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Feasibility study of dynamic thermal-modeling development using measurement and validation: Case study of indoor fish farm

环境科学 相对湿度 湿度 能源消耗 曝气 热舒适性 热的 环境工程 气象学 工程类 废物管理 地理 结构工程 电气工程
作者
Jabin Goo,Young Hoon Kwak,Hakjong Shin,Ji‐Won Kim,Seng‐Kyoun Jo,Jung-Ho Huh
出处
期刊:Applied Thermal Engineering [Elsevier BV]
卷期号:228: 120512-120512 被引量:4
标识
DOI:10.1016/j.applthermaleng.2023.120512
摘要

Since energy consumption in buildings relies primarily on their purpose as opposed to the type of occupants, relatively high energy is also consumed in the indoor fish farms where people do not live. In particular, energy consumption of indoor fish farms has been increasing for the high productivity and economic efficiency. Therefore, a feasibility study of dynamic thermal modeling development for indoor fish farms is critical to analyze energy consumption. In this study, an indoor fish farm thermal model was developed, and the feasibility of the model was analyzed using onsite indoor temperature, relative humidity, and water temperature data. Further, the major sensitive variables in model development and their effects were investigated. The errors between measured and predicted results obtained using the developed thermal model were within hourly acceptable limits, which means the developed thermal model was valid. The activity factor (AF) and air change per hour (ACH), which are the major sensitive variables, significantly affected thermal performance such as evaporation loss, indoor temperature, relative humidity and water temperature. The average indoor relative humidity increased by approximately 16% when AF increased from 0 to 2. The average evaporation loss of the fish farm increased by 20.9 kW to 49 kW when ACH was 10. Therefore, similar to an indoor fish farm, the effects of aeration, opening/closing of windows and doors must be considered for simulations during energy-model development and future improvements. The results suggest that the proposed thermal model for indoor fish farms can be used to develop an energy model and expanded to various fields for predicting energy consumption and optimal control.
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