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Início Arquivos Representantes Futuras reuniões American Society of Thermal and Fluids Engineering

ISSN On-line: 2379-1748

ISBN Imprimir: 978-1-56700-517-2 (Flash drive)

5-6th Thermal and Fluids Engineering Conference (TFEC)
May, 26–28, 2021 , Virtual


Get access pages 549-558
DOI: 10.1615/TFEC2021.env.032324


The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. Estimates suggest that between 5% and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. In order to target various anomalies in different component of a HVAC system, a component level model of the HVAC system is needed. As one of the necessary steps in developing this general framework for fault detection of a cooling system in a commercial building, the energy modeling of a crossflow cooling tower was targeted. This warranted a detailed physical model for a cooling tower based on first principle physics. A cooling tower model was built based on physical thermodynamics and heat and mass transfer principles using the effectiveness-NTU method for heat exchanger performance. The focus of the model was to use non-intrusive measurements and predict the exiting water temperature from the cooling tower given certain inputs. Compared with the existing models, the new model has two main advantages: (1) As an engineering model, it is formulated while taking into consideration an engineering perspective and involves fewer input variables and has an intuitive description of the cooling tower operation; (2) There is no iterative computation required and therefore is very easy to use for online optimization. The focus of the modeling was not to get the most accurate measurements, but rather to help in the aid of developing a system level cooling tower model which could be used as a part of an optimization framework for overall building energy consumption. With this goal, the model was still able to predict the results accurately within 10% when compared with the results from field experiments. The model uses readily available inputs like temperatures of the water entering and exiting the cooling tower, airflow rate of the multiple fans in the cooling tower and the ambient weather conditions. The model was validated with data from a cooling tower that was operating for around one month. The data was collected from a cooling tower satisfying the needs of a large commercial hospital building in Spain. The results from the model show that the model agrees with the experimental measurements within 10%. The model validation and results from a case-study are presented in this work.
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