Department of Applied Mechanics

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

Direct Numerical Simulation (DNS) studies of a fully developed turbulent channel flow are performed with sinusoidal surface texture to assess its ability to enhance turbulent skin-frictional drag reductions over superhydrophobic surfaces (SHS). The streamwise sinusoidal microgroove structure generates asymmetric secondary flows to induce an in-plane stationary distribution of spanwise velocity that oscillates in the streamwise direction. The transverse motions over a sinusoidal texture behave analogously to an existing drag-reduction technique by spanwise wall oscillations. The friction-drag levels and slip-lengths are precisely quantified for various streamwise wavelength configurations, and an optimum wavelength for the maximum reduction in turbulent drag is determined. The response of the near-wall turbulent structures to the sinusoidal microgrooves is also analysed. The transverse Stokes strain generates spanwise tilting in the near-wall streaks for large wavelengths, inducing an apparent drop in the wall-normal ejections and sweeps, thereby reducing the turbulent contribution to the wall-shear-stress. The transverse shear strain above the sinusoidal microgroove is demonstrated to be resembling a Stokes spatial layer (SSL), by comparing with existing analytical solutions of strain profiles for wall-forcing by spanwise spatial oscillations. © 2019 Elsevier Ltd

GENERAL

Computers and Fluids

Enhanced drag-reduction over superhydrophobic surfaces with sinusoidal textures: A DNS study

Ratcheting is a phenomenon of the progressive accumulation of permanent strain under stress controlled cyclic loading. In cyclic loading at very high stresses, large permanent strains get accumulated in certain materials leading to ductile failure in material, which is different from a usual fatigue fracture. To apprehend the behaviour of 15CDV6 steel in repeated stress control tensile loading at high-stress levels, experiments were carried out on uniaxial flat specimens. Different test parameters were varied to see their effect over the ratcheting behaviour of the material. Significant time-dependent inelastic behaviour is seen in the material and strain hardening during loading-unloading-reloading is prominent in the material. No fracture is observed during the entire test exercise. © 2019 Author(s).

AIP Conference Proceedings

Behaviour of a high strength low alloy steel under extreme repeated loading

A stress-state dependent cohesive model for fatigue is used for modelling mode-I crack initiation and growth. Plane strain simulations with cohesive elements whose traction-separation behaviour incorporates the stress-state of neighbouring continuum elements, were performed and compared with experimental data of an Aluminum alloy (AA 2219-T87). A combination of the fatigue damage model parameters is identified that is able to estimate the experimentally observed crack growth data. Further, a discussion is provided on the specific role of the stress-state on the initiation life and crack growth rates under sub-critical cyclic loading. © 2018 Elsevier Ltd

International Journal of Fatigue

Role of stress-state on initiation and growth of a fatigue crack

PURPOSE: To design a non-patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy. METHODS: We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions. The performance of five different classifiers was analyzed using these features to design an automated detection system. RESULTS: Among the five classifiers, decision tree, k-nearest neighbor, and support vector machine performed with sensitivity (specificity) of 79% (81%), 75% (85%), and 80% (86%), respectively. The other two, linear discriminant algorithm and Naive Bayes classifiers, performed with sensitivity (specificity) of 54% (94%), 47% (96%), respectively. CONCLUSIONS: The support vector machine-based seizure detection system showed better detection capability in terms of sensitivity and specificity measures as compared to linear discriminant algorithm, Naive Bayes, decision tree, and k-nearest neighbor classifiers. CONCLUSIONS: Our study shows that a generalized system to detect the electrical onset of seizures in temporal lobe epilepsy using scalp-recorded EEG is possible. If confirmed on a larger data set, our findings may have significant implications for the management of seizures, especially in patients with drug-resistant epilepsy.

Department Of Civil Engineering

Fulltext

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society

Improved Patient-Independent System for Detection of Electrical Onset of Seizures

We investigated the effects of the chirality of carbon nanotubes (CNTs) on water transport using molecular dynamics simulations. For the study, we considered CNTs with similar diameter and varying chiralities, obtained by altering the chiral indices (n,m) of the nanotubes. The tubes with an armchair (n = m) structure show the maximum streaming velocity, flux, flow rate enhancement and slip length, whereas the corresponding values are lower for chiral (n ≠ m) tubes, and are the lowest in zigzag (m = 0) CNTs. The difference in flow rates with varying tube structures can be primarily attributed to the alteration in potential energy landscape experienced by the water molecules, leading to changes in the friction coefficient at the fluid-solid interface. The water molecules experienced the least resistance to flow in an armchair tube, while the force exerted by the CNT surface on the water molecules increased monotonically with the change in the CNT type to chiral and then to zigzag. The chirality effects on water transport are, however, found to decrease with an increase in tube diameter. Furthermore, an analysis of the influence of the CNT type on ion (Na + or Cl - ) transport in water-filled CNTs showed the interaction energy of ions with water to be much higher than that with the CNT surface, demonstrating minimal dependence of ion transport on the chiral structure. Hence, the tube chirality should be considered an ineludible factor in controlling the water transport through CNTs and in the designing of novel devices in nanotechnology. © 2019 the Owner Societies.

Physical Chemistry Chemical Physics

Water flow in carbon nanotubes: The role of tube chirality

During polymer flooding in oil reservoir, miscible and immiscible viscous fingering can occur at the front and backside of the polymer bank, respectively. Polymer dispersion and adsorption control the polymer concentration profile and water viscosity. So transport parameters should affect immiscible viscous instability between the oil and polymer banks. Earlier the effects of transport parameters on the miscible viscous instability and the effects of capillarity and relative permeability on immiscible viscous instability were studied. In this paper the effects of dispersivity and nonlinear adsorption on the growth of immiscible viscous fingers are discussed. We modeled equilibrium adsorption using a Langmuir isotherm. The pattern and growth of miscible and immiscible viscous fingers for different transport parameters were compared using the root mean square of unstable interfaces. We have found that like miscible viscous instability, the immiscible fingering is also less for higher longitudinal dispersivity. For larger transverse dispersivity, miscible viscous fingering is almost suppressed but immiscible fingers of large wavelength still grow. Adsorption does not affect miscible viscous instability behind the polymer bank, but it reduces the width of polymer bank. When the adsorption is very high, the miscible viscous fingers can puncture the polymer bank and reach the oil bank. In the case of strong nonlinear adsorption (large value of B in the Langmuir isotherm), the saturation profile is very sharp and immiscible viscous fingers grow fast. For linear adsorption or small B, the saturation front is diffused and immiscible viscous instability is greatly suppressed. So the linear adsorption model underpredicts the growth of the immiscible viscous finger. © 2019 by Begell House, Inc.

Journal of Porous Media

Effects of polymer dispersion and adsorption on immiscible and miscible viscous instabilities during chemical enhanced oil recovery

Magnetorheological (MR) gels are a class of smart materials whose mechanical characteristics can be modified by the application of an external magnetic field. This paper aims at the investigation of the effect of the size of spherically shaped particles on the magnetorheological gels and the significance of its influence on the properties of the MR gels. MR gels are prepared using magnetite (Fe3O4) nanoparticles and/or carbonyl iron (CI) microparticles dispersed in a polymer gel. A free decay test apparatus built in-house is used to obtain the magnetic field dependent shear response of the gels under dynamic conditions at room temperature and the results are compared for varying sizes of particles. The results indicate a clear trend of increase in storage modulus of the magnetic gel with the decrease in the size of the particles under same conditions of magnetic field and mass fraction. For example, the increase in the shear storage moduli of the magnetic gel, prepared with a mass fraction of 20% of carbonyl iron particles of sizes 3.4 μm and 1.4 μm increase by 16.3% and 18% respectively of the zero field moduli, while the gel with the same mass fraction of magnetite nanoparticles increases to 28%. © 2019 Elsevier B.V.

Journal of Magnetism and Magnetic Materials

Significance of particle size on the improved performance of magnetorheological gels

Objective. This study introduces and evaluates a novel target identification method, latent common source extraction (LCSE), that uses subject-specific training data for the enhancement of detection of steady-state visual evoked potential (SSVEP). Approach. LCSE seeks to construct a common latent representation of the SSVEP signal subspace that is stable across multiple trials of electroencephalographic (EEG) data. The spatial filter thus obtained improves the signal-to-noise ratio (SNR) of the SSVEP components by removing nuisance signals that are irrelevant to the generalized signal representation learnt from the given data. In this study a comparison of SSVEP identification performance between the proposed method, extended canonical correlation analysis (ExtCCA) and multiset canonical correlation analysis (MsetCCA) was conducted using SSVEP benchmark data of 40 targets recorded from 35 subjects to validate the effectiveness of the LCSE framework. Main results. The results indicate that the LCSE framework significantly outperforms the other two methods in terms of both classification accuracy and information transfer rates (ITRs). Significance. The significant improvement in the target identification performance demonstrates that the proposed LCSE method can be seen as a promising potential candidate for efficient SSVEP detection in brain-computer interface (BCI) systems. © 2019 IOP Publishing Ltd.

Journal of Neural Engineering

Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain-computer interfaces

In recent decades, motor imagery (MI) based brain computer interface (BCI) is frequently used to control the external devices, but it has limited applications because of its lower classification accuracy (CA). The classification accuracy depends on the feature extraction techniques which is a challenging problem in the field of MI based BCI systems. In this paper, an efficient feature extraction and classification technique is proposed for the detection of multi-class (left hand, right hand, tongue and foot) MI movements with improved classification accuracy. The proposed phase space reconstruction (PSR) technique extracted efficient features during MI activities and the support vector machine (SVM) classifier was used to classify multi-class MI movements. The proposed technique and the classifier are tested on BCI competition-III (2005) dataset-IIIa which contains four-class of MI movements of the subjects. The results showed that the proposed technique improved the classification accuracy of MI signal and has better performance (%CA = 80.86% and Cohen's kappa coefficient (K)= 0.72) compared to several state-of-the-art techniques. © 2019 IEEE.

International IEEE/EMBS Conference on Neural Engineering, NER

Detection of Motor Imagery Movements Based on the Features of Phase Space Reconstruction

An attempt has been made in this Letter to analyse term (week of gestation (WOG) >37) and preterm (WOG ≤ 37) conditions using uterine electromyography (uEMG) signals and generalised Hurst exponent (GHE) features. For this analysis, public database signals recorded from the surface of abdomen are considered. Multifractal detrended fluctuation analysis is performed on the signals and the GHE is calculated. From the exponent, seven features are extracted and data-balancing based on synthetic minority over-sampling technique is used to retain a balanced feature contribution by the term and preterm records. Two classification algorithms namely, Naive Bayes and logistic regression (LR) are employed to classify the signals. Ten-fold cross validation approach is executed and the performance is validated using accuracy, precision and recall. The results show the uEMG signals exhibit multifractal characteristics and five GHE features are significant in distinguishing the term and preterm uEMG signals. The LR classifier gives the highest accuracy of 97.8%. Therefore, it appears that the multifractal Hurst exponent features in combination with LR classifier can be used as biomarkers for predicting the preterm or term delivery during the early stage of gestation. © 2019 The Institution of Engineering and Technology.