Dhananjay R. Mishra

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



Research Interests:


Dhananjay R. Mishra has obtained his bachelor’s degree (Mechanical Engineering) from Amravati University (M.S.), Master's degree (Production Engineering) in 2007 from BIT, Durg of Pt. Ravi Shankar Shukla University, Raipur (C.G.), and Doctor of Philosophy in Mechanical Engineering from National Institute of Technology Raipur, Raipur, on August 2016. He is working as an Assistant Professor (SG) in the Mechanical Engineering Department of Jaypee University of Engineering and Technology, Guna (M.P.). He has published more than 125 research articles in peer-reviewed international, national journals, and conferences. His area of interest includes Solar thermal Applications, Renewable Energy, Python Programming, Numerical Computations, Internal combustion engines, 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: https://doi.org/10.5815/ijmsc.2023.04.03, 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: https://doi.org/10.5815/ijmsc.2023.03.05, 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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