고층 주거건물의 공기유동 시뮬레이션 방법론

Airflow network simulation have been widely used for predicting the airflow pattern in high-rise residential buildings. Air-flow network modeling requires a lot of input data depending on the complexity of the building and it's surrounded environment. Field measurement must be conducted to collect the input data. However, measurement time and equipment are always limited and some parameters are hard to measure, so it is generally impossible to measure all input parameters. In this paper, a method to deduce resonable input data from the spot and short-term measurement results is proposed. This method includes a global optimization process to minimize the error between measured and simulated output data by genetic algorithm.