TY - JOUR
T1 - Online estimation of multicomponent heat flux using a system identification technique
AU - Khorrami, Masoud
AU - Samadi, Forooza
AU - Kowsary, Farshad
AU - Mohammadzaheri, Morteza
PY - 2013/5
Y1 - 2013/5
N2 - In this work system identification techniques are used to map the two-dimensional heat flux into the temperatures through a linear model supported by theoretical and numerical results. The basis of this analysis is a discrete version of the Burggraf Method saying a single component heat flux is a linear combination of the temperatures around the time of its occurrence. Taking the same approach, a linear model (i.e. a linear artificial neural network (ANN)) is employed to estimate a multicomponent heat flux as a linear function of the temperatures. A known heat flux is imposed to the direct model, then the history of heat flux-temperature data are fit to the linear mathematical model (i.e. a linear ANN) using system identification techniques. The achieved model estimates the heat flux based on a series of past and future temperatures and the estimated heat flux components are in a good agreement with the exact ones. Finally, the effect of some important factors on the results is investigated. The proposed solution to inverse heat conduction problems does not need thermophysical and geometrical parameters of the system and is robust against noises. It merely needs some series of heat flux-temperature data from solution of a reliable direct numerical model or experiment.
AB - In this work system identification techniques are used to map the two-dimensional heat flux into the temperatures through a linear model supported by theoretical and numerical results. The basis of this analysis is a discrete version of the Burggraf Method saying a single component heat flux is a linear combination of the temperatures around the time of its occurrence. Taking the same approach, a linear model (i.e. a linear artificial neural network (ANN)) is employed to estimate a multicomponent heat flux as a linear function of the temperatures. A known heat flux is imposed to the direct model, then the history of heat flux-temperature data are fit to the linear mathematical model (i.e. a linear ANN) using system identification techniques. The achieved model estimates the heat flux based on a series of past and future temperatures and the estimated heat flux components are in a good agreement with the exact ones. Finally, the effect of some important factors on the results is investigated. The proposed solution to inverse heat conduction problems does not need thermophysical and geometrical parameters of the system and is robust against noises. It merely needs some series of heat flux-temperature data from solution of a reliable direct numerical model or experiment.
KW - Inverse heat conduction
KW - Modelling
KW - Multicomponent heat flux
KW - Neural network
KW - Online estimation
UR - http://www.scopus.com/inward/record.url?scp=84877103094&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877103094&partnerID=8YFLogxK
U2 - 10.1016/j.icheatmasstransfer.2013.03.018
DO - 10.1016/j.icheatmasstransfer.2013.03.018
M3 - Article
AN - SCOPUS:84877103094
SN - 0735-1933
VL - 44
SP - 127
EP - 134
JO - International Communications in Heat and Mass Transfer
JF - International Communications in Heat and Mass Transfer
ER -