TY  - JOUR
A1  - Sadeghzadeh, Milad
A1  - Maddah, Heydar
A1  - Ahmadi, Mohammad Hossein
A1  - Khadang, Amirhosein
A1  - Ghazvini, Mahyar
A1  - Mosavi, Amir Hosein
A1  - Nabipour, Narjes
T1  - Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network
JF  - Nanomaterials
N2  - In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text
KW  - Wärmeleitfähigkeit
KW  - Fluid
KW  - Neuronales Netz
KW  - Thermal conductivity
KW  - Nanofluid
KW  - Artificial neural network
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200421-41308
UR  - https://www.mdpi.com/2079-4991/10/4/697
VL  - 2020
IS  - Volume 10, Issue 4, 697
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Mosavi, Amir
A1  - Shamshirband, Shahaboddin
A1  - Esmaeilbeiki, Fatemeh
A1  - Zarehaghi, Davoud
A1  - Neyshabouri, Mohammadreza
A1  - Samadianfard, Saeed
A1  - Ghorbani, Mohammad Ali
A1  - Nabipour, Narjes
A1  - Chau, Kwok-Wing
T1  - Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths
JF  - Engineering Applications of Computational Fluid Mechanics
N2  - This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.
KW  - Bodentemperatur
KW  - Algorithmus
KW  - Maschinelles Lernen
KW  - Neuronales Netz
KW  - firefly optimization algorithm
KW  - soil temperature
KW  - artificial neural networks
KW  - hybrid machine learning
KW  - OA-Publikationsfonds2019
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200911-42347
UR  - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1788644
VL  - 2020
IS  - Volume 14, Issue 1
SP  - 939
EP  - 953
ER  -