9,212 research outputs found
Linear Amplification in Nonequilibrium Turbulent Boundary Layers
Resolvent analysis is applied to nonequilibrium incompressible adverse pressure gradient (APG) turbulent boundary layers (TBL) and hypersonic boundary layers with high temperature real gas effects, including chemical nonequilibrium. Resolvent analysis is an equation-based, scale-dependent decomposition of the Navier Stokes equations, linearized about a known mean flow field. The decomposition identifies the optimal response and forcing modes, ranked by their linear amplification. To treat the nonequilibrium APG TBL, a biglobal resolvent analysis approach is used to account for the streamwise and wall-normal inhomogeneities in the streamwise developing flow. For the hypersonic boundary layer in chemical nonequilibrium, the resolvent analysis is constructed using a parallel flow assumption, incorporating Nâ‚‚, Oâ‚‚, NO, N, and O as a mixture of chemically reacting gases.
Biglobal resolvent analysis is first applied to the zero pressure gradient (ZPG) TBL. Scaling relationships are determined for the spanwise wavenumber and temporal frequency that admit self-similar resolvent modes in the inner layer, mesolayer, and outer layer regions of the ZPG TBL. The APG effects on the inner scaling of the biglobal modes are shown to diminish as their self-similarity improves with increased Reynolds number. An increase in APG strength is shown to increase the linear amplification of the large-scale biglobal modes in the outer region, similar to the energization of large scale modes observed in simulation. The linear amplification of these modes grows linearly with the APG history, measured as the streamwise averaged APG strength, and relates to a novel pressure-based velocity scale.
Resolvent analysis is then used to identify the length scales most affected by the high-temperature gas effects in hypersonic TBLs. It is shown that the high-temperature gas effects primarily affect modes localized near the peak mean temperature. Due to the chemical nonequilibrium effects, the modes can be linearly amplified through changes in chemical concentration, which have non-negligible effects on the higher order modes. Correlations in the components of the small-scale resolvent modes agree qualitatively with similar correlations in simulation data.
Finally, efficient strategies for resolvent analysis are presented. These include an algorithm to autonomously sample the large amplification regions using a Bayesian Optimization-like approach and a projection-based method to approximate resolvent analysis through a reduced eigenvalue problem, derived from calculus of variations.</p
Devices and methods for wet gas flow metering: a comprehensive review
Wet gas is commonly encountered in various industries, including energy, chemical, and electric power sectors. For example, natural gas extracted from production often contains small amounts of liquid, such as water and hydrocarbon condensates, which classifies it as wet gas. The presence of liquid within the gas poses challenges for accurate flow measurement. To improve the performances of wet gas flow metering methods, significant research and development efforts have been invested into the wet gas flow metering technologies due to their vital importance in the production, transfer, and trade benefits.
This paper presents a comprehensive overview of the recent development of wet gas flow metering. Firstly, a comprehensive discussion of the Lockhart-Martinelli parameter (Xlm) and its relation to the gas void fraction (Óg) is presented, which was mostly overlooked in previous wet gas research work. The occurrence of various flow patterns in wet gas conditions at different orientations (horizontal and vertical) was explored. Following an investigation of pressure impact on the wet gas flow patterns and development of the wet gas regions, a different test matrix for further research work was suggested. After a novel classification of wet gas measurement methods, the paper offers a detailed comparison of differential pressure (DP) meters including Venturi, Cone meter, and orifice meters, by considering both liquid and gas flow rate measurements. Secondly, the paper discusses and compares vortex flow meters, Coriolis and ultrasonic meters in comparison to DP meters. Notable phase fraction meters are also examined and compared to one another. Thirdly, the paper reviewed the concept of existing and potential hybrid wet gas meters, conducting a detailed discussion and comparison with commercial solutions by evaluating their ranges and accuracies. This assessment provides valuable insights into the capabilities of these hybrid meters, highlighting their potential to enhance the measurement of wet gas flow rates
Effects of anisotropy on the geometry of tracer particle trajectories in turbulent flows
Using curvature and torsion to describe Lagrangian trajectories gives a full description of these as well as an insight into small and large time scales as temporal derivatives up to order 3 are involved. One might expect that the statistics of these properties depend on the geometry of the flow. Therefore, we calculated curvature and torsion probability density functions (PDFs) of experimental Lagrangian trajectories processed using the Shake-the-Box algorithm of turbulent von Kármán flow, Rayleigh-Bénard convection and a zero-pressuregradient boundary layer over a flat plate. The results for the von-Kármán flow compare well with experimental results for the curvature PDF and numerical simulation of homogeneous and isotropic turbulence for the torsion PDF. For the experimental Rayleigh-Bénard convection, the power law tails found agree with those measured for von-Kármán flow. Results for the logarithmic layer within the boundary layer differ slightly, we give some potential explanation below. To detect and quantify the effect of anisotropy either resulting from a mean flow or large-scale coherent motions on the geometry or tracer particle trajectories, we introduce the curvature vector. We connect its statistics with those of velocity fluctuations and demonstrate that strong large-scale motion in a given spatial direction results in meandering rather than helical trajectories
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Collective motion in active matter
Active matter is the study of collective motion of systems of particles that are able to
consume energy in order to perform systematic motion. Such systems are abundant in nature and come on a large range of scales: from animal herds and bird flocks, to bacterial colonies, to active polymer suspensions. In this Thesis, we focus on the so-called wet active matter, where interactions between an active particle and its environment (usually a surrounding fluid, as in bacterial or active polymer suspensions) conserve momentum.
Dilute suspensions of motile bacteria are one of the best-studied examples of wet active matter. When the density of the suspension is sufficiently high enough, it exhibits a phenomenon often referred to as bacterial turbulence. When it occurs, the suspension exhibits strong orientational and velocity and correlations on large length scales, enhanced diffusion and mixing of the suspending fluid, and reduced apparent viscosity that vanishes at the onset of collective motion. While the transition to bacterial turbulence is reasonably well-understood, the properties of the turbulent phase are largely unknown.
The purpose of this Thesis is to study theoretically aspects of collective motion of
self-propelled particles in the presence of hydrodynamic interaction. Motivated by previous work on the onset of bacterial turbulence, first we develop and study a lattice model of the collective phase. Since the transition to bacterial turbulence was previously shown to be driven by the orientational degrees of freedom alone, we confine model microswimmers to a regular lattice and fix their positions. The dynamics of each microswimmer then comprise re-orientations in the velocity fields created by other microswimmers and random orientational changes representing bacterial tumble event. We observe that in the absence of tumbling, all dynamics cease after some initial time, yielding a frozen configuration. For sufficiently strong tumbling, these configurations melt, and we discuss the implications of this scenario for bacterial turbulence.
As the next step, we study partially filled lattices of microswimmer. In addition to the
dynamical rules presented above, each swimmer can now hop on the lattice with a hopping rate dependent on the surrounding velocity field. This hopping simultaneously represents self-propulsion, advection, and spatial diffusion exhibited by real microswimmers. The model also incorporates excluded volume interactions as only a single microswimmer is allowed to occupy a single lattice site. In the absence of hydrodynamic interactions, the model exhibits a motility-induced phase separation, typical of dry active systems. In the presence of hydrodynamic interactions, the system exhibits microphase separation instead, leading to a total disappearance of clusters. The latter state is shown to be hyperuniform and we discuss how hydrodynamic interactions affect the phase diagram of such systems.
Finally, we study another example of collective motion in active fluids. We consider the active liquid crystalline model that is often referred to as active gel or active nematic
model, that is widely used in studying mechanics of biological tissues, bacterial colonies, cell mechanics, etc. Previous work on these systems mostly focussed on the case of a highly packed suspension with a constant density of active agents. Here, motivated by our work above, we introduce an analogous model that allows for local density variations. We study the onset of spontaneous flows in this model and discuss how our observations differ from their constant-density counterparts
Formation and evolution of vortex rings with weak to moderate swirl and their implications for enhancing vortex ring circulation
The formation of swirling vortex rings and their early time evolution, resulting from the controlled discharge of an incompressible fluid into a stationary equivalent fluid
bulk, is explored both numerically and experimentally for swirl number S ∈ [0, 1]. For the numerical work, two practically realisable inlet conditions are investigated
with swirl simultaneously superposed onto a linear momentum discharge; the corresponding circulation based Reynolds number is 7500. The results reveal that, for S > 1/2, the addition of swirl promotes the breakdown of the leading primary vortex ring structure, giving rise to the striking feature of significant negative vorticity, or opposite sign vorticity (OSV), generation in the region surrounding the primary vortex ring core, whose strength scales with S2. Through a non-linear interaction with the vortex breakdown, the radius of the primary toroidal vortex core is rapidly
increased; consequently, the self-induced propagation velocity of the leading ring decreases with S and vortex stretching along the circular primary vortex core increases counteracting viscous diffusion effects. The latter governs the evolution of the peak vorticity intensity and the swirl velocity magnitude in the primary ring core, the circulation growth rate of this ring, as well as the vorticity intensity of the trailing jet and hence its stability. This combination of effects leads to an increased dimensionless kinetic energy for the primary ring with increasing S and results in an almost linearly decreasing circulation based formation number, F. In a rigorous complementary experimental investigation, OSV is observed by introducing swirl using a rotating pipe, varying the time period before the piston stroke to achieve the desired swirl strength at a Reynolds number of 1000. Rotating pipe is found to
generate a secondary flow altering the inlet condition. Nevertheless, it is observed, using short periods of pipe rotation and higher angular speed, that it is possible to
generate a swirling vortex ring with less OSV production and all the related effects discussed above. The relation between F and the radius of the vortex ring is investigated through manipulation of ring radius growth, achieved through its interaction with a preceding vortex ring. Reducing radius growth, facilitates an increase of the circulation of the vortex ring, which in turn affects its F value
Jets and instabilities in forced magnetohydrodynamic flows
Magnetic fields are present in the solar system and astrophysical bodies (e.g. the Sun's field, the Earth's field, and the fields of giant planets, stars and galaxies). Our research examines the effect of magnetic fields on these systems, extending the work of Meshalkin and Sinai (1961) & Manfroi and Young (2002). The results will be useful for understanding the effects of the magnetic field in more turbulent regimes, although this study is concerned with the instabilities associated with classical laminar flow. We aim to investigate the role played by the magnetic field in modifying the stability properties of planar-forced fluid flows. In the absence of magnetic fields, the flow found by a body force, and nonlinear interactions with Rossby waves result in the generation of strong zonal flows. However, we find that the presence of a weak magnetic field suppresses the zonal jet generation.
Here we study the instabilities of the Kolmogorov flow. We consider u_0=(0,sin x ) as a 2D incompressible flow. In the presence of a mean magnetic field, the dynamics are governed by the Navier–Stokes equations and the induction equation. We perform a classical linear analysis, in which growth rate, stability criteria, and MHD effects are derived. Instabilities are investigated associated with two magnetic field orientations, which can be x-directed (horizontal) or y-directed (vertical}) in our two-dimensional system to give an MHD version of Kolmogorov flow. In a basic equilibrium state magnetic field lines are straight for the case of vertical field and sinusoidal for horizontal field with an additional component of the external force balancing the resulting Lorentz force
Subgrid scale modeling for large eddy simulation of supercritical mixing and combustion
Large eddy simulation (LES) is a widely used modeling and simulation technique in turbulent flow research. While the LES methodology and accompanying subgrid scale (SGS) modeling have been developed and applied over decades, primarily in the context of ideal gas conditions, their extension to complex multi-physics flows encountered in aerospace propulsion requires further refinement. In particular, the application of LES to turbulent flows at supercritical conditions presents several new modeling challenges and uncertainties. The scope of this dissertation is to investigate the theoretical LES formalism and SGS modeling framework for multi-species turbulent mixing and combustion at supercritical pressures. The goal is to identify the deficiencies with the current methodology and to establish a refined and consistent framework that accurately accounts for all the necessary physics.
In this dissertation, a consistent theoretical formulation of the filtered governing equations for LES is derived. Direct numerical simulations (DNS) are performed for spatially evolving non-reacting and reacting mixing layers at supercritical pressures. The complete set of terms in the filtered equations are quantified and analyzed using the DNS datasets. Based on the analyses, two new groups of subgrid terms are identified as important quantities to account in the LES framework. Parametric analyses are performed as a function of the filter resolution to derive resolution considerations for practical LES applications.
The performance and accuracies of two state-of-the-art subgrid modeling approaches for the traditional subgrid fluxes are assessed. The study demonstrates the better performance of scale-similarity based models over the eddy-viscosity based approaches. The study also reveals the deficiencies of conventional subgrid modeling approaches for LES of supercritical combustion. To address the additional modeling requirement for the filtered equation of state, novel subgrid modeling approaches are proposed. The performance of these models are tested and good improvements are demonstrated.Ph.D
Learning and Control of Dynamical Systems
Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise.
In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems.
We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p
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