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  - Ahmadi, Mohammad Hossein
A1  - Baghban, Alireza
A1  - Sadeghzadeh, Milad
A1  - Zamen, Mohammad
A1  - Mosavi, Amir
A1  - Shamshirband, Shahaboddin
A1  - Kumar, Ravinder
A1  - Mohammadi-Khanaposhtani, Mohammad
T1  - Evaluation of electrical efficiency of photovoltaic thermal solar collector
JF  - Engineering Applications of Computational Fluid Mechanics
N2  - In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
KW  - Fotovoltaik
KW  - Erneuerbare Energien
KW  - Solar
KW  - Deep learning
KW  - Machine learning
KW  - Renewable energy
KW  - neural networks (NNs)
KW  - adaptive neuro-fuzzy inference system (ANFIS)
KW  - least square support vector machine (LSSVM)
KW  - photovoltaic-thermal (PV/T)
KW  - hybrid machine learning model
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200304-41049
UR  - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1734094
VL  - 2020
IS  - volume 14, issue 1
SP  - 545
EP  - 565
PB  - Taylor & Francis
ER  -