A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach
By Mohammad Hossein Ahmadi, M. Alhuyi Nazari, Omid Mahian, Roghayeh Ghasempour. Journal of Thermal Analysis and Calorimetry, 2018
144 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Mechanical Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/73a4396ffdefb66f19f6eebca8e2882bc7f30edf
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- M. Alhuyi Nazari
- Mohammad Hossein Ahmadi
- M. Ramezanizadeh
- Akbar Maleki
- Ravinder Kumar
- Marjan Goodarzi
- Mahyar Ghazvini
In Mechanical Engineering
- M. Ramezanizadeh
- M. Alhuyi Nazari
- Mohammad Hossein Ahmadi
- Akbar Maleki
- Ravinder Kumar
- Marjan Goodarzi
- Mahyar Ghazvini
In Renewable Energy, Sustainability and the Environment
Citations in the corpus
- Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network (2019, methodology): “[48] employed LSSVM-GA method to estimate the TC of Al2O3/EG nanofluid.”
- Experimental and numerical analysis of a nanofluidic thermosyphon heat exchanger (2018, background): “There are some studies which have focused on modeling the thermal resistance of the heat pipes systems by considering effective factors which give better insight into these parameters (Ahmadi et al., 2018).”
- Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid (2019, background): “The improvement in the thermal behavior of heat transfer devices utilized nanofluids is chiefly attributed to improved thermal conductivity [54, 55].”