Experimental study of redesigned draft tube of an Agnew microhydro turbine

被引：3
作者：
Mirzaei, Ammar [1 ]
Shojaeefard, Mohammad Hassan [2 ]
Babaei, Ali [2 ]
Yassi, Yousef [3 ]
机构：
[1] MAPNA Grp Co, R&D Dept, Tehran, Iran
[2] Iran Univ Sci & Technol, Sch Mech Engn, Tehran, Iran
[3] Iranian Res Org Sci & Technol IROST, Tehran, Iran
来源：
ENERGY CONVERSION AND MANAGEMENT | 2015年 / 105卷
关键词：
Microhydro turbine; Surrogate-based optimization; Pressure recovery factor; Energy loss coefficient; Swirling flow; RESPONSE-SURFACE METHODOLOGY; SHAPE OPTIMIZATION; DESIGN; APPROXIMATIONS;
D O I：
10.1016/j.enconman.2015.08.007
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
In this study, a surrogate-based optimization has been carried out for the components of an Agnew microhydro turbine. A neural network was constructed as the surrogate, while the Non-dominated Sorting Genetic Algorithm (NSGA-II) was used as the optimizer. The optimal design was found by numerical simulations, and the final design was manufactured and installed at the turbine outlet. The performance of the turbine components was then measured according to ASME performance test code. Comparison was carried out between the original draft tube and its modified under different operating conditions. The test results have confirmed that the pressure recovery factor of the new component increases by 20.3% and the loss coefficient diminishes by 4.0%, with regard to the original design under the best operating conditions. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码：488 / 497
页数：10
相关论文
共 31 条
  • [1] [Anonymous], 1992, ASME PERFR TEST COD
  • [2] Response surface methodology based optimization of diesel-n-butanol -cotton oil ternary blend ratios to improve engine performance and exhaust emission characteristics[J]. Atmanli, Alpaslan;Yuksel, Bedri;Ileri, Erol;Karaoglan, A. Deniz. ENERGY CONVERSION AND MANAGEMENT, 2015
  • [3] Optimum estimation and forecasting of renewable energy consumption by artificial neural networks[J]. Azadeh, A.;Babazadeh, R.;Asadzadeh, S. M. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2013
  • [4] Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-a-vis response surface methodology and artificial neural network[J]. Betiku, Eriola;Taiwo, Abiola Ezekiel. RENEWABLE ENERGY, 2015
  • [5] Optimization of micro-grid system using MOPSO[J]. Borhanazad, Hanieh;Mekhilef, Saad;Ganapathy, Velappa Gounder;Modiri-Delshad, Mostafa;Mirtaheri, Ali. RENEWABLE ENERGY, 2014
  • [6] A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. Deb, K;Pratap, A;Agarwal, S;Meyarivan, T. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002(02)
  • [7] Deb K., 2010, MULTIOBJECTIVE OPTIM
  • [8] Denton J.D., 2010, P ASME TURB EXP 2010
  • [9] Multi-objective optimisation of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables[J]. Fischer, Gunter Reinald;Kipouros, Timoleon;Savill, Anthony Mark. RENEWABLE ENERGY, 2014
  • [10] Fuller W. A., 2009, Measurement error models
1234