Print Email Facebook Twitter Data-driven optimization of building-integrated ducted openings for wind energy harvesting Title Data-driven optimization of building-integrated ducted openings for wind energy harvesting: Sensitivity analysis of metamodels Author Kaseb, Z. (TU Delft Intelligent Electrical Power Grids) Montazeri, H. (Eindhoven University of Technology) Date 2022 Abstract Metamodels are developed and used for aerodynamic optimization of a ducted opening integrated into a high-rise building to maximize the amplification factor within the duct. The duct consists of a nozzle, a throat, and a diffuser. 211 high-resolution 3D RANS CFD simulations are performed to generate training and testing datasets. The space-filling design and Genetic algorithm are used for data sampling and optimization, respectively. The performance of five commonly-used metamodels is systematically investigated: Response Surface Methodology (RSM), Kriging (KG), Neural Network (NN), Support Vector Regression (SVR), and Genetic Aggregation Response Surface (GARS). The investigation is based on (i) detailed in-sample and out-of-sample evaluations of the metamodels, (ii) annual available power in the wind (Pavailable), and (iii) annual energy production (AEP) for a 3-bladed horizontal-axis wind turbine (HAWT) installed in the mid-throat for the optimum designs obtained by the metamodels. The results show that converging-diverging ducted openings can magnify the experienced wind speed by the turbine and enhance the available wind power. In addition, the use of different metamodels can lead to a variation of up to 153% in the estimated Pavailable. For a small dataset, crude yet still acceptable accuracy can be achieved for Genetic Aggregation Response Surface and Kriging at a very low computational time. Subject Computational fluid dynamics (CFD)Design of experiments (DOE)Diffuser-augmented wind turbine (DAWT)Machine learningSurrogate modelZero-energy building To reference this document use: http://resolver.tudelft.nl/uuid:cb8bb9ce-7d90-4070-ac17-b55c8f55d605 DOI https://doi.org/10.1016/j.energy.2022.124814 ISSN 0360-5442 Source Energy, 258 Part of collection Institutional Repository Document type journal article Rights © 2022 Z. Kaseb, H. Montazeri Files PDF 1_s2.0_S0360544222017170_main.pdf 10.79 MB Close viewer /islandora/object/uuid:cb8bb9ce-7d90-4070-ac17-b55c8f55d605/datastream/OBJ/view