Pankaj Dumka

Work place: Department of Mechanical Engineering, Jaypee University of Engineering and Technology, Guna-473226, Madhya Pradesh, India



Research Interests: Engineering Thermodynamics, Computational Fluid Dynamics, Computational Engineering, Solar Water Desalination, Heat & Mass Transfer, Fluid Mechanics


Pankaj Dumka has completed his B. Tech. (Mechanical Engineering) from Inderprastha Engineering College, Ghaziabad in 2007, M. Tech. from Indian Institute of Technology, Kanpur in “Fluids and Thermal Sciences” in 2010, and PhD from Jaypee University of Engineering and Technology, Guna in 2021. He has been working with the Jaypee University of Engineering and Technology as an assistant professor in the Department of Mechanical Engineering since 2011. He has published more than 40 research articles in reputed SCI and SCOPUS indexed Journals. His area of interest includes Thermodynamics, Fluid Dynamics, Computational Fluid Dynamics, Numerical Computations, Python programming, and Solar water desalination.

Author Articles
Modelling Taylor's Table Method for Numerical Differentiation in Python

By Pankaj Dumka Rishika Chauhan Dhananjay R. Mishra

DOI:, Pub. Date: 8 Dec. 2023

In this article, an attempt has been made to explain and model the Taylor table method in Python. A step-by-step algorithm has been developed, and the methodology has been presented for programming. The developed TT_method() function has been tested with the help of four problems, and accurate results have been obtained. The developed function can handle any number of stencils and is capable of producing the results instantaneously. This will eliminate the task of hand calculations and the use can directly focus on the problem solving rather than working hours to descretize the problem.

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Integration based on Monte Carlo Simulation

By Priyanshi Mishra Pramiti Tewari Dhananjay R. Mishra Pankaj Dumka

DOI:, Pub. Date: 8 Aug. 2023

In this short article an attempt has been made to model Monte Carlo simulation to solve integration problems. The Monte Carlo method employs random sampling and the theory of big numbers to generate values that are very close to the integral's true solution. Python programming has been used to implement the developed algorithm for integration. The developed Python functions are tested with the help of six different integration examples which are difficult to solve analytically. It has been observed that that the Monte Carlo simulation has given results which are in good agreement with the exact analytical results.

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