Multi-layer flows of immiscible fractional Maxwell fluids in a cylindrical domain

Laminar unsteady multilayer axial flows of fractional immiscible Maxwell fluids in a circular cylinder are investigated. The flow of fluids is generated by a time-dependent pressure gradient in the axial direction and by the translational motion of a cylinder along his axis. The considered mathematical model is based on the fractional constitutive equation of Maxwell fluids with Caputo time-fractional derivatives. Analytical solutions for the fractional differential equations of the velocity fields with boundary and interfaces conditions have been determined by using the Laplace transform coupled with the Hankel transform of order zero and the Weber transform of order zero. The influence of the memory effects on the motion of the fluid has been investigated for the particular case of three fractional Maxwell fluids. It is found that for increasing values of the fractional parameter the fluid velocity is decreasing. The memory effects have a stronger influence on the velocity of the second layer.     

Analysis of peristaltic flow of Jeffrey six constant nano fluid in a vertical non-uniform tube

Peristaltic flow of non-Newtonian nano fluid through a non-uniform surface has been investigated in this paper. The fluid motion along the wall of the surface is caused by the sinusoidal wave traveling with constant speed. The governing equations are converted into cylindrical coordinate system and assuming low Reynolds number and long wave length partial differential equations are simplified. Analytically solutions of the problem are obtained by utilizing the homotopy perturbation method (HPM). In order to insight the impact of embedded parameters on temperature, concentration and velocity some graphs are plotted for different peristaltic waves. At the end, some observations were made from the graphical presentation that velocity, pressure rise and nano particle concentration are increasing function of thermophoresis parameter N_t while temperature and frictional forces show opposite trend.     

An adaptive smooth unsaturated bistable stochastic resonance system and its application in rolling bearing fault diagnosis

An adaptive smooth unsaturated bistable stochastic resonance (ASUBSR) system for bearing fault signal detection is established. Based on the problem of output saturation and poor low-frequency suppression performance of classical bistable stochastic resonance (CBSR) system, an SUBSR with unsaturated characteristics is proposed. An ASUBSR system is designed by extracting the envelope spectrum of the input signal and resampling it to satisfy the adiabatic approximation condition, combining high-pass filter to filter out low-frequency interference, and using genetic algorithm to select the optimal system parameters. Through simulations and experiments, we found that the system can effectively suppress the interference of low-frequency and high-frequency, indicates that the system performs like a band-pass filter, and the output signal-to-noise ratio is better than that of the CBSR system. The proposed ASUBSR system has great application in the field of fault detection of rolling bearings.     

Testing the energy diffusion approximation for the escape of a Brownian particle from a potential pocket

For the first time, the energy diffusion approximation is confronted at the percent level with the exact numerical modeling of thermal decay of a metastable state. This model is useful in many branches of natural sciences: e.g. in biology, nuclear physics, chemistry, etc. The exact (within the statistical errors about 2%) quasistationary decay rates result from the Langevin equations for the coordinate and conjugated momentum. For the energy (or action) diffusion approach, a Langevin-type equation for the action is constructed, validated, and solved numerically. The comparison of these two approaches is performed for four potentials (two of which are anharmonic) in a wide range of two dimensionless scaling parameters: i) the governing parameter G reflecting how high is the barrier with respect to the temperature and ii) the damping parameter φ expressing the friction strength. It turns out that the energy diffusion approach produces the rate which comes into 50%-agreement with the exact one only at φ < 0.02. Thus, we quantify, for the first time by our knowledge, the condition φ ≪ 1 known in the literature.     

A GPU-based algorithm for efficient LES of high Reynolds number flows in heterogeneous CPU/GPU supercomputers

Αn optimized MPI+OpenACC implementation model that performs efficiently in CPU/GPU systems using large-eddy simulation is presented. The code was validated for the simulation of wave boundary-layer flows against numerical and experimental data in the literature. A direct Fast-Fourier-Transform-based solver was developed for the solution of the Poisson equation for pressure taking advantage of the periodic boundary conditions. This solver was optimized for parallel execution in CPUs and outperforms by 10 times in computational time a typical iterative preconditioned conjugate gradient solver in GPUs. In terms of parallel performance, an overlapping strategy was developed to reduce the overhead of performing MPI communications using GPUs. As a result, the weak scaling of the algorithm was improved up to 30%. Finally, a large-scale simulation (Re = 2 × 10⁵) using a grid of 4 × 10⁸ cells was executed, and the performance of the code was analyzed. The simulation was launched using up to 512 nodes (512 GPUs + 6144 CPU-cores) on one of the current top 10 supercomputers of the world (Piz Daint). A comparison of the overall computational time showed that the GPU version was 4.2 times faster than the CPU one. The parallel efficiency of this strategy (47%) is competitive compared with the state-of-the-art CPU implementations, and it has the potential to take advantage of modern supercomputing capabilities.     

多集值信息表中的多粒度模糊决策论粗糙集

多粒度粗糙集和决策论粗糙集是Pawlak粗糙集的重要推广,目前已成为人工智能研究的热点.然而,它们大多处理的都是单值信息系统中的问题.而实际生活中绝大多数都是处理多值问题,为了解决这一问题,在多集值信息表中将多粒粗糙集与模糊决策论粗糙集相结合进行研究,提出了其在乐观,悲观情形下的上下近似,研究了一些相关性质并给出了多集值信息表中的多粒度模糊决策论粗糙集精度、粗度的概念,最后通过一个具体例子验证其有效性.     

Generation of strongly non-Gaussian stochastic processes by iterative scheme upgrading phase and amplitude contents

Random excitations, such as wind velocity, always exhibit non-Gaussian features. Sample realisations of stochastic processes satisfying given features should be generated, in order to perform the dynamical analysis of structures under stochastic loads based on the Monte Carlo simulation. In this paper, an efficient method is proposed to generate stationary non-Gaussian stochastic processes. It involves an iterative scheme that produces a class of sample processes satisfying the following conditions. (1) The marginal cumulative distribution function of each sample process is perfectly identical to the prescribed one. (2) The ensemble-averaged power spectral density function of these non-Gaussian sample processes is as close to the prescribed target as possible. In this iterative scheme, the underlying processes are generated by means of the spectral representation method that recombines the upgraded power spectral density function with the phase contents of the new non-Gaussian processes in the latest iteration. Numerical examples are provided to demonstrate the capabilities of the proposed approach for four typical non-Gaussian distributions, some of which deviate significantly from the Gaussian distribution. It is found that the estimated power spectral density functions of non-Gaussian processes are close to the target ones, even for the extremely non-Gaussian case. Furthermore, the capability of the proposed method is compared to two other methods. The results show that the proposed method performs well with convergence speed, accuracy, and random errors of power spectral density functions.     

Swimming of micro-organism over an oscillatory stretched surface filled with a magnetic third-grade nanofluid: An application of activation energy

During the past few years, abundant involvement of nanoparticles in improving the thermal extrusion systems and energy resources attracted the attention of numerous scientists recently. The significance of nanofluid in terms of working liquid directed for the enhancement of solar energy and thermal extrusion performances. Therefore, the present analysis deals with the thermal performances of bioconvection flow with nanoparticles suspended in a non-Newtonian fluid. Considering that the flow has been induced due to periodically accelerated surface. The activation energy consequences are also employed in the concentration equation. The flow problem is initially formulated in the form of partial differential equations. The dimensionless variables are reported to renovate such equations in the dimensionless style, which are tackled analytically by employing the homotopy analysis method. The significance of various physical parameters is estimated for the relevant distribution of velocity, temperature, concentration, and motile micro-organisms. The dimensionless local Nusselt number, local Sherwood number, and motile density number are numerically iterated via flow parameters. A convergence analysis is also presented. The detected observation can involve theoretical significance in various engineering processes, bio-fuel cells, solar energy systems, and enhancement of extrusion systems.