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A methodology for non-destructive simultaneous estimation of spatially varying thermal conductivity and heat capacity in 2D solid objects was developed that requires only boundary measurements of temperatures. The spatial distributions were determined by minimizing the normalized sum of the least-squares differences between measured and calculated values of the boundary temperatures. Computing time was significantly reduced for the entire inverse parameter identification process by utilizing a metamodel created by an analytical response surface supported by an affordable number of numerical solutions of the temperature fields obtained by the high fidelity finite element analyses. The minimization was performed using a combination of particle swarm optimization and the BFGS algorithm. The methodology has shown to accurately predict linear and nonlinear spatial distributions of thermal conductivity and heat capacity in arbitrarily shaped multiply-connected 2D objects even in situations with noisy measurement data thus proving that it is robust and accurate. The current drawback of this method is that it requires an a priori knowledge of the general spatial analytic variation of the physical properties. This can be remedied by representing such variations using products of infinite series such as Fourier or Chebyshev and determining correct values of their coefficients.
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Non-Destructive Estimation of Spatially Varying Thermal Conductivity in 3D Objects Using Boundary Thermal Measurements
A methodology for non-destructive, accelerated inverse estimation of spatially varying material properties using only boundary measurements is presented. The spatial distribution of diffusion coefficient in 3D solid object is determined by minimizing the sum of the least-squares difference between measured and calculated values. The forward problem is solved using the finite volume and finite element methods, both of which were compared against analytical solution. The inverse problem was solved using an optimization technique to minimize the sum of the least-square errors. The non-destructive estimation was accelerated by the use of surrogate models to solve the forward problem. The presented methodology is applied to measurements containing varying levels of noise. Finally, it is used to detect both the location, size and shape of a subdomain within a solid object and material property of the subdomain material.
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- Award ID(s):
- 1642253
- PAR ID:
- 10072433
- Date Published:
- Journal Name:
- International journal of thermal sciences
- Volume:
- 118
- ISSN:
- 1778-4166
- Page Range / eLocation ID:
- 488-496
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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