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Regularization methods w ere combined w ith line-of-sight tunable diode laser absorption spectroscopy( TDLAS) to measure nonuniform temperature distribution. Relying on measurements of 12 absorption transitions of w ater vapor from 1300 nm to 1350 nm,the temperature probability distribution of nonuniform temperature distribution,for w hich a parabolic temperature profile is selected as an example in this paper,w as retrieved by making the use of regularization methods. To examine the effectiveness of regularization methods,truncated singular value decomposition( TSVD),Tikhonov regularization and a revised Tikhonov regularization method w ere implemented to retrieve the temperature probability distribution. The results derived by using the three regularization methods w ere compared w ith that by using constrained linear least-square fitting. The results show that regularization methods not only generate closer temperature probability distributions to the original,but also are less sensitive to measurement noise. Particularly,the revised Tikhonov regularization method generate solutions in better agreement w ith the original ones than those obtained by using TSVD and Tikhonov regularization methods. The results obtained in this w ork can enrich the temperature distribution information,w hich is expected to play a more important role in combustion diagnosis.
Regularization methods w ere combined w ith line-of-sight tunable diode laser absorption spectroscopy (TDLAS) to measure nonuniform temperature distribution. Relying on measurements of 12 absorption transitions of w ater vapor from 1300 nm to 1350 nm, the temperature probability distribution of nonuniform temperature distribution, for w hich a parabolic temperature profile is selected as an example in this paper, w as retrieved by making the use of regularization methods. revised Tikhonov regularization method w ere implemented to retrieve the temperature probability distribution. The results derived by using the three regularization methods w ere compared w ith that by using constrained linear least-square fitting. The results show that regularization methods not only generate closer temperature probability distributions to the original, but also are less sens Particularly, the revised Tikhonov regularization method generate solutions in better agreement w ith the original ones than those obtained by using TSVD and Tikhonov regularization methods. The results obtained in this w ork can enrich the temperature distribution information, w hich is expected to play a more important role in combustion diagnosis.