scholarly journals Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

Nanomaterials ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 697 ◽  
Author(s):  
Milad Sadeghzadeh ◽  
Heydar Maddah ◽  
Mohammad Hossein Ahmadi ◽  
Amirhosein Khadang ◽  
Mahyar Ghazvini ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Sol Gel ◽  
Back Propagation ◽  
Thermal Conductivity Ratio ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
The Heat Transfer Coefficient

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.

Comparing Regional Sustainability and Transportation Sustainability at the Metropolitan Level in the U.S. using Artificial Neural Network Clustering Techniques

2021 ◽  
pp. 036119812110095
Author(s):  
Haiqing Liu ◽  
Na Chen ◽  
Xinhao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Efficient Method ◽  
Network Clustering ◽  
Self Organizing Map ◽  
Regional Sustainability ◽  
Transportation Sustainability ◽  
Artificial Neural ◽  
The U.S

Regional sustainability and transportation sustainability have been intensely discussed and analyzed in recent decades. Though the use of indicators has been adopted in those models, debates continue on what indicators should be used and how to optimize the number of indicators. This results in the lack of a comprehensive and efficient method to assess and compare the sustainability of a sub-system, such as transportation system, and overall regional sustainability. A thorough literature review is conducted to identify indicators used to assess regional sustainability and transportation sustainability. Then, based on the available data, two sets of indicators for regional sustainability and transportation sustainability are identified and calculated respectively for the 382 metropolitan statistical areas (MSAs) in the U.S. A self-organizing map, which is a type of artificial neural network, is used to cluster the MSAs and compare their regional sustainability and transportation sustainability as well as to investigate the relationships among indicators. The results show that MSAs with a higher score on regional sustainability do not necessarily have a higher score on transportation sustainability. Some MSAs that are geographically close to each other have similar scores in regional sustainability and transportation sustainability. These findings provide insights to decision makers that the assessment of sustainability should consider both correlation and heterogeneity of different indicators within a region. Therefore, it is important to develop a comprehensive and efficient method to evaluate the role of sustainability in one urban sub-system, such as transportation, in the overall regional sustainability.

Modeling Thermal Conductivity Ratio of CuO/Ethylene Glycol Nanofluid by Using Artificial Neural Network

2018 ◽  
Vol 388 ◽  
pp. 39-43 ◽  
Author(s):  
Mohammad Alhuyi Nazari ◽  
Mohammad Hossein Ahmadi ◽  
Giulio Lorenzini ◽  
Heydar Maddah ◽  
Morteza Fahim Alavi ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Ethylene Glycol ◽  
Correlation Coefficient ◽  
Thermal Conductivity Ratio ◽  
Conductivity Ratio ◽  
Artificial Neural ◽  
Input Variables ◽  
Trained Neural Network

The thermal conductivity of nanofluids depends on several factors such as temperature, concentration, and temperature. These parameters have the most significant effect on thermal conductivity compared with other factors. In the present study, the accuracy of trained Perceptron neural network with 10 neurons and three input variables including size of nanoparticles, temperature, and concentration is evaluated. The sum of squared errors and the correlation coefficient of the trained neural network are equal to 0.99293 and 0.00031, respectively.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural

scholarly journals Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

2021 ◽  
pp. 506-526
Author(s):  
Nisha Thakur ◽  
Sanjeev Karmakar ◽  
Sunita Soni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Back Propagation ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

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