International Journal for Numerical Methods in Fluids最新文献

A modified fifth-order WENO-Z scheme is proposed by analogy with the non-normalized weights of the reformulated fifth-order adaptive order (AO) WENO scheme. We show that if the original fifth-order WENO-AO scheme is rewritten as the form of the conventional WENO combination, the resulting non-normalized weights can be divided into three parts: a constant one term, a local stencil smoothness measure term and a global stencil smoothness measure term. In order to make use of the latter two terms for constructing a modified WENO-Z scheme with enhanced performance, we change the form of the third term and introduce an adaptive scaling factor to adjust the contributions from the second and third terms. Numerical examples show that the modified fifth-order WENO-Z scheme has the advantage of high resolution in smooth regions and sharp capturing of discontinuities, and it can obtain evidently better results for shocked flows with small-scale structures compared with the recently developed WENO-Z+ and WENO-Z+M schemes.

Multi-objective optimization of ship form can effectively reduce ship energy consumption, and is one of the important research topics of green ships. However, the computational cost of numerical simulation based on computational fluid dynamics (CFD) theory is relatively high, which affects the efficiency of optimization. Traditional subjective weighting methods mostly rely on expert's experience, which affects the scientificity of optimization. This paper effectively integrates the CFD method, the improved multi-objective optimization algorithm and the objective weighting method to build a ship form multi-objective optimization framework. Conduct multi-objective optimization research on resistance and seakeeping performance of a very large crude oil carrier (KVLCC) ship. The improved bare-bones multi-objective particle swarm optimization (IBBMOPSO) algorithm is used to obtain the pareto front, and the kernel principal component analysis (KPCA) method is used to objectively assign the weight of each target. Finally, the optimal ship form scheme with high satisfaction was obtained. The multi-objective optimization framework constructed in this paper can provide a certain theoretical basis and technical support for the development of ship greening and digital transformation.

Maxwell fluid flow over a curved surface with the impacts of nonlinear convection and radiation, temperature-dependent properties, and magnetic field are investigated. The governing equations of the physical system are solved using wavelet based physics informed neural network, a machine learning technique. This is an unsupervised method, and the solutions have been obtained without knowing the numerical solution to the problem. Given the nonlinearity of the coupled equations, the methodology used is flexible to implement, and the activation function used improves the accuracy of the solution. We approximate the unknown functions using different neural network models and determine the solution by training the network. The special case of the obtained results is examined with the available results in the literature for validation of the proposed methodology. It is observed that the proposed approach gives reliable results for the analyzed problem of study. Further, an analysis of the influence of flow parameters (deborah number, variable thermal conductivity and viscosity parameter, velocity slip parameter, temperature ratio parameter, suction parameter, and convection parameters) on temperature and fluid flow velocity is carried out. It is observed that as the flow parameter Deborah number, velocity slip parameter, and viscosity parameter increase, there is a decline in velocity and an enhancement in temperature. This study of fluid flow over a curved surface has applications in the polymer industry, which plays an important role in the manufacturing of contact lenses.

This paper focuses on a general computational framework to unify both Lagrangian staggered-grid hydrodynamic (SGH) and cell-centered hydrodynamic (CCH) methods. One challenge is that artificial viscosity has contained empirical parameters in the SGH method for seven decades. To address this challenge, a new relationship between pressure and velocity is constructed using specific volume as a medium. Another challenge is that entropy is increasing in isentropic flows for the CCH method. To overcome this second challenge, the forces acting on a target cell are split into linear and quadratic terms in the CCH method. The numerical results of the two methods are almost identical. The scheme is more general than both existing SGH and CCH methods.

A directionally-split volume-of-fluid (VOF) methodology for evolving interfaces under curvature-dependent speed is devised. The interface is reconstructed geometrically and the volume fraction is advected with a technique to incorporate a topological volume conservation constraint. The proposed approach uses the idea that the role of curvature in a speed function $��V�$ is analogous to the role of viscosity in the corresponding hyperbolic conservation law to propagate complex interfaces where singularities may exist. The approach has the advantage of simple implementation and straightforward extension to more complex multiphase systems by formulating the interface evolution problem using energy functionals to derive an expression for the interface-advecting velocity. The numerical details of the volume-of-fluid based formulation are discussed with emphasis on the importance of curvature estimation. Finally, canonical curves and surfaces traditionally investigated by the level set (LS) method are tested with the devised approach and the results are compared with existing work in LS.

The finite volume method (FVM) is widely adopted in many different applications because of its built-in conservation properties, its ability to deal with arbitrary mesh and its computational efficiency. In this work, we consider the Rhie–Chow stabilized Box method (RCBM) for the approximation of the Stokes problem. The Box method (BM) is a piecewise linear Petrov–Galerkin formulation on the Voronoi dual mesh of a Delaunay triangulation, whereas the Rhie–Chow (RC) stabilization is a well known stabilization technique for FVM. The first part of the article provides a variational formulation of the RC stabilization and discusses the validity of crucial properties relevant for the well-posedness and convergence of RCBM. Moreover, a numerical exploration of the convergence properties of the method on 2D and 3D test cases is presented. The last part of the article considers the theoretically justification of the well-posedness of RCBM and the experimentally observed convergence rates. This latter justification hinges upon suitable assumptions, whose validity is numerically explored.

This article constructs a new two-level explicit/implicit numerical scheme in an approximate solution for the two-dimensional time fractional mobile/immobile advection-dispersion problem. The stability and error estimates of the proposed technique are deeply analyzed in the $��{�L�}^{�\infty �}�(�0�,�T�;�{�L�}^{�2�}�)�$-norm. The developed approach is less time consuming, fourth-order in space and temporal accurate of order $��O�\left(�{�k�}^{�2�-�\frac{�\lambda �}{�2�}�}�\right)�$, where $��k�$ is the time step and $��\lambda �$ denotes a positive parameter less than 1. This result shows that the two-level explicit/implicit formulation is faster and more efficient than a large class of numerical schemes widely discussed in the literature for the considered problem. Numerical experiments are performed to verify the theoretical studies and to demonstrate the efficiency of the new numerical method.

The solution of reactive computational fluid dynamics (CFD) simulations is accelerated by the implementation of a hybrid central processing unit/graphics processing units (CPU/GPU) Finite Volume solver based on the operator-splitting strategy, where the chemistry integration is treated independently of the flow solution. The integration of ordinary differential equations (ODEs) describing the finite-rate chemical kinetics is solved by an adaptive multi-block explicit solver on GPUs, while the load of the fluid solution is distributed on a multicore CPU algorithm. The resulting speed-up for reactive CFD simulations is up to 10$��\times �$; the performance gain increases with the size of the mechanism. The proposed implementation is general and can be applied to any CFD problem where the governing equations for the fluid transport are coupled with an ODE system. Code validation is performed against reference solutions on a selection of test cases involving reacting flows.

In order to shorten the optimization cycle of ship design optimization and solve the time-consuming problem of computational fluid dynamics (CFD) numerical calculation, this paper proposes a multi-precision back-propagation neural network (MP-BP) approximation technology. Fewer high-precision ship samples and more low-precision ship samples were used to construct an approximate model, back-propagation (BP) neural network was used to train multi-precision samples. So that the approximate model is as close as possible to the real model, and achieving the effect of high-precision approximation model. Subsequently, numerical verification and typical hull form verification are given. Based on CFD and Rankine theory, the multi-objective design optimization framework for ship comprehensive navigation performance is constructed. The multi-objective approximation model of KCS ship is constructed by MP-BP approximation technology, and optimized by particle swarm optimization (PSO) algorithm. The results show that the multi-objective optimization design framework using the MP-BP approximation model can capture the global optimal solution and improve the efficiency of the entire hull form design optimization. It can provide a certain degree of technical support for green ship and low-carbon shipping.