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

This study examines the performance of the refined time-reversal method (RTRM), which recommends best reconstruction frequency as probing frequency and uses an extended mode of the reconstructed signal for computing the damage/similarity index for damage detection under a varying thermal environment. The experiments were performed on a 3 mm aluminum plate with a block mass-type damage at three excitation frequencies: the best reconstruction frequency, the sweet spot frequency at which only S0 mode is actuated, and an arbitrarily chosen frequency. Measurements were taken at two temperatures, 25∘ C and 50∘ C. The percent similarity between the reconstructed and original input signals showed a consistent decrease with the increase in damage height at the best reconstruction frequency. However, at the other excitation frequencies, the change in the similarity with the increase in damage height did not follow a consistent trend, which can lead to erroneous information about damage size. The percent similarity changes significantly with temperature rise at the sweet spot frequency and the arbitrarily chosen frequency. However, it showed minimal change with temperature rise at the best reconstruction frequency for undamaged and damaged states of different severity, making the RTRM suitable for baseline-free damage detection and sizing even under temperature variations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Lecture Notes in Civil Engineering

Damage Detection Using Refined Time Reversal Method of Lamb Waves Under Varying Temperatures

What makes Total fringe order photoelasticity (TFP) the ideal digital photoelastic technique for industrial applications is the sufficiency of a single colour isochromatic image for fringe order evaluation and the convenience of capturing this image using easily accessible, relatively cheap hardware. In TFP, the colour information in the application image is matched with an experimental colour calibration table. A simple least squares colour matching is insufficient for this due to errors caused by the repetition of colour in the isochromatics. This is rectified through a process called fringe order refinement. Among the existing fringe order refining methods, the combination of – flexible seeding, window search method, and FRSTFP scanning – has proved to be the best choice for solving a variety of problems for the complete model domain. The successful application of this combination requires the right choice of parameter values for its constituent methods. A systematic parametric study of these refining parameters is a lacuna in the literature, which is addressed in this work, by selecting a variety of problems. The study has revealed the interconnection between the refining parameters, which has helped to formulate a set of recommendations for selecting the same. It is found that some of the existing understandings on refining parameters require a relook. The difficulty in refining fringe fields having multiple fringe gradient reversals with a single set of parameters is illustrated, and how this can be overcome by local refinement is brought out. Additionally, a methodology to track the source of refining error is proposed. © 2022 Elsevier Ltd

Optics and Lasers in Engineering

Framework to select refining parameters in Total fringe order photoelasticity (TFP)

Objective: The first objective of this paper is to establish a relationship between brain activities and motor imagery (MI) movements through mapping functions. The second objective is to distinguish left and right hands MI movements of the subjects. Approach: In this paper, the dynamical behavior of brain activities is well explained by the discrete wavelet transform (DWT) and phase space reconstruction (PSR) techniques. The DWT is employed to identify μ and β frequency bands from raw electroencephalogram (EEG) signals. In order to extract rest and MI features and their corresponding brain patterns, a PSR-based common spatial pattern (CSP) technique is applied to both the frequency bands. The obtained MI features used to train a support vector machine (SVM) model to detect MI movements. Results: The proposed method is tested on benchmark BCI competition (II, III and IV) datasets. The mentioned technique yields a higher value of classification accuracy (%CA), Cohen's kappa coefficients (K) and information transfer rate (ITR) in bits per trial. Significance: The μ band is highly responsive than the β band in MI period. The PSR is a powerful graphical technique to investigate the brain activities. © 2022 Elsevier Ltd

Biomedical Signal Processing and Control

Investigation of the dynamical behavior of brain activities during rest and motor imagery movements

Gas hydrate sediments are considered as one of the potential sources of unconventional energy because of their high energy density and presence of abundant subsurface reserves in order to meet the rising global energy demand. Methane gas hydrate sediments generally occur naturally in permafrost regions and in deep-sea continental shelves where the water depth is more than 500 m below the mean sea level. These deep-water sediments have a high concentration of methane hydrates. Efficiently extracting (safe and economical) these unconventional energies will help in mitigating the energy demand of a country. The current study focuses on predicting the geo-mechanical response of gas hydrates using elastic-plastic constitutive relations and compare with the existing elemental experimental results. From the numerical integration of these advanced models, it is observed that the HISS-MH model could simulate realistic material response under triaxial conditions. Further, an investigation is also carried out to understand the effect of hydrate saturation and Over Consolidation Ratio (OCR) on the geo-mechanical response. There is an increase in stiffness, peak stress, stress dilatancy ratio and softening response with the increase in hydrate saturation. The response is due to the enhancement of cementation between soil particles with hydrate saturation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Department Of Civil Engineering

Numerical Study on the Effect of Hydrate Saturation on the Geo-Mechanical Behavior of Gas Hydrate Sediments

Diffuser is a flow device which is used to reduce the velocity of flow. The performance of a diffuser is evaluated using metrics like flow uniformity and pressure recovery. The present work is part of development of a new compact diffuser design that can be incorporated in a heat exchanger for flow distribution. The design concept employs several baffles arranged as frustum of cones with an aerofoil cross-section along the flow direction. The performance enhancement is obtained by optimal positioning of these baffles, and this problem is solved as a numerical optimisation problem. The performance of the diffuser is evaluated using a CFD model, and the same is used for computing the design sensitivity and to perform optimisation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Department of Engineering Design

Department Of Chemical Engineering

Lecture Notes in Mechanical Engineering

Parametric Modelling, Analysis and Design of Compact Diffuser for Heat Exchanger

To ensure safe and efficient operation, operators in process industries have to make timely decisions based on time-varying information. A holistic assessment of operators’ performance is, therefore, challenging. Current approaches to operator performance assessment are subjective and ignore operators’ cognitive behavior. In addition, these cannot be used to predict operators’ expected responses during novel situations that may arise during plant operations. The present study seeks to develop a human digital twin (HDT) that can simulate a control room operator’s behavior, even during various abnormal situations. The HDT has been developed using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. It mimics a human operator as they monitor the process and intervene during abnormal situations. We conducted 426 trials to test the HDT’s ability to handle disturbance rejection tasks. In these simulations, we varied the reward and penalty parameters to provide feedback to the HDT. We validated the HDT using the eye gaze behavior of 10 human subjects who completed 110 similar disturbance rejection tasks as that of the HDT. The results indicate that the HDT exhibits similar gaze behaviors as the human subjects, even when dealing with abnormal situations. These indicate that the HDT’s cognitive capabilities are comparable to those of human operators. As possible applications, the proposed HDT can be used to generate a large database of human behavior during abnormalities which can then be used to spot and rectify flaws in novice operator’s mental models. Additionally, the HDT can also enhance operators’ decision-making during real-time operation. Copyright © 2023 Balaji, Shahab, Srinivasan and Srinivasan.

Fulltext

Frontiers in Human Neuroscience

ACT-R based human digital twin to enhance operators’ performance in process industries

This paper presents a novel hybrid two-element scanner (HyTES) for high-frequency ultrasound imaging. The proposed scanner utilizes a combination of a focused and an unfocused high-frequency transducer elements to improve the quality of reconstructed image. An experimental prototype of the proposed scanner is made to serve as proof-of-concept. The scan data obtained from the proposed scheme is beamformed using the recently proposed non-linear beamformer of F-DewMAS, and the results are compared with state-of-the-art monostatic Delay-And-Sum (DAS) and recently proposed Virtual -source-in-front of the transducer with DAS (VS-DAS) beamforming that uses data from only the focused element. As a minor innovation, Virtual Source beamforming is adapted to Delay-Multiply-And-Sum to develop VS-DMAS, which is utilized to reconstruct two element data and the results are compared with other methods. The proposed scanning technology with F-DewMAS beamforming is able improve the image quality by more than 52 % and 63 % in terms of resolution and by more than 44 % and 57 % in terms of CNR and GCNR compared to the recent VS-DAS and monostatic DAS, respectively. Further, the newly proposed VS-DMAS was able to improve resolution and contrast compared to existing methods. However, the performance in terms of the image quality metrics were not as much as obtained with the proposed HyTES. The proposed technology has the potential to improve the image quality without the need for an array, manufacturing of which is a challenge at High-frequencies. © 2022 IEEE.

IEEE International Ultrasonics Symposium, IUS

A Novel Two-element Scanner for High-frequency Ultrasound Imaging

Operator training is critical to ensure safe operation in safety-critical domains such as chemical process industries. Training enhances the operator's understanding of the process, which is then encapsulated as mental models. Typically, the operator's learning in traditional training programs is assessed using expert judgment or in terms of process- and operator action-based metrics. These assessment schemes, however, ignore the cognitive aspects of learning, such as mental model development and cognitive workload. The HMM-based model proposed in Part 1 offers a systematic way to quantify operators' cognition during abnormalities. In this Part 2, we show that the cognitive behaviors displayed by expert operators can be represented as target values on the HMM's state transitions and emission probability distributions. Further, we propose two axioms of learning that can capture the evolution of the operator's mental models as they learn the causal relationships in the process and gain expertise in handling abnormal situations. We validate the proposed axioms by conducting training experiments involving 10 participants performing 486 tasks. Our results reveal that the axioms can accurately assess the progress of operators' learning. © 2022 Elsevier Ltd

Journal of Loss Prevention in the Process Industries

HMM-based models of control room operator's cognition during process abnormalities. 2. Application to operator training

Fouling in heat exchangers is unavoidable and causes performance degradation; hence, occasional cleaning is essential. An effective cleaning strategy requires knowledge of foulants that can be obtained by laboratory tests. However, these are unaffordable for small and medium-scale industries. Model-based methods have been proposed for foulant characterization. However, they fail when exchangers are under temperature control and varying inlet conditions. This paper proposes a simple methodology to directly estimate foulants' characteristics using excess thermal and hydraulic loads. Specifically, it first uses changes in the pressure drop and mass flowrate vis-à-vis the exchanger's clean state to estimate foulant thickness. Subsequently, the estimated thickness is used along with temperature measurements to estimate the thermal conductivity. The methodology is demonstrated on a heat exchanger in two cases– with fouling only on tube side, and on both shell and tube sides. Our results show that the estimates have an error of less than 3% in all cases. A key advantage of the proposed methodology is that it can accommodate setpoint changes and disturbances that are common in industrial practise. © 2022 Institution of Chemical Engineers

Chemical Engineering Research and Design

A simple model-based methodology to characterize foulants in heat exchangers using excess thermal and hydraulic loads

Fouling degrades the overall efficiency of the heat exchanger networks (HENs), which results in a significant economic loss. The mitigation of fouling in an operational HEN is carried out by optimizing the cleaning schedules of the heat exchangers. Although such approach can save costs, it is subjected to the exact implementation of the optimal cleaning schedule. Usually, the small and medium-scale process industries face difficulties in implementing such solutions due to limited resources, which forces them to rely on suboptimal cleaning schedules, such as postponing or avoiding few cleaning tasks. This work addresses this gap by optimizing the cleaning schedule considering the maintenance resource limitation. Our approach considers a mixed-integer linear programming (MILP) based optimization considering groupings of heat exchangers based on their spatial locations for ease of maintenance The proposed formulation is applied on a HEN with linear and asymptotic fouling, with and without cleaning cost. The results show that the approach can prevent a considerable economic loss, which would incur due to suboptimal cleaning schedules due to resource limitations. © 2022 Elsevier B.V.