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  - INPR
A1  - Abbas, Tajammal
A1  - Kavrakov, Igor
A1  - Morgenthal, Guido
A1  - Lahmer, Tom
T1  - Prediction of aeroelastic response of bridge decks using artificial neural networks
N2  - The assessment of wind-induced vibrations is considered vital for the design of long-span bridges. The aim of this research is to develop a methodological framework for robust and efficient prediction strategies for complex aerodynamic phenomena using hybrid models that employ numerical analyses as well as meta-models. Here, an approach to predict motion-induced aerodynamic forces is developed using artificial neural network (ANN). The ANN is implemented in the classical formulation and trained with a comprehensive dataset which is obtained from computational fluid dynamics forced vibration simulations. The input to the ANN is the response time histories of a bridge section, whereas the output is the motion-induced forces. The developed ANN has been tested for training and test data of different cross section geometries which provide promising predictions. The prediction is also performed for an ambient response input with multiple frequencies. Moreover, the trained ANN for aerodynamic forcing is coupled with the structural model to perform fully-coupled fluid--structure interaction analysis to determine the aeroelastic instability limit. The sensitivity of the ANN parameters to the model prediction quality and the efficiency has also been highlighted. The proposed methodology has wide application in the analysis and design of long-span bridges.
KW  - Aerodynamik
KW  - Artificial neural network
KW  - Ingenieurwissenschaften
KW  - Bridge
KW  - Bridge aerodynamics
KW  - Aerodynamic derivatives
KW  - Motion-induced forces
KW  - Bridges
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200225-40974
N1  - This is the pre-peer reviewed version of the following article: 
https://www.sciencedirect.com/science/article/abs/pii/S0045794920300018?via%3Dihub, https://doi.org/10.1016/j.compstruc.2020.106198
ER  -