Department of Mechanical 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 nonlinear effects induced on a nonspherical object exposed to an acoustic standing wave offer acoustic radiation force and torque, resulting in the dynamical motion of the object. Here, we study the translational and rotational motion of an oblate shaped particle exposed to standing bulk acoustic waves in a microchannel using numerical simulations. Acoustic pressure and velocity fields are obtained from a numerical model, and a perfectly matched layer boundary condition is used to simulate the particle dynamics. A systematic parametric study is carried out to understand the effects of initial orientation, aspect ratio, size, and initial location of the particle on the translational and rotational motion, by considering the acoustic streaming effects. In this paper, we reveal that the particle undergoes rotation to minimize the acoustic radiation torque potential when the minor axis of the particle is not in line with the acoustic pressure wave direction. We find that the direction of rotation changes from anticlockwise to clockwise beyond a critical aspect ratio of the particle. The location of maximum torque and consequently particle rotation shift closer to the pressure node with increased particle size for a constant aspect ratio. Our results show that a particle positioned closer to the pressure node rapidly rotates, attributed to a sharp increase in acoustic radiation torque acting on it owing to a higher torque potential. In this paper, we shed light on the dynamical motion of an oblate shaped particle exposed to acoustic standing waves which may be relevant in understanding the dynamics of an elongated micro-organism or biological cells. © 2022 American Physical Society.

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Physical Review Fluids

Dynamical motion of an oblate shaped particle exposed to an acoustic standing wave in a microchannel

The main purpose is to monitor the sensitivity of graphene nanoplatelet (GNP)–doped polymethyl methacrylate (PMMA) sensor under temperature-loaded aluminium 2024-T351 under mechanical loading. The objective is to develop smart polymer nanocomposites using spray-coated graphene nanoplatelet (GNP)–doped polymethyl methacrylate on the surface of aluminium beams (2024-T351) and monitor gauge factor (GF) at different temperatures during monotonic load. The methods used for this work are nanofabrication of GNPs/PMMA sensor, spray coating, temperature-loaded aluminium specimens, optical spectroscopy, Fourier Transfer Infrared Spectroscopy (FTIR) and Raman characterization. In this work, 9 specimens were prepared by spray coating with different intrinsic resistances, namely, 500 Ω, 1 kΩ, 3 kΩ, 9 kΩ, 21 kΩ, 30 kΩ, 55 kΩ, 65 kΩ and 82 kΩ by scotch tape erosion method. Amongst all, 9 kΩ has shown good signature for the highest gauge factor (GF) as 72 more than 500 Ω which showed 14 as the lowest GF before heating. Similarly, 9 kΩ showed 86, 59 and 44 as GF during heat, post-heating and cold conditions, respectively. And 500 Ω showed 24, 12 and 27 as GF during heat, post heat and cold heat, respectively. Here, increment of gauge factor slope for 500 Ω,1 kΩ, 3 kΩ, 82 kΩ, 30 kΩ, 20 kΩ, 55 kΩ, 65 kΩ and 9 kΩ with an increase in temperature at 30 °C, 33 °C, 38 °C, 46 °C and 53 °C has been presented. The change in morphology of sensors has been verified with a scanning electron microscope (SEM) and error analysis of GF is studied analytically. Here, it is concluded, with an increment of temperature, electrical resistance increases but with morphology degradation, the gauge factor of sensors is reduced. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Journal of Nanoparticle Research

Graphene nano-platelet (GNP)–doped poly (methyl methacrylate) (PMMA) spray-coated piezoresistive-based 2D strain sensor under temperature environment on aluminium alloy 2024-T351

Ultrasonic guided waves play an essential role in determining the severity of defects in plate-pipe-like structures. One of the parameters that influence the decision to replace a defective structure is the thickness remaining in the structure. Hence, a precise measurement of the remaining thickness is essential in many industries. The objective of this paper is the quantification of defects in small thickness structures. The cut-off property of the guide wave mode is applied to quantify the lowest remnant thickness of defects in small thickness structures. Implementing the cut-off approach in small thickness structures is challenging because of the small wavelength requirements. A linear array transducer, capable of selectively exciting the transducer elements, is used for this application to transmit and receive wave modes. The study focuses on the A1 Lamb wave mode and its cut-off behaviors. The remnant thickness reflects the A1 mode if the frequency of the wave mode is lower than the cut-off frequency of the defect. The reflection of the A1 mode from the defect is analyzed at multiple frequencies utilising the wavelength constrained excitation. The frequency below which the A1 mode appears indicated the remnant thickness of the defect. Furthermore, an advanced excitation approach using the broadband A1 mode identifies the remnant thickness values from a single experiment. In addition, rather than specifying a range of remnant thickness, it precisely quantified its value. This approach was capable of identifying remnant thickness within a maximum error of 2.3% among the set of examples selected. © 2022 Author(s).

Journal of Applied Physics

Remnant thickness quantification in small thickness structures utilising the cut-off property of A1 Lamb wave mode employing linear array elements

The passage of a bubble through an immiscible horizontal liquid-liquid interface has a wide range of applications, from chemical processes to microfluidic devices. Buoyancy governs this passage of the bubble, and at the liquid-liquid interface, the bubble encounters a downward pulling force due to surface tension. Depending on the volume of the bubble, it may pass through or become trapped at the interface. In this study, for the first time, we proposed the idea of trapped bubble removal from a liquid-liquid interface with the aid of a wettability-patterned cone. The bubble detachment dynamic is investigated using numerical results and theoretical analysis. The effect of fluid properties and cone parameters on bubble detachment has been extensively studied. It is found that density contrast (ρr) and viscosity contrast (μr) of both the liquids, surface tension ratio (σr), bubble diameter (d0), wettability of the cone (θ), and cone angle (α) play a crucial role in bubble detachment. Here, we studied the effect of each parameter on the bubble detachment and, based on that, identified two distinct regimes, e.g., detached regime and non-detached regime. The regime map is represented by two non-dimensional groups βco and ψ, which are functions of Bond number (Bo), Ohnesorge number (Oh), α, and θ. Furthermore, the transport characteristics of the bubble on the cone after the detachment indicate that the bubble velocity decreased as it moved from the narrower to the wider section of the cone. These findings could be useful in the removal of trapped bubbles from a liquid-liquid interface in small-scale chemical industries. © 2022 Author(s).

Physics of Fluids

A wettability pattern-mediated trapped bubble removal from a horizontal liquid-liquid interface

With the increasing water demand around the world, researchers are looking for new innovative techniques to meet this demand. Since available water resources are limited and only a few technologies like desalination are available which can be used to increase the water supply above the hydrological cycle to meet the rapidly increasing water demand. One such new emerging technology to increase the water supply is Atmospheric Water Harvesting (AWH), where the water vapors which are found everywhere in the air are condensed to obtain fresh water. The AWH system can run on low-grade energy like waste heat or solar energy, which is abundantly available in arid regions having water scarcity and can help in the decentralization of water resources by eliminating the need for power grids. In this paper, we have analyzed and compared some new innovative technologies of AWH systems after defining them mathematically and modeling them. The technologies will be compared based on various parameters such as the amount of water produced per day, the Gained Output Ratio (GOR) of the cycle under varying ambient conditions, such as dry bulb temperature (DBT), relative humidity (RH) and mass flow ratio (MFR). The results show that about 15kgh-1 of water may be harvested with a GOR of about 0.28 for 30oC DBT, 60%RH and 80oC desiccant inlet temperature. © 2022 Begell House Inc.. All rights reserved.

Proceedings of the Thermal and Fluids Engineering Summer Conference

NUMERICAL ANALYSIS OF THE PERFORMANCE OF ATMOSPHERIC WATER HARVESTING SYSTEM

Water is among the most fundamental needs of any society. Studies forecast that by 2030 half of the world's population will be living in areas of high-water stress due to increasing population and water footprint. Desalination can be an alternative to increase the influx of water, and many countries in the Middle East are currently relying on it for their water requirement. The primary desalination techniques are based on phase change of water, due to which they are energy-intensive, which leads to an imbalance of water-energy nexus. Therefore, we need a desalination technique that can run on renewable energy to mitigate the energy footprint. Membrane distillation (MD) is a thermally-driven separation process. It has lots of potentials as it works on lower temperature and pressure range that alternate energy sources can provide. However, the conduction heat loss and temperature polarization in MD make the process less energy efficient. Photothermal membrane distillation (PMD), a process in which the localized heating at the surface of the feed side of the membrane by solar irradiation alone drives the distillation process, can be an effective solution as it eliminates the requirement of input feed heating and lower operating velocity to overcome the problem faced in conventional MD. In this study, the PMD system is modelled mathematical and numerical analysis of the same is carried out for the optimum working performance of the system. Preliminary results indicate that with optimized heat recovery, the performance of the system can be further improved (10%-20%). © 2022 Begell House Inc.. All rights reserved.

Numerical Investigation of Photothermal Membrane Distillation

This paper explores the feasibility of machine learning algorithms on nonlinear ultrasonics for classification of the austenitic stainless-steel material subjected to different annealing conditions. The material that is isothermally annealed at 1323 K for different soaking times showed a marginal variation in its nonlinearity parameter at larger mean grain sizes. The grain growth during annealing followed the Arrhenius type equation fairly well, which has been verified using a genetic algorithm approach. The machine learning algorithms are trained using features such as the ratio of the harmonic amplitudes, root-mean-square value, and the phase difference between the fundamental and second harmonic components derived from the nonlinear ultrasonic response. Upon evaluating the performance of decision tree and ensemble learning algorithms in the classification of annealed materials, it was observed that the LPBoost classifier has the highest accuracy of 97%. According to the results, it is concluded that a machine learning strategy based on a minimal number of features can effectively classify specimens that are otherwise indistinguishable in their nonlinear response. This research takes a step forward to the automation of non-destructive testing toward Industrial Revolution 4.0. The results also pointed out the necessity of parameter fusion in non-destructive decision making. © 2022 Author(s).

A machine learning approach to nonlinear ultrasonics for classifying annealing conditions in austenitic stainless steel

The impact of interlaminar microstructure on the induction heating patterns of CFRP laminates is investigated using thermography technique. Different consolidation methods were used to obtain the varying interlaminar distances. When excited by a circular induction coil, the induction heating pattern of laminates shifted from a circular to a rectangular shape as the interlaminar distance increased. The shift in the heating patterns is caused by the increase in fiber separations at the interface, which leads to fewer electrical contacts. The effect of varying interlaminar distance was modeled as an effective through-thickness electrical conductivity in the numerical model. When the separation between the fibers increased from 8 ± 3.6 µm to 15.3 ± 3.8 µm, the ratio of junction heating to fiber heating increased from 60% to 135%. © 2022 Elsevier Ltd

Materials Today Communications

The influence of interlaminar microstructure on the induction heating patterns of CFRP laminates

In this paper, we proposed a data-driven spatio-temporal deep learning (SDL) model, to simulate forward and reflected ultrasonic wave propagation in the 2D geometrical domain, by implementing the convolutional long short-term memory (ConvLSTM) algorithm. The SDL model learns underlying wave physics from the spatio-temporal datasets. Two different SDL models are trained, with the following time-domain finite element (FE) simulation datasets, by applying: (1) multi-point excitation sources inside the domain and (2) single-point excitation sources on the edge of the different geometrical domains. The proposed SDL models simulate ultrasonic wave dynamics, for the forward ultrasonic wave propagation in the different geometrical domains and reflected wave propagation phenomenon, from the geometrical boundaries such as curved, T-shaped, triangular, and rectangular domains, with varying frequencies and cycles. The SDL is a reliable model, which generates simulations faster than the conventional finite element solvers. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Fulltext

Applied Sciences (Switzerland)

Implementing Data-Driven Approach for Modelling Ultrasonic Wave Propagation Using Spatio-Temporal Deep Learning (SDL)

Hypothesis: A liquid droplet on a rigid polydimethylsiloxane (PDMS) substrate exhibits a higher receding contact angle (θr), therefore, recedes earlier than its softer counterpart. The three-phase contact line of a suspension droplet on a composite rigid-soft PDMS substrate can be selectively tuned wherein the contact line recedes on the rigid substrate sooner and approaches toward the softer side, with microparticles eventually being deposited in the softer substrate region. Experiments: A composite PDMS substrate containing soft cores of various shapes (circular and non-circular) surrounded by rigid matrices was fabricated by employing 3D printing and soft lithography. A sessile suspension droplet containing spherical microparticles was deposited on the composite substrate and evaporated under ambient conditions. The evaporation dynamics was recorded and analyzed. Findings: Evaporation-induced patterning (in circular, triangular, and rectangular areas) with sizes ranging from microns to millimetres were obtained. For the first time, by varying the ratio of the rigid-soft regions in the PDMS substrate, we were able to obtain different deposition sizes and shapes from an identical droplet. Instead of using lithographically patterned substrate, our simple methodology by using 3D printing and soft lithography opened up a new avenue for patterning microparticles based on a rigid-soft composite substrate. © 2022 The Authors