@Article{hmt.7.15,
AUTHOR = {Sharath Kumar, Harsha Kumar, N. Gnanasekaran},
TITLE = {A NEURAL NETWORK BASED METHOD FOR ESTIMATION OF  HEAT GENERATION FROM A TEFLON CYLINDER},
JOURNAL = {Frontiers in Heat and Mass Transfer},
VOLUME = {7},
YEAR = {2016},
NUMBER = {1},
PAGES = {1--7},
URL = {http://www.techscience.com/fhmt/v7n1/54687},
ISSN = {2151-8629},
ABSTRACT = {The paper reports the estimation of volumetric heat generation (qv) from a Teflon cylinder. An aluminum heater, which acts as a heat source, is 
placed at the center of the Teflon cylinder. The problem under consideration is modeled as a three dimensional steady state conjugate heat 
transfer from the Teflon cylinder. The model is created and simulations are performed using ANSYS FLUENT to obtain temperature data for the 
known heat generation qv. The numerical model developed using ANSYS acts as a forward model. The inverse model used in this work is 
Artificial Neural Network (ANN). Estimation of heat generation is carried out by minimizing the error between the simulated temperature and 
the experimental/surrogated temperature. The efficacy of the ANN method is explored for the estimation of unknown heat generation as both 
forward model and inverse model. The concept of Asymptotic Computational Fluid Dynamics (ACFD) is introduced as a fast forward model 
which is obtained by performing CFD simulations. The unknown heat generation is estimated for the surrogated data using ANN. In order to 
mimic experiments, noise is added to the surrogated data and estimation of heat generation is also carried out for the perturbed/noise added 
temperature data.},
DOI = {10.5098/hmt.7.15}
}