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PRINCE VICTOR JENIES C

Roll Number: AM16D020
PH.D - Applied Mechanics
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Keywords:
DNS, CFD, Numerical study, turbulence, fluid mechanics, jet flows
I am Prince Victor Jenies doing PhD in the Applied Mechanics department (fluid mechanics group).
Skill Set:
Technical Skills: Matlab, CFD, ParaView.
Computational Skills: ANSYS-Fluent, ICEM-CFD, ANSA-meshing, FORTRAN, Matlab, Linux, Latex.
Lab Affiliations:
Center Name: Computational Flow Turbulence and Combustion lab

Abstract:

Effects of a pintle-shaped orifice on a planar turbulent jet flow at Reynolds number 4000, based on the inlet bulk mean velocity and the jet width, are studied using direct numerical simulations. Flapping of the jet along with a low frequency modulation of the Kelvin-Helmholtz (KH) instability, in the presence of a pintle-shaped orifice, is observed. To compare the pintle-jet behaviour, a free-jet is simulated as a reference case. The effects of the near-field region on the far-field flow characteristics have been investigated. In both the cases, the KH instability in the near-field influences the far- field jet, whereas the pintle-jet also exhibits a low-frequency flapping. In addition, oblique vortex pattern has been observed in the case of pintle-jet. The far-field flow statistics of the pintle-jet with a top-hat inlet interestingly agree with those of the free-jet with a hyperbolic tangent inlet. Temporal variation of the jet characteristics have been analysed using spatio-temporal plots. In addition, the large and small scale turbulent motion have been studied using three anisotropic invariant maps (turbulence triangles, eigenvalue and barycentric maps). Moreover, that the barycentric map gives a better visualization of the anisotropic behaviour has been observed in the current study.
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Vinothini S

Roll Number: AM18D005
PH.D - Applied Mechanics
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Keywords:
Biomedical, Healthcare, Signal processing, Image Processing, Video Processing, Vital sign, Machine Learning, Deep Learning, Artificial Intelligence (AI), Python, TensorFlow, Keras, MatLAB, GCP, AWS, Azure, Camera, Raspberry Pi, Arduino, Instrumentation
Research scholar
Skill Set:
Technical Skills: Signal Processing, Image Processing, Object detection, Machine learning, Deep Learning, Research, Arduino, Raspberry Pi
Computational Skills: MatLAB, Python
Lab Affiliations:
Center Name: Non-Invasive Imaging and Diagonstic Laboratory - IIT Madras, Peter L. Reichertz Instituts für Medizinische Informatik - TU Braunschweig

Abstract:

Preterm birth is one of the most critical public health concern, causing maternal and fetal mortality and morbidity. Early identification of preterm birth allows for timely intervention to postpone labor by prescribing appropriate medications and liftstyle interventions. Clinically available medical devices for measurement of uterine contractions are intra uterine pressure catheters, and tocodynamometer. However, these devices suffer from limitations, such as invasiveness, and measurement inaccuracy. In recent research, non-invasive uterine electromyography (uEMG) has been used to assess contractions for detecting premature birth by capturing anomalous changes in the uterus. Analysis of multi-channel uEMG signals has been shown to provide significant information about the underlying synchronous activity of the uterus. This research work attempts to investigate the topological and cyclostationary variations for detecting preterm birth using uEMG signals in different channels. The suitable features are extracted from these signals that characterise the changes in distinguishing term and preterm conditions with varied gestational weeks. Fusion-based approaches are implemented to construct a reliable model. Thus, the proposed approaches characterize the variations in uEMG signals.
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Priyan Bhattacharya

Roll Number: CH16D202
PH.D - Chemical Engineering
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Keywords:
Perfect Adaptation, Near Perfect Adaptation, Control Theoretic Approaches, Systems Theoretic Approaches, Biochemical Networks, Design Principles for adaptation, Systems Biology, Mathematical Biology
I am a final-year integrated Ph.D. (M.S. + Ph.D) research scholar in Control theory and Systems biology. Previously, I obtained my B-Tech in Electronics and Communication Engineering from the West Bengal University of Technology. in my Ph.D., I developed systems and control-theoretic approaches to identify adaptive biological networks. I am interested in research-oriented industry positions concerning Control theory, Systems theory, and Systems and Mathematical Biology.
Skill Set:
Technical Skills: Applied Mathematics, Control Theory, Systems Biology, Time Series Forecasting, Analytical thinking.
Computational Skills: MATLAB R Python
Lab Affiliations:
Center Name: Initiative fir Biology and Sestems mEdicine (IBSE)

Abstract:

Theoretical investigations to understand the emergence of any biological property require unraveling the specific interconnections (design principles) among the constituents of the underlying biological network. Adaptation, the tendency of every living organism to regulate its essential activities in the presence of environmental fluctuations, is one such important property that has gained significant attention in the field of systems and synthetic biology. In this work, we present generic methodologies inspired by systems theory to discover the design principles for adaptation in four different contexts– i) local, perfect adaptation for deterministic disturbance ii) robust adaptation for deterministic disturbance, iii) local adaptation in the presence of noise, and iv) global, perfect adaptation for a deterministic disturbance with arbitrary magnitude. In all the cases, firstly, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as design requirements for the underlying networks. Thus, contrary to the existing approaches, the proposed methodologies provide an exhaustive set of admissible network structures without resorting to computationally burdensome brute-force techniques. Further, the proposed frameworks do not assume any prior knowledge about the particular rate kinetics, thereby rendering the conclusions generalizable to a wide range of rate kinetics. The proposed method for the first case reveals that a network of any size requires the presence of either a specific class of negative feedback or a feedforward structure with incoherency between the forward paths from the input to the output node to produce a perfectly adaptive response in the presence of a small step-type disturbance. In the second scenario, we show that networks with a particular class of negative feedback can provide robust, perfect adaptation. Interestingly, we also proved that adding negative feedback loops to an incoherent feed-forward motif significantly betters the performance in imperfect adaptation. Further, in the third scenario, we argue that the structural requirements for perfect adaptation and noise filtering are in contradiction for a specific class of locally adaptive networks. To circumvent this bottleneck, we propose a sequential technique for designing networks that can achieve local, perfect adaptation with increased (compared to the pure adaptive module) output Signal to Noise Ratio (SNR). Finally, we propose a nonlinear systems theory-driven approach that attempts to find out the network structures admissible for perfect adaptation when subjected to external disturbances of arbitrary magnitude. We showed that the set of globally adaptive network structures serves as a strict subset of the set of locally adaptive networks. Contrary to the inferences drawn by our analysis in the linear domain, we show that the incoherent feed-forward module is indeed prone to loss of modularity, therefore, likely to show retroactivity in all practical scenarios. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic, and robust framework for designing various kinds of biological networks.
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Puneet Siwach

Roll Number: CH17D013
PH.D - Chemical Engineering
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Keywords:
Electrochemistry, Photoelectrochemistry, Light Emitting Materials, Semiconductor materials, Optoelectronics, Halide Perovskites
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Lab Affiliations:
Center Name: Solar Energy Research Group (SERG), Chemical Engineering Laboratory

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Fredy Francis

Roll Number: EE14D020
PH.D - Electrical Engineering
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Keywords:
Optical Communication, Repeaters, Regenerators, EDFA, Multihop links. FSO
Fredy Francis received the B. Tech degree in Electronics and Communication from Viswajyothi College of Engineering and Technology, Vazhakulam, India, in 2012 and M. Tech degree in OptoElectronics and Communication Systems from Government Model Engineering College, Kochi, India, in 2014. He is currently pursuing Ph.D. degree in Electrical Engineering from the Indian Institute of Technology-Madras, Chennai, India. His research interests include optical communication, amplifiers, FSO, single photon sources, and microwave photonics. He was a member and held leadership roles in IEEE and OSA.
Skill Set:
Technical Skills: Optical Links noise Modeling
Computational Skills: MATLAB, Python, C, C++, OptiSystem, Lumerical, OptiLux, VHDL, Java, Assembly Languages (8085, 8051, PIC), QtOctave, OptSim, OptiSPICE, APSS, Latex
Lab Affiliations:
Center Name: Optical Communication Engineering and Networking Lab (OCEAN LAB)

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Roll Number: EE17D035
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Keywords:
Skilled with microelectronics engineering with clear understanding of semiconductor device physics, possessing expertise in fabrication and characterization and data-analysis of semiconductor devices.
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Anjith Kumar

Roll Number: ME15D201
PH.D - Mechanical Engineering
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Keywords:
Fluid dynamics, Atomization, Data Science, Machine Learning, Project management
I am a data scientist and researcher with experience in developing an atomizer to produce uniform spray distribution and investigating physical phenomena involved in jet break-up. I have presented my work at conferences and published in Atomization and Sprays journal. I have also worked on personal projects involving customer segmentation using K-means clustering and stock price prediction using ARIMA and LSTM models. I am skilled in feature engineering, technical analysis, and statistical modeling. As a research fellow at IIT-Madras, I conducted experiments on impacting-freezing dynamics of water droplets and investigated condensation and boiling flow patterns in micro/mini-channels. I have experience in designing injectors, developing test rigs, and processing flow images using MATLAB. In my previous role, I modified the geometry of a rotary atomizer for spray simulations and analyzed the spray characteristics for three different liquids. I have also held positions of responsibility as Secretary of KSAA and Captain of the School Cricket Team. Overall, I am passionate about applying data science techniques to solve complex problems and am committed to delivering high-quality research and analysis
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Technical Skills:
Computational Skills: Python, SQL, MATLAB, SolidWorks, Ansys Fluent
Lab Affiliations:
Center Name: National Centre for Combustion Research and Development

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Ajay Kumar Jaiswal

Roll Number: ME17D039
PH.D - Mechanical Engineering
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Shaikh Sufyan Muneer Ahmed

Roll Number: MM17D202
PH.D - Metallurgical and Materials Engineering
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Keywords:
materials research, materials development, alloy development, high-temperature materials, metallurgy, materials characterization, product development, project management, failure analysis, research, Process, Structure, Property Analysis, metals and alloys, benchmarking, cost benefit analysis, problem solving, technical discussions, MATLAB, physical metallurgy, supplier development, cross functional collaboration, collaboration, interpersonal skill, SEM, EDS, atomistic modeling, optimization, superalloys, high entropy alloys, ductility, deformability, mechanical property, modeling, modelling, materials modeling, materials modelling, thermodynamics, Laboratory Management, Liaisoning, Scanning Electron Microscopy, Methods Development, Cross-functional Collaboration, Optical Microscopy, Heat Treatment, Interpersonal skill, Materials Structure Analysis, Product Benchmarking, Problem Solving, Structure-Property Correlation, Technical Report Writing, Cost-Benefit Analysis
I am developing alloys for high-temperature structural applications using ab-initio atomistic modeling. My PhD thesis is on understanding the effect of alloying elements on the deformation and strengthening behavior of refractory metal alloys. I have extensive experience in Project Management, Physical Metallurgy, Failure Investigations, and Process-Structure-Property-Performance analysis using Advanced Metallography & Scanning Electron Microscopy. I am a practitioner of First-principles Thinking and Problem Solving. I have 7+ years of diverse experience in Project Management, Team Management, Industrial & Academic Research, and Cross-functional Collaborations. I like solving business problems using the First-principles approach by discussing complex ideas in simple terms with Customers, Suppliers, Internal Teams, and Executive Management. In my previous job at Bekaert India Technical Center, I led a team of 4 materials analysts in the metallurgical characterization of tire cords, circlips, automotive springs, and wire rods using Advanced Metallography and Scanning Electron Microscope equipped with Energy Dispersive Spectroscopy (SEM-EDS). I have been trained in microstructural analysis in Belgium and China. I have also been trained in process engineering in India, the Czech Republic, and Indonesia. I have successfully renewed the IATF 16949 certification of the Materials Lab, led more than 52 materials investigations, managed a lab with an annual budget of about INR 2.2 million, worked on cross-functional projects with teams from 5 countries, and led a team of 4 analysts. My frequent interactions with the Indian Steelmakers/Suppliers, Customers, Product Development teams, Purchase Teams, and my international counterparts from 5 countries have improved my Communication & Presentation skills, Problem-solving skills, and Persuasion skills. I believe the next revolution in Materials Development will happen at the confluence of Physics, Computer Science, and Materials Science & Engineering, with Materials Informatics and Integrated Computational Materials Engineering (ICME) accelerating the Materials Development cycles.
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Center Name: Materials Design Laboratory

Abstract:

A common strategy to ductilize refractory metals and alloys is to decrease their valence electron concentration (VEC). Many of the earlier studies which used the VEC strategy to ductilize refractory alloys ignored the thermodynamic stability of the resultant alloy. Also, Re addition to ductilize W remains an exception to the low VEC strategy. The present work gives a fundamental explanation of the role of both high- and low-valency elements and enthalpy of formation in ductilizing refractory metals and alloys. We first developed a rule-of-mixture (ROM) based methodology to quickly capture the global trends in refractory high entropy alloys (RHEA). Using the developed method, 252 RHEA are studied on their melting point, density, Young’s modulus, % atomic size difference, VEC, and specific heat at constant pressure and at 1273 K. We found that Ti, Zr, Hf are ductilizing the alloys and making them light; whereas Cr, Mo and W, are reducing the alloys’ ductility and making them heavy. The ROM technique can act as a useful tool to study a large number of alloy systems, without requiring heavy computational resources. In subsequent chapters, we used first-principles density functional theory (DFT) simulations to assess the intrinsic ductility parameter (D) which is the ratio of surface energy and unstable stacking fault energy, enthalpy of formation , root-mean-squared lattice distortion (RLD), and the barrier to slip plane glide of 25 binary, six ternary, three quaternary, and two quinary equiatomic refractory alloys. The errors associated with the unstable stacking fault energy due to the change in the bonding environment are quantified. We show that the enthalpy of formation strongly influences the change in USFE of concentrated refractory alloys as compared to their composition averaged value. This work unravels the role of enthalpy of formation in increasing the ductility of equiatomic binary refractory alloys apart from dictating their thermodynamic stability. Based on the observations from the binary alloys, a methodology has been developed to down-select alloy systems which can prevent the composition explosion as we move from binary to higher-order systems. We found that the large RLD is not exclusive to RHEA alone, and it can occur in binary alloys as well. The RLD may not be sufficient enough to influence the dislocation core, but the enthalpy of formation variation along the dislocation line (due to local chemistry change) can lead to a wavy dislocation . A sufficiently large positive enthalpy for rotation is a primary requirement for intrinsic ductility of RHEA. Therefore, the large enthalpy of formation should be compensated by a sufficiently large entropy for the alloy to remain in single phase.
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SACHIN C N

Roll Number: PH16D045
PH.D - Physics
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