Institution
Indian Institute of Technology Bhubaneswar
Education•Bhubaneswar, India•
About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Higgs boson. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.
Topics: Large Hadron Collider, Higgs boson, Graphene, Particle swarm optimization, Ultimate tensile strength
Papers
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TL;DR: In this article, the authors investigated the temperature dependent Raman spectroscopy behavior of a few layered MoS2 and WS2 nanosheets synthesized using simple hydrothermal method.
Abstract: We have systematically investigated the temperature dependent Raman spectroscopy behavior of a few layered MoS2 and WS2 nanosheets synthesized using simple hydrothermal method. Our result reveals A1g and E12g modes soften as temperature increases from 77 K to 623 K. This behavior can be explained in terms of a double resonance process which is active in single- and few layer thick nanosheets. The frequency shifts and peak broadening can provide unambiguous, nondestructive, and accurate information of a few layered MoS2 and WS2. This mechanism can also be applicable in characterizing the structural, optical, electronic, and vibrational properties of other emerging layered materials.
203 citations
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TL;DR: In this paper, a mix design methodology for GPC with the main focus on achieving better compressive strength in an economical way for different alkaline solutions to binder proportions was proposed.
202 citations
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TL;DR: The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.
Abstract: This paper presents a data-mining-based intelligent differential protection scheme for the microgrid. The proposed scheme preprocesses the faulted current and voltage signals using discrete Fourier transform and estimates the most affected sensitive features at both ends of the respective feeder. Furthermore, differential features are computed from the corresponding features at both ends of the feeder and are used to build the decision tree-based data-mining model for registering the final relaying decision. The proposed scheme is extensively validated for fault situations in the standard IEC microgrid model with wide variations in operating parameters for radial and mesh topology in grid-connected and islanded modes of operation. The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.
201 citations
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TL;DR: In this paper, interpretive structural modeling (ISM) is employed to extract the interrelationships among the identified behavioural factors and their interactions which help to attain green-enabled needs.
Abstract: Green supply chain management (GSCM) integrates ecological concepts with those of supply chain management in order to minimize energy and material usage and to reduce adverse impacts of supply chain activities on the environment. GSCM implementation in mining industries depends largely upon certain factors which are influenced by human behaviours. Human behaviour is dynamic in nature and the relationships between them continuously evolve and change. In this ever-changing context, therefore, identifying and ranking the behavioural factors that affect GSCM implementation becomes essential. This can be taken as a reference by the decision makers while deciding the hierarchy of action necessary for effective implementation of green practices in mining supply chains. The present research attempts to explore various behavioural factors affecting GCSM practices and their interactions which help to attain green-enabled needs. Interpretive structural modelling (ISM) is employed in this research to extract the interrelationships among the identified behavioural factors.
199 citations
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TL;DR: In this paper, thermal conductivity enhancer (TCE) is used to improve the thermal performance of phase change material (PCM) based heat sinks in cooling of electronic components.
Abstract: Generally, the commercial and industrial electronic devices are required to be operated under 100 °C.Therefore, there is a need to remove heat effectively from these devices under different loading conditions. Till now, Phase Change Material (PCM) based heat sinks are emerging as one of the effective techniques for removal of heat from the electronic devices. However, the low thermal conductivity of PCM situates a hindrance to the development. Thus, current research focuses on improving the thermal performance of PCM using thermal conductivity enhancer (TCE). At present internal fins, metallic foams and nano particles are mixed with PCM to enhance the performance of heat sinks. These are called as thermal conductivity enhancers. This article reviews methodologically various papers on the methods used for enhancement of PCM performance in cooling of electronic components. The effect of various parameters influencing the performance of the TCE-PCM based heat sinks are discussed in systematic order. The performance of these heat sinks under constant and variable thermal load are also evaluated. Out of these three TCE, metallic foams in heat sinks provides a higher surface area to volume ratio, good thermal conductivity and considerable weight advantage.
198 citations
Authors
Showing all 1220 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gabor Istvan Veres | 135 | 1349 | 96104 |
Márton Bartók | 76 | 622 | 26762 |
Kulamani Parida | 70 | 469 | 19139 |
Seema Bahinipati | 65 | 526 | 19144 |
Deepak Kumar Sahoo | 62 | 438 | 17308 |
Krishna R. Reddy | 58 | 400 | 11076 |
Ramayya Krishnan | 52 | 195 | 10378 |
Saroj K. Nayak | 49 | 149 | 8319 |
Dipak Kumar Sahoo | 47 | 234 | 7293 |
Ganapati Panda | 46 | 356 | 8888 |
Raj Kishore | 45 | 149 | 6886 |
Sukumar Mishra | 44 | 405 | 7905 |
Mar Barrio Luna | 43 | 179 | 5248 |
Chandra Sekhar Rout | 41 | 183 | 7736 |
Subhransu Ranjan Samantaray | 39 | 167 | 4880 |