A novel inverse method for identification of 3D thermal conductivity coefficients of anisotropic media by the boundary element analysis. (October 2015)

• Record Type: Journal Article
• Authors: Hematiyan, M.R.; Khosravifard, A.; Shiah, Y.C.
• Is Part Of: International journal of heat and mass transfer. Volume 89(2015:Oct.)
• Page Start: 685
• Page End: 693
• Publication Date: 2015-10
• Subjects: Thermal conductivity tensor -- Anisotropic heat conduction -- Boundary element method -- Inverse problem -- Multi-experiment based inverse method; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2015.05.034
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: This paper presents an expedient numerical approach to inversely identify the 3D heat conductivity coefficients of anisotropic media. The use of more than one set of data leads to faster convergence of the inverse analysis. By taking principal conductivities, the three nonlinear inequality constraints for the off-diagonal components of the conductivity tensor shall disappear. The multi-experiment based inverse method yields reliable solution. Abstract: This paper presents a new numerical inverse method for identifying the six components of the thermal conductivity tensor of a 3D anisotropic medium with an arbitrary shape. The unknowns are inversely calculated using heat conduction problems with extra information at some boundary sampling points. For better stability, the inverse method uses data supplied from more than one steady-state heat conduction problem. Since all sampling points are taken to be on boundary surfaces for the sake of easy access, the boundary element method (BEM) for direct calculation is employed for the sensitivity analyses. The off-diagonal components of the thermal conductivity tensor must satisfy three nonlinear inequality constraints, which make the inverse analysis even more challenging. To overcome this difficulty, the inverse problem is formulated in terms of the principal thermal conductivities along with the rotation angles of principal axes, by which the three constraints disappear. For the inverse analysis, the damped Gauss–Newton method is adopted for the optimization process. In the end, numerical examples are presented, showing that the proposed method can yield reliable solutions even in cases with relatively large measurement error and with initial guesses far from the exact solution.