Rheological modeling of multi-phase shear thickening fluid using an intelligent methodology

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Gürgen S., Sofuoğlu M. A., Kuşhan M. C.

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, cilt.42, sa.11, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 11
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s40430-020-02681-z
  • Dergi Adı: JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: Shear thickening fluid, Silicon carbide, Rheology, Intelligent modeling, STAB RESISTANCE, PARTICLE-SIZE, TEMPERATURE, ADDITIVES
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Shear thickening fluid (STF) has been extensively utilized in various engineering applications due to its unique characteristic, which is increasing viscosity under loading. Although single-phase STF is known for a long time, STF has been introduced to a novel concept, namely multi-phase STF, which includes particle additives in the suspension. In the STF adaptation to different systems, STF rheology is the key factor that should be tuned precisely for efficient usage. For this reason, STF rheology has been investigated by many researchers; however, experimental measurements are highly tedious and time-consuming. At this stage, theoretical modeling emerges as a wise choice; however, these models lead to a heavy computational burden due to the complex STF rheology. Therefore, we benefited from an intelligent modeling methodology that is highly effective for modeling and optimizing the complex and nonlinear relationships as such in multi-phase STF rheology. In this study, the rheology of multi-phase STF is investigated for the first time using an intelligent model to the best of our knowledge. According to the results, intelligent modeling is very efficient due to its parameter-free algorithm and successful fitting capability.