(SoE) School of Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

The National Capital Region (NCR) encircling the capital of India is the one of the most polluted regions in the world. Poor air quality is a cause of a number of diseases and reduction in life span. Particulate matter (PM) is the most significant as well as the most hazardous air pollutant in this region. This work proposes to build models to analyze and forecast PM concentrations at a location in the NCR. The correlation between PM concentrations in different seasons and with meteorological parameters and other air pollutants is studied to determine the most suitable explanatory variables for building the forecast models. The performance of the proposed models is evaluated with the help of variable importance ranking (VIR), partial plots and measures such as mean error, absolute mean error and root mean square error. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

World University root node

Computer Science and Engineering

Journal of Ambient Intelligence and Humanized Computing

Prediction and analysis of particulate matter (PM2.5 and PM10) concentrations using machine learning techniques

High-entropy alloys (HEAs) with multiple principal elements represent a paradigm shift in structural alloy design and show excellent surface degradation resistance in corrosive environment. Here, the tribo-corrosion response of laser-engineered net-shaped CoCrFeMnNi HEA was evaluated in 3.5 wt% NaCl solution at room temperature. The additively manufactured (AM-ed) CoCrFeMnNi showed five times lower wear rate, regenerative passivation, and nobler corrosion potential during tribo-corrosion test compared to its arc-melted counterpart. A significant anisotropy was seen in the tribo-corrosion response with 45° to the build direction showing better performance compared to tests along the build direction and perpendicular to it. The open circuit potential curves were characterized by a sharp drop to more negative values as wear began, followed by continuous change for the active tribo-corrosion duration and finally a jump to nobler value at the end of the test indicating excellent surface re-passivation for the AM-ed alloy. The superior tribo-corrosion resistance of AM-ed CoCrFeMnNi was attributed to the refined microstructure and highly protective surface passivation layer promoted by the sub-grain cellular structure formed during additive manufacturing. These results highlight the potential of utilizing additive manufacturing of HEAs for use in extreme environments that require a combination of tribo-corrosion resistance, mechanical durability, extended service life, and net shaping with low dimensional tolerance. © 2021, The Author(s).

Mechanical Engineering

Fulltext

npj Materials Degradation

Tribo-corrosion response of additively manufactured high-entropy alloy

Placement of items on the shelf space of retail stores significantly impacts the revenue of the retailer. Since customers typically tend to buy sets of items (i.e., itemsets) together, several research efforts have been undertaken towards facilitating itemset placement in retail stores for improving retailer revenue. However, they fail to consider that the time-period of expiry can vary across items i.e., some items expire sooner than others. This leads to loss of opportunity towards improving retailer revenue. Hence, we propose PEAR, which is a Product Expiry-Aware and Revenue-conscious itemset placement scheme for improving retailer revenue. Our key contributions are three-fold. First, we introduce the problem of addressing retail itemset placement when the items can be associated with different time-periods of expiry. Second, we propose the expiry-aware PEAR scheme for efficiently identifying and placing high-revenue itemsets for improving retailer revenue. Third, we conduct a performance study with two real datasets to demonstrate that PEAR is indeed effective in improving retailer revenue w.r.t. a reference scheme. © 2021 IEEE.

2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021

PEAR: A Product Expiry-Aware and Revenue-Conscious Itemset Placement Scheme

Networks are everywhere and their applications in various fields such as computer science, biological science, economics, and chemical engineering attracted attention of many researchers. Many complex systems in the real world can be represented by networks or graphs. Link prediction is one of the most important tasks in network analysis, thus attracting tremendous research interests in the last decades. In this paper, we present a novel algorithm for link prediction that works efficiently for both unipartite graphs and bipartite graphs. Our novel algorithm is based on the concept of eigenvectors and shortest distance between the nodes. We used the Peron–Frobenius theorem of node importance for link prediction. Four metrics, namely AUC, precision, prediction-power, and precision@K, were computed and compared with fourteen baseline algorithms to test the performance of the proposed algorithm. Testing was done on the thirteen datasets, and experimental results show that the proposed method outperforms the baseline algorithm on the basis of four given metrics. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

Social Network Analysis and Mining

A novel similarity measure for the link prediction in unipartite and bipartite networks

Amplitude death (AD) has been recently identified as a phenomenon that can be exploited to stop unwanted large amplitude oscillations arising from instabilities in engineering systems. These oscillations are a consequence of the occurrence of dynamic instability, for example, the flutter instability, which results in the manifestation of sustained limit cycle oscillations. Recent studies have demonstrated amplitude death in coupled aeroelastic systems with identical parameters using suitable reactive coupling. Deriving impetus from the same, the dynamical signatures of coupled non-identical aeroelastic systems under a variety of coupling characteristics are investigated in the present study. The coupling characteristics between the individual airfoils here are assumed to possess both reactive and dissipative terms and are represented via a linear torsional spring and a damper, respectively. Explicit parameter mismatch is introduced via the use of different structural parameters such as frequency ratio and air-mass ratio for the individual airfoils. We demonstrate that a nonlinear coupled aeroelastic system with parameter mismatch and combined coupling characteristics gives rise to broader regimes of AD in aeroelastic systems. Specifically, the possibility of encountering large amplitude oscillations, usually found with pure reactive coupling can be avoided by adding a dissipative coupling term. On introducing dissipative coupling, the regime of AD was found to increase substantially, for both identical and non-identical scenarios, which in turn aids in serving as an effective tool to be developed further toward the application of flutter instability suppression. © 2021 Author(s).

Chaos

Effect of parameter mismatch and dissipative coupling on amplitude death regime in a coupled nonlinear aeroelastic system

The deterioration of fluid machines due to slurry and cavitation erosion significantly impairs their serviceability. In the present work, we investigated the influence of deep cryogenic treatment (DCT) on the slurry and cavitation erosion resistance of WC-10Co-4Cr coatings developed using the detonation spraying technique. For comparison, hydroturbine steels and other conventional (Alumina and Stellite 6) coatings were also investigated. All thermal spray coatings showed typical lamellar structure along with the presence of pores and splat boundaries. Among all the coatings, the WC-10Co-4Cr showed the highest slurry (up to 15 times) and cavitation (2 times) erosion resistance owing to high hardness and fracture toughness. Post DCT, the WC-10Co-4Cr coating showed further improvement due to reduced porosity and improved hardness without decrement in fracture toughness. As a result, the DCT coating showed 1.5 to 4.2 times improved slurry erosion resistance than the as-sprayed counterpart, along with 1.6 times higher cavitation erosion resistance. The improved tribological performance of the coating after DCT is associated with enhanced hardness due to the presence of nano precipitates and densification as analyzed using electrochemical techniques. The topological analysis of the eroded surfaces indicated micro-cutting, micro-cracking, and delamination as the primary mechanism controlling the erosion behavior of the coatings. © 2021 Elsevier Ltd

Renewable Energy

Enhanced slurry and cavitation erosion resistance of deep cryogenically treated thermal spray coatings for hydroturbine applications

We discuss the implementation of a coupled Multi-phase particle in cell method (MPPIC) and Volume of Fluid (VOF) flow solver to simulate particles in a fluid flow field having a free surface. Using the solver developed using OpenFOAM 4.1 library, we study the flow of particles inside a highly anisotropic turbulent flow field of a hydrocyclone. The free surface from the air-core inside the hydrocyclone is resolved using the VOF method coupled with the LES turbulence model. We test multiple sub-grid scale model and find the dynamic k-equation model to have the best-predicting capability of the mean and turbulent flow field as well as the air-core shape. Using the established flow field, we study the particle flow properties and its effect on the fluid flow field using a four-way coupled description between MPPIC and VOF. Important flow properties such as the particle flow field velocity and turbulence modulation with varying particle concentration were studied. It was found that even with low solid concentration (0.3–1.5% by volume) there is a quite substantial increase in the fluctuating flow field (around 12%) and with a further increase (1.8%) we observe a decrease in the turbulent levels by approximately 6%. © 2020 Elsevier B.V.

Separation and Purification Technology

Effect of particle concentration on turbulent modulation inside hydrocyclone using coupled MPPIC-VOF method

In this paper, we study the problem of designing prefix-free encoding schemes having minimum average code length that can be decoded efficiently under a decode cost model that captures memory hierarchy induced cost functions. We also study a special case of this problem that is closely related to the length limited Huffman coding (LLHC) problem; we call this the soft-length limited Huffman coding problem. In this version, there is a penalty associated with each of the n characters of the alphabet whose encodings exceed a specified bound D(≤ n) where the penalty increases linearly with the length of the encoding beyond D. The goal of the problem is to find a prefix-free encoding having minimum average code length and total penalty within a pre-specified bound P. This generalizes the LLHC problem. We present an algorithm to solve this problem that runs in time O(nD). We study a further generalization in which the penalty function and the objective function can both be arbitrary monotonically non-decreasing functions of the codeword length. We provide dynamic programming based exact and PTAS algorithms for this setting. © Shashwat Banchhor, Rishikesh Gajjala, Yogish Sabharwal, and Sandeep Sen.

Leibniz International Proceedings in Informatics, LIPIcs

Generalizations of Length Limited Huffman Coding for Hierarchical Memory Settings

Climate change is a topic that is frequently debated on social media. A vast majority in the debate cite scientific evidence to recognize the existence of a man-made climate change and its impacts on environment as well as society. The opinion of the masses is critical to dealing with various issues arising due to climate change, such as global warming. In this work, we study people’s opinion on climate change and analyze the data to identify the common topics which garner discussion. Our aim is to analyze the dataset, explore the popular belief of a region and then derive the possible explanation in terms of different factors. This analysis could help us in determining the extent to which different factors affect people’s opinion. By building sentiment analysis models, performing topic modelling and using other appropriate technologies, we can visualise the sentiment pattern to understand the factors affecting them. © 2021, Springer Nature Switzerland AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Analyzing Tweets to Understand Factors Affecting Opinion on Climate Change

Mobile application based ride-hailing systems, e.g., DiDi, Uber have become part of day to day life and natural choices of transport for urban commuters. However, the pick-up demand in any area is not always matching with the supply or drop-off request in the same area. Urban planners and researchers are working hard to balance this demand and supply situation for taxi requests. The existing approaches have mainly focused on clustering of the spatial regions to identify hotspots, which refer to the locations with a high demand for pick-up requests. In our study, we determined that if the hotspots focus on the clustering of high demand for pick-up requests, most of the hotspots pivot near the city center or two-three spatial regions, ignoring the other parts of the city. In this work, we proposed a method, which can help in finding a local hotspot to cover the whole city area. We proposed a dominating set problem based solution, which covers every part of the city. This will help the drivers looking for near-by next customer in the region wherever they drop their last customer. It will also reduce the waiting time for customers as well as for a driver looking for next pick-up request. This would maximize their profit as well as help in improving their services. © 2021, Springer Nature Switzerland AG.