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Institution

National Institute of Technology, Karnataka

EducationMangalore, Karnataka, India
About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Corrosion & Cloud computing. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the second-order Boltzmann equation method (LBM) with multiple relaxation time (MRT) model is applied to investigate lid-driven flow in a 3D, rectangular cavity, and compare the results with flow in an equivalent two-dimensional (2D) cavity.
Abstract: Purpose - The purpose of this paper is to apply lattice Boltzmann equation method (LBM) with multiple relaxation time (MRT) model, to investigate lid-driven flow in a three-dimensional (3D), rectangular cavity, and compare the results with flow in an equivalent two-dimensional (2D) cavity. Design/methodology/approach - The second-order MRT model is implemented in a 3D LBM code. The flow structure in cavities of different aspect ratios (0.25-4) and Reynolds numbers (0.01-1000) is investigated. The LBM simulation results are compared with those from numerical solution of Navier-Stokes (NS) equations and with available experimental data. Findings - The 3D simulations demonstrate that 2D models may predict the flow structure reasonably well at low Reynolds numbers, but significant differences with experimental data appear at high Reynolds numbers. Such discrepancy between 2D and 3D results are attributed to the effect of boundary layers near the side-walls in transverse direction (in 3D), due to which the vorticity in the core-region is weakened in general. Secondly, owing to the vortex stretching effect present in 3D flow, the vorticity in the transverse plane intensifies whereas that in the lateral plane decays, with increase in Reynolds number. However, on the symmetry-plane, the flow structure variation with respect to cavity aspect ratio is found to be qualitatively consistent with results of 2D simulations. Secondary flow vortices whose axis is in the direction of the lid-motion are observed; these are weak at low. Reynolds numbers, but become quite strong at high Reynolds numbers. Originality/value - The findings will be useful in the study of variety of enclosed fluid flows.

35 citations

Journal ArticleDOI
01 Feb 2015
TL;DR: SVM and hybrid of Particle Swarm Optimization with SVM (PSO-SVM) are developed to predict damage level of non-reshaped berm breakwaters.
Abstract: Particle Swarm Optimization (PSO) is used to optimize the support vector machine (SVM).Models are trained on the data set obtained from experimental data.PSO-SVM with polynomial kernel function performs better than other kernel functions.Different soft computing models results are compared.PSO-SVM is computationally efficient as compared to ANFIS. The damage analysis of coastal structure is very much essential for better and safe design of the structure. In the past, several researchers have carried out physical model studies on non-reshaped berm breakwaters, but failed to give a simple mathematical model to predict damage level for non-reshaped berm breakwaters by considering all the boundary conditions. This is due to the complexity and non-linearity associated with design parameters and damage level determination of non-reshaped berm breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO-SVM) are developed to predict damage level of non-reshaped berm breakwaters. Optimal kernel parameters of PSO-SVM are determined by PSO algorithm. Both the models are trained on the data set obtained from experiments carried out in Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, India. Results of both models are compared in terms of statistical measures, such as correlation coefficient, root mean square error and scatter index. The PSO-SVM model with polynomial kernel function outperformed other SVM models.

35 citations

Journal ArticleDOI
TL;DR: In this article, the authors focused on direct metal laser sintering of Inconel-625 super alloy and its characterization and showed that with increase in laser scan speed and hatch spacing there was an increase in microhardness of the sintered parts while there was a decrease in dimensional accuracy and density.

35 citations

Journal ArticleDOI
TL;DR: In this paper, the authors gave an overview of some of the applications of remote sensing in the field of satellite oceanography and concluded that even though IR and microwave radiometers can be used for measuring temperatures at different depths in oceans, better choice is to use microwave data as it has got the advantage of penetrating through clouds and also it gives a clear view in all weather conditions except rain.

35 citations

Journal ArticleDOI
TL;DR: A magnetically separable, active nickel hydroxide (Brønsted base) coated nanocobalt ferrite catalyst has been developed for oxidation of alcohols and improved catalytic activity and selectivity were obtained.
Abstract: A magnetically separable, active nickel hydroxide (Bronsted base) coated nanocobalt ferrite catalyst has been developed for oxidation of alcohols. High surface area was achieved by tuning the particle size with surfactant. The surface area of 120.94 m2 g–1 has been achieved for the coated nanocobalt ferrite. Improved catalytic activity and selectivity were obtained by synergistic effect of transition metal hydroxide (basic hydroxide) on nanocobalt ferrite. The nanocatalyst oxidizes primary and secondary alcohols efficiently (87%) to corresponding carbonyls in good yields.

35 citations


Authors

Showing all 5100 results

NameH-indexPapersCitations
Ajay Kumar5380912181
Bhiksha Raj5135913064
Alexander P. Lyubartsev491849200
Vijay Nair4742510411
Sukumar Mishra444057905
Arun M. Isloor382616272
Vinay Kumaran362624473
M. C. Ray301152662
Airody Vasudeva Adhikari301192832
Ian R. Lane271292947
D. Krishna Bhat26951715
Anurag Kumar261262276
Soma Biswas251272195
Chandan Kumar25661806
H.S. Nagaraja23901609
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202351
2022175
2021938
2020893
2019838
2018740