scholarly journals A Sphere Held Fixed in a Poiseuille Flow near a Rough Wall

2021 ◽  
Vol 17 (3) ◽  
pp. 289-306
Author(s):  
K. Lamzoud ◽  
◽  
R. Assoudi ◽  
F. Bouisfi ◽  
M. Chaoui ◽  
...  
Keyword(s):  
Spherical Particle ◽  
Poiseuille Flow ◽  
Stokes Equations ◽  
Analytical Calculation ◽  
Rigid Wall ◽  
Rough Wall ◽  
Reciprocal Theorem ◽  
Drag Forces ◽  
Sphere Radius ◽  
Lift And Drag

We present here an analytical calculation of the hydrodynamic interactions between a smooth spherical particle held fixed in a Poiseuille flow and a rough wall. By the assumption of a low Reynolds number, the flow around a fixed spherical particle is described by the Stokes equations. The surface of the rigid wall has periodic corrugations, with small amplitude compared with the sphere radius. The asymptotic development coupled with the Lorentz reciprocal theorem are used to find the analytical solution of the couple, lift and drag forces exerted on the particle, generated by the second-order flow due to the wall roughness. These hydrodynamic effects are evaluated in terms of amplitude and period of roughness and also in terms of the distance between sphere and wall.

scholarly journals Deep learning of vortex-induced vibrations

2018 ◽  
Vol 861 ◽  
pp. 119-137 ◽  
Author(s):  
Maziar Raissi ◽  
Zhicheng Wang ◽  
Michael S. Triantafyllou ◽  
George Em Karniadakis
Keyword(s):  
Neural Networks ◽  
Velocity Field ◽  
Deep Neural Networks ◽  
Stokes Equations ◽  
Scattered Data ◽  
Space Time ◽  
Dynamic Motion ◽  
Drag Forces ◽  
Vortex Induced Vibrations ◽  
Lift And Drag

Vortex-induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier–Stokes equations coupled with the structure’s dynamic motion equation. In the first case, given scattered data in space–time on the velocity field and the structure’s motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure’s dynamic motion. In the second case, given scattered data in space–time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.

scholarly journals Deep Learning of Vortex Induced Vibrations

2018 ◽  
Author(s):  
Maziar Raissi ◽  
Zhicheng Wang ◽  
Michael S Triantafyllou ◽  
George Karniadakis
Keyword(s):  
Neural Networks ◽  
Velocity Field ◽  
Deep Neural Networks ◽  
Stokes Equations ◽  
Scattered Data ◽  
Space Time ◽  
Dynamic Motion ◽  
Drag Forces ◽  
Vortex Induced Vibrations ◽  
Lift And Drag

Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics (CFD) methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier-Stokes equations coupled with the structure's dynamic motion equation. In the first case, given scattered data in space-time on the the velocity field and the structure's motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure's dynamic motion. In the second case, given scattered data in space-time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems is part of our ongoing development of physics-informed learning machines, where the traditional CFD methodology is abandoned in favor of deep neural networks inference, circumventing the tyranny of elaborate mesh generation.

Influence of Wall Proximity on the Lift and Drag of a Particle in an Oscillatory Flow

2004 ◽  
Vol 127 (3) ◽  
pp. 583-594 ◽  
Author(s):  
Paul F. Fischer ◽  
Gary K. Leaf ◽  
Juan M. Restrepo
Keyword(s):  
Lift Force ◽  
Degrees Of Freedom ◽  
Oscillatory Flow ◽  
Numerical Solutions ◽  
Stokes Equations ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Drag Forces ◽  
Lift And Drag ◽  
Two Parameters

We report on the lift and drag forces on a stationary sphere subjected to a wall-bounded oscillatory flow. We show how these forces depend on two parameters, namely, the distance between the particle and the bounding wall, and on the frequency of the oscillatory flow. The forces were obtained from numerical solutions of the unsteady incompressible Navier–Stokes equations. For the range of parameters considered, a spectral analysis found that the forces depended on a small number of degrees of freedom. The drag force manifested little change in character as the parameters varied. On the other hand, the lift force varied significantly: We found that the lift force can have a positive as well as a negative time-averaged value, with an intermediate range of external forcing periods in which enhanced positive lift is possible. Furthermore, we determined that this force exhibits a viscous-dominated and a pressure-dominated range of parameters.

Internal laminar flow

2018 ◽  
Author(s):  
Marcel Escudier
Keyword(s):  
Poiseuille Flow ◽  
Stokes Equations ◽  
Circular Cross Section ◽  
Navier Stokes ◽  
Parallel Plates ◽  
Concentric Cylinder ◽  
Navier Stokes Equations ◽  
Concentric Cylinders ◽  
Constant Viscosity ◽  
Pressure Driven Flow

In this chapter it is shown that solutions to the Navier-Stokes equations can be derived for steady, fully developed flow of a constant-viscosity Newtonian fluid through a cylindrical duct. Such a flow is known as a Poiseuille flow. For a pipe of circular cross section, the term Hagen-Poiseuille flow is used. Solutions are also derived for shear-driven flow within the annular space between two concentric cylinders or in the space between two parallel plates when there is relative tangential movement between the wetted surfaces, termed Couette flows. The concepts of wetted perimeter and hydraulic diameter are introduced. It is shown how the viscometer equations result from the concentric-cylinder solutions. The pressure-driven flow of generalised Newtonian fluids is also discussed.

scholarly journals Preliminary Results on the Dynamics of a Pile-Moored Fish Cage with Elastic Net in Currents and Waves

2020 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
Gianluca Zitti ◽  
Nico Novelli ◽  
Maurizio Brocchini
Keyword(s):  
Numerical Model ◽  
Laboratory Experiments ◽  
Numerical Models ◽  
Offshore Structures ◽  
Fish Farm ◽  
Maximum Displacement ◽  
Drag Forces ◽  
Real Size ◽  
Lift And Drag ◽  
Mesh Grid

Over the last decades, the aquaculture sector increased significantly and constantly, moving fish-farm plants further from the coast, and exposing them to increasingly high forces due to currents and waves. The performances of cages in currents and waves have been widely studied in literature, by means of laboratory experiments and numerical models, but virtually all the research is focused on the global performances of the system, i.e., on the maximum displacement, the volume reduction or the mooring tension. In this work we propose a numerical model, derived from the net-truss model of Kristiansen and Faltinsen (2012), to study the dynamics of fish farm cages in current and waves. In this model the net is modeled with straight trusses connecting nodes, where the mass of the net is concentrated at the nodes. The deformation of the net is evaluated solving the equation of motion of the nodes, subjected to gravity, buoyancy, lift, and drag forces. With respect to the original model, the elasticity of the net is included. In this work the real size of the net is used for the computation mesh grid, this allowing the numerical model to reproduce the exact dynamics of the cage. The numerical model is used to simulate a cage with fixed rings, based on the concept of mooring the cage to the foundation of no longer functioning offshore structures. The deformations of the system subjected to currents and waves are studied.

Machine learning–based reduced-order modeling of hydrodynamic forces using pressure mode decomposition

2021 ◽  
pp. 095441002199986
Author(s):  
Hassan F Ahmed ◽  
Hamayun Farooq ◽  
Imran Akhtar ◽  
Zafar Bangash
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Stokes Equations ◽  
Decomposition Analysis ◽  
Hydrodynamic Forces ◽  
Reduced Order Modeling ◽  
Navier Stokes ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Lift And Drag

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients.

Stability of Hagen-Poiseuille flow with superimposed rigid rotation

1976 ◽  
Vol 73 (1) ◽  
pp. 153-164 ◽  
Author(s):  
P.-A. Mackrodt
Keyword(s):  
Poiseuille Flow ◽  
Pipe Flow ◽  
Stokes Equations ◽  
Three Dimensional ◽  
Reynolds Numbers ◽  
Neutral Curve ◽  
Rigid Rotation ◽  
Transition To Turbulence ◽  
Navier Stokes ◽  
Navier Stokes Equations

The linear stability of Hagen-Poiseuille flow (Poiseuille pipe flow) with superimposed rigid rotation against small three-dimensional disturbances is examined at finite and infinite axial Reynolds numbers. The neutral curve, which is obtained by numerical solution of the system of perturbation equations (derived from the Navier-Stokes equations), has been confirmed for finite axial Reynolds numbers by a few simple experiments. The results suggest that, at high axial Reynolds numbers, the amount of rotation required for destabilization could be small enough to have escaped notice in experiments on the transition to turbulence in (nominally) non-rotating pipe flow.

Computational Aerodynamics of a Winston-Cup-Series Race Car

2002 ◽  
Author(s):  
Oktay Baysal ◽  
Terry L. Meek
Keyword(s):  
Present Model ◽  
Pressure Coefficient ◽  
The Body ◽  
Race Car ◽  
Drag Forces ◽  
Surface Pressure Distribution ◽  
Computational Aerodynamics ◽  
Baseline Case ◽  
Quantitative Results ◽  
Lift And Drag

Since the goal of racing is to win and since drag is a force that the vehicle must overcome, a thorough understanding of the drag generating airflow around and through a race car is greatly desired. The external airflow contributes to most of the drag that a car experiences and most of the downforce the vehicle produces. Therefore, an estimate of the vehicle’s performance may be evaluated using a computational fluid dynamics model. First, a computer model of the race car was created from the measurements of the car obtained by using a laser triangulation system. After a computer-aided drafting model of the actual car was developed, the model was simplified by removing the tires, roof strakes, and modification of the spoiler. A mesh refinement study was performed by exploring five cases with different mesh densities. By monitoring the convergence of the computed drag coefficient, the case with 2 million elements was selected as being the baseline case. Results included flow visualization of the pressure and velocity fields and the wake in the form of streamlines and vector plots. Quantitative results included lift and drag, and the body surface pressure distribution to determine the centerline pressure coefficient. When compared with the experimental results, the computed drag forces were comparable but expectedly lower than the experimental data mainly attributable to the differences between the present model and the actual car.

Vibration Excitation Forces in a Normal Triangular Tube Bundle Subjected to Two-Phase Cross Flow

2011 ◽  
Author(s):  
E. S. Perrot ◽  
N. W. Mureithi ◽  
M. J. Pettigrew ◽  
G. Ricciardi
Keyword(s):  
Tube Bundle ◽  
Cross Flow ◽  
Random Excitation ◽  
Two Phase ◽  
Constant Frequency ◽  
Fluid Forces ◽  
Drag Forces ◽  
Wide Range ◽  
Drag And Lift ◽  
Lift And Drag

This paper presents test results of vibration forces in a normal triangular tube bundle subjected to air-water cross-flow. The dynamic lift and drag forces were measured with strain gage instrumented cylinders. The array has a pitch-to-diameter ratio of 1.5, and the tube diameter is 38 mm. A wide range of void fraction and fluid velocities were tested. The experiments revealed significant forces in both the drag and lift directions. Constant frequency and quasi-periodic fluid forces were found in addition to random excitation. These forces were analyzed and characterized to understand their origins. The forces were found to be dependent on the position of the cylinder within the bundle. The results are compared with those obtained with flexible cylinders in the same tube bundle and to those for a rotated triangular tube bundle. These comparisons reveal the influence of quasi-periodic forces on tube motions.

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