Qualification: 
Ph.D, M.Tech
Email: 
js_pradeep@blr.amrita.edu

Dr. Pradeep S. Jakkareddy has joined Amrita fraternity in the Department of Mechanical Engineering as Assistant Professor (Sr. Gr) on 2nd August 2017. He received his Ph. D. from Indian Institute of Technology Madras in 2017. His research interests include Inverse Heat Transfer, Computational Heat transfer, Bayesian inference, Artificial Neural Network, Genetic Algorithm, Liquid crystal thermography, etc.

He has worked as a Teaching Assistant at Indian Institute of Technology-Madras for a period of 4 years. He has served as an Assistant Professor in Department of Mechanical Engineering at S.K.S.V.M.A. College of Engineering and Technology, Karnataka for a period of 3 years. He has been a Project Assistant at Indian Institute of Science (IISc), Bangalore during his M. Tech. project work for a period of 10 months.

To his achievements, Dr. Pradeep had received a fellowship equivalent to Institute Post-Doctoral Fellow at Indian Institute of Technology, Madras during March 2017 to July 2017.

He possesses 4 international conference publications and 2 international publications to his credit, yet another two in communication.

Education

  • Ph.D. in 2017
    From: Indian Institute of Technology Madras, Chennai
  • M.Tech./M.E./M.S. in 2010
    From: P. D. A. College of Engineering, Kalburgi
  • B.Tech./B.E. 2007
    From: S.K.S.V.M.A.C.E.T. Lakshmeshwar

Major Research Interests

  • Inverse Heat Transfer, Experimental Heat Transfer

Certificates, Awards & Recognitions

  • Fellowship equivalent to Institute Post-Doctoral Fellow at IIT Madras, Chennai.
  • Certificate of outstanding contribution in reviewing awarded September 2017, International Journal of Thermal Sciences
  • Certificate of Reviewing awarded since March 2019, International Journal of Heat and Mass Transfer.
  • Member of National advisory committee, International conference on Thermofluids-2020 at KIIT, Bhubaneshwar.

Workshops / Seminars / Conferences Attended

  • Dr. Pradeep S. Jakkareddy has attended a workshop on Thermal Management: An Overview, Challenges, and Solutions”, organized by SVNIT, Surat on November 2 - 7, 2020.
  • Dr. Pradeep S. Jakkareddy has attended a workshop on Introduction to Machine Learning (Seminar)”, organized by MathWorks on October 29, 2020 Via Webex.
  • Dr. Pradeep S. Jakkareddy has attended a workshop on “Next Generation Electronic Systems: Heterogeneous Integration, Thermal and Power Management, Related Machine Learning.(Webinar)”, organized by Co-hosted by Binghamton University, IIT Madras and IIT Ropar during October 6 - 8, 2020 Through Zoom app.

Publications

Publication Type: Conference Paper

Year of Publication Title

2021

A. Rupendra S. Prasad, Krishna, M. Venkata, and Dr. Pradeep S. Jakkareddy, “Natural Convection Heat Transfer on the Strip Heaters Flushed on the Vertical Flat Plate: A Numerical Study”, in Proceedings of International Conference on Thermofluids, Singapore, 2021.[Abstract]


In the present study, a CFD analysis of a vertical plate with heat generating sources subjected to natural convection phenomenon is performed. The effect of each individual strip heater over the flat plate assembly which mimics the cooling of the electronic systems is studied. The geometry, the grid generation and the simulations with appropriate boundary conditions are carried out using COMSOL Multiphysics. Mesh independence test has been carried out. Prior to the actual simulations, the numerical code is validated. A test case of constant temperature vertical plate is simulated and the obtained results, i.e., the average heat transfer coefficient is compared with the standard empirical correlation results. Influence of the strip heaters on the vertical plate is elucidated in terms of temperature, velocity and heat transfer coefficient variations

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Publication Type: Conference Proceedings

Year of Publication Title

2020

S. Gollamudi and Dr. Pradeep S. Jakkareddy, “An Inverse Technique to Estimate the Heat flux of a Slab with Transient Heat Conduction”, ENERGY, MATERIALS SCIENCES & MECHANICAL ENGINEERING – 2020/ Lecture Notes in Mechanical Engineering, Springer/ organized by Department of Mechanical Engineering, NIT Delhi/ Online. 2020.

2020

V. Pradeep Yellala and Dr. Pradeep S. Jakkareddy, “Effect of Perforated pin-fin and Plate-fin Heat Sinks on Heat Transfer Enhancement: A Review of Recent Literature”, The Second International Conference on manufacturing, material science and engineering (ICMMSE 2020)/ AIP/ CMRIT Hyderabad/ Online. 2020.

Publication Type: Journal Article

Year of Publication Title

2020

S. Kumar, Dr. Pradeep S. Jakkareddy, and Balaji, C., “A novel method to detect hot spots and estimate strengths of discrete heat sources using liquid crystal thermography”, International Journal of Thermal Sciences, vol. 154, 2020.[Abstract]


This paper reports the results of an investigation to solve the inverse problem of estimating the strengths of different strip heat sources embedded in a flat plate under laminar steady-state forced convection and by way of this propose a novel method to detect hot spots from remote measurements. A Bayesian framework is adopted to infer the strength of the heat sources from thermochromic liquid crystal (TLC) temperature measurements. This framework consists of the forward model, the measured data, and the inverse model. The forward model simulates the conjugate three-dimensional heat transfer problem with the specified thermophysical properties, and the boundary conditions. The input data for the forward model is a combination of different heat source strengths, and the output is temperature data obtained at the bottom surface of the cork. The input-output data of the numerical simulations are used to build a proxy or surrogate (artificial neural network, ANN) that acts as a replacement for the actual forward model to increase the computational speed and decrease the computational time while solving the inverse problem. Calibrated thermochromic liquid crystal sheets are attached at the bottom surface of the cork for mapping the temperature data, so that the top surface where the convection takes place is undisturbed. In the inverse model, Bayesian statistics, along with the Gibbs sampling algorithm is adopted for analyzing the posterior distribution to estimate the mean, the maximum a posteriori and the standard deviation of the heat source strengths. Validation and robustness of the inverse methodology have been examined. The estimated heat source values are input to the forward model to determine the hot spot temperatures on the flat plate. This is a key spin-off from the present study, wherein based on temperature measurements at a convenient place, the hot spot in a geometry can be remotely ‘estimated.’ A comparison of the simulated and the measured values of the hot spot temperatures is reported for different flow Reynolds numbers.

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2018

Dr. Pradeep S. Jakkareddy and Balaji, C., “Estimation of Local Heat Transfer Coefficient from Natural Convection Experiments using Liquid Crystal Thermography and Bayesian Method”, Experimental Thermal and Fluid Science, vol. 97, pp. 458 - 467, 2018.[Abstract]


In this work, an inverse methodology is developed for estimating the local heat transfer coefficients on a vertical plate embedded with the three discrete heat sources, under steady state natural convection, with the temperatures measured at the adiabatic surface without disturbing the fluid flow, using simple conduction/surrogate model and Bayesian inference. Liquid crystal thermography (LCT), an optical measurement method based on the colour-temperature relationship of thermochromic liquid crystal sheet (TLC) is used to determine the temperature field of the adiabatic surface. Bayesian framework with Metropolis Hastings-Markov chain Monte Carlo (MH-MCMC) sampling method is considered for exploring the posterior distribution to estimate the parameters in terms of point estimates like mean, Maximum a posteriori (MAP) and standard deviation. A parity plot between simulated (using retrieved parameters) and measured TLC temperatures shows good agreement

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2018

Dr. Pradeep S. Jakkareddy and Balaji, C., “A Non-intrusive Technique to Determine the Spatially Varying Heat Transfer Coefficients in a Flat Plate with Flush Mounted Heat Sources”, International Journal of Thermal Sciences, vol. 131, pp. 144 - 159, 2018.[Abstract]


In this work, a novel experimental technique is developed to estimate spatially varying heat transfer coefficients from a flat plate with flush mounted discrete heat sources, using Bayesian inference with temperature measurements from liquid crystal thermography (LCT) at an adiabatic surface of the plate without disturbing the fluid flow. Steady state, laminar forced convection experiments have been done on a flat Bakelite plate with three identical embedded discrete aluminium heat sources of dimensions 0.16 × 0.06 × 0.015 (l × w × t all in m). The variation of local convective heat transfer coefficient is obtained in the form of a Nusselt number correlation Nu=aReb(x/l)c. This correlation is first developed by limited numerical simulations for two dimensional conjugate convection. With this correlation, a computationally less complex problem of conjugate conduction in the flat plate also known as the forward model is repeatedly solved for various values of 'a', 'b' and 'c' to obtain the temperature distributions at select points on the adiabatic surface using COMSOL. A surrogate model obtained by Artificial Neural Networks (ANN) built upon the data from these simulations then replaces the forward model. This surrogate model is used to drive a Markov Chain Monte Carlo based Metropolis Hastings algorithm to generate the samples to the forward model to solve the inverse problem of getting 'a', 'b' and 'c' from temperature measurements at the adiabatic surface. Bayesian framework is then adopted to compare the experimental and the simulated temperatures to generate posteriors and the mean, maximum a posteriori and standard deviation of the parameters 'a', 'b' and 'c' are estimated. The effect of number of samples and the temperature points on the performance of the estimation process has been reported. Finally, with the retrieved values of 'a', 'b' and 'c' temperature distributions are obtained by solving the conduction problem and these are compared with those actually measured with TLC.

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2017

Dr. Pradeep S. Jakkareddy and Balaji, C., “A methodology to determine boundary conditions from forced convection experiments using liquid crystal thermography”, Heat and Mass Transfer , vol. 53, no. 2, pp. 519-535, 2017.

2016

Dr. Pradeep S. Jakkareddy and Balaji, C., “Estimation of spatially varying heat transfer coefficient from a flat plate with flush mounted heat sources using Bayesian inference”, Journal of Physics: Conference Series, vol. 745, p. 032094, 2016.[Abstract]


This paper employs the Bayesian based Metropolis Hasting - Markov Chain Monte Carlo algorithm to solve inverse heat transfer problem of determining the spatially varying heat transfer coefficient from a flat plate with flush mounted discrete heat sources with measured temperatures at the bottom of the plate. The Nusselt number is assumed to be of the form Nu = aReb(x/l)c. To input reasonable values of ’a’ and ‘b’ into the inverse problem, first limited two dimensional conjugate convection simulations were done with Comsol. Based on the guidance from this different values of ‘a’ and ‘b’ are input to a computationally less complex problem of conjugate conduction in the flat plate (15mm thickness) and temperature distributions at the bottom of the plate which is a more convenient location for measuring the temperatures without disturbing the flow were obtained. Since the goal of this work is to demonstrate the eficiacy of the Bayesian approach to accurately retrieve ‘a’ and ‘b’, numerically generated temperatures with known values of ‘a’ and ‘b’ are treated as ‘surrogate’ experimental data. The inverse problem is then solved by repeatedly using the forward solutions together with the MH-MCMC aprroach. To speed up the estimation, the forward model is replaced by an artificial neural network. The mean, maximum-a-posteriori and standard deviation of the estimated parameters ‘a’ and ‘b’ are reported. The robustness of the proposed method is examined, by synthetically adding noise to the temperatures.

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Courses Taught

  • Gas Dynamics and Jet Propulsion, U.G. 2017-18 odd semester.
  • Heat Transfer, U.G. 2017-18, 2018-19, and 2019-20, 21 even semester.
  • Thermodynamics, U.G. 2018-19, and 2019-20 odd semester.
  • Advanced Heat Transfer, P.G. 2018-19, 2019-20, and 2020-21 odd semester.
  • Design and Optimization of Thermal systems, P.G. 2018-19 even semester.
  • Cooling of Electronic Systems, P.G. 2019-20 even semester.

Student Guidance

Undergraduate Students

Sl. No.

Name of the Student(s)

Topic

Status

Year of Completion

1

1) Prithvi Sanker J

2) Sharath R

3) Saravanan R

A fast inverse method to estimate the thermal conductivity of a square plate(Teflon) under natural convection

Completed

2019

2

1) AKURATHI RUPENDRA SIVA PRASAD

2) THIRUMOORTHI R

3) BHUKKE SIVAVISWAK

4) MEDISETTI VENKATA KRISHNA

Natural convection heat transfer on the strip heaters flushed on the vertical flat plate:

a numerical study

Completed

2020

Postgraduate Students

Sl. No.

Name of the Student(s)

Topic

Status

Year of Completion

1

Maseedu Srikanth

Experimental investigation of linseed oil bio-Diesel blends on a Diesel Engine

Completed

2019

2

Akash Kumar Dey

Applications of Thermoacoustic cooling in chillers

Completed

2019

3

Yellala Venkata Pradeep

Augmentation of heat transfer with and without perforations in the extended surfaces – a review

Completed

2020