Multi-objective optimization of a dimpled channel for heat transfer augmentation

Staggered arrays of dimples printed on opposite surfaces of a cooling channel is formulated numerically and optimized with hybrid multi-objective evolutionary algorithm and Pareto optimal front. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by three non-dimensional geometric design variables composed of dimpled channel height, dimple print diameter, dimple spacing, and dimple depth, to maximize heat transfer rate compromising with pressure drop. Twenty designs generated by Latin hypercube sampling were evaluated by Reynolds-averaged Navier–Stokes solver and the evaluated objectives were used to construct Pareto optimal front through hybrid multi-objective evolutionary algorithm. The optimum designs were grouped by k-means clustering technique and some of the clustered points were evaluated by flow analysis. With increase in dimple depth, heat transfer rate increases and at the same time pressure drop also increases, while opposite behavior is obtained for the dimple spacing. The heat transfer performance is related to the vertical motion of the flow and the reattachment length in the dimple.     

Clustering solver for displacement-based numerical homogenization

Based on strain-clustering via k-means, we decompose computational domain into clusters of possibly disjoint cells. Assuming cells in each cluster take the same strain, we reconstruct displacement field. We further propose a new way to condensate the governing equations of displacement-based finite element method to reduce the complexity while maintain the accuracy. Numerical examples are presented to illustrate the efficiency of the clustering solver. Numerical convergence studies are performed for the examples. Avoiding complexities which is common in existing clustering analysis methods, the proposed clustering solver is easy to implement, particularly for numerical homogenization using commercial softwares.     

A revised key agreement protocol based on chaotic maps

Very recently, Chen et al. proposed a security-enhanced key agreement protocol based on Chebyshev chaotic map. They claimed that the proposed protocol can achieve session key agreement shared among the server and user with security and users anonymity. Although, in this paper, we will prove that Chen et al.’s protocol cannot guarantee security and user anonymity against internal adversary who is a legal user. Furthermore, we give some improvements to dominate the mentioned shortcomings. The analysis shows that our proposed improvements are secure and efficient.     

An image processing method for feature extraction and dynamic tracking of particle clusters in CFBs

A new image processing method based on the high-speed camera is proposed to identify, locate, and track clusters. The instantaneous characteristic parameters of particle clusters in the riser of the circulating fluidized bed (CFB) can be acquired, such as solids holdup, vertical velocity, lateral displacement, aspect ratio and near-circularity. Experiments were carried out with glass bead particles, river sand particles and FCC particles. The time series of images of gas–solid flow in a CFB riser with a 100 mm × 25 mm cross-section and 3.2 m in length were obtained using high-speed cameras. The k-means++ clustering algorithm is utilized to identify the clusters, centroid is applied to locate the clusters, and the cross-correlation algorithm is employed to track the specific clusters and number them to get the instantaneous characteristic parameters. The results illustrate that the shapes of clusters in the center area are closest to circle, moving upwards at a uniform speed, while the clusters in the side-wall area are mostly elongated or long chain-like, moving slowly downwards. In the transition area, the clusters are more complex, moving upwards at a constant speed, and having large lateral displacement. The results show that the image processing method used in this study is successful in acquiring the dynamic and structural parameters of the clusters simultaneously.     

Chaotic ant swarm for the traveling salesman problem

Chaotic ant swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in continuous search space. However, it is not able to solve the combinational optimization problem directly, e.g., the traveling salesman problem (TSP). To tackle this problem, we propose a new method to solve the traveling salesman problem based on chaotic ant swarm, CAS-TSP for short. The CAS-TSP is developed by introducing a mapping from continuous space to discrete space, reverse operator and crossover operator into the CAS. Computer simulations demonstrate that the CAS-TSP is capable of generating optimal solution to instances of the TSPLIB in almost all test problems of sizes up to 150. Also a comparative computational study shows that this CAS-TSP algorithm is an efficient tool for solving TSP and this heuristic is competitive also with other heuristics.     

A three-party password-based authenticated key exchange protocol with user anonymity using extended chaotic maps

In this paper, we propose a scheme utilizing three-party password-based authenticated key exchange protocol with user anonymity using extended chaotic maps, which is more efficient and secure than previously proposed schemes. In order to enhance the efficiency and security, we use the extended chaotic maps to encrypt and decrypt the information transmitted by the user or the server. In addition, the proposed protocol provides user anonymity to guarantee the identity of users, which is transmitted in the insecure public network.     

Parameters identification for chaotic systems based on a modified Jaya algorithm

In this paper, parameters identification for chaotic systems using a modified Jaya algorithm is considered. Firstly, the objective function is formulated based on the variance rate between the responses acquired from the measurement and calculation. Then, the Jaya algorithm is put forward to solve this nonlinear optimization problem. To enhance the performance of the original Jaya, a one-step K-means clustering mechanism and a new updated equation for the best-so-far solution are introduced. To demonstrate the effectiveness of the suggested method, benchmark functions are firstly employed to conduct optimize. Afterward, numerical simulations, including a jerk circuit chaotic system, a hyper-chaotic system and a synchronized chaotic system are used to verify the present algorithm. The simulation results illustrates that the proposed algorithm for chaotic systems is a promising tool with higher identification accuracy, faster convergence rate, as well as stronger robustness.     

On new chaotic mappings in symbol space

A well known chaotic mapping in symbol space is a shift mapping.However,other chaotic mappings in symbol space exist too.The basic change is to consider the process not only at a set of times which are equally spaced,say at unit time apart(a shift mapping),but at a set of times which are not equally spaced,say if the unit time can not be fixed.The increasing mapping as a generalization of the shift mapping and the k-switch mapping are introduced.The increasing and k-switch mappings are chaotic.     

Diffuse manifold learning of the geometry of woven reinforcements in composites

When attempting to build mesoscale geometric models of woven reinforcements in composites based on X-ray microtomography data, we frequently run into ambiguous situations due to noise, particularly in contact zones between fiber tows, resulting in inadmissible cross-sectional shapes. We propose here a custom-built shape-manifold approach based on kernel PCA, k-means classification and Diffuse Approximation to identify, “repair” such badly segmented shapes in the feature space, and finally recover admissible shapes in the original space.     

A New Algorithm for Estimating Three-Phase Relative Permeability from Unsteady-State Core Experiments

We present a new history matching method based on a Genetic Algorithm to estimate three-phase k _r (relative permeability) from unsteady-state coreflood experiments. In this method, relative permeabilities (k _r) are represented by quadratic B-Spline functions. Adjustable coefficients in k _r functions are changed in an iterative process to minimize an objective function. The objective function is defined as the difference between the measures and simulated values of the pressure drop across the core and fluids recovery during the experiment. One of the main features of this approach is that water and gas relative permeabilities (k _rw and k _rg) are assumed to be functions of two independent saturations as opposed to most of the existing empirical k _r models in which k _rw and k _rg are assumed to be only dependent of their own saturations. Another important aspect of this algorithm is that it considers inequality constrains to ensure that physically acceptable k _r curves are maintained throughout the iterative optimization process. A three-phase coreflood simulator has been developed based on this methodology that generates best k _r values by matching experimental data. The integrity of the developed software was first successfully verified by using two sets of experimental three-phase k _r data published in the literature. Then, the results of some three-phase coreflood experiments carried out in our laboratory were used to obtain three-phase k _r curves by this approach.     

Parameter estimation of engineering turbulence model

A parameter estimation algorithm is introduced and used to determine the parameters in the standardk-∈ two equation turbulence model (SKE). It can be found from the estimation results that although the parameter estimation method is an effective method to determine model parameters, it is difficult to obtain a set of parameters for SKE to suit all kinds of separated flow and a modification of the turbulence model structure should be considered. So, a new nonlineark-∈ two-equation model (NNKE) is put forward in this paper and the corresponding parameter estimation technique is applied to determine the model parameters. By implementing the NNKE to solve some engineering turbulent flows, it is shown that NNKE is more accurate and versatile than SKE. Thus, the success of NNKE implies that the parameter estimation technique may have a bright prospect in engineering turbulence model research.     

Construction of a novel nth-order polynomial chaotic map and its application in the pseudorandom number generator

Chaotic maps with good chaotic performance have been extensively designed in cryptography recently. This paper gives an nth-order polynomial chaotic map by using topological conjugation with piecewise linear chaos map. The range of chaotic parameters of this nth-order polynomial chaotic map is large and continuous. And the larger n is, the greater the Lyapunov exponent is and the more complex the dynamic characteristic of the nth-order polynomial chaotic map. The above characteristics of the nth-order polynomial chaotic map avoid the disadvantages of one-dimensional chaotic systems in secure application to some extent. Furthermore, the nth-order polynomial chaotic map is proved to be an extension of the Chebyshev polynomial map, which enriches chaotic map. The numerical simulation of dynamic behaviors for an 8th-order polynomial map satisfying the chaotic condition is carried out, and the numerical simulation results show the correctness of the related conclusion. This paper proposed the pseudorandom number generator according to the 8th-order polynomial chaotic map constructed in this paper. Using the performance analysis of the proposed pseudorandom number generator, the analysis result shows that the pseudorandom number generator according to the 8th-order polynomial chaotic map can efficiently generate pseudorandom sequences with higher performance through the randomness analysis with NIST SP800-22 and TestU01, security analysis and efficiency analysis. Compared with the other pseudorandom number generators based on chaotic systems in recent references, this paper performs a comprehensive performance analysis of the pseudorandom number generator according to the 8th-order polynomial chaotic map, which indicates the potential of its application in cryptography.

     

Detecting unstable periodic orbits and unstable quasiperiodic orbits in vibro-impact systems

In this paper, unstable dynamics is considered for the models of vibro-impact systems with linear differential equations coupled to an impact map. To provide a skeleton for the organization of chaotic attractors, we propose a method for detecting unstable periodic orbits embedded in chaotic attractors through a combination of unconstrained optimization technique and Poincaré map. Three numerical examples from different vibro-impact models demonstrate that the strategy can efficiently detect unstable periodic orbits in chaotic attractors. In order to explore the mechanism responsible for the creation of multi-dimensional tori attractors, we also present another method to detect unstable quasiperiodic orbits embedded multi-dimensional tori attractors by examining a specially transient time series. The upper bound and lower bound of the transient time series (in the Poincaré map) can be obtained by analyzing transient Lyapunov exponent and transient Lyapunov dimension. Some examples verify the effectiveness of the numerical algorithm.     

Efficient detection of the quasi-periodic route to chaos in discrete maps by the three-state test

The three-state test (3ST) is a method based on ordinal pattern analysis for detecting chaos and determining the period in time series. For some well-known chaotic dynamical systems, we showed that the test behaves similar to Lyapunov exponents. However, the 3ST is detecting quasi-periodic motions both as regular and non-regular. In this paper, we propose to use the sensitivity of its chaos indicator \(\lambda \) on time delay for clear discernment between quasi-periodic and chaotic dynamics. Simulation results obtained using the logistic map and the sine-circle map attest that the sensitivity of \(\lambda \) on time delay is sufficient for the detection of the periodic and quasi-periodic route to chaos. A comparison with the permutation entropy confirms the effectiveness of the 3ST for the analysis of discrete time series data.     

Strongly singular and hypersingular integrals for aeroacoustic incident fields

Using the Burton and Miller formulation to predict the scattering of flow‐induced noise by a body immersed in the flow requires the near‐field pressure and pressure gradient incident on the body. In this paper, Lighthill's acoustic analogy is used to derive formulations for the near‐field pressure and pressure gradient at any point within the flow noise source region, including points on the body. These near‐field formulations involve strongly singular and hypersingular volume and surface integrals. To evaluate these singular integrals, an effective singularity regularization technique is derived. An analytical source distribution is used to demonstrate the accuracy of the method. A cell‐averaged representation of this analytical source distribution, similar to the data stored by computational fluid dynamics solvers, is also created. A piecewise linear, continuous source distribution is generated from these cell‐average values, producing a C⁰ distribution. A k‐exact reconstruction technique is then used to create high‐order polynomials of the solution variables for each volume cell. These high‐order polynomials are constructed from its cell average value and the average values of the nearby cells. The source distribution created using the k‐exact reconstruction is discontinuous across cell boundaries but exhibits a smooth polynomial distribution within each cell. The near‐field pressure and pressure gradient predicted using these reconstructed source distributions are compared with the results obtained using the analytical distribution. Copyright © 2014 John Wiley & Sons, Ltd.     

Finite element implementation of two‐equation and algebraic stress turbulence models for steady incompressible flows

The main purpose of this paper is to describe a finite element formulation for solving the equations for k and ε of the classical k–ε turbulence model, or any other two‐equation model. The finite element discretization is based on the SUPG method together with a discontinuity capturing technique to deal with sharp internal and boundary layers. The iterative strategy consists of several nested loops, the outermost being the linearization of the Navier–Stokes equations. The basic k–ε model is used for the implementation of an algebraic stress model that is able to account for the effects of rotation. Some numerical examples are presented in order to show the performance of the proposed scheme for simulating directly steady flows, without the need of reaching the steady state through a transient evolution. Copyright © 1999 John Wiley & Sons, Ltd.     

Hygric characterization of woven glass/epoxy composites

Hygric behavior of woven glass/epoxy composites is investigated by characterizing them using a novel experimental technique calledhunch-up technique. Based on one-dimensional diffusion theory, the through-thickness distribution of moisture concentration is derived and experimentally predicted for a laminate exposed to water. A procedure is prescribed to determine average axial hygric strain from the measured hunch-up distance of an axially constrained specimen exposed to water. The experimental results show that the hunch-up distance is extremely sensitive to moisture expansion. There results also give excellent confirmation to the theoretical predictions. An error analysis reveals that the hunch-up technique can reduce errors by an order of magnitude better than traditional measurement techniques.     

Image compression and encryption algorithm based on uniform non-degeneracy chaotic system and fractal coding

This paper focuses on the design of chaotic image compression encryption algorithms. Firstly, we design a uniform non-degenerate chaotic system based on nonlinear filters and the feed-forward and feed-back structure. Theoretical and experimental analyses indicate that the system can avoid the drawbacks of the existing chaotic systems, such as chaos degradation, uneven trajectory distribution, and weak chaotic behavior. In addition, our chaotic system can produce chaotic sequences with good pseudo-random characteristics. Then, we propose a fractal image compression algorithm based on adaptive horizontal or vertical (HV) partition by improving the baseline HV partition and the time-consuming global matching algorithm. The algorithm does not need to implement time-consuming global matching operations. In addition, analysis results demonstrate that our fractal image compression algorithm can reconstruct the original image with high quality under ultra-high compression ratios. Finally, to protect the confidentiality of images, we propose a chaotic fractal image compression and encryption algorithm by using our chaotic system and fractal image compression algorithm. The algorithm achieves excellent diffusion and confusion abilities without using the hash value of plain images. Therefore, it avoids the failure of decryption caused by the tampering of hash value during the transmission process, and can well resist differential attacks and chosen-ciphertext attacks. In addition, simulation results show the algorithm is efficient and robust.

     

Numerical and experimental investigation of turbulent flow in a rectangular duct

Details of the turbulent flow in a 1:8 aspect ratio rectangular duct at a Reynolds number of approximately 5800 were investigated both numerically and experimentally. The three-dimensional mean velocity field and the normal stresses were measured at a position 50 hydraulic diameters downstream from the inlet using laser doppler velocimetry (LDV). Numerical simulations were carried out for the same flow case assuming fully developed conditions by imposing cyclic boundary conditions in the main flow direction. The numerical approach was based on the finite volume technique with a non-staggered grid arrangement and the SIMPLEC algorithm. Results have been obtained with a linear and a non-linear (Speziale) k–ε model, combined with the Lam–Bremhorst damping functions for low Reynolds numbers. The secondary flow patterns, as well as the magnitude of the main flow and overall parameters predicted by the non-linear k–ε model, show good agreement with the experimental results. However, the simulations provide less anisotropy in the normal stresses than the measurements. Also, the magnitudes of the secondary velocities close to the duct corners are underestimated. © 1998 John Wiley & Sons, Ltd.     

Calculation of three‐dimensional turbulent flow with a finite volume multigrid method

A numerical method for the efficient calculation of three‐dimensional incompressible turbulent flow in curvilinear co‐ordinates is presented. The mathematical model consists of the Reynolds averaged Navier–Stokes equations and the k–ε turbulence model. The numerical method is based on the SIMPLE pressure‐correction algorithm with finite volume discretization in curvilinear co‐ordinates. To accelerate the convergence of the solution method a full approximation scheme‐full multigrid (FAS‐FMG) method is utilized. The solution of the k–ε transport equations is embedded in the multigrid iteration. The improved convergence characteristic of the multigrid method is demonstrated by means of several calculations of three‐dimensional flow cases. Copyright © 1999 John Wiley & Sons, Ltd.