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At least 145 records · Page 8

High-Fidelity Velocity and Concentration Measurements of Turbulent Buoyant Jets

Accurate models of turbulent buoyant flows are essential for the design of the cooling circuit of nuclear reactors and passive safety systems. However, available models fail to fully capture the physics of turbulent mixing when buoyancy becomes predominant with respect to momentum. Therefore, high-fidelity experiments of well-controlled fundamental flows are needed to develop and validate more accurate models. We analyze experiments of positive and negative turbulent buoyant jets, both in uniform and stratified environments, with the aim of understanding the thermal hydraulics of turbulent mixing with variable density and providing high-fidelity data for the development and validation of turbulence models. Non-intrusive, simultaneous particle image velocimetry and laser-induced fluorescence measurements were carried out to acquire instantaneous velocity and concentration fields on a vertical section parallel to the axis of a jet in the self-similar region. The refractive index matching method was applied to measure high-resolution buoyant jets with up to 8.6% density difference. These data are free of the typical errors that characterize optical measurements of buoyancy-driven flows (e.g. natural and mixed convection) where the refractive index of the fluid is inhomogeneous throughout the measurement domain. Turbulent statistics and entrainment of buoyant jets in uniform and stratified environments are presented. These data are compared with non-buoyant jets in a uniform environment, as a reference to investigate the effects of buoyancy and stratification on turbulent mixing. The results will be used for the assessment of current turbulence models and as a basis for the development of a new model that captures turbulent mixing.

laser induced fluorescence↗

Dynamics of the 4-day wave in the Southern Hemisphere polar stratosphere

Horizontal wind and temperature data are employed to investigate the dynamics of the 4-day wave in the Southern Hemisphere polar stratosphere. They were derived from synoptic maps of satellite-measured brightness temperatures, which were generated using the fast Fourier synoptic mapping technique of Salby (1982). Circulation statistics from these data are compared to those from the NMC operational stratospheric analyses, demonstrating improvements afforded by detailed treatment of asynoptic sampling effects. The 40-day wave is isolated using temporally filtered data. Several events of wave growth and decay are observed in the upper stratosphere during August 1980. Derived zonal-mean and eddy statistics suggest that the 4-day wave results from an instability of the zonal-mean flow near 55-60S, at and above 1 mb. It is inferred from climatological data that the source of the instability is the 'double-jet' structure in the upper stratosphere and mesosphere.

Randel, William J.↗

Statistical study of reconnection exhausts in the solar wind

Magnetic reconnection is a fundamental process that changes magnetic field configuration and converts a magnetic energy to flow energy and plasma heating. This paper presents a survey of the plasma and magnetic field parameters inside 418 reconnection exhausts identified in the WIND data from 1995-2012. The statistical analysis is oriented on the re-distribution of the magnetic energy released due to reconnection between a plasma acceleration and its heating. The results show that both the portion of the energy deposited into heat as well as the energy spent on the acceleration of the exhaust plasma rise with the magnetic shear angle in accord with the increase of the magnetic flux available for reconnection. The decrease of the normalized exhaust speed with the increasing magnetic shear suggests a decreasing efficiency of the acceleration and/or the increasing efficiency of heating in high-shear events. However, we have found that the already suggested relation between the exhaust speed and temperature enhancement would be rather considered as an upper limit of the plasma heating during reconnection regardless of the shear angle.

79 ASTRONOMY AND ASTROPHYSICS↗

Large-deviation joint statistics of the finite-time Lyapunov spectrum in isotropic turbulence

One of the hallmarks of turbulent flows is the chaotic behavior of fluid particle paths with exponentially growing separation among them while their distance does not exceed the viscous range. The maximal (positive) Lyapunov exponent represents the average strength of the exponential growth rate, while fluctuations in the rate of growth are characterized by the finite-time Lyapunov exponents (FTLEs). In the last decade or so, the notion of Lagrangian coherent structures (which are often computed using FTLEs) has gained attention as a tool for visualizing coherent trajectory patterns in a flow and distinguishing regions of the flow with different mixing properties. A quantitative statistical characterization of FTLEs can be accomplished using the statistical theory of large deviations, based on the so-called Cramér function. To obtain the Cramér function from data, we use both the method based on measuring moments and measuring histograms and introduce a finite-size correction to the histogram-based method. We generalize the existing univariate formalism to the joint distributions of the two FTLEs needed to fully specify the Lyapunov spectrum in 3D flows. The joint Cramér function of turbulence is measured from two direct numerical simulation datasets of isotropic turbulence. Results are compared with joint statistics of FTLEs computed using only the symmetric part of the velocity gradient tensor, as well as with joint statistics of instantaneous strain-rate eigenvalues. When using only the strain contribution of the velocity gradient, the maximal FTLE nearly doubles in magnitude, highlighting the role of rotation in de-correlating the fluid deformations along particle paths. We also extend the large-deviation theory to study the statistics of the ratio of FTLEs. The most likely ratio of the FTLEs λ{sub 1} : λ{sub 2} : λ{sub 3} is shown to be about 4:1:−5, compared to about 8:3:−11 when using only the strain-rate tensor for calculating fluid volume deformations. The results serve to characterize the fundamental statistical and geometric structure of turbulence at small scales including cumulative, time integrated effects. These are important for deformable particles such as droplets and polymers advected by turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Data-driven Mori–Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori–Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

97 MATHEMATICS AND COMPUTING↗

Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor

Reduced models describing the Lagrangian dynamics of the velocity gradient tensor (VGT) in homogeneous isotropic turbulence (HIT) are developed under the physics-informed machine learning (PIML) framework. We consider the VGT at both Kolmogorov scale and coarse-grained scale within the inertial range of HIT. Building reduced models requires resolving the pressure Hessian and subfilter contributions, which is accomplished by constructing them using the integrity bases and invariants of the VGT. The developed models can be expressed using the extended tensor basis neural network (TBNN) introduced by Ling et al. [J. Fluid Mech. 807, 155 (2016)]. Physical constraints, such as Galilean invariance, rotational invariance, and incompressibility condition, are thus embedded in the models explicitly. Our PIML models are trained on the Lagrangian data from a high-Reynolds number direct numerical simulation (DNS). To validate the results, we perform a comprehensive out-of-sample test. We observe that the PIML model provides an improved representation for the magnitude and orientation of the small-scale pressure Hessian contributions. Statistics of the flow, as indicated by the joint PDF of second and third invariants of the VGT, show good agreement with the “ground-truth” DNS data. A number of other important features describing the structure of HIT are reproduced by the model successfully. We have also identified challenges in modeling inertial range dynamics, which indicates that a richer modeling strategy is required. This helps us identify important directions for future research, in particular towards including inertial range geometry into the TBNN.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Freight Analysis Framework Version 5 (FAF5) Base Year 2017 Data Development Technical Report

The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive national picture of freight movements among states and major metropolitan areas by all modes of transportation. The latest of this data series is FAF5, which is the fifth generation FAF and is benchmarked on Commodity Flow Survey (CFS) 2017. Except for FAF1 that provided estimates for truck, rail, and water tonnage for calendar year 1998, later generations of FAF (FAF2 through FAF5) were built based on their benchmark year CFS data, for 2002, 2007, 2012, and 2017 respectively. The FAF is produced under a partnership between Bureau of Transportation Statistics (BTS) and Federal Highway Administration (FHWA). As a major data product of the FAF program, the FAF regional database provides a national picture of freight flows to, from, and within the United States (among regions and states), by commodity and mode for the base year, as well as for forecasts up to 30 years into the future in a 5-year interval. Additional FAF data products also include FAF network flows database, where truck movements are routed onto the national highway network, estimates of annual projections, and synchronized historical data series. This report is a technical document prepared to describe the data sources and methodologies applied in the process of building the FAF5 base-year 2017 regional database, released as FAF5.0 in February 2021. This report offers a description of the diverse data sources and modeling methods used in constructing the base year FAF5 regional database. The FAF5 base-year database is used as the base for development of forecasts and for assignment of truck flows on highway network. Similarly, the FAF5 base-year database will be used as the base to generate FAF5 annual estimates. In addition to this report, users are encouraged to refer to the FAF5 User’s Guide, which provides basic information of the data, including definitions of the data attributes, information on how to access the data and tool, as well as detailed data dictionary and code tables.

42 ENGINEERING↗

Isotherms for Water Adsorption on Molecular Sieve 3A: Influence of Cation Composition

This study is part of our continuing efforts to address engineering issues related to the removal of tritiated water from off-gases produced in used nuclear fuel reprocessing facilities. In the current study, adsorption equilibrium of water on molecular sieve 3A beads was investigated. Adsorption isotherms for water on the UOP molecular sieve 3A were measured by a continuous-flow adsorption system at 298, 313, 333, and 353 K. Experimental data collected were analyzed by the Generalized Statistical Thermodynamic Adsorption (GSTA) isotherm model. The K + /Na + molar ratio of this particular type of molecular sieve 3A was ~4:6. Our results showed that the GSTA isotherm model worked very well to describe the equilibrium behavior of water adsorption on molecular sieve 3A. The optimum number of parameters for the current experimental data was determined to be a set of four equilibrium parameters. This result suggests that the adsorbent crystals contain four energetically distinct adsorption sites. In addition, it was found that water adsorption on molecular sieve 3A follows a three-stage adsorption process. This three-stage adsorption process confirmed different water adsorption sites in molecular sieve crystals. In addition, the second adsorption stage is significantly affected by the K + /Na + molar ratio. In this stage, the equilibrium adsorption capacity at a given water vapor pressure increases as the K + /Na + molar ratio increases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Heat-release equation of state and thermal conductivity of warm dense carbon by proton differential heating

Warm dense carbon is generated at 0.3–2.0 g/cc and 1–7 eV by proton heating. The release equation of state (EOS) after heating and thermal conductivity of warm dense carbon are studied experimentally in this regime using a Au/C dual-layer target to initiate a temperature gradient and two picosecond time-resolved diagnostics to probe the surface expansion and heat flow. Comparison between the data and simulations using various EOSs and thermal conductivity models is quantified with a statistical χ2 analysis. In conclusion, out of seven EOS tables and five thermal conductivity models, only L9061 with the Lee-More model provides a probability above 50% to match all data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The minimal flow unit in near-wall turbulence

Direct numerical simulations of unsteady channel flow were performed at low to moderate Reynolds numbers on computational boxes chosen small enough so that the flow consists of a doubly periodic (in x and z) array of identical structures. The goal is to isolate the basic flow unit, to study its morphology and dynamics, and to evaluate its contribution to turbulence in fully developed channels. For boxes wider than approximately 100 wall units in the spanwise direction, the flow is turbulent, and the low-order turbulence statistics are in good agreement with experiments in the near-wall region. For a narrow range of widths below that threshold, the flow near only one wall remains turbulent, but its statistics are still in fairly good agreement with experimental data when scaled with the local wall stress. For narrower boxes only laminar solutions are found. In all cases, the elementary box contains a single low-velocity streak, consisting of a longitudinal strip on which a thin layer of spanwise vorticity is lifted away from the wall.

Jimeez, Javier↗

Patterns in simulated turbulent channel flow

The surface stress is a key diagnostic in wall bounded turbulent flows. Large fluctuations in the stress are believed to be associated with intermittent 'bursting' events during which a large proportion of the turbulence production takes place. If this is so, then a detailed investigation of the structure of the surface stress and its spatial relationship to events within the flow could have wide application in drag reduction and other aspects of flow control. The initial phase of this project, therefore, concentrated on the surface stress field. The first objective is to carry out a statistical analysis of the instantaneous surface stress in a simulated turbulent channel flow, including comparison with multipoint experimental data from a zero pressure gradient turbulent boundary layer. The second objective is to apply a simple pattern recognition procedure to educe the characteristic spatial structure of various flow diagnostics. The final objective is to extend the pattern recognition analysis to examine the whole three dimensional structure of the flow.

Stretch, D.↗

Investigation of Particle Sampling Bias in the Shear Flow Field Downstream of a Backward Facing Step

The flow field about a backward facing step was investigated to determine the characteristics of particle sampling bias in the various flow phenomena. The investigation used the calculation of the velocity:data rate correlation coefficient as a measure of statistical dependence and thus the degree of velocity bias. While the investigation found negligible dependence within the free stream region, increased dependence was found within the boundary and shear layers. Full classic correction techniques over-compensated the data since the dependence was weak, even in the boundary layer and shear regions. The paper emphasizes the necessity to determine the degree of particle sampling bias for each measurement ensemble and not use generalized assumptions to correct the data. Further, it recommends the calculation of the velocity:data rate correlation coefficient become a standard statistical calculation in the analysis of all laser velocimeter data.

Meyers, James F.↗

Time-History Statistics of Soot Formation in A Model Gas Turbine Combustor

Soot formation is a complex dynamic and intermittent process determined by properties of the fuel, combustor design, and combustor operation. Although the major steps in soot formation (i.e., formation of precursors, inception, growth and evolution) are similar for a variety of carbonaceous fuels, applications, and operating conditions, it remains unclear when the temporal transition between these steps occurs. An engineering prediction tool coupled with computational fluid physics (CFD), therefore needs to accurately model all these complex steps. To develop such a model, we propose the time-history concept for understanding the time dependency of soot formation as a function of local properties (i.e., temperature, velocity, local fuel air ratio, etc.). We continue our previous work with modeling the DLR aero-combustor [1] with our updated in-house CFD code, Open National Combustion Code (OpenNCC), that now includes a Multiple Time-Scale Flamelet Progress Variable approach and a the semi-empirical two-equation soot model. We injected massless tracer particles upstream of the injector region of the combustor to collect time-history statistics of the solution variables. The correlations between the collected statistics with respect to the experimental soot volume fraction data showed that time-history effect of certain flow variables, including turbulent kinetic energy (TKE), and multiple species is indeed important for soot formation. We then conducted a time-history based correlation analysis to determine the key species and the concentration ranges critical for soot formation (C6H5-based nucleation, acetylene-based surface growth, and oxidation with OH and O2). Based on the time-history correlation coefficient (THCC) analysis, we propose possible modifications to improve the current two-equation model.

LES↗

Discrete Mathematical Approaches to Graph-Based Traffic Analysis

Modern cyber defense and anlaytics requires general, formal models of cyber systems. Multi-scale network models are prime candidates for such formalisms, using discrete mathematical methods based in hierarchically-structured directed multigraphs which also include rich sets of labels. An exemplar of an application of such an approach is traffic analysis, that is, observing and analyzing connections between clients, servers, hosts, and actors within IP networks, over time, to identify characteristic or suspicious patterns. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. In this paper, we consider traffic analysis of Netflow using both basic graph statistics and two new mathematical measures involving labeled degree distributions and time interval overlap measures. We do all of this over the VAST test data set of 96M synthetic Netflow graph edges, against which we can identify characteristic patterns of simulated ground-truth network attacks.

Joslyn, Cliff A.↗

Effect of Boundary-Layer Bleed Hole Inclination Angle and Scaling on Flow Coefficient Behavior

Phase II data results of the Fundamental Inlet Bleed Experiments study at NASA Glenn Research Center are presented which include flow coefficient behavior for 21 bleed hole configurations. The bleed configurations are all round holes with hole diameters ranging from 0.795 to 6.35 mm, hole inclination angles from 20deg to 90deg, and thickness-to-diameter ratios from 0.25 to 2.0. All configurations were tested at a unit Reynolds number of 2.46 10(exp 7)/m and at discrete local Mach numbers of 1.33, 1.62, 1.98, 2.46, and 2.92. Interactions between the design parameters of hole diameter, hole inclination angle, and thickness-to-diameter as well as the interactions between the flow parameters of pressure ratio and Mach number upon the flow coefficient are examined, and a preliminary statistical model is proposed. An existing correlation is also examined with respect to this data.

Eichorn, Michael B.↗

Multiscale Flow for robust and optimal cosmological analysis

We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing. Multiscale Flow uses hierarchical decomposition of cosmological fields via a wavelet basis and then models different wavelet components separately as Normalizing Flows. The log-likelihood of the original cosmological field can be recovered by summing over the log-likelihood of each wavelet term. This decomposition allows us to separate the information from different scales and identify distribution shifts in the data such as unknown scale-dependent systematics. The resulting likelihood analysis can not only identify these types of systematics, but can also be made optimal, in the sense that the Multiscale Flow can learn the full likelihood at the field without any dimensionality reduction. We apply Multiscale Flow to weak lensing mock datasets for cosmological inference and show that it significantly outperforms traditional summary statistics such as power spectrum and peak counts, as well as machine learning–based summary statistics such as scattering transform and convolutional neural networks. We further show that Multiscale Flow is able to identify distribution shifts not in the training data such as baryonic effects. Finally, we demonstrate that Multiscale Flow can be used to generate realistic samples of weak lensing data.

79 ASTRONOMY AND ASTROPHYSICS↗

A comparison of limited-area energetic processes between observations and primitive equation model predictions

Energetic analyses of the NMC initial conditions and NMC six-layer primitive equation operational prediction model 12-hr forecast for a developing cyclone are presented. Consideration is given to the total kinetic energy, the energetics of the divergent and nondivergent flows and the baroclinic (vertical shear flow) and barotropic (vertical mean flow) components of the kinetic energy. It is found that the model initial conditions lose 10-15% of the kinetic energy at various levels compared to a limited-area multivariate statistical analysis of the observational data, leading to a decrease in the horizontal kinetic energy flux, a misrepresentation of the synoptic scale wave system in the 12-hr forecast. Similar results are obtained for the nondivergent flow, while the divergent flow energetics are not reproduced accurately by the model. The horizontal flux terms of the vertical mean and vertical shear energetics are also not found to be reproduced in the upper levels, although horizontal flux contributions to the baroclinic component are improved at middle and lower levels. Finally, vertical shear kinetic energy generation is found to be well represented in the model prediction, however kinetic energy conversion between vertical shear and mean flow is not reproduced in the lower layer.

Alpert, J. C.↗

Particle Loading Dynamics of SRNL Electrostatic Particle Collectors - 19398

The Department of Energy is interested in decontamination methods and tools for the removal of airborne radioactive particulate from facilities prior to decommissioning. Electrostatic particle collectors (EPCs) have historically been used industrially to scrub particles from air streams for large scale operations, and Savannah River National Laboratory (SRNL) researchers have expanded on the fundamental collection capabilities of EPCs to develop unique samplers focused on particulate effluent collection. One distinct advantage of an electrostatic particle collection over filtration or inertial impaction-based methods is the low pressure drop associated with prolonged use of EPCs. Electrostatic particle collectors previously developed by SRNL have proven effective in the collection of atmospheric aerosols in multiple field trials, and their applicability as a decontamination method for airborne particulates. Initial experiments using aerosolized NaCl were performed to determine EPC collection efficiencies, the times required to achieve a desired particle loading, and the size distribution of collected particulates. The amount of NaCl collected on Si substrates was found to increase linearly with time (∼ 2 mg per hour) and showed little sign of change after 24 hours of collection. Experiments showed that NaCl, or a similar aerosol, would likely continue to collect unperturbed until physical limitations within the EPC are reached. Collection efficiencies (i.e., the number of particles collected vs. the number of particles entering the EPC) were calculated independently using an assessment scheme based on Particle Depletion Dynamics (PDD) and by Computer Controlled Scanning Electron Microscope (CCSEM) analysis of collection substrates for a range of particle sizes. For the ACE 2.0 with a corona voltage of 15 kV, collection efficiencies were determined using PDD to be 54, 29, and 13% for the flow rates 100, 300, and 490 L/min respectively. While CCSEM data suggests a very high collection efficiency for the MiniACE, the static sampling rate of 28.1 L/min (1 ft{sup 3}/min) was found to be too low volume for PDD to provide statistically meaningful data. Number density maps of NaCl particles detected via CCSEM revealed that particles are preferentially collected near the EPC inlet (i.e., opposite the direction of airflow) when using slower flow rates. Particles collected using higher flow rates (e.g., 300 and 490 L/min) were collected more evenly across the substrate. When segregated by size, smaller (e.g., 200-500 nm) particles are collected evenly across the collection substrates. As particle sizes increase they are collected preferentially near the EPC inlet, but not in sufficient quantity to obstruct airflow and hinder long-term operations. Subsequent deployment of the SRNL developed system into plutonium-238 contaminated hot cells will be performed at Savannah River Site Building 235-F. Presented in this work are the testing and considerations utilized by SRNL to show this technique as a viable means of rapidly decontaminating airborne contaminated areas. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗