The paper aims to explore the application of CyclePad in modelling air standard thermodynamic cycles. CyclePad is a powerful software tool designed for the simulation and analysis of various thermodynamic cycles. This paper provides an in-depth investigation into its capabilities and effectiveness in modelling air standard cycles, including the analysis of performance parameters such as efficiency, work output, and heat transfer. To explore the potential of CyclePad, Carnot, Otto, Stirling, Ericsson, Diesel, and Dual cycles were explored first thermodynamically and then modelled using the software. These cycles were tested against practical numerical problems, and it has been observed that the results obtained from the CyclePad are in agreement with the existing literature. Moreover, to understand the impact of input parameters on the performance of cycle output and efficiency sensitivity analysis was performed and reported. The results obtained are very encouraging and stem from the fact the CyclePad can be used effectively to understand and analysis any thermodynamic cycle (both open and close) having any level of complexity.

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.

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.