Computational and experimental optimization of the exhaust air energy recovery wind turbine generator. (15th October 2016)

• Record Type: Journal Article
• Authors: Tabatabaeikia, Seyedsaeed; Ghazali, Nik Nazri Bin Nik; Chong, Wen Tong; Shahizare, Behzad; Izadyar, Nima; Esmaeilzadeh, Alireza; Fazlizan, Ahmad
• Is Part Of: Energy conversion and management. Volume 126(2016)
• Page Start: 862
• Page End: 874
• Publication Date: 2016-10-15
• Subjects: Computational optimization -- Computational fluid dynamic (CFD) -- Response surface methodology (RSM) -- Wind turbine -- Exhaust air recovery systems -- Noise pollution; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2016.08.039
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Studying the viability of harvesting wasted energy by exhaust air recovery generator. Optimizing the design using response surface methodology. Validation of optimization and computation result by performing experimental tests. Investigation of flow behaviour using computational fluid dynamic simulations. Performing the technical and economic study of the exhaust air recovery generator. Abstract: This paper studies the optimization of an innovative exhaust air recovery wind turbine generator through computational fluid dynamic (CFD) simulations. The optimization strategy aims to optimize the overall system energy generation and simultaneously guarantee that it does not violate the cooling tower performance in terms of decreasing airflow intake and increasing fan motor power consumption. The wind turbine rotor position, modifying diffuser plates, and introducing separator plates to the design are considered as the variable factors for the optimization. The generated power coefficient is selected as optimization objective. Unlike most of previous optimizations in field of wind turbines, in this study, response surface methodology (RSM) as a method of analytical procedures optimization has been utilised by using multivariate statistic techniques. A comprehensive study on CFD parameters including the mesh resolution, the turbulence model and transient time step values is presented. The system is simulated using SST K-ω turbulence model and then both computational and optimization results are validated by experimental data obtained in laboratory. Results show that the optimization strategy can improve the wind turbine generated power by 48.6% compared to baseline design. Meanwhile, it is able to enhance the fan intake airflow rate and decrease fan motor power consumption. The obtained optimization equations are also validated by both CFD and experimental results and a negligible deviation in range of 6–8.5% is observed.