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The purpose of this paper is the examination of fluid flow around NACA0012 airfoil, with the aim of the numerical validation between the experimental results in the wind tunnel and the Lattice Boltzmann method (LBM) analysis, for the medium Reynolds number (Re = 191,000). The LBM–large Eddy simulation (LES) method described in this paper opens up opportunities for faster computational fluid dynamics (CFD) analysis, because of the LBM scalability on high performance computing architectures, more specifically general purpose graphics processing units (GPGPUs), pertaining at the same time the high resolution LES approach.

Process starts with data collection in open-circuit wind tunnel experiment. Furthermore, the pressure coefficient, as a comparative variable, has been used with varying angle of attack (2°, 4°, 6° and 8°) for both experiment and LBM analysis. To numerically reproduce the experimental results, the LBM coupled with the LES turbulence model, the generalized wall function (GWF) and the cumulant collision operator with D3Q27 velocity set has been used. Also, a mesh independence study has been provided to ensure result congruence.

The proposed LBM methodology is capable of highly accurate predictions when compared with experimental data. Besides, the special significance of this work is the possibility of experimental and CFD comparison for the same domain dimensions.

Considering the quality of results, root-mean-square error (RMSE) shows good correlations both for airfoil’s upper and lower surface. More precisely, maximal RMSE for the upper surface is 0.105, whereas 0.089 for the lower surface, regarding all angles of attack.

The purpose of this paper is to discuss a data interpolation method of curved surfaces from the point of dimension reduction and manifold learning.

Instead of transmitting data of curved surfaces in 3D space directly, the method transmits data by unfolding 3D curved surfaces into 2D planes by manifold learning algorithms. The similarity between surface unfolding and manifold learning is discussed. Projection ability of several manifold learning algorithms is investigated to unfold curved surface. The algorithms’ efficiency and their influences on the accuracy of data transmission are investigated by three examples.

It is found that the data interpolations using manifold learning algorithms LLE, HLLE and LTSA are efficient and accurate.

The method can improve the accuracies of coupling data interpolation and fluid-structure interaction simulation involving curved surfaces.

The purpose of this paper is to propose a new strong-form numerical method, called the free element method, for solving general boundary value problems governed by partial differential equations. The main idea of the method is to use a locally formed element for each point to set up the system of equations. The proposed method is used to solve the fluid mechanics problems.

The proposed free element method adopts the isoparametric elements as used in the finite element method (FEM) to represent the variation of coordinates and physical variables and collocates equations node-by-node based on the newly derived element differential formulations by the authors. The distinct feature of the method is that only one independently formed individual element is used at each point. The final system of equations is directly formed by collocating the governing equations at internal points and the boundary conditions at boundary points. The method can effectively capture phenomena of sharply jumped variables and discontinuities (e.g. the shock waves).

a) A new numerical method called the FEM is proposed; b) the proposed method is used to solve the compressible fluid mechanics problems for the first time, in which the shock wave can be naturally captured; and c) the method can directly set up the system of equations from the governing equations.

This paper presents a completely new numerical method for solving compressible fluid mechanics problems, which has not been submitted anywhere else for publication.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.

Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.

While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.

It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

A precise and rapid temperature cycling of a small volume of fluid is vital for an effective DNA replication process using the polymerase chain reaction (PCR). The purpose of this paper is to study the velocity and temperature fields inside a rotating PCR‐tube during cooling of the enclosed liquid.

The velocity and temperature fields inside a rotating PCR‐tube during cooling of the enclosed liquid are studied. By using computational fluid dynamics, the time development of the flow can be investigated in detail. Owing to the rotation, the flow exhibits features which could never arise in a non‐rotating system.

An intricate azimuthal boundary layer flow is presented and explained. The inherent problem of stratification of the temperature is discussed, and different methods towards a remedy are presented. By analyzing the governing equations, some properties of the flow observed in the simulations are explained. It is shown that increasing the rate of rotation does not improve temperature homogenization.

The simulations were performed for a limited number of temperature boundary conditions, as well as a specific simulation geometry.

The analytical and simulation results offer fundamental insight into the physics behind increased DNA duplication. Further simulations offer possible design improvements.

While many studies have probed the effects of buoyancy in rotating cylinders and the development of boundary layers in stratified flows in conical containers rotating around their axis of symmetry, little work has been specifically focused on the case where the axis of rotation is normal to the direction of the stratification, which is the case in the present study.

Outlines a general methodology for the solution of the system of algebraic equations arising from the discretization of the field equations governing coupled problems. Considers that this discrete problem is obtained from the finite element discretization in space and the finite difference discretization in time. Aims to preserve software modularity, to be able to use existing single field codes to solve more complex problems, and to exploit computer resources optimally, emulating parallel processing. To this end, deals with two well‐known coupled problems of computational mechanics – the fluid‐structure interaction problem and thermally‐driven flows of incompressible fluids. Demonstrates the possibility of coupling the block‐iterative loop with the nonlinearity of the problems through numerical experiments which suggest that even a mild nonlinearity drives the convergence rate of the complete iterative scheme, at least for the two problems considered here. Discusses the implementation of this alternative to the direct coupled solution, stating advantages and disadvantages. Explains also the need for online synchronized communication between the different codes used as is the description of the master code which will control the overall algorithm.

The purpose of this paper is to propose an effective methodology for the industrial design of tangential inlet cyclone separators that is based on the fully three-dimensional (3D) simulation of the flow field within the cyclone coupled with an effective genetic algorithm.

The proposed fully 3D computational fluid dynamics (CFD) model makes use of the Reynold stress model for the accurate prediction of turbulence, while the particle trajectories are simulated using the one-way coupling discrete phase, which is a model particularly effective in case of low concentration of dust. To validate the CFD model, the numerical predictions are compared with experimental data available in the scientific literature. Eight design parameters were chosen, with the two objectives being the minimization of the pressure drop and the maximization of the collection efficiency.

The optimization procedure allows the determination of the Pareto Front, which represents the set of the best geometries and can be instrumental in taking an optimal decision in the presence of such a trade-off between the two conflicting objectives. The comparison among the individuals belonging to the Pareto Front with a more standard cyclone geometry shows that such a CFD global search is very effective.

The proposed procedure is tested for specific values of the operating conditions; however, it has general validity and can be used in place of typical procedures based on empirical models or engineers’ experience for the industrial design of tangential inlet cyclone separators with low solid loading.

Such an optimization process has never been proposed before for the design of cyclone separators; it has been developed with the aim of being both highly accurate and compatible with the industrial design time.

Computational fluid dynamics (CFD) technique is the most commonly used numerical approach to simulate fluid flow behaviour. Owing to its computationally, cost-intensive nature CFD models may not be easily and quickly deployable. In this regard, this study aims to present a support vector machine (SVM)-based metamodelling approach that can be easily trained and quickly deployed for carrying out large-scale studies.

Radial basis function and ε^*-insensitive loss function are used as kernel function and loss function, respectively. To prevent overfitting of the model, five-fold cross-validation root mean squared error is used while training the SVM metamodel. Rather than blindly using any SVM tuning parameters, a particle swarm optimisation (PSO) is used to fine-tune them. The developed SVM metamodel is tested using various error metrics on disjoint test data.

Using the SVM metamodel, a parametric study is conducted to understand the effect of various factors influencing the behaviour of the turbulent fluid flow in the pipe bend with CFD simulation data set. Based on the parametric study carried out, it is seen that the diametric position has the most effect on dimensionless axial velocity, whereas Reynolds number has the least effect.

This paper provides an effective PSO-tuned SVM metamodelling approach, which may be used as a significant cost-saving approach to quickly and accurately estimate fluid flow characteristics that, in general, require the use of expensive CFD models.

The purpose of this paper is to present different approaches for applying macroscopic boundary conditions in hybrid multiscale modelling.

Molecular dynamics (MD) was employed for the microscopic simulations. The continuum boundary conditions were applied either through rescaling of atomistic velocities or resampling based on velocity distribution functions.

The methods have been tested for various fluid flows with heat transfer scenarios. The selection of the most suitable method is not a trivial task and depends on a number of factors such as accuracy requirements and availability of computational resource.

The applicability of the methods has been assessed for liquid and gas flows. Specific parameters that affect their accuracy and efficiency have been identified. The effects of these parameters on the accuracy and efficiency of the simulations are investigated. The study provides knowledge regarding the development and application of boundary conditions in multiscale computational frameworks.