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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Mechanism for particle fractionalization and universal edge physics in quantum Hall fluids

Abstract Advancing a microscopic framework that rigorously unveils the underlying topological hallmarks of fractional quantum Hall (FQH) fluids is a prerequisite for making progress in the classification of strongly-coupled topological matter. We present a second-quantization framework that reveals an exact fusion mechanism for particle fractionalization in FQH fluids, and uncovers the fundamental structure behind the condensation of non-local operators characterizing topological order in the lowest-Landau-level. We show the first exact analytic computation of the quasielectron Berry connections leading to its fractional charge and exchange statistics, and perform Monte Carlo simulations that numerically confirm the fusion mechanism for quasiparticles. We express the sequence of (bosonic and fermionic) Laughlin second-quantized states, highlighting the lack of local condensation, and present a rigorous constructive subspace bosonization dictionary for the bulk fluid. Finally, we establish universal long-distance behavior of edge excitations by formulating a conjecture based on the DNA, or root state, of the FQH fluid.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Numerical and Physical Simulation of the Fluid Flow in a Beam Blank Mold Fed Through the Center of the Web

Fluid flow inside a beam blank mold fed through a three-port SEN (two lateral ports and one bottom port), positioned at the center of the mold, has been investigated. Literature survey shows that this kind of configuration is not frequently used. It had been shown that it is possible to get a symmetrical flow with this configuration. The downward inclination of the ports should influence the transient fluid flow. The greater the downward inclination, the greater is the instability of the jet and of the interface between the immiscible fluids that simulate the slag/metal behavior. The flow characterization was made by dye dispersion, PIV technique, measurement of the meniscus oscillation using ultrasonic sensors, and CFD simulations. The slag/metal behavior was simulated using water and oils with different physical properties. The results from PIV as well as from observations of the water–oil interface have been used to validate the CFD simulations. Oils with density close to water resulted in more instability at the interface with entrainment starting from fluid flow rate of 125 L/min (equivalent to casting speed of 1 m/min). Decreasing the viscosity of slag (oils) and increasing the casting velocity (water flow rate) result in reduction of the interfacial stability.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Simple physics of the partly pinned fluid systems

In this paper, we consider some aspects of the physics of the partly pinned (PP) systems obtained by freezing in place particles in equilibrium bulk fluid configurations in the normal (nonglassy) state. We first discuss the configurational overlap and the disconnected density correlation functions, both in the homogeneous and heterogeneous cases, using the tools of the theory of adsorption in disordered porous solids. The relevant Ornstein-Zernike equations are derived, and asymptotic results valid in the regime where the perturbation due to the pinning process is small are obtained. Second, we consider the homogeneous PP lattice gas as a means to make contact between pinning processes in particle and spin systems and show that it can be straightforwardly mapped onto a random field Ising model with a strongly asymmetric bimodal distribution of the field. Possible implications of these results for studies of the glass transition based on PP systems are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Physics of nanoscale immiscible fluid displacement

In this work, we investigate immiscible fluid displacement at small scales where slip lengths are on the order of characteristic system sizes, whereby Cox's law is not expected to be valid. Molecular dynamics simulations show that in this limit hydrodynamic bending becomes small and interfaces remain approximately spherical. In this case the only relevant angle for describing the interface shape is the dynamic microscopic angle at the fluid-solid interface. In our simulations, this angle is found to be described well by the molecular-kinetic theory originally proposed by Blake and Haynes. In general, this implies a different functional dependence between the contact angle (and related quantities) and the flow speed (or capillary number); this is demonstrated for the case of the force on the boundary for immiscible fluid displacement in a two-dimensional channel.

42 ENGINEERING↗
DOE OSTI2022

Prospects of federated machine learning in fluid dynamics

Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, processing units, neural network based technologies, and sensor adaptations. So far in many applications in fluid dynamics, machine learning approaches have been mostly focused on a standard process that requires centralizing the training data on a designated machine or in a data center. In this article, we present a federated machine learning approach that enables localized clients to collaboratively learn an aggregated and shared predictive model while keeping all the training data on each edge device. We demonstrate the feasibility and prospects of such a decentralized learning approach with an effort to forge a deep learning surrogate model for reconstructing spatiotemporal fields. Our results indicate that federated machine learning might be a viable tool for designing highly accurate predictive decentralized digital twins relevant to fluid dynamics.

36 MATERIALS SCIENCE↗
NASA NTRS1987

Inlets, ducts and nozzles

The internal fluid mechanics research program in inlets, ducts, and nozzles is described. The program consists of a balanced effort between the development of computational tools and the conduct of experimental research. The experiments are designed to better understand the fluid flow physics, to develop new or improved flow models, and to provide benchmark quality data sets for validation of the computational methods. The inlet, duct, and nozzle research program is described according to three major classifications of flow phenomena: highly three-dimensional flow fields; shock-boundary layer interactions; and shear layer control. Specific examples of current and future elements of the research program are described for each of these phenomena. In particular, the highly three-dimensional flow field phenomena is highlighted by describing the computational and experiemental research program in transition ducts having a round-to-rectangular area variation. In the case of shock-boundary layer interactions, the specific details of research for normal shock-boundary layer interactions are described. For shear layer control research in vortex generators and the use of aerodynamic excitation for enhancement of the jet mixing process are described. Future research in inlets, ducts, and nozzles will include more emphasis on three-dimensional full Navier-Stokes methods and corresponding experiments designed to concentrate on the appropriate three-dimensional fluid flow physics.

Abbott, John M.↗
DOE OSTI2013

Liquid-liquid-solid transition in viscoelastic liquids

Liquid-liquid-solid transitions (LLST) are known to occur in confined liquids, exist in supercooled liquids and emerge in liquids driven from equilibrium. Molecular dynamics (MD) simulations claim many successes in forecasting the phenomena. The transitions are also studied in the framework of thermodynamics based methods and minimalistic models. In here, the proposed approach is derived in the framework of continuum and includes spatial and temporal dynamic heterogeneities; the approach is meant to capture the material behavior at small scales. We conjecture that the liquid-like and solid-like behaviors are dissimilar enough for the two to be governed by different constitutive relations. In this way, we gain additional degree of freedom, which is found essential when predicting the transitional phenomena. As a result, we derive the LLST criteria for liquids in equilibrium, during steady flow and at transient conditions. Lastly, we forecast short-lived LLSTs in human blood during cardiac cycle.

SELF-ASSEMBLY↗
DOE OSTI2021

Inverse design of equilibrium cluster fluids applied to a physically informed model

Inverse design strategies have proven highly useful for the discovery of interaction potentials that prompt self-assembly of a variety of interesting structures. However, often the optimized particle interactions do not have a direct relationship to experimental systems. In this work, we show that Relative Entropy minimization is able to discover physically meaningful parameter sets for a model interaction built from depletion attraction and electrostatic repulsion that yield self-assembly of size-specific clusters. Here, we then explore the sensitivity of the optimized interaction potentials with respect to deviations in the underlying physical quantities, showing that clustering behavior is largely preserved even as the optimized parameters are perturbed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

A locally refined rectangular grid finite element method - Application to computational fluid dynamics and computational physics

The present FEM technique addresses both linear and nonlinear boundary value problems encountered in computational physics by handling general three-dimensional regions, boundary conditions, and material properties. The box finite elements used are defined by a Cartesian grid independent of the boundary definition, and local refinements proceed by dividing a given box element into eight subelements. Discretization employs trilinear approximations on the box elements; special element stiffness matrices are included for boxes cut by any boundary surface. Illustrative results are presented for representative aerodynamics problems involving up to 400,000 elements.

Young, David P.↗
NASA NTRS1985

Analysis of physical-chemical processes governing SSME internal fluid flows

The basic issues concerning the physical chemical processes of the Space Shuttle Main Engine are discussed. The objectives being to supply the general purpose CFD code PHOENICS and the associated interactive graphics package - GRAFFIC; to demonstrate code usage on SSME related problems; to perform computations and analyses of problems relevant to current and future SSME's; and to participate in the development of new physical models of various processes present in SSME components. These objectives are discussed in detail.

Singhal, A. K.↗
DOE OSTI2023

Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach

Multiphase Computational Fluid Dynamics (MCFD) based on the two-fluid model is considered a promising tool to model complex two-phase flow systems. MCFD simulation can predict local flow features without resolving interfacial information. As a result, the MCFD solver relies on closure relations to describe the interaction between the two phases. Those empirical or semi-mechanistic closure relations constitute a major source of uncertainty for MCFD predictions. In this paper, we leverage a physics-informed uncertainty quantification (UQ) approach to inversely quantify the closure relations’ model form uncertainty in a physically consistent manner. This proposed approach considers the model form uncertainty terms as stochastic fields that are additive to the closure relation outputs. Combining dimensionality reduction and Gaussian processes, the posterior distribution of the stochastic fields can be effectively quantified within the Bayesian framework with the support of experimental measurements. As this UQ approach is fully integrated into the MCFD solving process, the physical constraints of the system can be naturally preserved in the UQ results. Here, in a case study of adiabatic bubbly flow, we demonstrate that this UQ approach can quantify the model form uncertainty of the MCFD interfacial force closure relations, thus effectively improving the simulation results with relatively sparse data support.

42 ENGINEERING↗
DOE OSTI2014

Influence of system size on the properties of a fluid adsorbed in a nanopore: Physical manifestations and methodological consequences

Hysteresis and discontinuities in the isotherms of a fluid adsorbed in a nanopore in general hamper the determination of equilibrium thermodynamic properties, even in computer simulations. A way around this has been to consider both a reservoir of small size and a pore of small extent in order to restrict the fluctuations of density and approach a classical van der Waals loop. We assess this suggestion by thoroughly studying through Monte Carlo simulations and density functional theory the influence of system size on the equilibrium configurations of the adsorbed fluid and on the resulting isotherms. We stress the importance of pore-symmetry-breaking states that even for modest pore sizes lead to discontinuous isotherms and we discuss the physical relevance of these states and the methodological consequences for computing thermodynamic quantities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Fluidization of collisionless plasma turbulence

In a collisionless, magnetized plasma, particles may stream freely along magnetic field lines, leading to “phase mixing” of their distribution function and consequently, to smoothing out of any “compressive” fluctuations (of density, pressure, etc.). This rapid mixing underlies Landau damping of these fluctuations in a quiescent plasma—one of the most fundamental physical phenomena that makes plasma different from a conventional fluid. Nevertheless, broad power law spectra of compressive fluctuations are observed in turbulent astrophysical plasmas (most vividly, in the solar wind) under conditions conducive to strong Landau damping. Elsewhere in nature, such spectra are normally associated with fluid turbulence, where energy cannot be dissipated in the inertial-scale range and is, therefore, cascaded from large scales to small. By direct numerical simulations and theoretical arguments, it is shown here that turbulence of compressive fluctuations in collisionless plasmas strongly resembles one in a collisional fluid and does have broad power law spectra. This “fluidization” of collisionless plasmas occurs, because phase mixing is strongly suppressed on average by “stochastic echoes,” arising due to nonlinear advection of the particle distribution by turbulent motions. Other than resolving the long-standing puzzle of observed compressive fluctuations in the solar wind, our results suggest a conceptual shift for understanding kinetic plasma turbulence generally: rather than being a system where Landau damping plays the role of dissipation, a collisionless plasma is effectively dissipationless, except at very small scales. The universality of “fluid” turbulence physics is thus reaffirmed even for a kinetic, collisionless system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

A Study of Physics-Informed Deep Learning for System Fluid Dynamics Closures

The sub-grid-scale (SGS) physics models, or so-called closure relations (CR), are essential in thermal-fluid modeling and simulation codes, ranging from large-eddy simulation to system-level thermo-fluid dynamic problems. While details vary, all thermo-fluid codes solve coupled mass-momentum-energy conservation partial differential equations (PDE). Traditionally, the analytical form of closure models has to be known, and then we calibrate the model using data from a set of applicable experiments. Characteristically, it takes long effort to gain insights and mechanistic understanding needed for developing closure models. Such a model is a compact form of data. The long time required for new model development limits the pace of model application while dealing with new geometries, new coolants or system conditions, particularly in newly designed systems. Alternative to mechanistic and semi-analytical models, it is possible to discover the pattern of closure relations by applying the modern machine learning methods to the body of relevant experimental data. Deep learning (DL) is a relatively new branch in the machine learning family, and a major breakthrough has been achieved since Hinton introduces a fast algorithm to efficiently train a feed-forward neural network (NN) with multilayer perceptrons. Generally, any NN with more than two layers is considered as deep learning, and it contains a large number of hyper-parameters to achieve pattern recognition or regression for complex datasets. Hornik shows that multilayer NN is compatible with universal approximation theorem. There is no constraint for a multilayer NN to capture the properties of any measurable information. This capability makes DL attractive for closure models in a system thermal-hydraulic code. As system geometry and/or operating conditions change, new physical experiments (increasingly sophisticated) are conducted to support evaluation and revision of closure models. Such a so-called data-driven modeling relaxes the structural form of system codes to directly assimilate data instead of hard-wiring the model to pre-calibrated correlations. The objective of this research is to enable cost-effective development of a DL-based closure that reflects the data. The previous work on applying machine learning techniques to fluid dynamics focused on statistical learning with basic NN from direct numerical simulations (DNS) dataset. In the present work, we show that DL-based closure models can cause the fluid calculation to diverge if multilayer NN is used. To address the issue, we investigate conditions by which the DL-based closure models work compatibly, stably, and effectively with PDE-constrained forward prediction problems. Optimally meeting the compatibility conditions constitutes a physics-informed deep learning (PIDL) strategy. We refer to this process as PDE-constrained machine learning or PDE-ML. To develop the technical basis for PDE-ML using DL-based fluid dynamics closure, numerical experiments are performed for manufactured problems of increasing complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗