WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - Scopus: 1Genetic Algorithm Optimization of Langevin Thermostat and Thermal Properties of Graphene-Aluminum Nanocomposites: a Molecular Dynamics(Iop Publishing Ltd, 2024) Toprak, KasimThe thermal properties of a laminated structure of graphene-coated aluminum composite nanomaterial were investigated through non-equilibrium molecular dynamics (NEMD) simulations to address the problem of temperature deviation in the thermostat volume applied. This paper presents a new insight into the best values of timestep and Langevin thermostat damping parameters for each atom in the nanomaterial with different size configurations using the genetic algorithm (GA) method by considering the timestep and thermostat damping parameters for each atom type, as well as the thickness of the nanomaterial, the thermostat, buffer, and heat flow lengths. The initial population results indicate that the thermostat temperature deviation increases with higher thermostat damping coefficients and timestep. However, the deviation decreases significantly with increased heat flow and thermostat lengths. Variations in buffer length and aluminum thickness do not have a significant effect on temperature. The application of a GA for optimization leads to a decrease in thermostat temperature deviation. The optimized parameters resulted in better thermostat temperature deviations when analyzing the temperature, aluminum thickness, and both buffer and thermostat lengths. Additionally, the thermal conductivity of aluminum-graphene nanomaterial decreases with increasing temperature, buffer length, and aluminum thickness, but increases by up to 9.85% with increasing thermostat length.Article Citation - WoS: 3Citation - Scopus: 3Understanding Neural Network Tuned Langevin Thermostat Effect on Predicting Thermal Conductivity of Graphene-Coated Copper Using Nonequilibrium Molecular Dynamics Simulations(Iop Publishing Ltd, 2024) Toprak, KasimCopper has always been used in thermoelectric applications due to its extensive properties among metals. However, it requires further improving its heat transport performance at the nanosized applications by supporting another high thermal conductivity material. Herein, copper was coated with graphene, and the neural network fitting was employed for the nonequilibrium molecular dynamics simulations of graphene-coated copper nanomaterials to predict thermal conductivity. The Langevin thermostat that was tuned with a neural network fitting (NNF), which makes up the backbone of deep learning, generated the temperature difference between the two ends of the models. The NNF calibrated the Langevin thermostat damping constants that helped to control the temperatures precisely. The buffer and thermostat lengths were also analyzed, and they have considerable effects on the thermostat temperatures and a significant impact on the thermal conductivity of the graphene-coated copper. Regarding thermal conductivity, the four different shapes of vacancy defect concentrations and their locations in the graphene sheets were further investigated. The vacancy between the thermostats significantly decreases the thermal conductivity; however, the vacancy defect in thermostats does not have a similar effect. When the graphene is placed between two copper blocks, the thermal conductivity decreases drastically, and it continues to drop when the sine wave amplitude on the graphene sheet increases.Article Citation - WoS: 2Citation - Scopus: 2Longitudinal Thermal Conductivity of Cu-Swcnt Core-Shell Nanowire: Molecular Dynamics Simulations(Begell House inc, 2023) Toprak, Kasim; Bayazitoglu, YildizThe 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.