Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

This work develops an inverse methodology to jointly estimate the thermal and mass diffusivities of a solute-solvent system. An inverse parameter estimation framework using a combination of evolutionary simulation and optimization algorithms namely Artificial Neural Network (ANN) and Genetic Algorithm (GA) is discussed. The direct problem is first solved by assuming thermal and mass diffusivities as constants and then as functions of temperature and concentration. Synthetic experimental data is generated by sprinkling noise sampled from three probability distributions namely Gaussian, Uniform and Logistic on the data obtained from direct numerical simulations carried out using prescribed values of diffusivities. This data is then fed into the inverse framework to estimate the parameters of interest and systematically quantify the associated uncertainties. In case of diffusivities dependent on temperature and concentration both, the higher order parameters are also jointly retrieved and associated uncertainties are quantified. Three sets of experiments namely diffusion of heat in water due to applied temperature difference, isothermal diffusion of salt solution in fresh water and diffusion of salt solution in fresh water along with simultaneous counter current heat diffusion are performed. The time evolving temperature and concentration fields are captured in the form of an interferogram using a Mach-Zehnder interferometer illuminated by a monochromatic laser source (Helium-Neon laser, λ = 632.8 nm). The temperature and concentration data thus obtained is used to estimate the thermal and mass diffusivities of the solute-solvent system using the inverse framework. The results obtained are compared against literature to validate the proposed parameter estimation methodology. © 2017 Elsevier Masson SAS

Department of Mechanical Engineering

World University root node

International Journal of Thermal Sciences

Joint estimation of thermal and mass diffusivities of a solute-solvent system using ANN-GA based inverse framework

This study presents a methodology to estimate the in-plane thermal conductivity of copper clad board used in electronic applications. Experiments are performed in vacuum environment and an inverse heat conduction problem (IHCP) is solved employing artificial neural networks to estimate the in-plane thermal conductivity. A comparison of estimates of thermal conductivity as obtained by solving the inverse problem using back propagation artificial neural networks trained using two algorithms namely Levenberg-Marquardt and Scaled Conjugate Gradient are presented. © Published under licence by IOP Publishing Ltd.

Fulltext

IOP Conference Series: Materials Science and Engineering

Estimation of in-plane thermal conductivity of copper clad board by inverse analysis using artificial neural networks

A coupled experimental and theoretical study is reported here on friction hydro-pillar processing of AISI 4140 steel, which is a novel solid-state joining technique to repair and fill crack holes in thick-walled components by an external stud. The stud is rotated and forced to fill a crack hole by plastic flow. During the process, frictional heating occurs along the interface of the stud and the wall of crack hole leading to thermal softening of the stud that eases its plastic deformation. The effect of the stud force, its rotational speed and the total processing time on the rate of heat generation and resulting transient temperature field is therefore examined to correlate the processing variables with the joint structure and properties in a systematic and quantitative manner, which is currently scarce in the published literature. The results show that a gentler stud force rate and greater processing time can promote proper filling of the crack hole and facilitate a defect-free joint between the stud and original component. © The Minerals, Metals & Materials Society and ASM International 2018.

Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science

Friction hydro-pillar processing of a high carbon steel: Joint structure and properties

Landform attributes derived from digital elevation models (DEMs) are the most commonly used factors to predict soil depths on hillslopes. However, the selection of an appropriate algorithm to calculate terrain attributes and identify an optimal DEM resolution remains ambiguous. In this study, we propose a method to use high-resolution DEM and spatial soil depth data to obtain terrain attributes and identify the optimal DEM resolution to predict soil depths at hillslope scales. A geophysical method (ground penetrating radar, GPR) was used to investigate the reasons for the optimal DEM resolution findings. Point-scale soil depth from 116 sites and elevation data were collected from two adjacent headwater hillslopes (H1: 0.42 ha and H2: 0.31 ha) at the Hemuqiao hydrological experimental station in Southeast China. The elevation datasets were collected using a total station at a variable spacing level and were then used to derived DEMs at nine spatial resolutions: 0.25, 0.50, 0.75, 1.00, 2.00, 3.50, 5.00, 7.50 and 10.00 m. Nine primary and secondary topographic attributes using the nine spatial resolution DEMs were then derived. Two different algorithms (D8 and D ∞) for calculating contributing areas and related secondary topographic attributes were compared. We used and compared both linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN) models for soil depth prediction. Results demonstrated that the two models performed well for predicting soil depth. Specifically, MLR performed better than the non-linear model of ANNs. Additionally, we found that the multiple-direction algorithm (D ∞) allowed flow divergence and avoided abrupt changes in soil depth predictions (orphan cells) and should be adopted for model construction. The D ∞ algorithm performed better in divergent areas, such as ridges and side slopes, and the D ∞ algorithm also worked well in convergent areas, such as valleys. Moreover, our results demonstrated that moderate (e.g., 2.00 m) resolution topographic attributes, instead of the finest resolution, achieved the best prediction with the lowest root mean square error (RMSE) and mean absolute error (MAE) and the highest values of the coefficient of determination (R2). Moreover, the GPR results indicated that the valley accumulated more soils than side-slope areas, and a sharp increase in soil depth was found in areas adjacent to the valley. Comparing the optimal DEM resolution and valley width obtained by GPR, we found that average valley width (AVW) should be considered a good measure for choosing the optimal DEM resolution for soil depth prediction. © 2017 Elsevier B.V.

Department of Civil Engineering

Catena

Selection of optimal scales for soil depth prediction on headwater hillslopes: A modeling approach

We report hydrogen peroxide (H2O2)sensing using free standing graphene oxide thin films prepared using a cost effective scalable approach. Such sensors may find application in pharmaceutical and food processing industries. © 2018 Author(s).

Department of Physics

AIP Conference Proceedings

Free standing graphene oxide film for hydrogen peroxide sensing

This paper proposes a probabilistic approach for the solution of elastic buckling of columns, involving uncertainties, using stochastic element free Galerkin method. In the present work, modulus of elasticity is modeled as a homogeneous random field. Karhunen-Loeve expansion and shape function method are used to represent random field and their effectiveness is compared in modeling the same in a computationally viable manner. Both Gaussian and non-Gaussian field are considered for the present study. The stochastic eigenvalue problem is solved for first and second moment characteristics of buckling load, using perturbation analysis. Numerical examples of columns with different boundary conditions are solved. Monte Carlo simulation is used as a validation tool. The obtained results are found in good agreement with those obtained by Monte Carlo simulation. © 2017 Elsevier Ltd

Computers and Structures

Stochastic meshfree method for elastic buckling analysis of columns

Department of Mathematics

arXiv preprint arXiv:1807.

A 1-point Quadrature domain of order 1 not biholomorphic to a balanced domain

We show that the structure of proper holomorphic maps between the n-fold symmetric products, n ≥ 2, of a pair of noncompact Riemann surfaces X and Y, provided these are reasonably nice, is very rigid. Specifically, any such map is determined by a proper holomorphic map of X onto Y . This extends existing results concerning bounded planar domains, and is a non-compact analogue of a phenomenon observed in symmetric products of compact Riemann surfaces. Along the way, we also provide a condition for the complete hyperbolicity of all n-fold symmetric products of a noncompact Riemann surface. © 2018 Deutsche Mathematiker Vereinigung.

Documenta Mathematica

Proper holomorphic mappings onto symmetric products of a Riemann surface

In recent years, Inconel 718 is widely used in industries like aerospace, oil and gas, and automobile due to their unique combination of hardness, strength, temperature, and corrosion resistance properties. However, these materials pose challenges during conventional machining due to their high work hardening tendency, poor thermal conductivity, and chemical affinity towards the tool material. Consequently, rapid tool wear during the machining of Inconel 718 results in poor productivity. To overcome these issues, a non-contact hybrid electrical discharge and arc machining (HEDAM) process has been developed to machine such difficult-to-cut materials. Firstly, the design and development of the HEDAM circuit has been discussed. Subsequently, the working principle of the newly developed process has been studied followed by its performance comparison with the conventional EDM process. In addition, the material removal mechanism has also been discussed in detail. Finally, experimental results were reported, where holes were drilled using the developed HEDAM process. The HEDAM process has exhibited significant improvements in terms of material removal rate (MRR) which is around 12 times higher and electrode wear ratio (EWR) that is half when compared to conventional EDM. This high performance HEDAM process resulted in successful and efficient drilling of Inconel 718. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

International Journal of Advanced Manufacturing Technology

On the design and application of hybrid electrical discharge and arc machining process for enhancing drilling performance in Inconel 718

We investigate the evaporation-driven pattern formation in drying drops containing mixtures of polystyrene and soft microgel particles. The well-known coffee-rings that form when drops containing polystyrene particles are dried can be completely undone in the presence of a small quantity of soft colloids. The addition of soft colloids facilitates the adsorption of polystyrene particles to the water-vapor interface leading to a steep increase in their concentration and also imparts viscoelasticity to the interface. Time-resolved video microscopy is used to conclusively show the formation of a gel-like particle-laden interface. The mean square displacement of the polystyrene particles adsorbed to the interface confirms their immobile nature at the interface. This viscoelastic interface almost prevents the bulk flow-assisted migration of polystyrene particles toward the drop edge, leading to the suppression of coffee-ring effect and the formation of uniform particulate deposits. Copyright © 2018 American Chemical Society.