Systematic research of e-learning platforms for solving challenges faced by Indian engineering students

Purpose – As educational institutes began to address the challenges posed by COVID-19, e-learning came to the foreground as the best bet left. This study is in quest of revealing engineering student's perceptions of the available e-learning platforms, thus surfacing the underlying bottlenecks. Further, it aims at providing solutions that would help enhance the e-learning experience not only in pandemic times but also in the long run. Design/methodology/approach – This holistic research begins with a comprehensive comparative study about the available e-learning platforms, followed by a primary data analysis through an online survey of 364 engineering students from various colleges and branches. The collected data was analyzed to detect bottlenecks in online learning and suggestions are given for solving some challenges. Findings – On a five-point Likert scale, the available e-learning platforms garnered ratings ranging from 2.81 to 3.46. Google meet was the most preferred platform. However, with a net promoter score (NPS) of 30.36, Microsoft Teams emerged as the most satisfying platform. Technical shortcomings clubbed with psychological and biological factors were found to be taking a toll on e-learning. Research limitations/implications – This innovative research is based on the perceptions of engineering students hailing majorly from Indian cities, and hence, it may be having educational stream bias and geographical bias. The research could be further extended to cover rural areas and global trends in e-learning. Originality/value – The research offers a thorough analysis of e-learning platforms, as seen through the lens of engineering students. Furthermore, the analysis does not constrain itself to the technicalities and thus proves to be an all-encompassing one, potent enough to surface critical issues marring the e-learning experience

Modeling, Analysis and Control of Nonlinear Switching Systems

The first part of this two-part thesis examines the reverse-flow operation of auto-thermal methane reforming in a microreactor. A theoretical study is undertaken to explain the physical origins of the experimentally observed improvements in the performance of the reverse-flow operation compared to the unidirectional operation. First, a scaling analysis is presented to understand the effect of various time scales existing within the microreactor, and to obtain guidelines for the optimal reverse-flow operation. Then, the effect of kinetic parameters, transport properties, reactor design and operating conditions on the reactor operation is parametrically studied through numerical simulations. The reverse-flow operation is shown to be more robust than the unidirectional operation with respect to both optimal operating conditions as well as variations in hydrogen throughput requirements. A rational scheme for improved catalyst placement in the microreactor, which exploits the spatial temperature profiles in the reactor, is also presented. Finally, a design modification of the microreactor called "opposed-flow" reactor, which retains the performance benefits of the reverse-flow operation without requiring the input / output port switching, is suggested. In the second part of this thesis, a novel simulation-based Approximate Dynamic Programming (ADP) framework is presented for optimal control of switching between multiple metabolic states in a microbial bioreactor. The cybernetic modeling framework is used to capture these cellular metabolic switches. Model Predictive Control, one of the most popular advanced control methods, is able to drive the reactor to the desired steady state. However, the nonlinearity and switching nature of the system cause computational and performance problems with MPC. The proposed ADP has an advantage over MPC, as the closed-loop optimal policy is computed offline in the form of so-called value or cost-to-go function. Through the use of an approximation of the value function, the infinite horizon problem is converted into an equivalent single-stage problem, which can be solved online. Various issues in implementation of ADP are also addressed.Ph.D.Committee Chair: Jay H. Lee; Committee Member: Andrei G. Fedorov; Committee Member: Athanasios Nenes; Committee Member: Martha Gallivan; Committee Member: Matthew Realf

The Effect of Catalyst Placement on the Stability of a U-Bend Catalytic Heat-Recirculating Micro-Combustor: A Numerical Investigation

This study investigates the combined effect of catalyst placement and solid thermal conductivity on the stability of a U-bend catalytic heat-recirculating micro-combustor. The CFD code ANSYS Fluent 2020 R1 was used for two-dimensional simulations of lean premixed propane/air combustion by varying the inlet gas velocity, i.e., the input power. Three configurations were compared at low (3 W/(m K)) and high (30 W/(m K)) wall thermal conductivity: (A) the configuration in which both inner and outer walls are catalyst coated; (B) only the inner wall is catalyst coated; and (C) only the outer wall is catalyst coated. Numerical results show that, at low thermal conductivity, configuration (B) exhibits the same resistance to extinction as configuration (A), whereas at high thermal conductivity, configurations (B) and (C) exhibit much lower resistance to blowout than configuration (A). Accordingly, for low-power systems, which typically lose stability via extinction and thus require low-conductive materials, an optimal catalyst placement can be the partial coating of configuration (B). Conversely, for high-power systems, which are prone to blowout and thus require high-conductivity materials, a full coating of both the inner and outer walls is needed to guarantee higher stability. To elucidate these findings, a detailed analysis of the combustion behavior of the three configurations is presented

Transport Phenomena in Microscale Reacting flows

status: publishe