International Journal of Automotive and Mechanical Engineering

This paper focuses on the optimization of a crankshaft using ANSYS software in terms of weight and strength. The initial designs of the crankshaft, piston, and connecting rod were created using SolidWorks. The force generated by the gas during the combustion process was calculated to be 12017 N. Next, the SolidWorks assembled system was imported into ADAMS View software for simulation, which revealed a time-variant force of the crankpin of 14049 N. The calculated value was verified with results obtained from analytical calculations, showing a deviation of 0.23%. Finite element analysis was done for the crankshaft using ANSYS transient structural after applying loadings and boundary conditions. The optimization process aimed to minimize the crankshaft's weight while maintaining its strength and durability. The results of the ANSYS simulations showed a weight reduction of 2.5% from the original 2.983 kg to 2.907 kg, while maintaining the required strength and durability. The optimized crankshaft was compared to its original design in terms of fatigue life, weights, and stresses. The maximum von Mises stress was reduced by 16%, shear stress by 3.5%, and deformation by 3.5%, which were validated through analytical calculations. The crankshaft analysis resulted in a significant increase in fatigue life, calculated to be infinite under the given conditions. To conclude, the objective to optimize the crankshaft for performance and efficiency was achieved, demonstrating a 2.5% weight reduction and substantial improvements in fatigue life and stress distribution, proving the effectiveness of ANSYS software for the design optimization process.

This paper presents a formulation and analysis of a low computation Predictive Functional Control (PFC), which is a simplified version of the more advanced Model Predictive Control (MPC) for an Adaptive Cruise Control (ACC) system by using a representation of first order closed-loop transfer function. In this work, a non-linear mathematical model of vehicle longitudinal dynamics is considered as a control plant. Then, a simple Proportional Integral (PI) controller is employed as an inner loop to identify the first-order relationship between its actual and desired trajectory speed according to the reasonable time constant based on the logical response of pedals pressing. To directly control the whole plant, the PFC is formulated as an outer loop to track the desired speed together with the convergence rate based on a user preference while satisfying constraints related to acceleration and safe distancing. Since PFC is formulated based on the first-order transfer function, the prediction and tuning processes are straightforward and specific to this system. The simulation results confirm that the proposed controller managed to track the desired speed while maintaining a comfortable driving response. Besides, the controller also can retain safe distancing during the car following application, even in the presence of unmeasured disturbance. In summary, this framework can avoid the need to formulate an inverse non-linear model that is typically used when deploying a hierarchical control structure to compute the throttle and brake pedals pressing as it has been replaced with an inner loop PI controller. The performance also is comparable yet more conservative due to the simplification. These findings can become a good reference for designing and improving the ACC controller, as the framework can be easily generalized for any type of vehicle for future work.

An optimal wing configuration is crucial for achieving the best performance during various flight phases, including take-off, cruising, and landing. Such configurations also contribute to maximizing the aircraft's cruising range. This study compares the aerodynamic performance of NACA 43018 wings under different conditions: without high-lift devices, with a slotted flap, and with a combination of a leading-edge slat and slotted flap. Numerical simulations were conducted using the k-ɛ Realizable turbulence model at twelve different angles of attack, with a flow speed of 120 m/s. The results demonstrate that multiple-element wings significantly improve aerodynamic performance, particularly at low angles of attack, by reducing the induced drag coefficient and delaying flow separation.

Magnetorheological (MR) actuators represent an important class of semi-active devices that have received extensive investigation and deployment in the field of vibration reduction systems. Their notable features include a significant reduction in energy consumption, along with an impressive tunable range of continuously controllable damping forces. The force output of these devices is a complex function that involves two hysteretic mechanisms: magnetic and mechanical (i.e. hydraulic). While the total hysteresis mechanism of these devices has been the subject of considerable study, comparatively little attention has been paid to their magnetic hysteretic behavior. In this study, the authors examine the behavior of the dual coil MR actuator’s control circuit and attempt to extract the magnetic flux information from the laboratory measurements of electrical signals applied to it. The study is further enhanced by the incorporation of the Bouc-Wen (B-W) hysteretic unit, which serves to replicate the flux-current (or magnetic) hysteretic relationship. The B-W model's parameters are identified through the use of a hybrid algorithm, namely the particle swarm optimization and fmincon hybrid optimizing strategy. It incorporates the advantages of both algorithms, resulting in an average improvement of 0.38% in standard deviation compared to fmincon, across 1A to 5A, when comparing the experimental and simulation data. This strategy is employed to fit the model predictions to the flux data, derived from the reconstructed flux and current in time histories. The findings of the study demonstrate that the B-W model is an effective tool for predicting the variation in magnetic flux in response to an exciting current. The results can be implemented for prototyping or validating a model-based controller for MR actuator systems.

A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.