Thermophysical properties of hybrid nanofluids and the proposed models: An updated comprehensive study
Thermal performance of energy conversion systems is one of the most important goals to
improve the system's efficiency. Such thermal performance is strongly dependent on the …
improve the system's efficiency. Such thermal performance is strongly dependent on the …
A critical review of specific heat capacity of hybrid nanofluids for thermal energy applications
Nanofluids have gained tremendous research interests in diverse fields of study due to their
improved properties, especially as heat transfer fluids. Numerous studies have revealed the …
improved properties, especially as heat transfer fluids. Numerous studies have revealed the …
Exploring the specific heat capacity of water-based hybrid nanofluids for solar energy applications: A comparative evaluation of modern ensemble machine learning …
The current study aims to give insight into the usefulness of three underutilized yet
exceptionally efficient machine learning approaches in estimating the specific heat capacity …
exceptionally efficient machine learning approaches in estimating the specific heat capacity …
Significance of EMHD graphene oxide (GO) water ethylene glycol nanofluid flow in a Darcy–Forchheimer medium by machine learning algorithm
The low heat efficiency of base fluids is a key problem among investigators. To address this
issue, investigators utilize tiny-sized (1–100 nm) metal solid material inside the base fluids to …
issue, investigators utilize tiny-sized (1–100 nm) metal solid material inside the base fluids to …
An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks
AB Çolak - International Journal of Energy Research, 2021 - Wiley Online Library
In this study, the effect of the amount of data used in the design of artificial neural networks
(ANNs) on the predictive accuracy of ANNs was investigated. Five different ANNs were …
(ANNs) on the predictive accuracy of ANNs was investigated. Five different ANNs were …
Optimized ANFIS models based on grid partitioning, subtractive clustering, and fuzzy C-means to precise prediction of thermophysical properties of hybrid nanofluids
Applying machine learning algorithms in the prediction of nanofluids' thermophysical
properties such as density, viscosity, thermal conductivity (TC), and specific heat capacity …
properties such as density, viscosity, thermal conductivity (TC), and specific heat capacity …
Comparative analysis to study the Darcy–Forchheimer Tangent hyperbolic flow towards cylindrical surface using artificial neural network: an application to Parabolic …
Solar thermal collectors convert sunlight into useful thermal energy by absorbing its
incoming radiation. Concentrated solar power technologies use the parabolic trough solar …
incoming radiation. Concentrated solar power technologies use the parabolic trough solar …
[HTML][HTML] Comparative study of artificial neural network versus parametric method in COVID-19 data analysis
Since the previous two years, a new coronavirus (COVID-19) has found a major global
problem. The speedy pathogen over the globe was followed by a shockingly large number …
problem. The speedy pathogen over the globe was followed by a shockingly large number …
Experimental study for thermal conductivity of water‐based zirconium oxide nanofluid: develo** optimal artificial neural network and proposing new correlation
AB Colak - International Journal of Energy Research, 2021 - Wiley Online Library
In this study, five different water based ZrO2 nanofluids were prepared at volumetric
concentrations of 0.0125%, 0.025%, 0.05%, 0.1%, and 0.2%. In the preparation of …
concentrations of 0.0125%, 0.025%, 0.05%, 0.1%, and 0.2%. In the preparation of …
A new study on the prediction of the effects of road gradient and coolant flow on electric vehicle battery power electronics components using machine learning …
AB Çolak - Journal of Energy Storage, 2023 - Elsevier
Transistor and diode junction temperature is an important factor in battery electric vehicle
systems. High junction temperatures can lead to increased power losses and reduce the …
systems. High junction temperatures can lead to increased power losses and reduce the …