基于NSGA-II的喷水推进进气道多目标水力优化

R. Zhu, P. Cao, Yang Wang, Chao Ning
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引用次数: 1

摘要

进气道是喷水推进的组成部分,由于其形状特性,在进气道出口段会发生流动畸变。由于进气道的流动特性对推进效率的影响非常明显,因此有必要对进气道进行重新设计。本文给出了一种通过形状优化来降低进气管道内流动畸变和功率损失的系统方法。此外,还研究了流动畸变的机理。以30节航速的船舶为研究对象,选择了平整式进气道。根据风管纵向中段13个特征参数的几何关系,分别为斜坡角α、上唇半径R3、下唇半径R4和唇高h,设置4个特征参数作为优化变量。随后,利用拉丁超立方采样(Latin Hypercube Sampling, LHS)构建样本空间,并在0 ~ 1范围内对参数进行归一化处理。利用商业软件CFX,在SST k-ω湍流模型驱动下完成了数值模拟。采用基于非支配排序遗传算法II (NSGA-II)的多目标优化,实现出口截面非均匀性最小化和唇部最小压力最大化。利用径向基函数(RBF)神经网络逼近变量与目标之间的函数关系,将其应用于NSGA-II中,得到Pareto Front。从帕累托前线选取最小非均匀性点和折衷点(即既满足最小非均匀性又满足唇部最大最小压力的点)。对于取舍点的特征参数,斜坡角为31.91°,上唇半径为11.42 mm,下唇半径为400.97 mm,唇高为55.43 mm。最小不均匀点的特征参数分别为30.22°、25.59 mm、166.65 mm和89.90 mm。最后,对原型和优化后的管道出流特性进行了比较。在取舍点上,唇部最小压力增加66.40%至−24488.93 Pa,非均匀性下降4.56%至0.1571。最小点的非均匀性为0.1481,降低了10.02%。通过对管道形状的优化,有效地抑制了二次流(迪安涡)。通过本文的研究,可以更好地理解喷水推进进气道内的流场。
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Multi-Objective Hydraulic Optimization on Intake Duct of Water-Jet Propulsion Using NSGA-II
Flow distortions occur at the outlet section of the intake duct owing to its shape properties, which is a component of water-jet propulsion. Since the noticeable influence of intake’s flow characteristics upon propulsive efficiency, it’s necessary to focus on intake duct redesign. In this paper, a systematic methodology for reducing flow distortions and power losses within the intake duct through a shape optimization process was obtained. In addition, the mechanism of flow distortions was also developed. The flush type inlet applied in the marine vessel with the speed of 30 knots was chosen as research project. Four characteristic parameters were set as optimization variables depending on the geometrical relationship of thirteen characteristic parameters referred to the duct longitudinal midsection, which were the ramp angle α, the radius of the upper lip R3, the radius of the lower lip R4 and the lip height h respectively. Subsequently, a sample space was built by Latin Hypercube Sampling (LHS) and the parameters were normalized in the range of 0 to 1. With the commercial software CFX, the numerical simulation was accomplished driven by SST k-ω turbulence model. Multi-objective optimization based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was utilized to minimize the non-uniformity at outlet section and maximize the minimal pressure at lip simultaneously. Moreover, the Radial Basis Function (RBF) neural network was employed to approximate the functional relationship between variables and objectives, which could be applied in the NSGA-II to get the Pareto Front. The minimum non-uniformity point and the trade-off point (The point both satisfies the minimum non-uniformity and the maximum minimal pressure at lip strategically) were selected from the Pareto Front. With regard to the characteristic parameters of the trade-off point, the ramp angle, the radius of the upper lip, the radius of the lower lip and the lip height are 31.91°, 11.42 mm, 400.97 mm and 55.43 mm respectively. Meanwhile, the characteristic parameters of the minimum non-uniformity point are 30.22°, 25.59 mm, 166.65 mm and 89.90 mm respectively. Ultimately, the duct outflow characteristics of prototype and optimization are compared. In terms of the trade-off point, the minimal pressure at lip increases 66.40% to −24488.93 Pa and the non-uniformity has a drop of 4.56% to 0.1571. The non-uniformity of the minimum point is 0.1481 which is reduced by 10.02%. Through the optimization of duct shape, the secondary flow (Dean vortices) is suppressed effectively. This paper is expected to provide a better comprehension of the flow field within the intake duct of water-jet propulsion.
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