Estimation of heat transfer model parameters by using recursive least square (RLS) method for a shape memory alloy wire

ÖNDER E. T., SÜMER B., BAŞLAMIŞLI S. Ç.

JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, cilt.34, sa.4, ss.379-392, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 4
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1177/1045389x221105892
  • Dergi Adı: JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.379-392
  • Anahtar Kelimeler: Shape memory alloy, heat transfer model, parameter estimation, recursive least square method, SMA PHENOMENOLOGICAL MODEL, HEATING/COOLING RATE, IDENTIFICATION, DESIGN
  • Hacettepe Üniversitesi Adresli: Evet

Özet

The thermal model of shape memory alloy (SMA) wires is improved by identifying the convectional heat transfer model (htm) parameters. Recursive Least Square (RLS) Method is used to estimate heat transfer coefficient, specific heat and latent heat coefficient. The parameters are estimated altogether with the surface area, volume and density of the wire. Also, after measuring the resistance by using multimeter, the value of each parameter was separately determined. Heat transfer coefficient is ambient dependent and dominant in the heat transfer model. The estimation of heat transfer coefficient is important and this is shown in this study. The proposed method builds an improved heat transfer model for SMA wire by using a single set of experimental data. Performance of the estimation with RLS is validated by using the measured data. It is shown that heat convection coefficient can be estimated with high accuracy. Thus, it is revealed that addition of the latent heat term during phase transformation into the model resulted in a more accurate model. The accuracy of the model can be enhanced by 28% by the addition of latent heat term.