Analysis of geometrical parameter sensitivity and mechanism of flow loss in exhaust volute based on particle swarm optimization

IF 5.8 1区 工程技术 Q1 ENGINEERING, AEROSPACE Aerospace Science and Technology Pub Date : 2025-02-01 DOI:10.1016/j.ast.2024.109803
Dongliang Sun , Xiaolong Tang , Xiaoquan Yang , Jue Ding , Peifen Weng
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Abstract

To investigated the mechanism of flow loss occurs in gas-turbine exhaust volute, comprehensive analysis was conducted based on the traces of parametric optimizations. An original volute was parameterized by depicting the core-part with 6 parameters. Concerning the coefficients of total pressure loss and static pressure recovery, the volute was optimized, as a start of the investigation, by Particle Swarm Optimization (PSO) to generate 56 exhaust volute designs and 8 geometric parameter traces. The geometry evolution history was fully recorded by these traces during the optimization. Based on this, parameter sensitivity analyses were conducted by single, double and K-means-clustering-based comprehensive parameters. Furthermore, partial dependence plot (PDP) and individual conditional expectation plot (ICEP) were applied to enhance the expression of parameter sensitivity. This enables the detailed discussion of the mechanisms of flow loss occurs in exhaust volute. The results demonstrate that the Particle Swarm Optimization (PSO) algorithm is highly effective for optimizing the exhaust volute. After six rounds of optimization with eight particles per round, the total pressure loss coefficient at the exhaust volute outlet was reduced by 35%, while the static pressure recovery coefficient increased by 79%. The sensitivity analysis reveals that geometric parameters exhibit varying degrees of influence on aerodynamic performance, with diffuser length being the most critical factor. Notably, a shorter diffuser, constrained by the same external dimensions, tends to result in lower flow losses. Flow loss within the collector accounts for 71.7% of the total loss, which can be attributed to surface friction and turbulent dissipation. The latter is primarily driven by turbulent viscous dissipation, predominantly occurring in the collector section. Additionally, the size of large-scale vortices in the curved parts of the diffuser and collector, which contribute to turbulent dissipation, as well as the ratio of the average flow velocity to circulation speed in the collector, which affects wall friction, are key factors in flow loss generation.
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基于粒子群优化的排气蜗壳几何参数敏感性及流动损失机理分析
为了研究燃气轮机排气蜗壳内流动损失发生的机理,基于参数优化轨迹进行了综合分析。对原始蜗壳进行了6个参数的参数化。考虑总压损失系数和静压恢复系数,作为研究的开始,利用粒子群优化(PSO)对蜗壳进行了优化,生成了56个排气蜗壳设计和8个几何参数轨迹。这些轨迹完整地记录了优化过程中的几何演化历史。在此基础上,采用单均值、双均值和基于k均值聚类的综合参数进行参数敏感性分析。采用偏相关图(PDP)和个体条件期望图(ICEP)增强参数敏感性的表达。从而可以详细讨论排气蜗壳内流动损失发生的机理。结果表明,粒子群优化算法是一种高效的排气蜗壳优化算法。经过6轮优化,每轮优化8粒,排气蜗壳出口总压损失系数降低35%,静压恢复系数提高79%。灵敏度分析表明,几何参数对气动性能有不同程度的影响,其中扩压器长度是最关键的影响因素。值得注意的是,在相同外部尺寸的约束下,较短的扩压器往往会导致较低的流动损失。集热器内流动损失占总损失的71.7%,主要由表面摩擦和湍流耗散造成。后者主要由紊流粘性耗散驱动,主要发生在集热器段。此外,扩散器和集热器弯曲部位的大尺度涡的大小有助于湍流耗散,以及集热器内平均流速与循环速度的比值影响壁面摩擦,是流动损失产生的关键因素。
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来源期刊
Aerospace Science and Technology
Aerospace Science and Technology 工程技术-工程:宇航
CiteScore
10.30
自引率
28.60%
发文量
654
审稿时长
54 days
期刊介绍: Aerospace Science and Technology publishes articles of outstanding scientific quality. Each article is reviewed by two referees. The journal welcomes papers from a wide range of countries. This journal publishes original papers, review articles and short communications related to all fields of aerospace research, fundamental and applied, potential applications of which are clearly related to: • The design and the manufacture of aircraft, helicopters, missiles, launchers and satellites • The control of their environment • The study of various systems they are involved in, as supports or as targets. Authors are invited to submit papers on new advances in the following topics to aerospace applications: • Fluid dynamics • Energetics and propulsion • Materials and structures • Flight mechanics • Navigation, guidance and control • Acoustics • Optics • Electromagnetism and radar • Signal and image processing • Information processing • Data fusion • Decision aid • Human behaviour • Robotics and intelligent systems • Complex system engineering. Etc.
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