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Application GMDH artificial neural network for modeling of Al2O3/water and Al2O3/Ethylene glycol thermal conductivity
, Article International Journal of Heat and Technology ; Volume 36, Issue 3 , 2018 , Pages 773-782 ; 03928764 (ISSN) ; Hajizadeh, F ; Rahimzadeh, M ; Shafii, M. B ; Chamkha, A. J ; Lorenzini, G ; Ghasempour, R ; Sharif University of Technology
International Information and Engineering Technology Association
2018
Abstract
Thermal conductivity of nanofluids depends on several parameters including temperature, concentration, and size of nanoparticles. Most of the proposed models utilized concentration and temperature as influential factors in their modeling. In this study, group method of data handling (GMDH) artificial neural networks is applied in order to model the dependency of thermal conductivity on the mentioned factors. Firstly, temperature and concentration considered as inputs and a model is represented. Afterwards, the size of nanoparticles is added to the input variables and the results are compared. Based on obtained results, GMDH is an appropriate method to predict thermal conductivity of the...
Optimization and design of cooling systems using a hybrid neural network and genetic algorithm methodology
, Article Eighth International Conference on Advanced Computational Methods in Heat Transfer, HEAT TRANSFER VIII, Lisbon, 24 March 2004 through 25 March 2004 ; Volume 5 , 2004 , Pages 333-343 ; 14626063 (ISSN) ; Fardadi, M ; Bahoush, R ; Sharif University of Technology
2004
Abstract
In this paper a novel method for the design and optimization of cooling systems is presented. The numerical solution of free convection from a heated horizontal cylinder confined between adiabatic walls obtained from a finite element solver is used to propose a non-linear heat transfer model of GMDH type approach. In the context of GMDH model, three different methods depending on the structure of neural network are implemented. The system of orthogonal equations is solved using a SVD scheme. The coefficients of second order polynomials are computed and their behavior is discussed. In addition, to demonstrate the performance of the predicted model, the numerical data are divided into trained...
Prediction of pore facies using GMDH-type neural networks: a case study from the South Pars gas field, Persian Gulf basin
, Article Geopersia ; Volume 8, Issue 1 , March , 2018 , Pages 43-60 ; 22287817 (ISSN) ; Kadkhodaie, A ; Ahmadi, B ; Ahmadi, B ; Faraji, M. A ; Sharif University of Technology
University of Tehran
2018
Abstract
Pore facies analysis plays an important role in the classification of reservoir rocks and reservoir simulation studies. The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalan formations in the South Pars gas field. In the first step, pore facieswere determined based on Mercury Injection Capillary Pressure (MICP) data in corporation with the Hierarchical Clustering Analysis (HCA) method. Each pore facies represents a specific type of pore geometry indicating the interaction between the primary rock fabric and its diagenetic overprints. In the next step, polynomial meta-models were established based...
International Oil Price Time Series Prediction Using GMDH Neural Network and its Performance Comparison with MLP Neural Network and ARIMA Method
, M.Sc. Thesis Sharif University of Technology ; Haji, Alireza (Supervisor)
Abstract
Predicting oil prices, especially in exporting countries, will help governments in the policy-making process by obtaining a reliable estimate of oil revenues. The existence of a complex mechanism governing the process of oil price formation has reduced the efficiency of linear models in forecasting and led researchers to use nonlinear intelligent systems to predict oil prices. In this study, after a detailed study of the structure of artificial neural network, two models of neural network GMDH and MLP and ARIMA method have been used to predict oil price. There are important factors in the prediction process with neural networks, and if all these factors are selected correctly; One can expect...
Estimation of Pressure Fluctuation Coefficient in Stilling Basins Using Computational Intelligent Models
, M.Sc. Thesis Sharif University of Technology ; Shamsai, Abolfazl (Supervisor)
Abstract
Hydraulic jump is a significant hydraulic phenomenon that occurs in stilling basins and causes energy dissipation of water flow. Due to the severe pressure fluctuations, cavitation, and fatigue damage to concrete materials, hydraulic jump can cause damage to the stilling basin and its related components. Therefore, studying pressure fluctuations is one of the essential topics in the safe design and operation of stilling basins. Due to the nonlinear relationship between the effective variables in the pressure fluctuation phenomenon, the use of computational intelligent models that can extract the relationship between the effective variables is necessary. In this study, laboratory data...
Energy spectra unfolding of fast neutron sources using the group method of data handling and decision tree algorithms
, Article Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment ; Volume 851 , 2017 , Pages 5-9 ; 01689002 (ISSN) ; Esmaili Paeen Afrakoti, I ; Sharif University of Technology
Abstract
Accurate unfolding of the energy spectrum of a neutron source gives important information about unknown neutron sources. The obtained information is useful in many areas like nuclear safeguards, nuclear nonproliferation, and homeland security. In the present study, the energy spectrum of a poly-energetic fast neutron source is reconstructed using the developed computational codes based on the Group Method of Data Handling (GMDH) and Decision Tree (DT) algorithms. The neutron pulse height distribution (neutron response function) in the considered NE-213 liquid organic scintillator has been simulated using the developed MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif...