Reconstruction of selected operating parameters of a thermoelectric device
This paper presents preliminary research aimed at recognizing some selected operating parameters of a thermoelectric device. The inverse problem was formulated, for the solution of which a population heuristics (Ant Colony Optimization) was used. In the inverse task, selected parameters important for the cell operation were reconstructed based on relatively easy to obtain temperature measurements within heat exchangers and appropriate measurements of electrical quantities. The heuristics used, reconstructs the estimated variables, minimizing the differences between data from the measurements and data calculated in the model for their determined values. Since inverse tasks, as ill-conditioned problems, are characterized by high sensitivity to measurement errors, the tests began with calculations based on numerically generated data in order to fully maintain control of their disturbances.
Keywordsthermoelectricity, heat transfer, complex thermal system, thermal resistance, condition tracking, condition optimization, inverse problem, sensitivity analysis, ant colony optimization,
References R.C. Aster, B. Borchers, C.H. Thurber. Parameter estimation and inverse problems. Elsevier, 2019.
 R. Buchalik, I. Nowak, K. Rogozinski, G. Nowak. Detailed model of a thermoelectric generator performance.
Journal of Energy Resources Technology, 1–12 (12 pages), Paper No.: JERT-18-1924, 2019 (in press).
 M. Chen, S.S. Lu, B. Liao. On the figure of merit of thermoelectric generators. Journal of Energy Resources
Technology, 127(1): 37–41, 2005.
 L. Chen, J. Gong, F. Sun, C. Wu. Effect of heat transfer on the performance of thermoelectric generators.
International Journal of Thermal Sciences, 41(1): 95–99, 2002.
 M. Dorigo, M. Birattari. Ant colony optimization. Springer US, 2010.
 M. Kern. Numerical methods for inverse problems. ISTE and John Wiley & Sons, 2016.
 H. Lee, J. Sharp, D. Stokes, M. Pearson, S. Priya. Modeling and analysis of the effect of thermal losses on
thermoelectric generator performance using effective properties. Applied Energy, 211: 987–996, 2018.
 G.S. Nolas, J. Sharp, H.J. Goldsmid. Thermoelectrics: Basic principles and new materials developments.
Springer-Verlag Berlin Heidelberg New York, 2001.
 I. Nowak. Heuristics applying stochastic information as tools for thermoacoustic standing-wave engine optimization.
CAMES, 25(1): 3–19, 2018.
 H.W. Engl, M. Hanke, A. Neubauer. Regularization of inverse problems. Vol. 375. Springer Science & Business