Suraj Kumar

@iitm.ac.in

Post Doctoral Equivalent Fellow, Department of Mechanical Engineering
Indian Institute of Technology Madras

Suraj Kumar


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https://researchid.co/surajphd
Heat transfer researcher with excellent experience in the field of electronics cooling, thermal design of the spacecraft, nuclear fuel element cooling, and battery thermal management using efficient inverse methods

EDUCATION

MS and Ph.D. (Mechanical Engineering) | Indian Institute of Technology Madras| 2022
B.Tech (Mechanical Engineering) | Netaji Subhash Engineering College, Kolkata | 2016
12th (I.Sc, Math) | College of Commerce Patna | 2011
10th | H S Badarbali Shekhpura | 2009

RESEARCH INTERESTS

Inverse heat transfer, Optimization of thermal energy systems, Machine learning, Thermal management of electronics and
battery
16

Scopus Publications

Scopus Publications

  • A novel approach to estimate the specific heat capacity and volumetric heat generation of AMP20M1HD-A Li-ion pouch battery using MH-MCMC Bayesian inverse methodology
    Jithu J., Kasavajhula Naga Vasista, Suraj Kumar, Balaji Srinivasan, C. Balaji
    Applied Thermal Engineering, 2025
  • A novel method to predict a local Nusselt number profile on the hemispherical surface under air jet impingement cooling
    Suraj Kumar, Veerendra Kumar, B. Premachandran
    Applied Thermal Engineering, 2025
  • An inverse methodology to estimate the orthotropic thermal conductivities of AMP20M1HD-A pouch-type Li-ion battery
    Jithu J., Kasavajhula Naga Vasista, Suraj Kumar, Balaji Srinivasan, C. Balaji
    International Journal of Heat and Mass Transfer, 2025
  • Bayesian inverse estimation of thermophysical and radiative properties of flame-retardant fabrics for enhanced thermal protection in fireproof clothing
    Suraj Kumar, Bhavna Rajput, Apurba Das, Prabal Talukdar
    Thermal Science and Engineering Progress, 2025
  • Accurate heat flux estimation in continuous casting Molds via MH-MCMC Bayesian Inverse Method
    Suraj Kumar, Mohammad Tabish Siddiqui, Suvankar Ganguly, Prabal Talukdar
    Applied Thermal Engineering, 2024
  • An inverse methodology to estimate the thermal properties and heat generation of a Li-ion battery
    Suraj Kumar, Rajesh Akula, C. Balaji
    Applied Thermal Engineering, 2024
  • Estimation of thermophysical properties of a pouch-type Li-ion battery using an inverse methodology
    Jithu J, Kasavajhula Naga Vasista, Suraj Kumar, Balaji Srinivasan, C. Balaji
    Journal of Physics Conference Series, 2024
    The growing popularity of electric vehicles highlights the crucial role of batteries. Effective battery thermal management is crucial for improving performance, reliability, and safety, especially in tropical areas where overheating is a key challenge. This necessitates a comprehensive understanding of the thermophysical properties of batteries. The present study concerns the estimation of the temperature-dependent orthotropic thermophysical properties (kx , ky , kz , cp ) of the active material in a pouch-type Li-ion battery using an inverse methodology. An experimental study is conducted on a commercial AMP20M1HD-A Li-ion battery to measure the surface temperature at various locations using thermocouples. The forward model consists of the three-dimensional unsteady conduction problem and is solved in COMSOL using experimental boundary conditions. The data generated is used to train an Artificial Neural Network, which acts as a replacement for the forward model. The Metropolis Hasting-Markov Chain Monte Carlo algorithm along with the Bayesian inference inverse model is used for analyzing the posterior distribution and the average estimates for thermophysical properties are obtained. The temperature dependence study shows a significant correlation between temperature and battery thermophysical properties. The accuracy of the employed inverse model is validated by obtaining the surface temperature using the estimated thermophysical properties and comparing it with the measured surface temperature.
  • Systematic approach to estimate non-uniform heat generation rate in heat transfer problems using liquid crystal thermography and inverse methodology
    Suraj Kumar, C. Balaji
    Experimental Heat Transfer, 2023
    This paper presents an inverse methodology to estimate the parameters of the non-uniform heat generation (function estimation) within a flat plate assembly using steady-state conjugate heat transfer experiments on the flat plate assembly. Steady-state laminar conjugate forced convection experiments on a flat plate assembly are conducted on a horizontal wind tunnel to estimate the parameters of the non-uniform heat generation within flat plate assembly using the inverse methodology. Bayesian inference based Metropolis Hastings–Markov Chain Monte Carlo (MH–MCMC) algorithm and experimental temperatures are employed in the inverse methodology. The experimental temperatures are measured at convenient locations of the flat plate assembly using liquid crystal thermography. In order to accomplish the retrieval, first, steady-state experiments on only the cork material are conducted to estimate the thermal conductivity of the cork material accurately for use in the estimation of the heat generation rate so that the additional error due to uncertainty in the thermal conductivity of the cork material does not affect our final goal of estimating heat generation rate. Following this, steady-state experiments on the cork setup (consisting of a non-uniform heat generation heater and two symmetric cork plates) are conducted to ascertain the nature of heat generation of the heater using measured temperatures and fundamental rate laws. The priors are generated using coupled artificial neural network (ANN) and Levenberg–Marquardt (LM) algorithm for Bayesian inference. Using the Bayesian inference with priors, the parameters of non-uniform heat generation are then estimated in terms of the mean, maximum a posteriori with standard deviation. Finally, the simulated heat powers and temperatures are estimated with retrieved parameters of the non-uniform heat generation. These compared very well with the measured heat powers and temperatures. Finally, a recipe for solving a practical problem, in which only measured temperatures are available, is provided.
  • Prediction of Orthotropic Thermal Conductivities Using Bayesian-Inference from Experiments under Vacuum Conditions
    Suraj Kumar, Chakravarthy Balaji
    Heat Transfer Engineering, 2023
    This work reports a novel “divide and conquer” approach to estimate the principal thermal conductivities of an orthotropic material, specifically engineered with a view to demonstrate the potency of the inverse heat transfer method with unsteady temperature data. The sample is placed in a vacuum chamber maintained at a pressure of 8.6×10−7 mbar. The heat capacity of the engineered orthotropic material was determined via estimating the heat capacity of a solid SS304 in a sequential fashion. First steady-state experiments followed by a Bayesian estimation with the Metropolis Hastings-Markov Chain Monte Carlo method were done to obtain the thermal conductivity of a solid SS304 block. Using this as a prior, the heat capacity of solid SS304 was obtained through unsteady experiments followed by Bayesian estimation. The heat capacity of SS304 thus obtained is multiplied by the solidity of the engineered orthotropic material, and using this information, the three components of the orthotropic conductivity are estimated again using the Bayesian route. To expedite the estimation, a surrogate for the forward model was developed using artificial neural network. Finally, the retrieved parameters are used to determine the simulated temperatures through the forward model for the orthotropic material. These, when compared with the measured temperatures, gave excellent agreement.
  • Evaluating wear characteristics of self-lubricating composition for spools used in pneumatic valves
    Sivashankari Palaniswamy, K. Ajaykumar, S. Harish Kumar, K. R. Kavitha, S. Prakash
    Aip Conference Proceedings, 2020
    Aluminum Composite materials are mainly used for the application of light weight material in many aspects, the main application is considered for the front and rear spools in automobile. Apart from automobile field it is also used in various structural application and valves of transportation. The Aluminum Composites is to increase the strength and life span of the components. Basically, Most of the Aluminum Alloy does not satisfactory wear resistance. The focus of this research paper is to select a suitable Aluminum Alloy and composition that compensates high strength, wear resistance, and self-lubricating property. It is noted that spools are manufactured by using stainless steel which are more weight when compared to Aluminum alloy. Aluminum Alloy is reinforced with the composite material Molybdenum-Di-Sulphide which has a low friction, very good self-lubrication system in it. In this work, influence of Molybdenum-Di-Sulphide (MoS2) is added to increase the performance of the spools using Aluminum Alloy-7075 was studied. Stir casting technique is used to fabricate the hybrid composite with AA7075 with Molybdenum-Di-Sulphide (MoS2) at varying weight fractions such as 3%, 6% and 9% for improving wear properties. The Mechanical characteristics and Wear characteristics are analyzed to prove the strength and life span of the material composition. The wear and friction tests yield good results at higher sliding speed, low load and high composition. The main motive of this project is to increase the wear resistance and life time of the spools.
  • A novel method to detect hot spots and estimate strengths of discrete heat sources using liquid crystal thermography
    Suraj Kumar, Pradeep S. Jakkareddy, C. Balaji
    International Journal of Thermal Sciences, 2020
  • Design and thermal validation of four wheeler disc brake using different material
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Computational analysis of aerodynamic characteristics of dimple airfoil NACA 2412 at various angles of attack
    International Journal of Mechanical Engineering and Technology, 2018
  • A Markov Chain Monte Carlo-Metropolis Hastings Approach for the Simultaneous Estimation of Heat Generation and Heat Transfer Coefficient from a Teflon Cylinder
    Harsha Kumar, Sharath Kumar, Nagarajan Gnanasekaran, Chakravarthy Balaji
    Heat Transfer Engineering, 2018
  • A neural network based method for estimation of heat generation from a teflon cylinder
    Nagarajan NagarajanGnanasekaran, Sharath Kumar, Harsha Kumar
    Frontiers in Heat and Mass Transfer, 2016
  • Synergestic approach for the simultaneous estimation of heat transfer coefficient and heat flux using fin from steady state heat transfer experiments
    Harsha Kumar, Sharath Kumar, K. Srinivasa Sagar, Gnanasekaran N.
    International Symposium on Advances in Computational Heat Transfer, 2015