Ritwik Maiti | Mechanical Engineering | Best Researcher Award

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Dr. Ritwik Maiti | Mechanical Engineering | Best Researcher Award

Assistant Professor at Birla Institute of Technology Mesra, India

Summary:

Dr. Ritwik Maiti is an accomplished researcher in the field of fluid dynamics and granular flow, with a particular emphasis on the behavior of granular materials in various contexts such as silos, open channels, and underground cavities. His work has contributed significantly to understanding the flow of granular media in natural and industrial processes. Dr. Maiti has held prestigious research positions at the National University of Singapore and the University of Sheffield, where he worked on projects ranging from wind-tunnel tests to flow modeling in porous media. He is currently contributing to the academic and research community at Birla Institute of Technology Mesra, where he continues his innovative research on granular flows and their interactions with fluid dynamics.

Professional Profile:

👩‍🎓Education:

Dr. Ritwik Maiti is an Assistant Professor in the Department of Mechanical Engineering at Birla Institute of Technology, Mesra, Ranchi. He earned his Ph.D. in Mechanical Engineering from the Indian Institute of Technology Kharagpur (2011–2017), where his research focused on the dynamics of dense granular flows through silos, closed channels, and open channels. Dr. Maiti holds a Master of Engineering (M.E.) in Heat Power Engineering from Jadavpur University, Kolkata (2009–2011), and a Bachelor of Technology (B.Tech) in Mechanical Engineering from Kalyani Government Engineering College, West Bengal (2008).

🏢 Professional Experience:

Dr. Maiti has extensive research experience in both mechanical and civil engineering. From 2018 to 2021, he was a Research Fellow with the Fluid Mechanics Research Group at the National University of Singapore, where he worked on projects related to wind-tree interaction and the minimization of granular mixture segregation. Prior to this, he was a Research Associate at the University of Sheffield (2017–2018), where he focused on modeling flow through porous granular media as part of the Geotechnical Engineering Research Group. His professional expertise includes the design and development of experimental fluid flow facilities and the handling of advanced equipment such as high-speed cameras, particle image velocimetry, and particle analyzers.

Research Interests:

Dr. Maiti’s research interests lie at the intersection of fluid mechanics and granular flow. His areas of focus include:

  • Experimental Fluid Dynamics
  • Granular Flow Dynamics
  • Geophysical Flows and Avalanches
  • Granular Mixing and Segregation
  • Fluid-Structure Interaction
  • Impact Crater Analysis
  • Underground Cavity Collapse
  • Multiphase Flows
  • Discrete Element Model (DEM)
  • Computational Fluid Dynamics (CFD) and CFD-DEM Coupling

He is also skilled in high-speed photography, image processing, and the use of software such as Matlab, Autocad, and LIGGGHTS for simulation and analysis.

Author Metrics:

Dr. Maiti has published numerous articles in international journals and conferences, including:

  • 10 publications in top-tier journals such as Physics of Fluids, Powder Technology, and AIChE Journal.
  • Contributions to leading conferences such as the International Conference on Fluid Mechanics and Fluid Power and the International Conference on Multiphase Flow.
  • A book chapter published by Springer in 2017.
  • Several research papers currently under review in journals like Powder Technology and Ocean Engineering.

Dr. Maiti’s research on granular dynamics has garnered significant attention in his field, contributing valuable insights into both theoretical models and practical applications.

Top Noted Publication:

Experiments on Eccentric Granular Discharge from a Quasi-Two-Dimensional Silo

  • Authors: R. Maiti, G. Das, P.K. Das
  • Journal: Powder Technology
  • Volume: 301
  • Pages: 1054-1066
  • Year: 2016
  • Citations: 35
  • Summary: This study presents experimental investigations on granular discharge from a quasi-two-dimensional silo with an eccentric outlet. The paper discusses the flow behavior, discharge rates, and the formation of patterns in the granular material as it exits the silo. The experiments provide a detailed understanding of the flow field dynamics during eccentric discharge.

Granular Drainage from a Quasi-2D Rectangular Silo through Two Orifices Symmetrically and Asymmetrically Placed at the Bottom

  • Authors: R. Maiti, G. Das, P.K. Das
  • Journal: Physics of Fluids
  • Volume: 29 (10)
  • Year: 2017
  • Citations: 25
  • Summary: This research explores the granular flow through a rectangular silo with two bottom orifices, placed both symmetrically and asymmetrically. The work examines how different placement configurations of the orifices affect the flow and drainage dynamics of granular materials, contributing valuable insights into granular discharge mechanics.

Flow Field During Eccentric Discharge from Quasi-Two-Dimensional Silos—Extension of the Kinematic Model with Validation

  • Authors: R. Maiti, S. Meena, P.K. Das, G. Das
  • Journal: AIChE Journal
  • Volume: 62 (5)
  • Pages: 1439-1453
  • Year: 2016
  • Citations: 19
  • Summary: This paper extends a kinematic model to describe the flow field during eccentric discharge from a quasi-2D silo. The study provides experimental validation of the model and offers insights into the flow patterns and velocity fields of granular materials, expanding the understanding of discharge processes in industrial and natural granular systems.

Cracking of Tar by Steam Reforming and Hydrogenation: An Equilibrium Model Development

  • Authors: R. Maiti, S. Ghosh, S. De
  • Journal: Biomass Conversion and Biorefinery
  • Volume: 3
  • Pages: 103-111
  • Year: 2013
  • Citations: 6
  • Summary: This paper focuses on developing an equilibrium model for tar cracking using steam reforming and hydrogenation. The study addresses the challenges associated with tar removal in biomass gasification and proposes a model to predict the outcomes of chemical reactions involved in the process.

Self-Organization of Granular Flow by Basal Friction Variation: Natural Jump, Moving Bore, and Flying Avalanche

  • Authors: R. Maiti, G. Das, P.K. Das
  • Journal: AIChE Journal
  • Volume: 69 (1)
  • Article: e17943
  • Year: 2023
  • Citations: 2
  • Summary: This recent study investigates the self-organization phenomena in granular flows due to variations in basal friction. The paper describes natural jumps, moving bores, and flying avalanches in granular media, providing key insights into the mechanics of granular flow and segregation.

Conclusion:

Dr. Ritwik Maiti’s contributions to fluid dynamics and granular flow research, particularly in areas like silo flows and porous media, make him a strong candidate for the Best Researcher Award. His published work demonstrates both depth and innovation in key fields of mechanical engineering, and his international experience enhances his profile. While expanding his research into more applied fields and taking on greater leadership roles could strengthen his application, his current contributions to science are exceptional, positioning him well for recognition in the field of mechanical engineering research.

 

Xiaoming Li | Systems Engineering | Best Researcher Award

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Dr. Xiaoming Li, Systems Engineering, Best Researcher Award

Doctorate at Concordia University, Canada

Summary:

Dr. Xiaoming Li is an accomplished researcher and educator with extensive experience in artificial intelligence, machine learning, and optimization models for shared mobility and smart logistics. With a Ph.D. in Information and Systems Engineering from Concordia University, his work focuses on integrating data-driven approaches to optimize urban transportation systems and enhance sustainability. He has a strong background in software engineering and has contributed significantly to both academic research and industry projects. Dr. Li has received several awards, including the IEEE Outstanding Leadership Award and the Mitacs Accelerate Program Award. As a dedicated educator, he has taught and mentored students across various institutions, shaping the next generation of computer scientists.

Professional Profile:

👩‍🎓Education:

Ph.D. in Information and Systems Engineering

  • Concordia University, Montréal, QC, Canada
  • Cumulative GPA: 4.0 / 4.3

M.Sc. in Computer Software and Theory

  • Northeastern University, China
  • Cumulative GPA: top 16.7%

B.Sc. in Computer Science and Technology

  • Shenyang Aerospace University, China

Professional Experience:

Dr. Xiaoming Li currently serves as a Research Associate at Concordia University, Montréal, where he supervises interdisciplinary research in artificial intelligence and operations research for shared mobility-on-demand applications. His role involves designing deep learning models for time-series demand forecasts, developing data-driven stochastic optimization models for renewable energy mobility management, and creating an integrated optimization framework for sustainable crowd-shipping services. Previously, Dr. Li held a Research Assistant position at Concordia, working on data analysis and machine learning model development for various projects, including forecasting commodity rates and predicting hotel booking cancellations.

Dr. Li’s industrial experience includes internships at Ericsson’s Global Artificial Intelligence Accelerator, where he developed an energy-saving framework for 5G base stations, and Medialpha, where he optimized nurse routing and medical resource allocation using meta-heuristic algorithms. Additionally, he has substantial experience as a Software Engineer at Shenyang Aerospace University, leading projects and managing teams.

In academia, Dr. Li has served as an Adjunct Faculty at Vanier College, teaching database theory and application development, and as a Full-Time Lecturer at Shenyang Aerospace University, instructing on a variety of computer science courses and supervising numerous student projects.

Research Interest:

  • Machine Learning
  • Operations Research
  • Data Science
  • Data-Driven Optimization
  • Agent-Based Simulation Modeling
  • Shared Mobility
  • Smart Logistics
  • Sustainable Intelligent Transportation Systems

Publications Top Noted: 

GREEN: A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems

  • N. Lin, Y. Fan, L. Zhao, X. Li, M. Guizani
  • IEEE Transactions on Mobile Computing, 14, 2022.

BM-DDPG: An Integrated Dispatching Framework for Ride-Hailing Systems

  • J. Gao, X. Li, C. Wang, X. Huang
  • IEEE Transactions on Intelligent Transportation Systems, 23(8), 11666-11676, 2021.

Driver Guidance and Rebalancing in Ride-Hailing Systems Through Mixture Density Networks and Stochastic Programming

  • X. Li, J. Gao, C. Wang, X. Huang, Y. Nie
  • 2021 IEEE International Smart Cities Conference (ISC2), 1-7, 2021.

Learning-Based Open Driver Guidance and Rebalancing for Reducing Riders’ Wait Time in Ride-Hailing Platforms

  • J. Gao, X. Li, C. Wang, X. Huang
  • 2020 IEEE International Smart Cities Conference (ISC2), 1-7, 2020.

Ride-Sharing Matching Under Travel Time Uncertainty Through Data-Driven Robust Optimization

  • X. Li, J. Gao, C. Wang, X. Huang, Y. Nie
  • IEEE Access, 10, 116931-116941, 2022.