Contact angle hysteresis is an important physical phenomenon omnipresent in nature and various industrial processes, but its effects are not considered in many existing multiphase flow simulations due to modeling complexity. In this work, a multiphase lattice Boltzmann method (LBM) is developed to simulate the contact-line dynamics with consideration of the contact angle hysteresis for a broad range of kinematic viscosity ratios. In this method, the immiscible two-phase flow is described by a color-fluid model, in which the multiple-relaxation-time collision operator is adopted to increase numerical stability and suppress unphysical spurious currents at the contact line. The contact angle hysteresis is introduced using the strategy proposed by Ding and Spelt [Ding and Spelt, J. Fluid Mech. 599, 341 (2008)JFLSA70022-112010.1017/S0022112008000190], and the geometrical wetting boundary condition is enforced to obtain the desired contact angle. This method is first validated by simulations of static contact angle and dynamic capillary intrusion process on ideal (smooth) surfaces. It is then used to simulate the dynamic behavior of a droplet on a nonideal (inhomogeneous) surface subject to a simple shear flow. When the droplet remains pinned on the surface due to hysteresis, the steady interface shapes of the droplet quantitatively agree well with the previous numerical results. Four typical motion modes of contact points, as observed in a recent study, are qualitatively reproduced with varying advancing and receding contact angles. The viscosity ratio is found to have a notable impact on the droplet deformation, breakup, and hysteresis behavior. Finally, this method is extended to simulate the droplet breakup in a microfluidic T junction, with one half of the wall surface ideal and the other half nonideal. Due to the contact angle hysteresis, the droplet asymmetrically breaks up into two daughter droplets with the smaller one in the nonideal branch channel, and the behavior of daughter droplets is significantly different in both branch channels. Also, it is found that the contact angle hysteresis is strengthened with decreasing the viscosity ratio, leading to an earlier droplet breakup and a decrease in the maximum length that the droplet can reach before the breakup. These simulation results manifest that the present multiphase LBM can be a useful substitute to Ba et al. [Phys. Rev. E 88, 043306 (2013)PLEEE81539-375510.1103/PhysRevE.88.043306] for modeling the contact angle hysteresis, and it can be easily implemented with higher computational efficiency.

We report a facile approach to preparing laponite (LAP) bioceramics via sintering LAP powder compacts for bone tissue engineering applications. The sintering behavior and mechanical properties of LAP compacts under different temperatures, heating rates, and soaking times were investigated. We show that LAP bioceramic with a smooth and porous surface can be formed at 800°C with a heating rate of 5°C/h for 6 h under air. The formed LAP bioceramic was systematically characterized via different methods. Our results reveal that the LAP bioceramic possesses an excellent surface hydrophilicity and serum absorption capacity, and good cytocompatibility and hemocompatibility as demonstrated by resazurin reduction assay of rat mesenchymal stem cells (rMSCs) and hemolytic assay of pig red blood cells, respectively. The potential bone tissue engineering applicability of LAP bioceramic was explored by studying the surface mineralization behavior via soaking in simulated body fluid (SBF), as well as the surface cellular response of rMSCs. Our results suggest that LAP bioceramic is able to induce hydroxyapatite deposition on its surface when soaked in SBF and rMSCs can proliferate well on the LAP bioceramic surface. Most strikingly, alkaline phosphatase activity together with alizarin red staining results reveal that the produced LAP bioceramic is able to induce osteoblast differentiation of rMSCs in growth medium without any inducing factors. Finally, in vivo animal implantation, acute systemic toxicity test and hematoxylin and eosin (H&E)-staining data demonstrate that the prepared LAP bioceramic displays an excellent biosafety and is able to heal the bone defect. Findings from this study suggest that the developed LAP bioceramic holds a great promise for treating bone defects in bone tissue engineering.

Modern wind turbine airfoil designs are increasingly emphasizing low sensitivity to the leading edge roughness in addition to good aerodynamic performances under varying wind conditions. In this study, a multi-point robust design optimization method has been systematically established for the wind turbine airfoil. The objective is to not only maximize the lift-to-drag ratio and lift coefficient at and near the design point, but also minimize their sensitivity to the leading edge roughness associated with the geometry profile uncertainty. The geometry parameterization of the airfoil is conducted by the Bezier curves. In the robust optimization, the multiobjective genetic algorithm, Monte Carlo simulation technique, and artificial neural network model are used. The results show that both the robust Pareto optimal (RPO) and deterministic Pareto optimal (DPO) airfoils have higher lift-to-drag ratio and lift coefficient than the original design FX 63-137 at and near the design point. A smaller maximum camber and larger radii near the leading edge help the RPO airfoil outperform both the DPO and original ones in terms of low sensitivity to the leading edge roughness. This study may be useful to the future development of the wind turbine airfoils with higher efficiency and reliability.

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Modern aerodynamic optimization design methods for the industrial axial compressor cascade mainly aim at improving both design point and off-design point performance. In this study, a multi-point and multi-objective optimization design method is established for the cascade, particularly aiming at widening the operating range while maintaining good performance at the acceptable expense of computational load. The design objectives are to maximize the static pressure ratio and minimize the total pressure loss coefficient at the design point, and to maximize the operating range for the positive and negative incidences. To alleviate the computational load, a design of experiment (DOE)-based GA-BP-ANN model is constructed to rapidly approximate the cascade aerodynamic performance in the optimization process. The artificial neural network (ANN) is trained by the genetic algorithm (GA) technique and back propagation (BP) algorithm, where the training cascades are sampled by the DOE method and analysed by the computational fluid dynamics method. The multi-objective genetic algorithm is used to search for a series of Pareto-optimum solutions, from which an optimal cascade is found out whose objectives are all better than (ABT) those of the original design. The ABT cascade is characterized by the lower camber and higher turning angle, leading to better aerodynamic performance in a widened operating range. Compared with the original design, the ABT cascade decreases the total pressure loss coefficient by 1.54 per cent, 23.4 per cent, and 7.87 per cent at the incidences of 5 • , −9 • , and 13 • , respectively. The established optimization design method can be extended to the three-dimensional aerodynamic design of axial compressor blade.

Recently, there has been a renewed interest in the research of tandem compressor cascades due to the high stage pressure ratio and low control cost. Firstly, the computational fluid dynamics (CFD) method is employed to examine the particular aerodynamic performance of the tandem cascade. Then we propose an automatic multi-objective optimization design method of the tandem cascade for the superior aerodynamic performance under the multiple operation conditions. Particular efforts have been devoted to the gap geometry optimization in terms of the front and aft airfoil relative position, camber turning ratio as well as chord ratio. The multi-objective optimization algorithm comprises a refined multi-objective genetic algorithm (MOGA) and a developed artificial neural network (ANN) model which is used to fast approximate the aerodynamic performance of the tandem cascade. The results show that the tandem cascade outperforms the single cascade in terms of producing higher pressure ratio and lower losses while the operation range is rather narrow. The optimized all-better-than (ABT) tandem cascade has its design point performance significantly improved while the operation range slightly widened. We also find that a slight axial proximity and separation of the tandem airfoils are beneficial to widening the positive and negative operation range, respectively. This research is useful to the tandem compressor cascade design in minimizing the stage number of the engine compressors.

The Monte Carlo simulation method for turbomachinery uncertainty analysis often requires performing a huge number of simulations, the computational cost of which can be greatly alleviated with the help of metamodeling techniques. An intensive comparative study was performed on the approximation performance of three prospective artificial intelligence metamodels, that is, artificial neural network, radial basis function, and support vector regression. The genetic algorithm was used to optimize the predetermined parameters of each metamodel for the sake of a fair comparison. Through testing on 10 nonlinear functions with different problem scales and sample sizes, the genetic algorithm-support vector regression metamodel was found more accurate and robust than the other two counterparts. Accordingly, the genetic algorithm-support vector regression metamodel was selected and combined with the Monte Carlo simulation method for the uncertainty analysis of a wind turbine airfoil under two types of surface roughness uncertainties. The results show that the genetic algorithm-support vector regression metamodel can capture well the uncertainty propagation from the surface roughness to the airfoil aerodynamic performance. This work is useful to the application of metamodeling techniques in the robust design optimization of turbomachinery.

The impeller–volute tongue interaction strongly influences the aerodynamic performance of squirrel cage fan. To quantitatively evaluate the level of impeller–volute tongue interaction, we propose two parameters, i.e., recirculated flow coefficient and reversed flow coefficient based on a careful inspection of flow pattern near the volute tongue of a squirrel cage fan. Inspired by the good aerodynamic characteristics of owl wing, particular effort is made to develop a bionic design of volute tongue to improve the impeller–volute tongue match. The aerodynamic performances of both the squirrel cage fans with original volute tongue (OVT) and bionic volute tongue (BVT) are numerically and experimentally analyzed. The results show that, by employing the bionic design of volute tongue, the squirrel cage fan can achieve higher aerodynamic performance than that with OVT. Better match between impeller and volute tongue is obtained with smaller recirculated flow coefficient and reversed flow coefficient, validating the effectiveness of the proposed parameters to quantitatively evaluate the level of impeller–volute tongue interaction. In addition, the bionic design of volute tongue is beneficial for the improvement of flow quality and for the relief of abrupt pressure variation and axial nonuniformity of flow near the volute tongue. This work is helpful for a deep understanding of complex flow pattern in squirrel cage fan.