Publications

Measurements of charged-particle production in pp, p–Pb, and Pb–Pb collisions in the toward, away, and transverse regions with the ALICE detector are discussed. These regions are defined event-by-event relative to the azimuthal direction of the charged trigger particle, which is the reconstructed particle with the largest transverse momentum pTtrig in the range 8

Herein we report on the investigation of microwave-assisted catalytic transfer hydrogenation (TH) of carbonyl and nitro compounds by employing Ru(II) complexes bimetallic [(p-cymene)2(RuCl)2L1]2X (X = BF4(Cat2); X = PF6(Cat3)) and mononuclear [(p-cymene)(RuCl)L2]BF4(Cat4) (where L1= N,N'-(3,3',5,5'-tetraisopropyl-[1,1'-biphenyl]-4,4'-diyl)bis(1-(pyridin-2-yl)methanimine); L2 = N-(2,6-diisopropyl-phenyl)-1-(pyridin-2-yl)-methanimine). At a low catalyst loading of 0.01 mol% (Cat2/Cat3), a broad substrate scope has been achieved for aromatic as well as aliphatic ketones and aldehydes, with a short reaction time of just 10 minutes. Additionally, chemoselective hydrogenation of nitroaromatic compounds has been achieved under microwave irradiation by Cat2 within 5 minutes. Control experiments demonstrate that microwave heating conditions outperform conventional heating method in terms of improved catalytic activity and reaction efficiency. The bimetallic Cat2 catalyst can be used at a very low loading of 0.001 mol% to achieve the high TONs and TOFs of 7.7 × 104and 2.3 × 105h-1, respectively, for TH reaction. Spectrometry experiments for intermediate trapping have been used to propose a probable mechanism for TH of carbonyl compounds.

The common tool for assessment of breathing-type debonding in metallic or composite structures is nonlinear guided wave-based technique. The past studies show that with debonding size, the strength of the nonlinearity does not exhibit strictly increasing or decreasing trends, or that the monocity is valid up to a certain size limit of debonding. This paper presents the study of non-linear interaction of guided waves in the debonding interface of a metallic stiffened panel. The study attempts to establish a relationship between the contact energy generated by the contact acoustic nonlinearity (CAN) at the debonding interface and the associated nonlinearity strength for various debonding sizes at various excitation frequencies. A numerical model of the stiffened panels is developed in three-dimensional finite element (FE) and validated with experiments for the study of interaction of nonlinear guided waves. The validated FE model is used to conduct studies on nonlinear interactions in debonding. The outcome of this study contributes to a better understanding of how guided waves can be used to effectively assess the debonding in metallic stiffened panels by considering non-linear interactions at the debonding interface. The study also provides insights into a more accurate and consistent quantification of the debonding using higher harmonic signals and contact energy produced by non-linear interactions.

While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct instances of damage captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep-learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size predictions of stiffened panels using a vibration-based method. The results are satisfactory for both debonding location and size prediction. This domain transformation method can be used in any field in which experimental data for training machine-learning models is scarce.

Railway transportation is widely recognized as an environment-friendly and sustainable means for conveying freight and passengers over long distances. This article investigates the effectiveness of utilizing scrap tire rubber granules and geosynthetics to enhance track performance in response to the growing demands for railway transport and the consequent escalation of train-induced loading. A multi-faceted methodology, incorporating experimental, numerical, and analytical techniques, is employed to examine the efficacy of these sustainable approaches. Results from three-dimensional (3D) finite element (FE) analyses conducted on slab tracks for high-speed railways reveal that the addition of a resilient layer, comprising scrap tire rubber granules, reduces vertical stress within the track substructure. Laboratory investigations on an innovative composite material consisting of soil, scrap rubber granules, and polyurethane demonstrate its potential to enhance track performance. Findings from two-dimensional (2D) FE analyses conducted on pile-supported railway embankments highlight an enhanced transfer of load to the pile head following the installation of a geogrid layer at the embankment base. Finally, the results from the analytical approach indicate a reduction in track settlement and a decrease in the track geometry degradation rate on reinforcing the ballast layer with 3D cellular geoinclusion. The novelty of this study lies in the comprehensive assessment of the innovative composite material under drained and cyclic loading conditions, the investigation of the influence of train loading on geosynthetic tension and the load transfer mechanism in railway embankments, and the development of an innovative computational methodology capable of assessing the effectiveness of 3D cellular inclusions in improving the ballasted railway track performance. The findings from this article underscore the effectiveness of these sustainable approaches in mitigating the challenges posed by increased loads on railway tracks, providing valuable insights for the ongoing efforts to optimize railway transportation infrastructure.