Department Of Chemical Engineering

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

The primary objective of this project is to increase the fatigue life of aluminium wheels and to prevent the fatigue failure of the wheel above the bolt hole region. In the testing and validation of wheels, the cornering fatigue test (CFT) plays an important role in deciding the stiffness of the wheel. In CFT testing, the wheel will be fixed in the bottom flange, and the bending moment will be applied, according to the load rating of the wheel multiplied by its corresponding test factor. CFT testing results of aluminium wheels showed crack above the bolt hole region in most of the cases. To prevent this mode of failure, and also to improve the fatigue life of the wheel further, it has been found that increasing the localized hardness above the bolt hole region will improve the fatigue life in CFT. Laser peening is a surface hardening technique by which the surface hardness of the material will be enhanced, along with which hardness and compressive residual stresses will be imparted on the surface in which the treatment is done. The depth of the high magnitude compressive stresses will greatly improve the material’s resistance to fatigue failure. In aluminium wheels, laser peening was done circumferentially around the bolt hole region to induce localized compressive stress and increase the hardness in that area. The wheel in which laser peening was done was tested on CFT and showed a significant improvement in life. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

Department of Mechanical Engineering

World University root node

Lecture Notes in Mechanical Engineering

An innovative method of preventing bolt hole crack in aluminium wheels by using laser peening technique

The deduction of design principles for complex biological functionalities has been a source of constant interest in the fields of systems and synthetic biology. A number of approaches have been adopted, to identify the space of network structures or topologies that can demonstrate a specific desired functionality, ranging from brute force to systems theory-based methodologies. The former approach involves performing a search among all possible combinations of network structures, as well as the parameters underlying the rate kinetics for a given form of network. In contrast to the search-oriented approach in brute force studies, the present chapter introduces a generic approach inspired by systems theory to deduce the network structures for a particular biological functionality. As a first step, depending on the functionality and the type of network in consideration, a measure of goodness of attainment is deduced by defining performance parameters. These parameters are computed for the most ideal case to obtain the necessary condition for the given functionality. The necessary conditions are then mapped as specific requirements on the parameters of the dynamical system underlying the network. Following this, admissible minimal structures are deduced. The proposed methodology does not assume any particular rate kinetics in this case for deducing the admissible network structures notwithstanding a minimum set of assumptions on the rate kinetics. The problem of computing the ideal set of parameter/s or rate constants, unlike the problem of topology identification, depends on the particular rate kinetics assumed for the given network. In this case, instead of a computationally exhaustive brute force search of the parameter space, a topology–functionality specific optimization problem can be solved. The objective function along with the feasible region bounded by the motif specific constraints amounts to solving a non-convex optimization program leading to non-unique parameter sets. To exemplify our approach, we adopt the functionality of adaptation, and demonstrate how network topologies that can achieve adaptation can be identified using such a systems-theoretic approach. The outcomes, in this case, i.e., minimum network structures for adaptation, are in agreement with the brute force results and other studies in literature. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Department Of Biotechnology

Methods in Molecular Biology

Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities

The significance of operator training has dramatically increased due to complex automation strategies in modern process plants. It is reported that human errors account for 70% of the accidents in process industries, with inadequate training cited as one of the most common reasons for these incidents. Our previous work has shown the potential of eye-tracking to infer the mental state of control room operators. In this work, we propose a methodology that combines multi-scale data from the process simulator, control actions performed, and eye gaze data of the operators to evaluate their training outcomes. Specifically, we use fixation transition entropy, an eye-tracking metric, which can help infer the mental models of the process abnormalities developed by the operators during repeated control room tasks. Results indicate that the fixation transition entropy decreases on account of development of correct mental models of process while it remain at higher values when operator fails to update their mental models during plant abnormalities. Thus, the proposed metric can be used to gauge the development of operator's mental models during training to understand the transition from novice to becoming experts. © 2021 Elsevier B.V.

Computer Aided Chemical Engineering

Evaluating Control Room Operator Training Outcomes Through Eye Gaze Augmented Multi-Scale Data

The total temperature achieved by vitiation of air using hydrogen–oxygen combustion is predicted after heat transfer losses. The adiabatic temperature, combustion products, and its properties are acquired using GASEQ. The heat transfer losses include losses due to convection and radiation of the mixture. The hot jet from individual burner enters the preheater with a velocity, which in turn causes convection to occur from the gas to the surface of the preheater. The convection loss is predicted using basic convection heat transfer equation used for heat transfer of hot gases flowing over a plate. The radiation loss was predicted using the emittance of the combustion products at different temperatures using existing analytical data. The emittance of the mixture is a function of adiabatic temperature of the combustion, chamber pressure, and partial pressure of water vapour in the combustion product. The prediction is compared with experimental total temperature, and calculated using the chamber pressure PO. The predicted total temperature is also compared with thermocouple value in one of the tests. The prediction helps in the design of supersonic combustion experiments which are largely dependent on the total temperature of the preheater. The prediction was used get the total temperatures up to 1100 K in the preheater. © 2021, Springer Nature Singapore Pte Ltd.

Department of Aerospace Engineering

Prediction of Heat Losses in Scramjet Vitiator

The evaporation of colloidal dispersions is an elegant and straightforward route to controlled self-assembly of particles on a solid surface. In particular, the evaporation of particle laden drops placed on solid substrates has received considerable attention for more than two decades. Such particle filled drops upon complete evaporation of the solvent leave behind a residue, commonly called particulate deposit pattern. In these patterns, typically, more particles accumulate at the edge compared to the interior, a feature observed when coffee drops evaporate. Consequently, such evaporative patterns are called coffee stains. In this article, the focus is on the evaporation of highly dilute suspension drops containing particles of larger diameters ranging from 3 to 10 μm drying on solid substrates. This helps us to investigate the combined role of gravity-driven settling of particles and capillary flow-driven particle transport on pattern formation in drying drops. In the highly dilute concentration limit, the evaporative patterns are found to show a transition, from a monolayer deposit that consists of a single layer of particles, to a multilayer deposit as a function of particle diameter and initial concentration of particles in the drying drop. Moreover, the spatial distribution of particles as well as the ordering of particles in the deposit patterns are found to be particle size dependent. It is also seen that the order-disorder transition, a feature associated with the organization of particles at the edge of the deposit, observed typically at moderate particle concentrations, disappears at the highly dilute concentrations considered here. The evaporation of drops containing particles of 10 μm diameter, where the effect of gravity on the particle becomes significant, leads to uniform deposition of particles, i.e, suppression of the coffee-stain effect and to the formation of two-dimensional percolating networks. © 2021 American Chemical Society.

Langmuir

Further Insights into Patterns from Drying Particle Laden Sessile Drops

Microswimmers interacting with passive particles in confinement are common in many systems, e.g., spermatozoa encountering other cells or debris in the female reproductive tract or active particles interacting with polymers and tracers in microfluidic channels. The behaviour of such systems is driven by simultaneous, three way hydrodynamic interactions between the microswimmer, the passive particle and the microchannel walls. Therefore, in this work we investigate the hydrodynamic collision between a model microswimmer and a passive particle using three different methods: (i) the point particle approach, (ii) analytical calculations based on method of reflections, and (iii) lattice Boltzmann numerical simulations. We show that the hydrodynamic collision is essentially an asymmetric process-the trajectory of the microswimmer is altered only in an intermediate stage while the passive particle undergoes a three stage displacement with a net displacement towards or away from the microchannel walls. The path of the passive particle is a simple consequence of the velocity field generated by the swimmer: an open triangle in bulk fluid and a loop-like trajectory in confinement. We demonstrate the generality of our findings and conclude that the net displacement of the passive particle due to collision may be capitalised in order to develop applications such as size separation of colloidal particles and deposition of particles in the microchannel interiors. This journal is © The Royal Society of Chemistry.

Soft Matter

Hydrodynamic collision between a microswimmer and a passive particle in a micro-channel

Effective control of a blast furnace (BF) process requires accurate estimates of key process indicators (KPIs), namely, productivity, coke rate, direct reduction per cent, adiabatic flame temperature, bosh gas volume and top gas utilization. Some of these KPIs are obtained directly from the measurements, and some are derived by carrying out material and energy balances on measurements of different feeds, their compositions and temperatures. Due to errors in the measurements, the estimates of the KPIs can be inconsistent or misleading, which may result in misinterpretation of the current state of the BF process. Hence, it is necessary to reconcile the measurement data before these are used either for interpreting the current furnace state directly or as an input to other models. In the proposed methodology, data reconciliation and gross error detection techniques are used to improve the accuracy of the estimates of process variables and parameters, by ensuring that they satisfy process constraints such as elemental balances of iron, nitrogen, carbon, oxygen and hydrogen. Since the BF is a fed-batch process, a customized version of these techniques has been developed and applied real time to an operating BF. The method is shown to be useful in deriving consistent estimates of the hot metal production rate, identifying gross errors in the online gas analyser and for estimating unmeasured parameters, such as top gas flow rate, its moisture concentration and calorific value which are useful for the purpose of stove heating in the downstream process. © 2021 Curtin University and John Wiley & Sons, Ltd.

Department Of Civil Engineering

Department of Management Studies

Asia-Pacific Journal of Chemical Engineering

Application of data reconciliation and gross error detection techniques to enhance reliability and consistency of the blast furnace process data

The effect of inlet flow distribution on the performance characteristics of a catalytic converter is analyzed for two common inlet manifold designs. Three-dimensional steady-state computational fluid dynamics simulations are performed under isothermal and nonisothermal conditions for the entire converter comprising an inlet manifold, central monolith, and outlet section. The porous medium approximation is used for the catalytic monolith. The reaction mechanism for a diesel oxidation catalyst with five global reactions is employed. A parameter called the “flow distribution index” is defined in order to quantify the nonuniformity of the velocity profile within the monolith and hence analyze the effect of operating conditions for the two geometries. Under isothermal conditions, catalytic converters with straight and U-bend inlet manifolds behave similarly and closely match single channel predictions. However, under nonisothermal conditions, there is a considerable difference in performance between the two geometries because of temperature variations. The presence of heat effects in the nonisothermal case marginally improves the flow uniformity within the monolith. However, in the presence of heat effects, reactant conversion in the center of the monolith varies from the periphery and the single channel model slightly overestimates the conversion for both geometries. © 2021 American Chemical Society.

Industrial and Engineering Chemistry Research

Modeling the effects of the inlet manifold design on the performance of a diesel oxidation catalytic converter

High entropy alloys (HEAs) have drawn considerable attention owing to their unique properties such as high fracture toughness, good strength–ductility combination and enhanced corrosion resistance. In this study, the corrosion resistance dependence on the crystal structure and grain size of AlCoCrFeNi HEA is investigated. AlCoCrFeNi HEA with different mixture of body centered cubic (BCC) and face centered cubic (FCC) phases is produced using sequential alloying and tested for corrosion resistance in 3.5 wt% NaCl solution. CoNi+Fe+Cr+Al (84% BCC), FeCr+Ni+Al+Co (62% BCC) and AlNi+Co+Cr+Fe (38% BCC) alloy sequences have corrosion potential of −454, −299 and −524 mV (vs. SCE), and corrosion current of 14, 0.4 and 12 µA, respectively. FCC is a tight binding lattice with higher packing fraction than BCC which made it better for corrosion resistance, but FCC is rich in elements like Co, Ni, and Fe which are easily corroded. These two competing effects lead to a nearly optimum corrosion resistance for FeCr+Ni+Al+Co alloy sequence with 62% BCC and 32% FCC. It is also observed that an increase in grain size improves corrosion resistance. The influence of different chemical elements, crystal structure and microstructure (coarse vs. nanocrystalline) on corrosion resistance is discussed. © 2020 Elsevier B.V.

Journal of Alloys and Compounds

Effect of crystal structure and grain size on corrosion properties of AlCoCrFeNi high entropy alloy

Machine learning (ML) and artificial intelligence (AI) have remarkable abilities to classify, recognize, and characterize complex patterns and trends in large data sets. Here, we adopt a subclass of ML methods, viz., deep learning, and develop a general purpose AI tool - dPOLY - for analyzing molecular dynamics (MD) trajectories and predicting phases and phase transitions in polymers. An unsupervised deep neural network (DNN) is used within this framework to map a MD trajectory undergoing thermophysical treatment such as cooling, heating, drying, and compression to a lower dimension. A supervised DNN is subsequently developed based on the lower dimensional data to characterize the phases and phase transitions. As a proof of concept, we employ this framework to study the coil to globule phase transition of a model polymer system. We conduct coarse-grained MD simulations to collect MD trajectories of a single polymer chain over a wide range of temperatures and use the dPOLY framework to predict polymer phases. The dPOLY framework accurately predicts the critical temperatures for the coil to globule transition for a wide range of polymer sizes. This method is generic and can be extended to capture various other phase transitions and dynamical crossovers in polymers and other soft materials. It can also significantly accelerate polymer phase prediction and characterization. © 2021 American Chemical Society.