Faculty Publications

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Author: search.filters.author.Bhaktha, B.S.

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    Closed-Loop Vector Formulation in Euler’s Complex Numbers for Multi-Loop Planar Mechanisms With N-bars: A Novel Modeling Approach and Algorithm
    (Defense Scientific Information and Documentation Centre, 2023) Rahul, V.M.; Bhaktha, B.S.; Gangadharan, K.V.
    This paper presents a novel iterative algorithm incorporated in a user-friendly GUI for modeling the kinematics of multiple looped N-bar closed-loop mechanisms. Past research works have used custom coding or expensive commercial software to analyze the mechanisms of specific applications. The proposed algorithm focuses on kinematics and offers a quick, easy-to-use, cost-effective solution to analyze a wide range of generic mechanisms, reducing the need for custom coding and lowering computational costs. The algorithm employs algebraic equations, such as solving complex closed-loop vector equations using the Euler form of complex numbers, to simulate and derive the unknowns necessary to characterise any generic closed-loop mechanism. The Python code implemented in the algorithm adapts to various scenarios by utilising available information on the position, velocity, and acceleration variables of the mechanisms. The simulation tool can display real-time color contour plots (RGB color scale) for linear and angular velocities and accelerations, simulate mechanisms with multiple loops and switch configurations, and find inverse mechanisms. The approach for solving multiple loop problems and the algorithm utilized to solve the configurations, methods, equations used and GUI features implementation are all described in this study. The case study considered for a four-bar mechanism indicates a strong agreement between the results obtained from the proposed kinematics-based simulator and ANSYS software. These results demonstrate the simulator’s effectiveness in providing low-cost and user-friendly simulation results for various generic mechanisms involving multiple interconnected loops. © 2023, DESIDOC.
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    Driving Cycle-Based Design Optimization and Experimental Verification of a Switched Reluctance Motor for an E-Rickshaw
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bhaktha, B.S.; Jose, N.; Vamshik, M.; Pitchaimani, J.; Gangadharan, K.V.
    This article deals with the design and optimization of a 2 kW switched reluctance motor (SRM) for an electric rickshaw (E-rickshaw). Previously published research on SRM optimization has mostly focused on the optimization of their design and control variables only at the rated conditions. In electric vehicle (EV) applications, the load operating points (LOPs) of a traction motor are dynamic and spread widely across the torque speed envelope. To enhance their overall performance, it is vital to include them in the design optimization process; therefore, in this article, a novel procedure for implementing the multiobjective design optimization (MODO) of an SRM based on a driving cycle has been demonstrated. Higher starting torque and torque density with reduced electromagnetic losses throughout the driving cycle are established as the design objectives, subject to practical restrictions on current density and slot fill factor. The design objectives have been accurately evaluated through transient finite element analysis (FEA) and a computationally efficient SRM drive model (developed in MATLAB/Simulink) with consideration of the excitation control parameters. Kriging models have been constructed to reduce the computation cost of FEA during the optimization process. Then, a nondominated sorting genetic algorithm II (NSGA II) based multiobjective optimization coupled with the constructed Kriging models is conducted to generate a Pareto front. An optimal design that offers the best balance between the design objectives is selected from the Pareto-optimal set, and the dimensions of corresponding design variables are used to build a prototype. Finally, the static and dynamic performance of the SRM prototype are experimentally evaluated and validated with the FEA simulations. © 2024 IEEE.