  Current CFD calibration work has mainly focused on the CFD model calibration.
However no known work has considered the calibration of the CFD results. In
this paper, we take inspiration from the image editing problem to develop a
methodology to calibrate CFD simulation results based on sparse sensor
observations. We formulate the calibration of CFD results as an optimization
problem. The cost function consists of two terms. One term guarantees a good
local adjustment of the simulation results based on the sparse sensor
observations. The other term transmits the adjustment from local regions around
sensing locations to the global domain. The proposed method can enhance the CFD
simulation results while preserving the overall original profile. An experiment
in an air-conditioned room was implemented to verify the effectiveness of the
proposed method. In the experiment, four sensor observations were used to
calibrate a simulated thermal map with 167x365 data points. The experimental
results show that the proposed method is effective and practical.
