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Journal ArticleDOI

Experiment Driven Ann-GA Based Technique for Optimal Distribution of Discrete Heat Sources Under Mixed Convection

TL;DR: In this article, a completely experimental driven hybrid optimization strategy that combines Artificial Neural Network (ANN) with Genetic Algorithm (GA) is used to determine the optimal arrangement, such that, the maximum temperature excess is minimum among all the possible configurations.
Abstract: This article reports the results of mixed convection heat transfer studies from five heat sources (aluminum) mounted at different positions on a substrate board (Bakelite). The goal is to determine the optimal arrangement, such that, the maximum temperature excess is minimum among all the possible configurations. For accomplishing this, a completely experimental driven hybrid optimization strategy, that combines Artificial neural network (ANN) with Genetic algorithm (GA) is used. Initial optimization studies are carried out by employing a heuristic non-dimensional geometric parameter λ, which is identified to be the key parameter to decide the maximum temperature in the system.
Citations
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Journal ArticleDOI
TL;DR: In this article, an optimal configuration for the heat sources has been defined based on a geometric parameter λ, taken from literature, and the effect of substrate conduction on heat transfer is studied numerically considering board materials of different thermal conductivities.

45 citations

Journal ArticleDOI
01 Dec 2015
TL;DR: In this paper, hiding algorithms have been produced to fight three types of attacks: visual, structural, and statistical attacks, and the results of the proposed algorithm can efficiently embed a large quantity of data, up to 12bpp (bits per pixel), with better image quality.
Abstract: Steganography architecture with seven security layers. New steganography algorithm.Proposed new intelligent technique.Proposed seven layers of security.Extract byte characteristics.Construct image segmentation. A three-phase intelligent technique has been constructed to improve the data-hiding algorithm in colour images with imperceptibility. The first phase of the learning system (LS) has been applied in advance, whereas the other phases have been applied after the hiding process. The first phase has been constructed to estimate the number of bits to be hidden at each pixel (NBH); this phase is based on adaptive neural networks with an adaptive genetic algorithm using upwind adaptive relaxation (LSANN_AGAUpAR1). The LS of the second phase (LSANN_AGAUpAR2) has been introduced as a detector to check the performance of the proposed steganographic algorithm by creating a rich images model from available cover and stego images. The LS of the last phase (LSCANN_AGAUpAR3) has been implemented through three steps, and it is based on a concurrent approach to improve the stego image and defend against attacks. The adaptive image filtering and adaptive image segmentation algorithms have been introduced to randomly hide a compressed and encrypted secret message into a cover image. The NBH for each pixel has been estimated cautiously using 32 principle situations (PS) with their 6 branch situations (BS). These situations have been worked through seven layers of security to augment protection from attacks. In this paper, hiding algorithms have been produced to fight three types of attacks: visual, structural, and statistical attacks. The simulation results have been discussed and compared with new literature using data hiding algorithms for colour images. The results of the proposed algorithm can efficiently embed a large quantity of data, up to 12bpp (bits per pixel), with better image quality.

40 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the heat source layout optimization in two-dimensional heat conduction using simulated annealing (SA) method and found that SA can reduce the maximum temperature of the domain significantly compared to random distribution and bionic optimization.

32 citations

Journal ArticleDOI
TL;DR: Results indicate that through proper selection of the number of grid cells for placing the heat sources and a minimum inter-source spacing, the maximum temperature and temperature non-uniformity in the domain can be significantly reduced.

30 citations

Journal ArticleDOI
TL;DR: GAA and GARB were tested for predicting the discharge coefficient of a modified labyrinth side weir and the GARB method could successfully predict the accurate discharge coefficient even in cases where there is a limited number of train datasets available.

24 citations

References
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Journal ArticleDOI
TL;DR: This study shows that by integrating ANN with GA, the computational time can be reduced substantially in problems of this class by integrating the micro genetic algorithm with the Bayesian regularization algorithm.

77 citations


"Experiment Driven Ann-GA Based Tech..." refers methods in this paper

  • ...Madadi and Balaji [9] determined the optimal location of three heat sources under forced convection by combining a micro-GA with an ANN....

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  • ...EXPERIMENT DRIVEN ANN-GA BASED TECHNIQUE FOR OPTIMAL DISTRIBUTION OF DISCRETE HEAT SOURCES UNDER MIXED CONVECTION T. K. Hotta,1 C. Balaji,2 and S. P. Venkateshan2 1Center for Energy, Indian Institute of Technology Jodhpur, Rajasthan, India 2Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India This article reports the results of mixed convection heat transfer studies from five heat sources (aluminum) mounted at different positions on a substrate board (Bakelite)....

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Journal ArticleDOI
TL;DR: In this paper, the authors studied the natural convection heat transfer optimization through genetic algorithms and confirmed the methodology as computationally feasible for the optimal location of heat sources in a vertical wall.

58 citations

Journal ArticleDOI
TL;DR: In this article, the effects of Reynolds and Grashof numbers on these numbers were investigated and the effect of the buoyancy affected secondary flow and the onset of instability have been discussed.

51 citations


"Experiment Driven Ann-GA Based Tech..." refers background in this paper

  • ...[2] and Pirasaci and Sivrioglu [3] experimentally investigated the rate of heat transfer from an array of discrete heat sources (8 £ 4 in number) mounted on the top and bottom of a horizontal channel under mixed convection....

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Journal ArticleDOI
TL;DR: In this paper, a numerical study of laminar mixed-convection heat transfer to air from two identical protruding heat sources, which simulate electronic components, located in a two-dimensional horizontal channel, is presented.

43 citations


"Experiment Driven Ann-GA Based Tech..." refers methods in this paper

  • ...Hamouche and Bessaih [4] performed numerical simulations for the mixed convection air cooling of protruding discrete heat sources mounted on a horizontal channel to solve the conservation equations of mass, momentum, and energy using the finite volume method and SIMPLER algorithm; they found that the increase in separation distance, height, and width of the components has a considerable effect in enhancing the heat removal rate from the components....

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Journal ArticleDOI
TL;DR: In this paper, the optimization of location of heat sources in a square enclosure with natural convection is performed to maximize the global conductance in the enclosure, and an ANN is trained using the above data obtained from numerical solutions.

28 citations


"Experiment Driven Ann-GA Based Tech..." refers methods in this paper

  • ...and Chattopadhyay [8] determined the optimal location of heat sources in a square enclosure under natural convection by combining the GA with an ANN....

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  • ...Kadiyala NOMENCLATURE A area of the heat source (m2) F diffuse shape factor g acceleration due to gravity, 9.81m/s2 GrLh Grashof number, gbD T L 3 h/n 2 h heat transfer coefficient (W/m2K) I heat source input current (A) k thermal conductivity of air (W/mK) Lh characteristic length of the heat source, 4A/P (m) P perimeter of the heat source (m) q heat flux (W/m2) Q heat transfer rate (W) Re Reynolds number, U Lh/n Ri Richardson number, GrLh /Re 2 t thickness of the substrate (m) T temperature (K) U velocity of air in the channel (m/s) V heat source input voltage (V) Greek Symbols b isobaric thermal expansion coefficient of air, 1/Tmean (1/K) DTref reference temperature, QsupLh/Ak (K) 1 hemispherical emissivity of heat source of surface n kinematic viscosity of air (m2/s) s Stefan–Boltzmann constant, 5.67 £ 1028 (W/m2K4) u non-dimensional maximum temperature excess (Tmax 2 T1)/DTref Subscripts 1 ambient cond conduction conv convection hs heat source insu insulation max maximum rad radiation sub substrate sup supplied and Chattopadhyay [8] determined the optimal location of heat sources in a square enclosure under natural convection by combining the GA with an ANN....

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