Faculty Publications
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Item 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.Item Experimental investigation and optimization of performance, emission, and vibro-acoustic parameters of SI engine fueled with n-propanol and gasoline blends using ANN-GA coupled with NSGA3-modified TOPSIS hybrid approach(Elsevier Ltd, 2024) Kirankumar, K.R.; Kumar, G.N.; Kamath, N.; Gangadharan, K.V.In the present study, performance, emission, and vibro-acoustic studies were conducted on a spark ignition (SI) engine fueled with gasoline and an n-propanol blend at variable compression ratio (CR), speed, and propanol blend fraction (PBF). Experimental data were used to model an artificial neural network (ANN) trained with a genetic algorithm (GA). ANN predictive responses were employed to establish regression relationships between brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), oxides of nitrogen (NOx), carbon monoxide (CO), hydrocarbon (HC), resultant vibration acceleration (RVA), and sound pressure level (SPL) with operating parameters using response surface methodology (RSM). These models served as objective functions in the non-dominated sorting genetic algorithm-3 (NSGA3), a multi-objective optimization (MOO) technique, to optimize responses and obtain non-dominated solutions. These solutions were filtered using a modified technique for order preference by similarity to the ideal solution (TOPSIS) to obtain a compromised optimal solution. ANN-GA model outcomes showed high accuracy, with coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.979 to 0.993 and 0.0381 to 0.0643, respectively. NSGA3 coupled with modified TOPSIS identified optimal operating conditions at 1271.77 RPM, a CR of 11.96, and a PBF of 33.26 %. © 2024 Elsevier Ltd