Deciphering the optimal exergy field in closed-wet cooling towers using Bi-level reduced-order models. (1st January 2022)

• Record Type: Journal Article
• Authors: Qu, Jinghui; Li, Mingjian; He, Chang; Zhang, BingJian; Chen, QingLin; Ren, Jingzheng
• Is Part Of: Energy. Volume 238:Part A(2022)
• Publication Date: 2022-01-01
• Subjects: Reduced-order models -- Closed wet cooling towers -- Exergy fields -- Exergy efficiency ratio -- Stochastic optimization -- Stochastic reduced-order model; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.121766
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract : This paper introduces a bi-level reduced-order models (ROMs) approach for quickly deciphering the optimal exergy fields in closed wet cooling towers (CWCTs) with consideration of weather variations. First, an efficient sampling method based on stochastic reduced-order model is performed for the approximation of the multivariate probability distributions by generating a finite set of samples. The uncertainty associated with input variables is propagated via multi-sample CFD simulations of the CWCT model for each of the samples. The results of the state and output variables stored in the CFD solutions are used to construct the data-driven and physics-based ROMs by combining principal component analysis and artificial neural network methods. The constructed data-driven ROM is embedded in a sampling-based stochastic optimization model that seeks the maximization of the expected exergy efficiency ratio. The physics-based ROM is used to visualize the optimal field profiles of the thermal-, mechanical-, and chemical-exergy fluxes. Finally, the results of a case study demonstrate that the main strengths of the proposed approach is to simultaneously obtain the optimal exergy efficiency ratios and the exergy field profiles of the CWCT system in a computationally efficient manner. Graphical abstract: Image 1 Highlights: Bi-level model reductions are used for optimizing closed-wet cooling towers. Principal component analysis is combined with artificial neural network. Samples are generated by the stochastic reduced-order model approach. The probability distributions are propagated via multi-sample simulations. It takes a few seconds to visualize the spatially distributed exergy parameters.