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Time Traveling Regularization for Inverse Heat Transfer Problems

Author

Listed:
  • Elisan Dos Santos Magalhães

    (Heat Transfer Laboratory—LabTC, Institute of Mechanical Engineering—IEM, Federal University of Itajubá—UNIFEI, Campus Prof. José Rodrigues Seabra, Av. BPS, 1303, 37500-903 Itajubá, MG, Brazil)

  • Bruno De Campos Salles Anselmo

    (Heat Transfer Laboratory—LabTC, Institute of Mechanical Engineering—IEM, Federal University of Itajubá—UNIFEI, Campus Prof. José Rodrigues Seabra, Av. BPS, 1303, 37500-903 Itajubá, MG, Brazil)

  • Ana Lúcia Fernandes de Lima e Silva

    (Heat Transfer Laboratory—LabTC, Institute of Mechanical Engineering—IEM, Federal University of Itajubá—UNIFEI, Campus Prof. José Rodrigues Seabra, Av. BPS, 1303, 37500-903 Itajubá, MG, Brazil)

  • Sandro Metrevelle Marcondes Lima e Silva

    (Heat Transfer Laboratory—LabTC, Institute of Mechanical Engineering—IEM, Federal University of Itajubá—UNIFEI, Campus Prof. José Rodrigues Seabra, Av. BPS, 1303, 37500-903 Itajubá, MG, Brazil)

Abstract

This work presents a technique called Time Traveling Regularization (TTR) applied to an optimization technique in order to solve ill-posed problems. This new methodology does not interfere in the minimization technique process. The Golden Section method together with TTR are applied only to the objective function which will be minimized. It consists of finding an ideal timeline that minimizes an objective function in a defined future time step. In order to apply the proposed methodology, inverse heat conduction problems were studied. Controlled experiments were performed on 5052 aluminum and AISI 304 stainless steel samples to validate the proposed technique. One-dimensional and three-dimensional heat input experiments were carried out for the 5052 aluminum and AISI 304 stainless steel samples, respectively. The Sequential Function Specification Method (SFSM) was also used to be compared with the results of heat flux obtained by TTR. The estimated heat flux presented a good agreement when compared with experimental values and those estimated by SFSM. Moreover, TTR presented lower residuals than the SFSM.

Suggested Citation

  • Elisan Dos Santos Magalhães & Bruno De Campos Salles Anselmo & Ana Lúcia Fernandes de Lima e Silva & Sandro Metrevelle Marcondes Lima e Silva, 2018. "Time Traveling Regularization for Inverse Heat Transfer Problems," Energies, MDPI, vol. 11(3), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:507-:d:133693
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