Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm

In the present paper, a plate and frame heat exchanger is considered. Multi-objective optimization using genetic algorithm is developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coefficient. Vividly, considered objective functions are conflicting and no single solution can satisfy both objectives simultaneously. Multi-objective optimization procedure yields a set of optimal solutions, called Pareto front, each of which is a trade-off between objectives and can be selected by the user, regarding the application and the project’s limits. The presented work takes care of numerous geometric parameters in the presence of logical constraints. A sensitivity analysis is also carried out to study the effects of different geometric parameters on the considered objective functions. Modeling the system and implementing the multi-objective optimization via genetic algorithm has been performed by MATLAB.     

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.     

仿生多胞薄壁管耐撞性分析及优化

为提高薄壁管结构耐撞性，以雀尾螳螂虾螯为仿生原型，结合仿生学设计方法，设计一种含正弦胞元的多胞薄壁管结构。以初始峰值载荷、比吸能和碰撞力效率为耐撞性指标，通过有限元数值模拟分析了不同碰撞角度（0o、10o、20o和30o）条件下，仿生胞元数对薄壁管耐撞性的影响，通过多目标的复杂比例评估法获取仿生薄壁管的最优胞元数。基于不同碰撞角度权重因子组合，设置了4种单一角度工况和3种多角度工况，采用多目标粒子群优化方法获取了不同工况下薄壁管结构最优胞元高宽比和壁厚。复杂比例评估结果表明，胞元数为4的薄壁管为最优晶胞数仿生薄壁管。优化结果表明，单一角度工况下，最优结构参数高宽比的范围为0.88～1.50，壁厚的范围为0.36～0.60 mm，碰撞角度为0o和10o的最优高宽比明显小于碰撞角度为20o和30o的；多角度工况下，最优高宽比范围为1.01～1.10，壁厚范围为0.49～0.57 mm。     

Improved non-dominated sorting genetic algorithm （NSGA）-II in multi-objective optimization studies of wind turbine blades

The non-dominated sorting genetic algorithm (NSGA) is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multiobjective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.     

传热结构的多目标拓扑优化设计研究

深入分析了传热结构多目标拓扑优化设计中的几个关键问题。提出了基于结构柔度最小化和结构散热弱度最小化的多目标拓扑优化设计方法,建立了传热结构的多目标拓扑优化设计模型,推导了传热结构多目标拓扑优化中用于迭代分析求解的优化准则算法和敏度分析方程。通过数值计算验证了理论和算法的有效性。     

Multi-objective optimization of a guide vane in the turning region of a rotating U-duct to enhance heat transfer performance

An optimization has been performed for the design of a guide vane in the turning region of a rotating U-duct using the Kriging meta-model and a hybrid multi-objective evolutionary algorithm. Rotation of the U-duct is accompanied by the Coriolis force that causes a discrepancy in heat transfer between the trailing (pressure) and leading (suction) surfaces of the duct. For the optimization, three geometric variables related to the thickness, angle, and location of the guide vanes are selected as the design variables. A Kriging model is constructed to obtain a Pareto-optimal front through a multi-objective evolutionary algorithm. The values of the objective function at the design points are evaluated by Reynolds-averaged Navier–Stokes analysis. The shear stress transport model is used as the turbulence closure model in the analysis. The tradeoff between the two competing objective functions is discussed for Pareto-optimal solutions in the design space. The optimized guide vanes show an increase in heat transfer performance with a decrease in the friction loss in the turning region and downstream straight passage in comparison with the reference design.     

基于多目标优化策略的结构可靠性稳健设计

应用可靠性稳健优化设计理论和多目标决策方法,将结构可靠性稳健优化设计转化为多目标优化问题。运用灰色理论中的关联分析法,选取粒子群算法中的全局极值和个体极值,提出了灰色粒子群算法求解可靠性稳健优化设计问题。与传统方法相比,该方法简便易行并能迅速准确地得到结构可靠性稳健优化设计信息。     

基于改进粒子群算法的翼型多目标优化研究

在多目标优化研究中,为改善多目标粒子群算法的局部搜索能力,以标准粒子群算法为基础,引入单点模拟退火算法,局部进化最优个体,采用基于目标向量的共享函数法评价适应值.标准测试函数优化实例表明:本文算法比标准粒子群算法具有更好的收敛稳定性和收敛速度,收敛速度提高了近50%;针对某翼型的气动优化设计结果表明:改进算法有效缩短了优化时间,迭代代数由61减为49,调用CFD由4880减为4250次;阻力系数、升力系数、低头力矩系数分别改进了9.23%、0.42%、16.4%,取得了较好的优化效果.     

粒子群优化算法在传递对准中的应用

给出了一种基于粒子群优化算法的捷联惯导传递对准算法。简单分析了传递对准任务要求和主子惯导惯性器件输出之间的关系,将传递对准问题作为参数优化问题进行求解,给出了基于粒子群优化算法进行传递对准的数学模型。定义了传递对准的优化目标函数,介绍了粒子群优化算法及其应用于传递对准的具体算法设置。用粒子群优化算法求解目标函数的最小值,可获得主子惯导之间的失准角,进行一次校正即可完成传递对准过程。通过计算机仿真对算法进行了验证分析,在仿真条件下（陀螺精度为0.1°/h）,能达到方位0.1°的精度。与其他对准算法一样,算法受载体机动条件的影响较大,一般需要姿态机动来提高陀螺的信噪比。     

粒子群算法优化的捷联罗经初始对准方法

初始对准是惯导系统的关键技术,罗经法对准是实现捷联惯导系统初始对准的重要手段。罗经对准回路的参数选择直接影响对准结果的好坏。对于不同的捷联惯导系统,罗经回路的最优参数也是不同的。传统的方法是根据经验以及大量的反复试验确定罗经对准参数,不能保证对准参数为最优。针对此问题,提出以水平罗经对准回路阻尼振荡周期T_(d1)和航向罗经对准回路阻尼振荡周期T_(d2)为寻优目标,用粒子群算法对参数(T_(d1),T_(d2))进行寻优的方法,以确定出满足条件的最优对准参数,从而提高捷联罗经初始对准的性能。实验结果表明:粒子群算法能够快速、准确地搜索出罗经对准回路的最优参数,提高捷联罗经对准的性能。将粒子群算法应用到捷联罗经初始对准中是有效的。     

Multi-objective discrete optimization of laminated structures

The paper is dedicated to the multi-objective optimal design of laminated composite structures. In order to provide sound-engineering designs, a few alternative and/or conflicting objectives must be taken into account. It is reasonable to consider the multi-objective optimization as a sensible enrichment with respect to single objective optimization, since the solutions are enforced to result optimal at the same time with respect to different objectives. Multi-objective optimization methods gained in the last years a growing interest in engineering, due to the possibility to determine a design possessing at the same time optimality with respect to different conflicting requirements. This problem is approached and suitably solved by Evolution Strategies, a computational algorithm based on Darwinian theories, that allow to solve optimization problems without using gradient-based information on the objective functions and the constraints. The presence of multiple objectives has been taken into account coupling the algorithm with a cooperative game theoretic approach and, for sake of comparison, with other methods, such as weighted objectives or Trade-off. With the game theoretic approach, all objective functions have the same importance, and the optimal solution is found using a bargaining function. The ply orientations in the stacking sequence of the laminate are the assumed design variables, of discrete type, a common situation in engineering practice. The results obtained for two different typical laminate designs show the effectiveness of the proposed method.     

求解非线性方程组的混沌粒子群算法及应用

针对非线性方程组的求解在工程上具有广泛的实际意义,经典的数值算法如牛顿法存在其收敛性依赖于初值而实际计算中初值难确定的问题,提出以混沌粒子群算法求解非线性方程。它通过将混沌搜索机制有机地引入粒子群算法,使每个粒子从混沌搜索机制与粒子群算法搜索机制中获得适当的搜索方向,以混沌变量的遍历性增强粒子的搜索性能与更全面地应用目标函数的信息,并反映到逐代更新的个体极值和群体极值中,可更有效地调整粒子的移向并最终获得最优解。测试结果表明这一尝试的有效性。最后将所提的方法用于建立复合材料结构的疲劳寿命与应力、温度、湿度的关系模型。     

RELIABILITY-BASED DESIGN OF COMPOSITES UNDER THE MIXED UNCERTAINTIES AND THE OPTIMIZATION ALGORITHM

This paper proposed a reliability design model for composite materials under the mixture of random and interval variables. Together with the inverse reliability analysis technique, the sequential single-loop optimization method is applied to the reliability-based design of composites. In the sequential single-loop optimization, the optimization and the reliability analysis are decoupled to improve the computational efficiency. As shown in examples, the minimum weight problems under the constraint of structural reliability are solved for laminated composites. The Particle Swarm Optimization （PSO） algorithm is utilized to search for the optimal solutions. The design results indicate that, under the mixture of random and interval variables, the method that combines the sequential single-loop optimization and the PSO algorithm can deal effectively with the reliability-based design of composites.     

Multi-swingby optimization of mission to Saturn using global optimization algorithms

Based on the trajectory design of a mission to Saturn, this paper discusses four different trajectories in various swingby cases. We assume a single impulse to be applied in each case when the spacecraft approaches a celestial body. Some optimal trajectories ofEJS, EMS, EVEJS and EVVEJS flying sequences are obtained using five global optimization algorithms： DE, PSO, DP, the hybrid algorithm PSODE and another hybrid algorithm, DPDE. DE is proved to be supe- rior to other non-hybrid algorithms in the trajectory optimi- zation problem. The hybrid algorithm of PSO and DE can improve the optimization performance of DE, which is vali- dated by the mission to Saturn with given swingby sequences. Finally, the optimization results of four different swingby sequences are compared with those of the ACT of ESA.     

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.     

Multi-objective optimization of discrete film hole arrangement on a high pressure turbine end-wall with conjugate heat transfer simulations

This paper discussed a method of combining a full automatic multi-objective optimization and conjugate heat transfer calculation to obtain optimal cooling layouts on a transonic high pressure guide vane under a realistic turbine working condition. The improvement in cooling design from the optimized models was analyzed in detail, along with a discussion of sensitivities of two objective functions to five design variables. The full automatic method comprises the process of geometry creation, mesh generation, numerical solution and post data analysis. The vane is solid and the end-wall is arranged in a linear cascade. On the end-wall, film holes are all cylindrical and classified in five regions, with region A near the leading edge of the vane, region B near the suction side, regions C and D near the pressure side, and region E for the rest. Five design variables are three pitch-to-hole ratios in regions B, D, E and two compound angles of film holes in regions A and D. Two selected objective functions are area averaged overall cooling effectiveness of the end-wall and aerodynamic losses in a cross-plane at x/C_ax = 1.06 just downstream of the outlet of the cascade. For the optimization process, the multi-objective genetic algorithm based on the Non-dominated Sorted Genetic Algorithm-II was applied. The Latin hypercube sampling method was used to choose 21 experimental design points in the design space, which are also the sources for constructing the surrogate models with the Kriging model. The results demonstrate that the method using full automatic optimization and conjugate heat transfer calculation has achieved an increase of 8.7%–9.5% in area-averaged overall cooling effectiveness and a reduction of about 4.8%–6.1% in aerodynamic losses. The highest increase in cooling effectiveness exists in the region near the pressure side with a mild increase in the middle of the passage. The largest heat flux reduction exists in the regions near the pressure side and the crown of the suction side. The change of compound angle in region A near the leading edge has a negligible influence on overall cooling effectiveness but a high impact on aerodynamic losses. It's advisable to maintain the compound angle and pitch-to-diameter ratio at low values in region D near the pressure side to obtain high cooling performance.     

Multi‐objective optimization of a rotating cooling channel with staggered pin‐fins for heat transfer augmentation

A rotating channel with staggered pin‐fins is formulated numerically and optimized for performance (heat transfer/required pumping power) using a Kriging meta‐model and hybrid multi‐objective evolutionary algorithm. Two design variables related to cooling channel height, pin diameter, and spacing between the pins are selected for optimization, and two‐objective functions related to the heat transfer and friction loss are employed. A design of experiment is performed, and 20 designs are generated by Latin hypercube sampling. The objective function values are evaluated using a Reynolds‐averaged Navier–Stokes solver, and a Kriging model is constructed to obtain a Pareto‐optimal front through a multi‐objective evolutionary algorithm. Rotation in a cooling channel with staggered pin‐fins induces Coriolis force that causes a heat transfer discrepancy between the trailing (pressure) and leading (suction) surfaces, with a higher Nusselt number on the trailing surface. The tradeoff between the two competing objective functions is determined, and the distribution of the Pareto‐optimal solutions in the design space is discussed through k‐means clustering. In the optimal designs, with a decrease in spacing between the pins, heat transfer is enhanced whereas friction loss is increased. Copyright © 2011 John Wiley & Sons, Ltd.     

Rational approximation for fractional-order system by particle swarm optimization

In this paper, a rational approximation me-thod is proposed for the fractional-order system using the particle swarm optimization (PSO). Firstly, the approximation method for the fractional-order operator is studied, because a fractional-order system consists of many fractional-order operators. The coefficients of the transfer function are calculated using PSO with a fitness function under the continued fraction expansion (CFE) framework in the frequency domain. The average velocity of the particle swarm is defined to reflect the real state of particle swarm. To improve the global optimization and achieve a more satisfactory fitting result, comparing with the linear PSO, the chaotic optimization is combined with PSO. The numerical examples of fractional-order systems demonstrate the effectiveness of this method.     

Topology optimization of conjugate heat transfer systems: A competition between heat transfer enhancement and pressure drop reduction

Topology optimization method is developed for a multi-objective function combining pressure drop reduction and thermal power maximization (incompressible flows at low to moderate Reynolds numbers). Innovative optimal designs are obtained, discussed and presented on a Pareto-frontier. The numerical developments (continuous adjoint technique) have been conducted inside an open source CFD platform via the finite volume method. Comparisons have been presented with an optimal design obtained by a Lattice Boltzmann Method from the literature. Finally, this contribution presents and discuss several detailed numerical vitrification steps which are essential to be conducted in topology optimization method when applied with multi-objective functions.     

Genetic algorithm optimization for finned channel performance

Compared to a smooth channel,a finned channel provides a higher heat transfer coefficient;increasing the fin height enhances the heat transfer.However,this heat transfer enhancement is associated with an increase in the pressure drop.This leads to an increased pumping power requirement so that one may seek an optimum design for such systems.The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm.Each fin arrangement is considered a solution to the problem (an individual for genetic algorithm).An initial population is generated randomly at the first step.Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.