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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A better way to define dark matter haloes

ABSTRACT Dark matter haloes have long been recognized as one of the fundamental building blocks of large-scale structure formation models. Despite their importance – or perhaps because of it! – halo definitions continue to evolve towards more physically motivated criteria. Here, we propose a new definition that is physically motivated, effectively unique, and parameter-free: ‘A dark matter halo is comprised of the collection of particles orbiting in their own self-generated potential’. This definition is enabled by the fact that, even with as few as ≈300 particles per halo, nearly every particle in the vicinity of a halo can be uniquely classified as either orbiting or infalling based on its dynamical history. For brevity, we refer to haloes selected in this way as physical haloes. We demonstrate that (1) the mass function of physical haloes is Press–Schechter, provided the critical threshold for collapse is allowed to vary slowly with peak height; and (2) the peak-background split prediction of the clustering amplitude of physical haloes is statistically consistent with the simulation data, with accuracy no worse than ≈5 per cent.

Astronomy & Astrophysics↗
DOE OSTI2018

Neutron and Thermal Embrittlement of RPV Steels: An Overview

Because the reactor pressure vessel (RPV) represents the first structural line of defense against the release of radiation to the public, the design and fabrication of the RPV for any nuclear reactor facility is performed at very high standards in accordance with consensus codes that are based on mechanical and physical properties of the steels used to construct the vessel. Nuclear RPVs may weigh up to 800 tons with wall thicknesses up to approximately 330 mm and are clad on the inside with stainless-steel weld metal and given a final post-weld heat treatment. The RPV is a unique structural component in that it operates under high pressures and temperatures and is exposed to relatively high neutron radiation. Although typical RPV steels and welds have excellent fracture toughness at room temperature and above when put into service, the degrading effects of high-energy neutron irradiation can cause levels of irradiation-induced embrittlement in radiation-sensitive materials of concern for the structural integrity of the RPV. In recent decades, remarkable progress has been made in developing a mechanistic understanding of irradiation embrittlement. This progress includes developing physically based and statistically calibrated models of Charpy V-notch-indexed transition temperature shifts based on results from RPV surveillance programs complemented by significant results from comprehensive research experiments performed in test reactors. In addition, advances in elastic-plastic fracture mechanics allow for a relatively small number of relatively small specimens to characterize the fracture toughness of RPV steels with statistical confidence. This paper presents a review of the primary mechanical properties, test procedures, examples of applicable codes and standards, and specimen types used to characterize RPV steels and welds, the effects of neutron irradiation on those most relevant mechanical properties, and a brief review of the effects of thermal aging on RPV materials. The paper closes with a summary.

Nanstad, Randy K.↗
DOE OSTI2021

Toward Robust and Routine Determination of Mw for Small Earthquakes: Application to the 2020 Mw 5.7 Magna, Utah, Seismic Sequence

To better characterize seismic hazard, particularly, for induced seismicity, there is an increasing interest in methods to estimate moment magnitude (M w ) for small earthquakes. M w is generally preferred over other magnitude types, but, it is difficult to estimate M w for earthquakes with local magnitude (M L ) <3-3.5, using conventional moment tensor (MT) inversion. The 2020 M ww 5.7 Magna, Utah, seismic sequence provides an opportunity to illustrate and evaluate the value of spectral methods for this purpose. Starting with a high-quality seismic catalog of 2103 earthquakes (M L <5.6), we estimate M w using two independent spectral methods—one based on direct waves, yielding M w,direct , and the other based on coda waves, yielding M w,coda . For the direct-wave method, we present a non-parametric (NP) inversion scheme that solves for apparent geometrical spreading, G(R), and site effects (S), similar to other NP procedures that have been used to calibrate regional M L scales. The NP inversion is constrained using M ws derived from MTs for nine events in the Magna sequence. We recover statistically robust and physically reasonable G(R) and S and compute M w,direct for 635 Magna earthquakes down to M L 0.7. For the coda-wave method, we consider two separate calibration schemes involving previous MT solutions and compute M w,coda for 311 earthquakes down to M L 1.0. For 280 of the events that were processed with both methods—M w,direct and M w,coda —are strongly correlated (r = 0.98), with a mean difference of only 0.05. We compare M w,direct and M w,coda with M L and find reasonably good agreement for M L <3.6 with the theoretically predicted relationship of M w =(2/3)M L +C, in which C is a regional constant. Our results imply that seismic network operators can use spectral-based M w estimates to replace M L estimates for events with M L ≥1.0, and possibly smaller. The main requirement is the existence of a small number of MT solutions for calibration purposes.

58 GEOSCIENCES↗
DOE OSTI2014

Comparison of Fission Product Release Predictions Using PARFUME with Results from the AGR-1 Irradiation Experiment

This report documents comparisons between post-irradiation examination measurements and model predictions of silver (Ag), cesium (Cs), and strontium (Sr) release from selected tristructural isotropic (TRISO) fuel particles and compacts during the first irradiation test of the Advanced Gas Reactor program that occurred from December 2006 to November 2009 in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). The modeling was performed using the particle fuel model computer code PARFUME (PARticle FUel ModEl) developed at INL. PARFUME is an advanced gas-cooled reactor fuel performance modeling and analysis code (Miller 2009). It has been developed as an integrated mechanistic code that evaluates the thermal, mechanical, and physico-chemical behavior of fuel particles during irradiation to determine the failure probability of a population of fuel particles given the particle-to-particle statistical variations in physical dimensions and material properties that arise from the fuel fabrication process, accounting for all viable mechanisms that can lead to particle failure. The code also determines the diffusion of fission products from the fuel through the particle coating layers, and through the fuel matrix to the coolant boundary. The subsequent release of fission products is calculated at the compact level (release of fission products from the compact) but it can be assessed at the particle level by adjusting the diffusivity in the fuel matrix to very high values. Furthermore, the diffusivity of each layer can be individually set to a high value (typically 10-6 m2/s) to simulate a failed layer with no capability of fission product retention. In this study, the comparison to PIE focused on fission product release and because of the lack of failure in the irradiation, the probability of particle failure was not calculated. During the AGR-1 irradiation campaign, the fuel kernel produced and released fission products, which migrated through the successive layers of the TRISO-coated particle and potentially through the compact matrix. The release of these fission products was measured in PIE and modeled with PARFUME.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

AGR-2 safety test predictions using the PARFUME code

This report documents calculations performed to predict failure probability of TRISO-coated fuel particles and diffusion of fission products through these particles during safety tests following the second irradiation test of the Advanced Gas Reactor program (AGR-2). The calculations include the modeling of the AGR-2 irradiation that occurred from June 2010 to October 2013 in the Advanced Test Reactor (ATR) and the modeling of a safety testing phase to support safety tests planned at Oak Ridge National Laboratory and at Idaho National Laboratory (INL) for a selection of AGR-2 compacts. The heat-up of AGR-2 compacts is a critical component of the AGR-2 fuel performance evaluation, and its objectives are to identify the effect of accident test temperature, burnup, and irradiation temperature on the performance of the fuel at elevated temperature. Safety testing of compacts will be followed by detailed examinations of the fuel particles to further evaluate fission product retention and behavior of the kernel and coatings. The modeling was performed using the particle fuel model computer code PARFUME developed at INL. PARFUME is an advanced gas-cooled reactor fuel performance modeling and analysis code (Miller 2009). It has been developed as an integrated mechanistic code that evaluates the thermal, mechanical, and physico-chemical behavior of fuel particles during irradiation to determine the failure probability of a population of fuel particles given the particle-to-particle statistical variations in physical dimensions and material properties that arise from the fuel fabrication process, accounting for all viable mechanisms that can lead to particle failure. The code also determines the diffusion of fission products from the fuel through the particle coating layers, and through the fuel matrix to the coolant boundary. The subsequent release of fission products is calculated at the compact level (release of fission products from the compact). PARFUME calculates the release fraction as the ratio of the number of atoms released from the compact to the amount produced in the compact fuel kernels and through uranium contamination. These safety test predictions aim at giving guidance for the upcoming safety tests to be conducted at ORNL and INL. The AGR-2 compacts modeled in this study belong to the four U.S. capsules of the AGR-2 irradiation test train. For each capsule, the compacts with the lowest and highest burnups were selected to provide a prediction envelope. The objective of this document is to present results of calculations and analyses made by PARFUME on these selected AGR-2 compacts. These results include: • Fuel failure probability • Palladium penetration • Fractional release of fission products Details associated with completion of these calculations are provided in the remainder of this document: the AGR-2 Safety Testing plan is briefly introduced in Section 2, PARFUME modeling is outlined in Section 3, results are described in Section 4, conclusions are given in Section 5, and references are listed in Section 6.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Highly Resolved Reference Projections of Building Energy Use for the Contiguous United States: Building Sector Energy Baselines, Projection Methods, and Results

This report describes one methodology of projecting energy consumption of the US residential and commercial building sectors using NREL's ResStock™ and ComStock™ as well as growth rates derived from EIA's Annual Energy Outlook (AEO). The impetus for this work is to provide an intermediate method for compiling demand-side sectoral energy projections that is suitable for grid-scale analysis, such as NREL's Standard Scenarios. ResStock and ComStock are physics-based and statistically representative building stock models of the US residential and commercial sector, respectively. Using the 2012 actual meteorological year (AMY) weather data, the sectoral energy baselines are simulated and then segmented along key dimensions (e.g., geography, dwelling/building type). The segmented results are then scaled using the corresponding annual growth rates derived from the 2021 AEO reference case to produce energy projections out to 2050. The compiled result is a demand-side grid model (dsgrid) data set suitable for use in NREL's large-scale grid models, such as the Regional Energy Deployment System (ReEDS). This simple projection method does not endogenously represent how the building stock could evolve through time. Most notably, it does not reflect large-scale electrification, for example, the conversion of space heating, water heating, clothes drying, and cooking from primary fossil fuels to electricity, as this is not part of AEO's reference case assumptions. Nonetheless this approach is more resolved and potentially extensible compared to the current method used by Standard Scenarios's reference case, which augments a sector's total load based on a single growth rate from AEO.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

The 2.4 μm Galaxy Luminosity Function As Measured Using WISE. I. Measurement Techniques

The astronomy community has at its disposal a large back catalog of public spectroscopic galaxy redshift surveys that can be used for the measurement of luminosity functions (LFs). Utilizing the back catalog with new photometric surveys to maximum efficiency requires modeling the color selection bias imposed on the selection of target galaxies by flux limits at multiple wavelengths. The likelihood derived herein can address, in principle, all possible color selection biases through the use of a generalization of the LF, Φ(L), over the space of all spectra: the spectro-luminosity functional, Ψ[L{sub ν}]. It is, therefore, the first estimator capable of simultaneously analyzing multiple redshift surveys in a consistent way. We also propose a new way of parametrizing the evolution of the classic Schechter function parameters, L {sub ⋆} and ϕ {sub ⋆}, that improves both the physical realism and statistical performance of the model. The techniques derived in this paper are used in a companion paper by Lake et al. to measure the LF of galaxies at the rest-frame wavelength of 2.4 μm using the Widefield Infrared Survey Explorer (WISE).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys

We present a deep machine learning (ML)–based technique for accurately determining σ g and Ω m from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of N -body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological parameter values, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power spectrum–based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations, and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small (~0.07 h -3 Gpc 3 ) and the training data span a large parameter space (six cosmological and six HOD parameters), the CNN and hybrid CNN can constrain estimates of σ g and Ω m to ~3% and ~4%, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Comets and the formation of planets

Morphological study of the physical and dynamical processes of planet formation, with emphasis on the role of comet nuclei. A consistent model proposes the formation of comets and planets in preplanetary rings of the residual solar nebula, with subsequent ejection, chiefly by Jupiter, of the comets to Oort's (1950) sphere. Physically, dynamically, or statistically evaluated items include: (1) the total number and mass of comets in Oort's cloud; (2) reevaluation of the diameters and masses of comet nuclei; (3) the processes of nucleation from gravitational and 'Boltzmann' instabilities of gaseous media to agglomerations of particulate matter; and (4) the statistical-dynamical conditions and time scales of orbital interaction of comets with the planets and the consequences of disintegration.

Opik, E. J.↗
NASA NTRS1975

Statistical model for premixed turbulent flames

Statistical concepts and physical arguments are used to develop closure models for the mean energy equation governing one-dimensional, premixed, intense turbulent flames. It is proposed that the turbulent transport terms can be modeled by a turbulent conductivity based on turbulence kinetic energy (k) and energy dissipation rate (epsilon). The models obtained are used to derive correlations for the turbulent flame speed. Results show that there are three different flame regimes: a low-intensity turbulence, wrinkled flame; a high-intensity, fast-chemistry flame; and a high-intensity, finite-rate-chemistry flame.

Gouldin, F. C.↗
NASA NTRS1977

Analysis of turbofan propulsion system weight and dimensions

Weight and dimensional relationships that are used in aircraft preliminary design studies are analyzed. These relationships are relatively simple to prove useful to the preliminary designer, but they are sufficiently detailed to provide meaningful design tradeoffs. All weight and dimensional relationships are developed from data bases of existing and conceptual turbofan engines. The total propulsion system is considered including both engine and nacelle, and all estimating relations stem from physical principles, not statistical correlations.

Waters, M. H.↗
NASA NTRS1983

Lagrange thermodynamic potential and intrinsic variables for He-3 He-4 dilute solutions

For a two-fluid model of dilute solutions of He-3 in liquid He-4, a thermodynamic potential is constructed that provides a Lagrangian for deriving equations of motion by a variational procedure. This Lagrangian is defined for uniform velocity fields as a (negative) Legendre transform of total internal energy, and its primary independent variables, together with their thermodynamic conjugates, are identified. Here, similarities between relations in classical physics and quantum statistical mechanics serve as a guide for developing an alternate expression for this function that reveals its character as the difference between apparent kinetic energy and intrinsic internal energy. When the He-3 concentration in the mixtures tends to zero, this expression reduces to Zilsel's formula for the Lagrangian for pure liquid He-4. An investigation of properties of the intrinsic internal energy leads to the introduction of intrinsic chemical potentials along with other intrinsic variables for the mixtures. Explicit formulas for these variables are derived for a noninteracting elementary excitation model of the fluid. Using these formulas and others also derived from quantum statistical mechanics, another equivalent expression for the Lagrangian is generated.

Jackson, H. W.↗
NASA NTRS1987

Classification, seasonality and persistence of low-frequency atmospheric circulation patterns

The seasonality and persistence of the major modes of interannual variability in the low-frequency atmospheric circulation were studied using orthogonally rotated principle component analysis (RPCA) of Northern Hemisphere 1-month mean 700 mb heights. The twice-daily data for the 1950-1984 period were used. Winter results are similar to those of other recent RPCA and teleconnection studies. The strongest summer pattern is the North Atlantic Oscillation, which is also the strongest winter pattern; it systematically contracts northward in summer and expands southward in winter, being the only pattern found for every month of the year. The robustness of the RPCA results was examined through consistency with results of other studies and of adjacent month solutions within this study, as well as by replicating the results using 3-month and 10-day means of 700-mb height. It is concluded that the RPCA method provides a physically meaningful and statistically stable product with the simplicity of teleconnection patterns but with superior pattern choice and depiction.

Barnston, Anthony G.↗
NASA NTRS1987

Relativistic beaming and the nuclei of double-lobed quasars

If the simple relativistic beaming model for extragalactic radio sources is at all correct, Doppler boosting may bias us toward choosing those objects pointing nearly at us in flux-density limited samples. In order to minimize any such effects in statistical tests of physical theories of these objects, an attempt is made to define a complete sample of sources with random orientations. For a sample of 26 double-lobed quasars, the dependence of a number of observable features on the angle to the line of sight and on the superluminal velocity is examined, and the distribution of superluminal velocities for the sources is measured. Two new superluminals are reported, and their apparent velocities and nuclear strengths are discussed.

Hough, David H.↗
NASA NTRS1993

Structures Of Turbulent Boundary Layers

Report presents second part of two-part comprehensive study of coherent motions in, and structures of, turbulent boundary-layer flows. Study critically reviews current fragmented knowledge with view toward eventual unification and understanding of physical causes and statistical properties of turbulence phenomena.

Robinson, Stephen K.↗
NASA NTRS1999

The GEOS Ozone Data Assimilation System: Design and Validation

An ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center to provide global three-dimensional analyzed ozone mixing ratio and total column ozone. The Total Ozone Mapping Spectrometer (TOMS) total column ozone and the Solar Backscatter Ultraviolet (SBUV) or SBUV/2 partial ozone profile data are assimilated. The analyzed winds from the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) drive the ozone transport. Following every transport model timestep, the model prediction is combined with the observations using a global, physical-space based, statistical analysis scheme. Due to the smaller size of the ozone system than that of a global meteorological data assimilation system, new statistical analysis methodology, including anisotropic and flow-dependent forecast error correlation models, can be implemented and tested in the ozone system more easily. Sample results from the winter 1992 validation period are presented. There is a close agreement between the analyzed fields and the independent observations from ozone sondes and the Halogen Occultation Experiment (HALOE).

Stajner, I.↗