Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65237
Title: Robust optimal design of HVAC systems considering uncertainty and reliability
Authors: Cheng, Qi
Advisors: Wang, Shengwei (BSE)
Xiao, Fu (BSE)
Keywords: Heating.
Ventilation.
Air conditioning.
Issue Date: 2017
Publisher: The Hong Kong Polytechnic University
Abstract: This thesis presents a robust optimal design method of HVAC systems in buildings concerning the uncertainties of design inputs and the reliability of system components. The developed methods include uncertainty-based optimal design considering uncertainties only, robust optimal design concerning uncertainties and reliability, probabilistic approach for generating the cooling load distribution of required accuracy and reliability quantification methods (including Markov method and sequential Monte Carlo simulation). Monte Carlo simulation is a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. In this thesis, Monte Carlo simulation is used for generating cooling load distributions. In order to represent the characteristics of the uncertainties of design input in cooling load distribution, sufficient number of Monte Carlo simulation is required. A probabilistic approach is developed to determine the minimum number of Monte Carlo simulations for accuracy. Reliability analysis or assessment is necessary to avoid/reduce losses caused by both the normal situations and abnormal situations such as the failure of some components. Markov method and sequential Monte Carlo simulation are frequently used to conduct the reliability assessment in other fields such as electrical engineering. In this thesis, both the two methods are used to conduct the reliability assessment of HVAC system. Availability risk cost is considered as the index to evaluate the system reliability. An uncertainty-based optimal design is developed and used to optimize the chiller plant design. It ensures that the chiller plant operate at a high efficiency and the minimum annual total cost (including annual operational cost and annualized capital cost) could be achieved under various possible cooling load conditions, considering the uncertain variables in cooling load calculation (i.e., weather conditions). A case study on the chiller plant of a building in Hong Kong is conducted to demonstrate the design process and validate the uncertainty-based optimal design.
A robust optimal design method is proposed to optimize the design of chiller plants concerning impacts of uncertainty in the design input data and the system reliability in operation. Monte Carlo simulation is used to generate the cooling load distribution and Markov method is used to obtain the probability distribution of system states considering the different failure rates between constant-speed chillers and variable-speed chillers. A case study of a building in Hong Kong is conducted to demonstrate the design process and validate the robust optimal design method. Comparisons are made among the conventional design, uncertainty-based optimal design and robust optimal design. The results show that the system could operate at a relatively high efficiency and the minimum total annual total cost could be achieved under various possible cooling load conditions considering the uncertainties and system reliability. A robust optimal design method is proposed to optimize the design of chilled water pump systems while concerning the uncertainties of design inputs and models as well as the component reliability in operation. Monte Carlo simulation is used to generate the cooling load distribution and hydraulic resistance distribution by quantifying the uncertainties. Markov method is used to obtain the probability distribution of the system state. Under different control methods, this proposed design method minimizes the annual total cost. A case study on a building in Hong Kong is conducted to demonstrate the design process and validate the robust optimal design method. Results show that the system could operate at a relatively high efficiency and the minimum total life-cycle cost could be achieved. 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 accurate cooling load distributions, power consumptions and unmet cooling loads. 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.
Description: PolyU Library Call No.: [THS] LG51 .H577P BSE 2017 Cheng
vi, vi, 202 pages :color illustrations
URI: http://hdl.handle.net/10397/65237
Rights: All rights reserved.
Appears in Collections:Thesis

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