Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network

Leema, N.; Radha, P.; Vettivel, S.C.; Nehemiah, H. Khanna

doi:10.1016/j.matdes.2014.11.035

Cited by 29 publications

(12 citation statements)

References 32 publications

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“…The popularity of RBF neural networks compared to other kinds of neural networks concerns: (i) their capacity to approximate function problems, (ii) their simple and reduced architecture (composed only of three layers), (iii) their fast convergence properties and (vi) they have no local minima problems (Kasabov, 1998). Because of these advantages, RBF neural networks were applied in several disciplines, including in the photovoltaic process, to predict current-voltage characteristics and power-voltage (PeV) curves of a commercial PeV module (Bonanno et al, 2012), in medical diseases diagnosis (Qasem and Shamsuddin, 2011), in the identification of nuclear accidents (Gomes and Canedo Medeiros, 2015), in seismic inversions in petroleum exploration (Baddari et al, 2010), in image analysis (Cha and Kassam, 1996;Montazer and Giveki, 2015), in nanocomposite characterization of pore size measurement and wear model of a sintered CoppereTungsten (Leema et al, 2015), in the estimation of geotechnical parameters (Sinha and Wang, 2008;Mustafa et al, 2012), in geology to estimate the grade of an offshore placer gold deposit (Samanta and Bandopadhyay, 2009) and to assess rocky desertification in northwest Guangxi, China (Zhang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Preliminary study of relationships between thermal conductivity and petrophysical parameters in Hamra Quartzites reservoir, Hassi Messaoud field (Algeria)

Zerrouki

Géraud

Diraison

et al. 2019

Journal of African Earth Sciences

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In geothermic studies, the thermal conductivity (TC) is an essential parameter needed to calculate heat flow in rocks. It is obtained generally with high accuracy from laboratory measurements. The methodology proposed in this paper is to estimate the TC, which depends on many parameters; such as mineralogy, porosity, shape of voids and nature of contact between grains, based on linear and nonlinear relationships. In order to predict the thermal conductivity from other petrophysical parameters in the Hamra Quartzites reservoir, we have measured porosity, density and permeability, for dry samples taken from core wells. The correlation coefficients (R) were calculated between thermal conductivity and other petrophysical parameters for all samples. The results show that, the correlation coefficient is moderate between TC and the porosity, weak between TC, density and permeability. To improve these correlations, samples were classified into cemented and uncemented sets. A minor improvement on the correlation coefficients is noted between TC, density and porosity in uncemented samples, with values equal 0.51 and 0.73, respectively. The application of Radial Basis Function (RBF) neural networks, using density, porosity and permeability as inputs and thermal conductivity as output, permit us to predict the thermal conductivity with high precision. The correlation coefficient between TC estimated by the RBF neural network is the same as that measured in laboratory equaling 0.983.

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Section: Introductionmentioning

confidence: 99%

A Preliminary study of relationships between thermal conductivity and petrophysical parameters in Hamra Quartzites reservoir, Hassi Messaoud field (Algeria)

Zerrouki

Géraud

Diraison

et al. 2019

Journal of African Earth Sciences

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“…RBFNN is considered as feed-forward network made up of three layers of neurons. 38 Initial layer is input layer, it contains input data source nodes. Radial basis function is second layer, it denotes only hidden layer on network.…”

Section: Load Demand Prediction Using Rbfnnmentioning

confidence: 99%

Internet of things platform for energy management in multi‐microgrid system to enhance power quality: ARBFNOCS technique

Vinjamuri

Burthi

2021

Int J Numerical Modelling

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This manuscript proposes an Internet of Things (IoT) platform for energy management (EM) in multi‐microgrid (MMG) system to enhance the power quality with hybrid method. The proposed method is the consolidation of opposition based crow search optimizer (OCSO) and radial basis functional neural network (RBFNN), hence it called RBFNOCS technique. The main aim of this manuscript is to optimally managing the power and resources of distribution system (DS) by constantly track the data from IoT‐based communication framework. In the proposed work, every devices of home is interfaced with data acquisition module (DAM) that is IoT object along unique IP address resultant in large mesh wireless network. Here, the IoT‐based communication framework is used for facilitating the development of a demand response (DR) energy management system (EMS) for distribution system. The transmitted data is processed by RBFNOCS technique. By utilizing the RBFNOCS method, the active with reactive power processing for optimal capacity unbalance compensation smart VSIs share the obtainable neutral current (NC). Likewise, the DS IoT framework enhances these networks flexibility and gives feasible use of obtainable resources. Moreover, the RBFNOCS method is responsible for satisfying the total supply with energy demand. The proposed model is activated in MATLAB/Simulink site and the performance is compared with existing models, namely improved artificial bee colony, squirrel search algorithm and gravitational search algorithm based artificial neural network (SOGSNN), GOAPSNN, fruit fly optimization, and FORDF technique. When compared with the existing methods, the efficiency of the RBFNOCS method is 93.4501%.

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“…High mechanical and thermal conductivity properties are achieved only upon addition of 0.3 wt% graphene [37]. Mechanical milling is a simple method to prepare nanopowder where milling time plays very important role [38]. The milling time closely related to hardness of the composite and particles deformation.…”

Section: Hardnessmentioning

confidence: 99%

Effect of milling on dispersion of graphene nanosheet reinforcement in different morphology copper powder matrix

Ponraj

Vettivel

Azhagurajan³

et al. 2017

Surfaces and Interfaces

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The effective dispersion of Graphene Nanosheet (GNS) as reinforcement was studied with various morphology of Copper (Cu) powder as matrix. High energy milling was used for modifying the morphology of the Cu powders. The Cu powder with Spherical and dentric shape was used in this study. Using high energy milling both the powders were milled for 8 h and 16 h respectively. The morphology of Cu powders was altered as flake shape upon milling. GNS (2 wt. %) was added as reinforcement uniformly with various morphology Cu powder used as matrix. The effect of size and shape of Cu as matrix on Cu/GNS composite properties was comprehensively studied. The Cu/GNS composite was prepared using powder metallurgy technique. Using the different shape and size of the Cu as matrix the interface of GNS has been studied. The hardness properties of Cu/GNS composite are evaluated. The effect of GNS interface in Cu particles has a significant influence on mechanical properties of composites. The hardness of Cu matrix composite has improved up to 20% compared to that of pure Cu. Thus morphology of Cu has the ability to improve the mechanical properties with GNS reinforcement.

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Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network

Cited by 29 publications

References 32 publications

A Preliminary study of relationships between thermal conductivity and petrophysical parameters in Hamra Quartzites reservoir, Hassi Messaoud field (Algeria)

A Preliminary study of relationships between thermal conductivity and petrophysical parameters in Hamra Quartzites reservoir, Hassi Messaoud field (Algeria)

Internet of things platform for energy management in multi‐microgrid system to enhance power quality: ARBFNOCS technique

Effect of milling on dispersion of graphene nanosheet reinforcement in different morphology copper powder matrix

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