21 research outputs found

    A Data-Driven Approach for Generating Vortex Shedding Regime Maps for an Oscillating Cylinder

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    Recent developments in wind energy extraction methods from vortex-induced vibration (VIV) have fueled the research into vortex shedding behaviour. The vortex shedding map is vital for the consistent use of normalized amplitude and wavelength to validate the predicting power of forced vibration experiments. However, there is a lack of demonstrated methods of generating this map at Reynolds numbers feasible for energy generation due to the high computational cost and complex dynamics. Leveraging data-driven methods addresses the limitations of the traditional experimental vortex shedding map generation, which requires large amounts of data and intensive supervision that is unsuitable for many applications and Reynolds numbers. This thesis presents a data-driven approach for generating vortex shedding maps of a cylinder undergoing forced vibration that requires less data and supervision while accurately extracting the underlying vortex structure patterns. The quantitative analysis in this dissertation requires the univariate time series signatures of local fluid flow measurements in the wake of an oscillating cylinder experiencing forced vibration. The datasets were extracted from a 2-dimensional computational fluid dynamic (CFD) simulation of a cylinder oscillating at various normalized amplitude and wavelength parameters conducted at two discrete Reynolds numbers of 4000 and 10,000. First, the validity of clustering local flow measurements was demonstrated by proposing a vortex shedding mode classification strategy using supervised machine learning models of random forest and -nearest neighbour models, which achieved 99.3% and 99.8% classification accuracy using the velocity sensors orientated transverse to the pre-dominant flow (), respectively. Next, the dataset of local flow measurement of the -component of velocity was used to develop the procedure of generating vortex shedding maps using unsupervised clustering techniques. The clustering task was conducted on subsequences of repeated patterns from the whole time series extracted using the novel matrix profile method. The vortex shedding map was validated by reproducing a benchmark map produced at a low Reynolds number. The method was extended to a higher Reynolds number case of vortex shedding and demonstrated the insight gained into the underlying dynamical regimes of the physical system. The proposed multi-step clustering methods denoted Hybrid Method B, combining Density-Based Clustering Based on Connected Regions with High Density (DBSCAN) and Agglomerative algorithms, and Hybrid Method C, combining -Means and Agglomerative algorithms demonstrated the ability to extract meaningful clusters from more complex vortex structures that become increasingly indistinguishable. The data-driven methods yield exceptional performance and versatility, which significantly improves the map generation method while reducing the data input and supervision required

    THIESEL 2022. Conference on Thermo-and Fluid Dynamics of Clean Propulsion Powerplants

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    The THIESEL 2022. Conference on Thermo-and Fluid Dynamic Processes in Direct Injection Engines planned in Valencia (Spain) for 8th to 11th September 2020 has been successfully held in a virtual format, due to the COVID19 pandemic. In spite of the very tough environmental demands, combustion engines will probably remain the main propulsion system in transport for the next 20 to 50 years, at least for as long as alternative solutions cannot provide the flexibility expected by customers of the 21st century. But it needs to adapt to the new times, and so research in combustion engines is nowadays mostly focused on the new challenges posed by hybridization and downsizing. The topics presented in the papers of the conference include traditional ones, such as Injection & Sprays, Combustion, but also Alternative Fuels, as well as papers dedicated specifically to CO2 Reduction and Emissions Abatement.Papers stem from the Academic Research sector as well as from the IndustryXandra Marcelle, M.; Payri MarĂ­n, R.; Serrano Cruz, JR. (2022). THIESEL 2022. Conference on Thermo-and Fluid Dynamics of Clean Propulsion Powerplants. Editorial Universitat PolitĂšcnica de ValĂšncia. https://doi.org/10.4995/Thiesel.2022.632801EDITORIA

    Improved surface displacement estimation through stacking cross-correlation spectra from multi-channel imagery

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    Studying sporadic and complex geophysical surface flows, like earthquakes or sea surface circulation, are challenging cases. If a satellite is able to image an event, it becomes essential to pull out as much information as possible. In this contribution we demonstrate a method to increase the coverage and signal-to-noise ratio for displacement estimation, making such surface flow estimates more complete. We leverage upon the redundant offset information acquired by multi-channel push-broom imagery. The individual cross-correlation spectra (cross power spectral density; Fourier transform of the cross-correlation function) of different spectral bands are averaged in the frequency domain before sub-pixel offset-estimation by phase-plane fitting. The method is demonstrated near Kaikƍura, where in 2016 a surface rupture occurred. RapidEye data from two different dates were used to reconstruct the displacement. In addition, the circulation along the coast is estimated from data from a single date where multiple spectral bands were acquired within seconds which made stacking of cross-correlation spectra possible. The demonstrated methodology is applied to data from the already decommissioned RapidEye constellation, but can be adopted to other pushbroom systems, such as the Landsat legacy or Sentinel-2

    Exponential asymptotics for discrete Painlevé equations

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    In this thesis we study Stokes phenomena behaviour present in the solutions of both additive and multiplicative difference equations. Specifically, we undertake an asymptotic study of the second discrete PainlevĂ© equation (dPII) as the independent variable approaches infinity, and consider the asymptotic behaviour of solutions of the q-Airy equation and the first q-PainlevĂ© equation in the limits q□(→┮ ) 1 and n□(→┮ ) ∞. Exponential asymptotic methods are used to investigate Stokes phenomena and obtain uniform asymptotic expansions of solutions of these equations. In the first part of this thesis, we obtain two types of asymptotic expansions which describe vanishing and non-vanishing type solution behaviour of dPII. In particular, we show that both types of solution behaviour can be expressed as the sum of an optimally-truncated asymptotic series and an exponentially subdominant correction term. We then determine the Stokes structure and investigate Stokes behaviour present in these solutions. From this information we show that the asymptotic expansions contain one free parameter hidden beyond-all-orders and determine regions of the complex plane in which these asymptotic descriptions are valid. Furthermore, we deduce special asymptotic solutions which are valid in extended regions and draw parallels between these asymptotic solutions to the tronquĂ©e and tri-tronquĂ©e solutions of the second PainlevĂ© equation. In the second part of this thesis, we then extend the exponential asymptotic method to q-difference equations. In our analysis of both the q-Airy and first q-PainlevĂ© equations, we find that the Stokes structure is described by curves referred to as q-spirals. As a consequence, we discover that the Stokes structure for solutions of q-difference equations separate the complex plane into sectorial regions bounded by arcs of spirals rather than traditional rays

    Geometry identification and data enhancement for distributed flow measurements

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    The measurement of fluid motion is an important tool for researchers in fluiddynamics. Measurements with increasing precision did expedite the development of fluid-dynamic models and their theoretical understanding. Several well-established experimental techniques provide point-wise information on the flow field. In recent years novel measurement modalities have been investigated which deliver spatially resolved three-dimensional velocity measurements. Note that for methods such as particle tracking and tomographic particle imaging optical access to the flow domain is necessary. For other methods like magnetic resonance velocimetry, CT-angiography, or x-ray velocimetry this is, however, not the case. Such a property and also the fact that those methods are able to provide three-dimensional velocity fields in a rather short acquisition time makes them in particular suited for in-vivo applications. Our work is motivated by such non-invasive velocity measurement techniques for which no optical access to the interior of the geometry is needed and also not available in many cases. Here, an additional difficulty is that the exact flow geometry is in general not known a priori. The measurement techniques we are interested in, are extensions of already available medical imaging modalities. As a prototypical example, we consider magnetic resonance velocimetry, which is also suited for the measurement of turbulent fluid motion. We will also discuss computational examples using such measurement data. General purpose. Our main goal is a suitable post-processing of the available velocity data and also to obtain additional information. The measurements available from magnetic resonance velocimetry consist of several components given on a fixed field of view. The magnitude of the MRT signal corresponds to a proton density and thus e.g. the density of water molecules. Those data typically give a clear indication of the position and size of the flow geometry. The velocity data, on the other hand, are substantially perturbed outside the flow domain. This is a typical feature of measurements stemming from magnetic resonance velocimetry. Note that the surrounding noise usually has a notably higher magnitude than the actual measurements. Thus, a first necessary step will be to somehow separate the domain containing valuable velocity data from the noise surrounding it. For this reason, we apply some kind of image segmentation where we make use of the given den-sity image. Since the velocity values are given on the same field of view the segmentation directly transfers to those data. Due to the measurement procedure also the segmented velocity data are contaminated by measurement errors. Therefore, besides segmentation, additional post-processing is necessary in order to make the flow measurements available for further usage. In a second step, we propose a problem adapted data enhancement method which is able to provide a smoothed velocity field on the one hand, and also provides additional information on the other hand, like for instance the pressure drop or an estimate for the wall shear stress. The two main steps will therefore be: (i) The identification of the flow geometry, where we make use of the available density measurements. (ii) The denoising and improvement of the segmented velocity data, by using a suitable fluid-dynamical model. Outline. In part I of this thesis, we introduce our basic approach to the geom- etry identification and velocity enhancement problems described above. Both problems are formulated as optimal control problems governed by a partial differential equation and we shortly discuss some general aspects of the analysis and the solution of such problems in section 4. In part II, we thoroughly discuss and analyze the geometry identification problem introduced in section 2. The procedure is formulated as an inverse ill-posed problem and we propose a Tikhonov regularization for its stable solution. We show that the resulting optimal control problem has a solution and discuss its numerical treatment with iterative methods. Finally, a systematic discretization can be realized using finite elements which is also demonstrated by numerical tests. The velocity enhancement problem is introduced in part III. We propose a linearized flow-model which directly incorporates the available measurements. The resulting modeling error can be quantified in terms of the data error. The reconstruction method is then formulated as an optimal control problem subject to the linearized equations. We show the existence of a unique solution and derive estimates for the reconstruction error. Additionally, a reconstruction for the pressure is obtained for which we derive similar error estimates. We discuss the systematic discretization using finite elements and show preliminary computational examples for the verification of the derived estimates. In order to verify the applicability of the proposed methods to realistic data, we consider an application using experimental data in part IV. We use measurements of a human blood vessel stemming from magnetic resonance velocimetry obtained at the University Medical Center in Freiburg. After a suitable pre-processing of the available data, we apply the geometry identification method in order to obtain a discretization of the blood vessel. Using the generated mesh, we reconstruct an enhanced velocity field and the pressure from the available velocity data

    Mechanics of remora adhesion

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    Remora fishes are capable of rapid, reversible, and robust attachment to a wide variety of marine hosts both natural and artificial with widely varying geometric and material properties. Despite its unique abilities, the mechanisms responsible for remora attachment have received little attention in scientific literature in comparison to the number of works commenting on it. The objective of this work is to identify and quantify the behavior and limitations of the critical mechanisms responsible for remora attachment. Traditional dissection techniques were combined with high-resolution three-dimensional scans to characterize and identify critical structural metrics pertaining to remora morphology. The structural metrics were incorporated into simulations to predict remora behavior during attachment. Finally, experimental methods were performed on artificial tissues to validate model predictions when necessary. The work is of value to both the engineering and biological communities through the creation of design tools, analyses, data sets, and simulations that provide both quantitative design data for bioinspired devices and/or methodologies, but also insight into the behavior of the remora itself.Ph.D

    Model Order Reduction in Fluid Dynamics: Challenges and Perspectives

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    This chapter reviews techniques of model reduction of fluid dynamics systems. Fluid systems are known to be difficult to reduce efficiently due to several reasons. First of all, they exhibit strong nonlinearities — which are mainly related either to nonlinear convection terms and/or some geometric variability — that often cannot be treated by simple linearization. Additional difficulties arise when attempting model reduction of unsteady flows, especially when long-term transient behavior needs to be accurately predicted using reduced order models and more complex features, such as turbulence or multiphysics phenomena, have to be taken into consideration. We first discuss some general principles that apply to many parametric model order reduction problems, then we apply them on steady and unsteady viscous flows modelled by the incompressible Navier-Stokes equations. We address questions of inf-sup stability, certification through error estimation, computational issues and — in the unsteady case — long-time stability of the reduced model. Moreover, we provide an extensive list of literature references

    Volumetric measurements of the transitional backward facing step flow

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    The thesis describes state of the art volumetric measurement techniques and applies a 3D measurement technique, 3D Scanning Particle Tracking Velocimetry, to the transitional backward facing step flow. The measurement technique allows the spatial and temporal analysis of coherent structures apparent at the backward facing step. The thesis focusses on the extraction and interaction of coherent flow structures like shear layers or vortical structures

    Computer vision and optimization methods applied to the measurements of in-plane deformations

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