Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66024
Title: Sequential Monte Carlo simulation for robust optimal design of cooling water system with quantified uncertainty and reliability
Authors: Cheng, Q
Wang, S
Yan, C
Keywords: Cooling water system
Reliability
Robust optimal design
Sequential Monte Carlo simulation
Uncertainty-based design
Issue Date: 2017
Publisher: Pergamon Press
Source: Energy, 2017, v. 118, p. 489-501 How to cite?
Journal: Energy 
Abstract: Conventional design of cooling water systems mainly focused on the individual components of cooling water system, not the system as a whole. In this paper, a robust optimal design based on sequential Monte Carlo simulation is proposed to optimize the design of cooling water system. Monte Carlo simulation is used to obtain the cooling load distribution of required accuracy, power consumption and unmet cooling load. Convergence assessment is conducted to terminate the sampling process of Monte Carlo simulation. Under different penalty ratios and repair rates, this proposed design minimizes the annual total cost of cooling water system. A case study of a building in Hong Kong is conducted to demonstrate the design process and test the robust optimal design method. The results show that the minimum total cost could be achieved under various possible cooling load conditions considering the uncertainties of design inputs and reliability of system components.
URI: http://hdl.handle.net/10397/66024
ISSN: 0360-5442
EISSN: 1873-6785
DOI: 10.1016/j.energy.2016.10.051
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