Sürdürülebilir Kalkınma Amaçları

• 1

Yazarlar

• Bilen, Kadir(1)
• Elleithy, K.(1)
• Hasiloglu, Abdussamet(1)
• Kalkisim, Ahmet Tahir(1)
• Karaman, Safa(1)
• Kayacier, Ahmed(1)
• Sobh, T.(1)
• Yüksel, Ferhat(1)

• BildiriYalnız künye2017/r/gumushane/15278
  Artificial intelligent models for thermodynamic properties of R152a and R134a refrigerants in automobile air conditioning systems

  Kalkisim, Ahmet Tahir; Bilen, Kadir; Hasiloglu, Abdussamet; Sobh, T.; Elleithy, K.

  With the increase in environmental awareness, the quest for alternative refrigerants for automobile air conditioning systemsis a necessity for the protection of the ozone layer and the prevention of global warming. Therefore, it is important that the refrigerant to be used as an alternative must be at least thermodynamically sufficient and within specified environmental limits. In this study, R152a refrigerant which is an alternative gas for R134a that's use in automobile air conditioning systems will be removed within a certain schedule has been examined thermodynamically. Artificial intelligent model was generated and compared by using data obtained from the examination. Through this model, system was tested with untrained data. The generated model has been very close to test data and successful in high precision.

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• MakaleAçık Erişim2014Scopus SJRQ1/r/gumushane/15536
  Dynamic mechanical spectra of selected turkish honeys: Construction of predictive models for complex viscosity using two different nonlinear modeling techniques

  Kayacier, Ahmed; Yüksel, Ferhat; Karaman, Safa

  Dynamic oscillatory shear rheological characteristics of honey samples from different floral sources were evaluated using frequency sweep test and stress sweep test at three different temperatures (10, 15, and 20°C). All honey samples showed liquid-like behavior because the loss modulus was significantly greater than the elastic modulus. Pine honey showed the highest complex viscosity (86.33 Pa s at 10°C), while the lowest complex viscosity was observed in citrus honey (22.15 Pa s) at the same temperature. Temperature dependency of complex viscosity of honey samples was modeled by Arrhenius model and activation energy values of honeys ranged from 83.13 to 97.46 kJ/mol. An efficient predictive model for complex viscosity values of honeys was constructed using adaptive neuro fuzzy inference system that showed satisfactory prediction performance with high coefficient of determination (0.995) and low root mean square error (0.04) in the validation period compared to artificial neural networks.

  AtıfScopus8·WoS8·OpenAlex9

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