Indian Institute of Technology Bhubaneswar

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 & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Forecasting tropical cyclones in the Bay of Bengal using quasi-operational WRF and HWRF modeling systems: an assessment study

Abstract: The Hurricane Weather Research and Forecasting (HWRF) is increasingly becoming known for its better performance in simulating tropical cyclones (TCs) over different basins globally, and the Advanced Research version of the Weather Research and Forecasting (WRF) model is widely used for the same in both research and operational settings. These two models are now operational at India Meteorological Department (IMD) for TC predictions over the North Indian Ocean. The near real-time forecast of HWRF and WRF models is evaluated in a quasi-operational setup based on 62 forecast cases from 10 recent TCs during 2013–2017 over the Bay of Bengal. Multi-satellite estimated winds are used to compare initial and simulated vortex structures, and HWRF has been found to be better. The track prediction is comparable in both the models for shorter forecast lengths (up to 30 h), while the HWRF is skillful (by 27%) for longer forecast, thus leading to a better estimation of landfall position and time. HWRF is significantly better (error < 10 knots) in intensity prediction compared to the WRF model (~ 15 knots). Unlike the WRF model, the HWRF model produced an improved vertical structure of dynamic and thermodynamic processes and could be attributed to the state-of-the-art vortex initialization and relocation method, multiscale interaction and high resolution. The model-predicted vortex structures also supported the credibility of HWRF system. This work highlights the need for having improved initialization and high resolution for TC predictions over the region.

Search for Black Holes and Sphalerons in High-Multiplicity Final States in Proton-Proton Collisions at √s = 13 TeV

Abstract: A search in energetic, high-multiplicity final states for evidence of physics beyond the standard model, such as black holes, string balls, and electroweak sphalerons, is presented. The data sample corresponds to an integrated luminosity of 35.9 fb(-1) collected with the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV in 2016. Standard model backgrounds, dominated by multijet production, are determined from control regions in data without any reliance on simulation. No evidence for excesses above the predicted background is observed. Model-independent 95% confidence level upper limits on the cross section of beyond the standard model signals in these final states are set and further interpreted in terms of limits on semiclassical black hole, string ball, and sphaleron production. In the context of models with large extra dimensions, semiclassical black holes with minimum masses as high as 10.1 TeV and string balls with masses as high as 9.5 TeV are excluded by this search. Results of the first dedicated search for electroweak sphalerons are presented. An upper limit of 0.021 is set at 95% confidence level on the fraction of all quark-quark interactions above the nominal threshold energy of 9 TeV resulting in the sphaleron transition.

A Single Source Transformer-Less Boost Multilevel Inverter Topology With Self-Voltage Balancing

Abstract: In this article, a single source transformer-less boost multilevel inverter topology is proposed. The salient features of the proposed topology include voltage boost capability, reduced number of switches, and requirement of a single dc voltage source, which in turn reduces the overall cost and complexity. The proposed topology comprises a conventional boost converter and a diode–capacitor voltage multiplier circuit (VMC). The capacitors’ voltages of the VMC are self-balanced, which eliminates the requirement of any auxiliary circuit for capacitor voltage balancing. Moreover, due to the input inductor, it has less input current ripple and able to provide variable fractional voltage gain, which is a key requirement for maximum power point tracking in photovoltaic applications. In order to get an optimal harmonic profile of the output ac voltage, a fundamental frequency switching technique named as the fundamental frequency sine quantization method is adopted for the generation of the switching signals. Finally, a hardware prototype of the proposed 11-level boost inverter is developed and the experimental results along with the MATLAB/Simulink results are presented to validate the efficacy of the proposed converter.

Constrained portfolio asset selection using multiobjective bacteria foraging optimization

Abstract: Portfolio asset selection (PAS) is a challenging and interesting multiobjective task in the field of computational finance, and is receiving the increasing attention of researchers, fund management companies and individual investors in the last few decades. Selecting a subset of assets and corresponding optimal weights from a set of available assets, is a key issue in the PAS problem. A Markowitz model is generally used to solve this optimization problem, where the total profit is maximized, while the total risk is to be minimized. However, this model does not consider the practical constraints, such as the minimum buy in threshold, maximum limit, cardinality etc. The Practical constraints are incorporated in this study to meet a real world financial scenario. In the proposed work, the PAS problem is formulated in a multiobjective framework, and solved using the multiobjective bacteria foraging optimization (MOBFO) algorithm. The performance of the proposed approach is compared with a set of competitive multiobjective evolutionary algorithms using six performance metrics, the Pareto front and computational time. On examining the performance metrics, it is concluded that the proposed MOBFO algorithm is capable of identifying a good Pareto solution, maintaining adequate diversity. The proposed algorithm is also successfully applied to different cardinality constraint conditions, for six different market indices.