Gaseous Pollutent Source Term Estimation Based on Adjoint Probability and Regularization Method

Fast and accurate identification of source locations release rates is particularly important for improving indoor air quality ensuring the safety health people. Existing methods based on adjoint probability are difficult to distinguish rate dynamic sources, optimization algorithms regularization limited analysing only a small amount potential pollutant information. Therefore, this study proposed an algorithm combining equations models identify location intensity sources in entire computational domain room. Based validated CFD model, we first obtained series response matrices corresponding sensor position by solving equation, then used method Bayesian inference extrapolate The results shown that convenient feasible source. Compared with real intensity, constant lower than error threshold (10%) 97.4% time nodes, periodic 95.4% nodes. This research provides new perspective estimation