, J.; Hower, J.C.; Hsu-Kim, H.(2025). Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites. Environmental Science & Technology.

Coal ash composition↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Multi-fidelity modeling to predict the rheological properties of fiber suspensions

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.

Boodaghidizaji, Miad↗
DOE OSTI2019

A coupled thermo‐chemo‐mechanical reduced‐order multiscale model for predicting process‐induced distortions, residual stresses, and strength

Summary We study residual stresses and part distortion induced by a manufacturing process of a polymer matrix composite and its effect on the component strength. Unlike most of the thermo‐chemo‐mechanical models in the literature where governing multiphysics equations are directly formulated on the macroscale, we present a multiscale‐multiphysics approach. To address the enormous computational complexity involved, a reduced‐order homogenization was originally developed for a single physics problem is employed. The proposed reduced‐order two‐scale thermo‐chemo‐mechanical model has been validated for predicting part distortion beam strength in three‐point bending test. It is shown that while macroscopic stresses are relatively low, and therefore often ignored in practice, stresses at the scale of microconstituents are significant and may have an effect on the overall composite component strength.

Yuan, Zifeng↗
DOE OSTI2016

SU-F-R-51: Radiomics in CT Perfusion Maps of Head and Neck Cancer

Purpose: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. Methods: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters. All patients underwent pre-treatment CTP. 315 texture parameters were computed for three perfusion maps: blood volume, blood flow and mean transit time. The variability of texture parameters was tested with respect to non-standardizable perfusion computation factors (noise level and artery contouring) using intraclass correlation coefficients (ICC). The parameter with the highest ICC in the correlated group of parameters (inter-parameter Spearman correlations) was tested for its predictive value. The final model to predict tumor control was built using multivariate Cox regression analysis with backward selection of the variables. For comparison, a predictive model based on tumor volume was created. Results: Ten parameters were found to be stable in both HNC and lung cancer regarding potentially non-standardizable factors after the correction for inter-parameter correlations. In the multivariate backward selection of the variables, blood flow entropy showed a highly significant impact on tumor control (p=0.03) with concordance index (CI) of 0.76. Blood flow entropy was significantly lower in the patient group with controlled tumors at 18 months (p<0.1). The new model showed a higher concordance index compared to the tumor volume model (CI=0.68). Conclusion: The preselection of variables in the robustness study allowed building a predictive radiomics-based model of tumor control in HNC despite a small patient cohort. This model was found to be superior to the volume-based model. The project was supported by the KFSP Tumor Oxygenation of the University of Zurich, by a grant of the Center for Clinical Research, University and University Hospital Zurich and by a research grant from Merck (Schweiz) AG.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Probing the folded state and mechanical unfolding pathways of T4 lysozyme using all-atom and coarse-grained molecular simulation

The Bacteriophage T4 Lysozyme (T4L) is a prototype modular protein comprised of an N-terminal and a C-domain domain, which was extensively studied to understand the folding/unfolding mechanism of modular proteins. To offer detailed structural and dynamic insights to the folded-state stability and the mechanical unfolding behaviors of T4L, we have performed extensive equilibrium and steered molecular dynamics simulations of both the wild-type (WT) and a circular permutation (CP) variant of T4L using all-atom and coarse-grained force fields. Our all-atom and coarse-grained simulations of the folded state have consistently found greater stability of the C-domain than the N-domain in isolation, which is in agreement with past thermostatic studies of T4L. While the all-atom simulation cannot fully explain the mechanical unfolding behaviors of the WT and the CP variant observed in an optical tweezers study, the coarse-grained simulations based on the Go model or a modified elastic network model (mENM) are in qualitative agreement with the experimental finding of greater unfolding cooperativity in the WT than the CP variant. Interestingly, the two coarse-grained models predict different structural mechanisms for the observed change in cooperativity between the WT and the CP variant—while the Go model predicts minor modification of the unfolding pathways by circular permutation (i.e., preserving the general order that the N-domain unfolds before the C-domain), the mENM predicts a dramatic change in unfolding pathways (e.g., different order of N/C-domain unfolding in the WT and the CP variant). Based on our simulations, we have analyzed the limitations of and the key differences between these models and offered testable predictions for future experiments to resolve the structural mechanism for cooperative folding/unfolding of T4L.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Production of dark-matter bound states in the early universe by three-body recombination

The small-scale structure problems of the universe can be solved by self-interacting dark matter that becomes strongly interacting at low energy. A particularly predictive model for the self-interactions is resonant short-range interactions with an S-wave scattering length that is much larger than the range. The velocity dependence of the cross section in such a model provides an excellent fit to self-interaction cross sections inferred from dark-matter halos of galaxies and clusters of galaxies if the dark-matter mass is about 19 GeV and the scattering length is about 17 fm. Such a model makes definite predictions for the few-body physics of weakly bound clusters of the dark-matter particles. The formation of the two-body bound cluster is a bottleneck for the formation of larger bound clusters. We calculate the production of two-body bound clusters by three-body recombination in the early universe under the assumption that the dark matter particles are identical bosons, which is the most favorable case. If the dark-matter mass is 19 GeV and the scattering length is 17 fm, the fraction of dark matter in the form of two-body bound clusters can increase by as much as 4 orders of magnitude when the dark-matter temperature falls below the binding energy, but its present value remains less than 10 -6 . The present fraction can be increased to as large as 10 -3 by relaxing the constraints from small-scale structure and decreasing the mass of the dark matter particle.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Mechanics and energetics modeling of ball-milled metal foil and particle structures

The reported research establishes a semi-analytical computational predictive model of fractal microstructure in ball-milled metal foils and powder particulates, with emphasis on its transformation mechanics via an energy-based approach. The evolving structure is composed of reconfigurable warped ellipsoid material domains, subjected to collisions with the ball milling impactors following Brownian motion energetics. In the first step of the model, impacts are assumed to generate ideal Hertzian elastic stress fields, with associated bulk deformations quantified as per Castigliano's strain energy methods. In the second stage of the model, elastic energies are recast to produce frictional slip and plastic yield, thus resulting in surface micro-joints. Only two parameters of the model necessitate experimental calibration, performed by comparison of joint energy with laboratory tensile measurements on ball-milled multilayer Al-Ni foils. Model predictions of evolving internal microstructure are validated against SEM micrographs of Al-Ni powder particulate samples for different ball milling durations. Results demonstrate the capability of the model to accurately capture relevant fractal measures of the microstructure of ball-milled powders.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimizing detachment control using the magnetic configuration of divertors

Abstract As tokamak research moves to reactor conditions, the control of a stable, optimally-detached divertor plasma has become increasingly relevant. Simple predictions of such detachment control have been performed previously using the detachment location sensitivity (DLS) model (Lipschultz et al 2016 Nucl. Fusion 56 056007). In this study the DLS model is extended and combined with SOLPS-ITER simulations of isolated divertor grids to study the effects of alternate divertor magnetic field properties on detachment control. The DLS model predicts that divertors can achieve easier access to detachment through a long connection length, a high total flux expansion, and a high average magnetic field in the divertor compared with that at the x -point. SOLPS-ITER simulations suggest an even stronger impact of total flux expansion and connection length on detachment access than the DLS model predicts. In terms of detachment evolution, both simulation and modelling show a high gradient in the total magnetic field and low B p o l B are able to more easily keep a detachment front at a desired poloidal location. Regions with no stable detachment front locations can arise for high magnetic field gradients directed towards the target. Significant differences have been found between impurity scan simulations and the DLS model. These differences may be attributed to sources and sinks of power and electron pressure, and should be explored further in future work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bridging Experiment and Theory to Reveal Compounds in K–Zn(Cd)–Bi Systems

This study investigates the facile hydride synthesis method guided by theoretical predictions to explore the K–T–Bi (T = Zn, Cd) phase spaces. Using an adaptive genetic algorithm (AGA) and density functional theory (DFT), candidate compositions are identified for experimental validation via a facile hydrides route, permitting experimental screening of K–Zn–Bi and “empty” K–Cd–Bi systems. The previously reported KZnBi and KZn 2 Bi 2 are synthesized alongside newly discovered KCdBi and KCd 2 Bi 2 . While the AGA and DFT predict the stability of these compounds, structural predictions align with the experiment only for KZnBi and KZn 2 Bi 2 . Single-crystal X-ray structure refinements confirm that KZnBi and KZn 2 Bi 2 adopt the hexagonal ZrBeSi- and tetragonal ThCr 2 Si 2 -structure types, respectively. KCdBi has tetragonal PbClF-structure type and KCd 2 Bi 2 belongs to the ThCr 2 Si 2 -structure type. A trend based on the ratio of the metal ionic radii allows to rationalize variation in the structure types within the ATBi family (A = Li–Cs), correctly identifying KCdBi as isostructural to NaZnBi. Thermal stability studied by high-temperature powder X-ray diffraction reveals that Zn-containing compounds melt at higher temperatures (821 K for KZn 2 Bi 2 ) than Cd-containing KCd 2 Bi 2 (635 K). This study highlights the efficacy of combining rapid synthesis techniques with predictive modeling, though structural predictions show some limitations in accuracy.

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

Search for signatures of electroweakinos with photons, jets, and large missing transverse momentum in s=13 TeV pp collisions with the ATLAS detector

A search for final states characterised by at least one isolated high transverse-momentum photon, jets and large missing transverse momentum is presented. Such a final state might occur in gauge-mediated supersymmetric models where a pair of binos and higgsinos mix to form neutralinos, one of which decays into a photon plus a gravitino while the other decays into a Higgs boson, a Z boson or a photon, plus a gravitino. The search is performed using the full Run-2 data sample of 140 fb−1 of s=13$$ \sqrt{s}=13 $$ TeV proton-proton collisions collected by the ATLAS detector at the Large Hadron Collider. No significant excess of events is observed above the Standard Model prediction and model-dependent exclusion limits at the 95% confidence level are set. These limits are interpreted in terms of the masses of gauginos, which are excluded up to 1.2 TeV depending on their branching ratios, and on their branching ratios as a function of their mass.

Aad, G↗
DOE OSTI2015

Inclusive, prompt and non-prompt J/ψ production at mid-rapidity in Pb-Pb collisions at s N N = 2.76 $\sqrt{s_{\mathrm{NN}}}=2.76$ TeV

The transverse momentum (p T ) dependence of the nuclear modification factor RAA and the centrality dependence of the average transverse momentum < p T > for inclusive J/ψ have been measured with ALICE for Pb-Pb collisions at $\sqrt{s_{_\text {NN}}}$ = 2.76 TeV in the e+e– decay channel at mid-rapidity (|y| < 0.8). The < p T > is significantly smaller than the one observed for pp collisions at the same centre-of-mass energy. Consistently, an increase of R AA is observed towards low p T . These observations might be indicative of a sizable contribution of charm quark coalescence to the J/ψ production. Additionally, the fraction of non-prompt J/ψ from beauty hadron decays, f B , has been determined in the region 1.5 < p T < 10 GeV/c in three centrality intervals. No significant centrality dependence of fB is observed. Finally, the R AA of non-prompt J/ψ is discussed and compared with model predictions. The nuclear modification in the region 4.5 < p T < 10 GeV/c is found to be stronger than predicted by most models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Search for resonant and non-resonant Higgs boson pair production in the $b\overline{b}{\tau}^{+}{\tau}^{-}$ decay channel using 13 TeV $pp$ collision data from the ATLAS detector

A search for Higgs boson pair production in events with two $b$-jets and two $τ$-leptons is presented, using a proton–proton collision dataset with an integrated luminosity of 139 fb -1 collected at $\sqrt{s}$ = 13 TeV by the ATLAS experiment at the LHC. Higgs boson pairs produced non-resonantly or in the decay of a narrow scalar resonance in the mass range from 251 to 1600 GeV are targeted. Events in which at least one $τ$-lepton decays hadronically are considered, and multivariate discriminants are used to reject the backgrounds. No significant excess of events above the expected background is observed in the non-resonant search. The largest excess in the resonant search is observed at a resonance mass of 1 TeV, with a local (global) significance of 3.1$σ$ (2.0$σ$). Observed (expected) 95% confidence-level upper limits are set on the non-resonant Higgs boson pair-production cross-section at 4.7 (3.9) times the Standard Model prediction, assuming Standard Model kinematics, and on the resonant Higgs boson pair-production cross-section at between 21 and 900 fb (12 and 840 fb), depending on the mass of the narrow scalar resonance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Search for a light charged Higgs boson decaying to $ \mathrm{ c \bar{s} } $ in pp collisions at $ \sqrt{s} =$ 8 TeV

Our search for a light charged Higgs boson, originating from the decay of a top quark and subsequently decaying into a charm quark and a strange antiquark, is presented. The data used in the analysis correspond to an integrated luminosity of 19.7 fb -1 recorded in proton-proton collisions at √s = 8 TeV by the CMS experiment at the LHC. The search is performed in the process tt - W ± bH ∓ b - , where the W boson decays to a lepton (electron or muon) and a neutrino. Furthermore, the decays lead to a final state comprising an isolated lepton, at least four jets and large missing transverse energy. No significant deviation is observed in the data with respect to the standard model predictions, and model-independent upper limits are set on the branching fraction B(t→ H + b), ranging from 1.2 to 6.5% for a charged Higgs boson with mass between 90 and 160 GeV, under the assumption that B(H + →cs - ) = 100%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Source Apportionment of Stack Emissions from Research and Development Facilities Using Positive Matrix Factorization

Emissions from research and development (R&D) facilities are difficult to characterize due to the wide variety of processes used, changing nature of research, and large number of chemicals. Positive matrix factorization (PMF) was applied to volatile organic compounds (VOCs) concentrations measured in the main exhaust stacks of four different R&D buildings to identify the number and composition of major contributing sources. PMF identified from 9-11 source-related factors contributing to the stack emissions depending on the building. The factors that were similar between buildings were major contributors to trichloroethylene (TCE), acetone, and ethanol emissions. Several other factors had similar profiles for two or more buildings but not for all four. One factor for each building was a combination of p/m-xylene, o-xylene and ethylbenzene. At least one factor for each building was identified that contained a broad mix of many species and constraints were used in PMF to modify the factors to resemble more closely the off-shift concentration profiles. PMF accepted the constraints with little decrease in model fit. Although the PMF model predicted the profiles of the off-shift samples, the percent of total emissions was under-predicted by the model versus the measured data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗