No description available.

A complete understanding of the cooling process when hot rolling steel is essential to understanding how the quality of the steel is connected to the cooling. This is why it is of great interest to simulate this process. However traditional CFD methods are too expensive in terms of CPU time. Knowing that video games successfully simulate fluids in reasonable time, those methods could be useful for simulating the cooling process in steel manufacturing. This would mean a loss in accuracy that could be acceptable. In this thesis different methods used for fluid simulations have been studied. The Smoothed Particle Hydrodynamics (SPH) method has been chosen. The method has been implemented for simulating the cooling process in MATLAB, which is a matrix operation based programming tool. Convincing results have been achieved for a big scale, but problems still remain for an implementation on a small scale.

When applying optimization techniques to complex physical systems, using very large numerical models for the solution of a system of parameter dependent partial differential equations (PDEs) is usually intractable. Surrogate models are used to provide an approximation to the high fidelity models while being computationally cheaper to evaluate. Typically, for time dependent nonlinear problems a reduced order model is built using a basis obtained through proper orthogonal decomposition (POD) and Galerkin projection of the system dynamics. In this thesis we present theoretical and numerical results for parameter dependent model reduction techniques. The methods are motivated by the need for surrogate models specifically designed for nonlinear parameter dependent systems. We focus on methods in which the projection basis also depends on the parameter through extrapolation and interpolation. Numerical examples involving 1D Burgers' equation, 2D Navier-Stokes equations and 2D Boussinesq equations are presented. For each model problem comparison to traditional POD reduced order models will also be presented. / Ph. D.

This thesis deals with the development of a new adaptive algorithm for three-dimensional fluid flows based on a residual error estimator. The residual, known as the R –parameter has been successfully extended to three dimensions using a novel approach for arbitrary grid topologies. The computation of the residual error estimator in three dimensions is based on a least-squares based reconstruction and the order of accuracy of the latter is critical in obtaining a consistent estimate of the error. The R –parameter can become inconsistent on three–dimensional meshes depending on the grid quality. A Zero Mean Polynomial(ZMP) which is k–exact, and which preserves the mean has been used in this thesis to overcome the problem. It is demonstrated that the ZMP approach leads to a more accurate estimation of solution derivatives as opposed to the conventional polynomial based least-squares method. The ZMP approach is employed to compute the R –parameter which is the n used to derive the criteria for refinement and derefinement. Studies on three different complex test problems involving inviscid, laminar and turbulent flows demonstrate that the new adaptive algorithm is capable of detecting the sources of error efficiently and lead to accurate results independent of the grid topology.

Active matter theory studies the collective behaviour of self-propelled organisms or objects. Although the field has made great progress in the past decade, little is known of the role played by confinement and surfaces. This thesis analyses the self-organisation of dense bacterial suspensions in three different microchambers: flattened drops, racetracks and lattices of cavities. Suspensions of swimming bacteria are well-known to spontaneously form macroscopic quasi-turbulent patterns such as jets and swirls. Confinement inside flattened drops and racetracks stabilises their motion into a spiral vortex and wavy streams, respectively. We have quantitatively measured and analysed bacterial circulation and discovered cells at the interfaces to move against the bulk. To understand this phenomenon, we developed a method able to measure simultaneously the directions of swimming and of motion. Experiments in drops reveal that cells align in a helical pattern, facing outward and against the main bulk circulation. Likewise, bacteria in racetracks share a biased orientation against the overall stream. Particle-based simulations confirm these results and identify hydrodynamic interactions as the main driving force: bacteria generate long-range fluid flows which advect the suspension in the bulk against its swimming direction, resulting in the double-circulation pattern. We have finally injected dense suspensions of bacteria into lattices of cavities. They form a single vortex in each cavity, initially spinning clockwise or counterclockwise with equal probabilities. Changing the topology of the lattice and the geometry of connections between cavities allows us to control the lattice state (random, ferromagnetic, antiferromagnetic, or unstable). Edge currents along interfaces and connections appear to determine the lattice organisation. We finally propose an Ising model to understand experimental results and estimate Hamiltonian and interactions parameters. This work opens new perspectives for the study of active matter and, we hope, will have a great impact on the field.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19681号 / 工博第4136号 / 新制||工||1638(附属図書館) / 32717 / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 西脇 眞二, 教授 稲室 隆二, 教授 松原 厚 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

<p> An investigation of the technique of obtaining the wall shear stress in a two-phase flow, by measuring the mass transfer coefficient at the wall with the electrochemical method, has been completed.</p> <p> The experiments involved the measurement of flow rates, pressure drops, void fractions and mass transfer coefficients, for a cocurrent upwards gas-liquid flow in a vertical tube, 13 mm in diameter. The liquid phase was an electrolyte consisting of 1.0 to 3.0 molar sodium hydroxide, and 0.005 to 0.010 equimolar potassium ferricyanide and potassium ferrocyanide. The gas phase was nitrogen. The flow regimes studied were slug, churn and annular.</p> <p> Emphasis is placed on the measurements obtained with the electrochemical method. Its application, advantages and disadvantages are detailed. A series of single-phase experiments were performed to explore the characteristics of the method and to serve as benchmarks for the two-phase experiments.</p> <p> The space-time-averaged values of the mass transfer coefficient were found to give the wall shear stresses to an accuracy of ±20%. Frequency analysis of the local fluctuating values indicate that measurements of the local mass transfer coefficient can be used for flow regime identification.</p> <p> The theoretical flow regime map of Dukler and Taitel successfully predicted the flow regimes. The correlations of Griffith and Wallis, and Lockhart and Martinelli as modified by Davis, predicted the pressure drops and void fractions to an accuracy ±15% when applied to the appropriate flow regimes. As a further exercise, the force interactions between the phases, referred to as the interfacial shear terms, were calculated from both the measured and predicted void fractions and pressure drops.</p> / Thesis / Master of Engineering (MEngr)

This thesis describes the unstable dynamics of a gas jet impinging on a falling liquid film. This flow configuration is encountered in the jet wiping process, used in continuous coating applications such as the hot-dip galvanizing to control the thickness of a liquid coat on a moving substrate. The interaction between these flows generates a non-uniform coating layer, of great concern for the quality of industrial products, and results from a complex coupling between the interface instabilities of the liquid film and the confinement-driven instabilities of the impinging jet.Combining experimental and numerical methods, this thesis studied the dynamics of these flows on three simplified flow configurations, designed to isolate the key features of their respective instabilities and to provide complementary information on their mutual interaction. These configurations include the gas jet impingement on a falling liquid film perturbed with controlled flow rate pulsation, the gas jet impingement on a solid interface reproducing stable and unstable liquid film interfaces and a laboratory scaled model of the jet wiping process. Each of these configurations was reproduced on dedicated experimental set-up, instrumented for non-intrusive measurement techniques such as High-Speed Flow Visualization (HSFV) and Time-resolved Particle Image Velocimetry (TR-PIV) for the gas jet flow analysis, Laser Induced Fluorescence (LIF) tracking of the liquid interface, and 3D Light Absorption (LAbs) measurement of the liquid film thickness. To optimize the performances of these measurement techniques, several advanced data processing routines were developed, including a novel image pre-processing method for background removal in PIV and a dynamic feature tracking for the automatic detection of the jet flow and the liquid film interface from HSFV, LIF, and PIV videos.To identify the flow structures driving the unstable response of the jet flow, a novel data-driven modal decomposition was developed. This decomposition, referred to as Multiscale Proper Orthogonal Decomposition (mPOD), was validated on synthetic, numerical and experimental test cases and allowed for better feature extraction than classical alternatives such as Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD).The experimental work on these laboratory models was complemented with the analysis of several numerical simulations, including a classical 2D Unsteady Reynolds Averaged Navier Stokes (URANS) modeling of the gas jet impingement on a fixed interface, a 2D Variational Multiscale Simulation (VMS) with anisotropic mesh refinement of the gas jet impingement on a pulsing interface, and a 3D simulation of the jet wiping process combining Large Eddy Simulation (LES) on the gas side with Volume of Fluid (VOF) treatment of the liquid film flow. The experimental modal analysis on the dynamic response of the gas jet and the characterization of the pressure-velocity coupling in the numerical investigation allowed for a complete picture of the mechanism driving the jet oscillation and its possible impact on the liquid film.In parallel, several flow control strategies to prevent the jet oscillation were developed, tested numerically and experimentally in simplified conditions, and later implemented on the design of a new nozzle for the jet wiping process. This new nozzle was finally tested on a laboratory scale of the wiping process and its performances compared to single jet and multiple jet wiping configurations. In these three cases, the experimental work presents the modal analysis of the gas field using TR-PIV and mPOD, the liquid interface tracking via LIF, and the final coating thickness characterization via LAbs.The large spatiotemporally resolved experimental database allowed to give a detailed description of the jet wiping instability and to provide new insights on this fascinating fundamental and applied problem of fluid dynamics. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Simuler les réservoirs souterrains permet d’optimiser la production d’hydrocarbures. Les réservoirs naturellement ou hydrauliquement fracturés détiennent une part importante des réserves et exhibent un degré élevé d’hétérogénéité : les fractures, difficiles à détecter, impactent fortement la production via des réseaux préférentiels d’écoulement. Une modélisation précise de ces forts contrastes permettrait d’optimiser l’exploitation des ressources tout en maîtrisant mieux les risques environnementaux. L’enjeu est de prédire les processus d’écoulement multi échelles par un modèle simplement paramétrable. Une stratégie de simulations, qui améliore la fiabilité et les temps de calculs est mise au point dans cette thèse. Elle permet de simuler numériquement ou analytiquement la complexité d’un réservoir fracturé à grande échelle. Ces techniques dont l’intérêt est démontré sur un réservoir de roche mère trouvent des applications en géothermie ou dans la gestion des ressources en eau. / Fluid flow simulation is used to optimize oil and gas production. Naturally or hydraulically fractured reservoirs hold a significant part of reserves, difficult to assess. Fractures may create preferential flow paths heavily impacting fluid flow. Accurate modeling of fractured media accounting for strong contrasts would allow operators to optimize resources exploitation while better controlling environmental risks. Integrating sparse available data, we aim at predicting fluid flow processes occurring in the earth’s subsurface accounting for multi-scale fractures with a simply parameterized model. Improving the computational time and results reliability, we propose a full integrated strategy suitable for fractured reservoir specificities by simulating the fractures complexity on large scales. The techniques developed in this thesis, whose interest is demonstrated in an unconventional field case study, can find other applications in geothermal engineering and water resources management

<p dir="ltr">Machine learning and deep learning techniques now provide innovative tools for addressing problems in biological, engineering, and physical systems. Physics-informed neural networks (PINNs) are a type of neural network that incorporate physical laws described by partial differential equations (PDEs) into their supervised learning tasks. This dissertation aims to enhance PINNs with improved training techniques and loss functions to tackle the complex physics of viscoelastic flow and rheology more effectively. The focus areas of the dissertation are listed as follows: i) Assigning relative weights to loss terms in training physics-informed neural networks (PINNs) is complex. We propose a solution using numerical integration via backward Euler discretization to leverage statistical properties of data for determining loss weights. Our study focuses on two and three-dimensional Navier-Stokes equations, using spatio-temporal velocity and pressure data to ascertain kinematic viscosity. We examine two-dimensional flow past a cylinder and three-dimensional flow within an aneurysm. Our method, tested for sensitivity and robustness against various factors, converges faster and more accurately than traditional PINNs, especially for three-dimensional Navier-Stokes equations. We validated our approach with experimental data, using the velocity field from PIV channel flow measurements to generate a reference pressure field and determine water viscosity at room temperature. Results showed strong performance with experimental datasets. Our proposed method is a promising solution for ’stiff’ PDEs and scenarios requiring numerous constraints where traditional PINNs struggle. ii) Machine learning algorithms are valuable for fluid mechanics, but high data costs limit their practicality. To address this, we present viscoelasticNet, a Physics-Informed Neural Network (PINN) framework that selects the appropriate viscoelastic constitutive model and learns the stress field from a given velocity flow field. We incorporate three non-linear viscoelastic models: Oldroyd-B, Giesekus, and Linear PTT. Our framework uses neural networks to represent velocity, pressure, and stress fields and employs the backward Euler method to construct PINNs for the viscoelastic model. The approach is multistage: first, it solves for stress, then uses stress and velocity fields to solve for pressure. ViscoelasticNet effectively learned the parameters of the viscoelastic constitutive model on noisy and sparse datasets. Applied to a two-dimensional stenosis geometry and cross-slot flow, our framework accurately learned constitutive equation parameters, though it struggled with peak stress at cross-slot corners. We suggest addressing this by exploring smaller domains. ViscoelasticNet can extend to other rheological models like FENE-P and extended Pom-Pom and learn entire equations, not just parameters. Future research could explore more complex geometries and three-dimensional cases. Complementing Particle Image Velocimetry (PIV), our method can determine pressure and stress fields once the constitutive equation is learned, allowing the modeling of future fluid applications. iii) Physics-Informed Neural Networks (PINNs) are widely used for solving inverse and forward problems in various scientific and engineering fields. However, most PINNs frameworks operate within the Eulerian domain, where physical quantities are described at fixed points in space. We explore coupling Eulerian and Lagrangian domains using PINNs. By tracking particles in the Lagrangian domain, we aim to learn the velocity field in the Eulerian domain. We begin with a sensitivity analysis, focusing on the time-step size of particle data and the number of particles. Initial tests with external flow past a cylinder show that smaller time-step sizes yield better results, while the number of particles has little effect on accuracy. We then extend our analysis to a real-world scenario: the interior of an airplane cabin. Here, we successfully reconstruct the velocity field by tracking passive particles. Our findings suggest that this coupled Eulerian-Lagrangian PINNs framework is a promising tool for enhancing traditional experimental techniques like particle tracking. It can be extended to learn additional flow properties, such as the pressure field for three-dimensional internal flows, and infer viscosity from passive particle tracking, providing deeper insights into complex fluids and their constitutive models. iv) Time-fractional differential equations are widely used across various fields but often present computational and stability challenges, especially in inverse problems. Leveraging Physics-Informed Neural Networks (PINNs) offers a promising solution for these issues. PINNs efficiently compute fractional time derivatives using finite differences and handle other derivatives via automatic differentiation. This study addresses two inverse problems: (1) anomalous diffusion and (2) fractional viscoelasticity. Our approach defines residual loss scaled with the standard deviation of observed data, using numerically generated and experimental datasets to learn fractional coefficients and calibrate parameters for the fractional Maxwell model. Our framework demonstrated robust performance for anomalous diffusion, maintaining less than 10% relative error in predicting the generalized diffusion coefficient and the fractional derivative order, even with 25% Gaussian noise added to the dataset. This highlights the framework’s resilience and accuracy in noisy conditions. We also validated our approach by predicting relaxation moduli for pig tissue samples, achieving relative errors below 10% compared to literature values. This underscores the efficacy of our fractional model with fewer parameters. Our method can be extended to model non-linear fractional viscoelasticity, incorporate experimental data for anomalous diffusion, and apply it to three-dimensional scenarios, broadening its practical applications.</p>