Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

Browse

Filters

2023 - 20262

Settings

Has files: NoSubject: search.filters.subject.Thermal Conductivity

Search Results

Now showing 1 - 2 of 2
  • Article
    Development and Validation of Regression Model via Machine Learning to Estimate Thermal Conductivity and Heat Flow Using Igneous Rocks from the Dikili-Bergama Geothermal Region, Western Anatolia
    (Pergamon-Elsevier Science Ltd, 2026) Ayzit, Tolga; Sahin, Onur Gungor; Erol, Selcuk; Baba, Alper
    Thermal conductivity is a fundamental parameter that significantly influences the thermal regime of the lithosphere. It plays a crucial role in a variety of geological applications, including geothermal energy exploration, igneous system assessment, and tectonic modeling. In this study, a machine learning approach is used to predict the thermal conductivity of igneous rocks based on the composition of major oxides. A total of 488 samples from different regions of the world were analyzed. The thermal conductivity values ranged from 1.20 to 3.74 Wm(-1) K-1 and the mean value was 2.61 Wm(-1) K-1. The Random Forest (RF) algorithm was used, resulting in a high coefficient of determination (R-2 = 0.913 for training and R-2 = 0.794 for testing) and a root mean square error (RMSE) of 0.112 and 0.179, respectively. Significance analysis of the traits identified SiO2 (>40 %), Na2O (>15 %) and Al2O3 (>10 %) as the most influential predictors. The study presented results from the Western Anatolia region, where felsic rocks had the highest thermal conductivity (mean = 2.69 Wm(-)(1)K(-)(1)) compared to mafic (mean = 2.34 Wm(-)(1)K(-)(1)) and ultramafic rocks (mean = 2.39 Wm(-)(1)K(-)(1)). In addition, the study evaluated the predictive capabilities of machine learning models for the igneous rocks of the Dikili-Bergama region and compared the results with those of saturated models. Using these data, we calculated heat flow values of up to 400 mWm(-2) under saturated conditions in western Anatolia. These results highlight the value of integrating geochemical data with machine learning to improve geothermal resource exploration and lithospheric modeling.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Longitudinal Thermal Conductivity of Cu-Swcnt Core-Shell Nanowire: Molecular Dynamics Simulations
    (Begell House inc, 2023) Toprak, Kasim; Bayazitoglu, Yildiz
    The phonon thermal conductivity of copper core and armchair single-walled carbon nanotube shell (Cu-SWCNT) coaxial nanostructure is presented using the non-equilibrium molecular dynamics (NEMD) simulations method. The study aims to investigate how the ultrathin Cu nanowire affects the thermal conductivity of Cu-SWCNT. The results have revealed that the thermal conductivity of Cu-SWCNT is more than two orders of magnitude higher than that of the Cu core with the contribution of the SWCNT shell. The influences of length, chirality, defect, and core filling on the thermal conductivity of Cu-SWCNT are studied using the two most used C-C potentials, the AIREBO and Tersoff potentials. The bare SWCNT and Cu-SWCNT simulation results revealed that the thermal conductivity using the AIREBO potential is lower than that of Tersoff. Although the thermal conductivity increases with the length of the coaxial tube, it decreases with the chirality and the filling ratio. Increasing the chirality of SWCNT and the Cu core-filling ratio can boost the core copper's contributions to the thermal conductivity, reducing the overall thermal conductivity. The lengths of the thermostat and buffer regions do not significantly affect the thermal conductivity. In addition, the vacancy concentration in heat flow regions effectively reduces thermal conductivity, whereas the vacancy in the thermostat regions does not have a significant effect. The thermal rectification factor defined as changing the imposed heat flux direction is up to 1.73% for the Cu-SWCNT and 2.63% for the SWCNT.