The simulation results for airflow in high-rise buildings may not match actual behavior due to various airflow paths and impact factors, such as temperature, wind direction, wind velocity, and building characteristics (airtightness, layout, and others). When conducting whole-building airflow simulation for high-rise buildings, it is important to reasonably define uncertain input parameters in order to minimize the difference between measurement and simulation results. This study proposes a calibration method for airflow simulation modeling of high-rise buildings using the data-driven method and estimating uncertain input data through output data.The output data for comparing measured values with simulated values were focused on pressure distributions in buildings, because this value indicates the driving force of the airflows between building partitions and in the entire building. Based on the measured pressure distribution of the analyzed building, the unknown input parameters were deduced primarily using the Thermal Draft Coefficient (TDC). The uncertain input parameters (leakage area, flow coefficient, and exponent) were then optimized using Genetic Algorithm (GA). Through the processes suggested in this study, the uncertain input parameters for whole-building airflow simulation can be optimized, and the discrepancy between simulation results and measured values can be minimized. The advantage of this method is that the input parameters can be optimized for reasonable simulation modeling using limited measurement results in high-rise buildings.
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