Inverse estimation of indoor airflow patterns using singular value decomposition

The fast pace in the development of indoor sensors and communication technologies is allowing a great amount of sensor data to be utilized in various areas of indoor air applications, such as estimating indoor airflow patterns. The development of such an inverse model and the design of a sensor system to collect appropriate data are discussed in this study. Algebraic approaches, including singular value decomposition (SVD), are evaluated as methods to inversely estimate airflow patterns given limited sensor measurements. In lieu of actual sensor data, computational fluid dynamics data are used to evaluate the accuracy of the airflow patterns estimated by the inverse models developed in this study. It was found that the airflow patterns estimated by the linear inverse SVD model were as accurate as those estimated by the nonlinear inverse-multizone model. For the zones tested, sensor measurements along on the walls and near the inlet and outlet provided the greatest improvement in the accuracy of the estimated airflow patterns when compared with the results using measurements from other locations.