BibTeX
@INPROCEEDINGS{
         Karalashvili2009Saa,
       author = "M. Karalashvili and A. Mhamdi and G. F. Dietze and H. M. B{\"u}cker and A.
         Vehreschild and R. Kneer and C. H. Bischof and W. Marquardt",
       title = "Sensitivity analysis and identification of an effective heat transport model in wavy
         liquid films",
       booktitle = "Progress in Computational Heat and Mass Transfer: Proceedings of the 6th
         International Conference on Heat and Mass Transfer, Guangzhou, China, May~18--21, 2009",
       editor = "A. A. Mohamad and R. Bennacer and M. El-Ganaoui and K. Nandakumar and S. Huang",
       publisher = "South China University of Technology",
       pages = "644--651",
       abstract = "Thin liquid films flowing down vertical or inclined planes are of high industrial
         relevance. Nevertheless, a predictive transport model describing the effects of wave-induced
         intensification of heat and mass transport for the design of technical systems does not yet exist.
         In this paper, a systematic approach for the identification of a suitable transport model (structure
         and parameters) for the effective heat transport coefficient in the reduced system of heat transport
         equations is presented. For this transport coefficient two different model structures are proposed.
         An investigation of parameter identifiability based on local sensitivity analysis is carried out for
         both model structures prior to model identification. It is shown that the number of model parameters
         can be significantly reduced by targeted selection of the identifiable parameter subsets. This
         facilitates to reduce the overall computational effort. After setting up the best identifiable
         parameter sets for each model structure, a nonlinear, constrained least-squares parameter estimation
         problem is stated and solved using standard solution methods. Automatic differentiation techniques
         are applied for efficient sensitivity computations as well as for the gradients calculation within
         the parameter estimation step. Finally, the best model candidate is determined using statistical
         model discrimination techniques.",
       year = "2009",
       ad_area = "Process Engineering",
       ad_tools = "ADiMat"
}