نشریه مهندسی مکانیک مدرس
سال بیست و یکم شماره 8 (امرداد 1400)

With the increasing number of road accidents and driver assistance systems development, the automated vehicles importance has increased more than ever. As the issue of automated vehicles comes up, attending to their safety, and the impact of the other vehicles in traffic flow on their performance dramatically increased. One of the most important problems for automated vehicles is designing and controlling the trajectory regarding the surrounding vehicles in transient dynamic traffic conditions during complicated maneuvers. Although various studies have been performed in the field of lane change in dynamic traffic conditions and even in critical high speed, considering the transient dynamic conditions has been limited to the beginning of the maneuver and no solution has been provided for the surrounding vehicles’ immediate changes during the maneuver. The algorithm presented in this paper is able to design new safe optimized trajectories according to the sudden decisions of the surrounding vehicles during the lane change maneuver, also ensures collision avoidance in the whole maneuver via vehicle’s simultaneous longitudinal and lateral control. After evaluating the decision-making unit’s performance by real driving tests, the presented algorithm is simulated with different scenarios in complicated transient dynamic traffic conditions by using MATLAB software and its desired performance has been proven in the dynamic environment of IPG CarMaker, in the presence of surrounding vehicles.

The Resin transfer molding (RTM) process is a closed mold process to produce composite products in mass production. This method includes various parameters such as mold temperature, vacuum pressure, injection point, fiber volume fraction, injection pressure and so on. It is only by controlling these parameters that parts with the desired surface quality and high properties can be achieved. In this study, the effect of input parameters of resin transfer molding process including vacuum pressure, injection point and mold temperature on mold filling time, curing time, tensile strength and flexural strength of carbon-epoxy composite was investigated. Each of the input parameter has been investigated at three levels using the RSM full factorial method. Finally, after making carbon/epoxy composite samples with this process, their evaluation in terms of filling time, curing time and mechanical properties have been investigated. The results showed that the injection point has a significant effect on the mechanical properties and production volume of the part in the Resin transfer molding process. According to the results, the optimal sample was determined by prioritization mechanical properties with mold temperature parameters of 106.7° C, vacuum pressure of 0.83 bar and injection point B (one resin input in the middle of the part and two vacuum outputs at the end of the part). Tensile strength of the sample was 546.7 MPa, flexural strength was 759.2 MPa, curing time was 7.3 minutes and mold filling time was 8.6 minutes by software.

In internal combustion engines, due to cyclic loading and wear between the upper surface of the piston ring and the upper groove of the piston, the fretting fatigue phenomenon could occur. In this research, the effect of lubrication and the heat treatment on the fretting fatigue behavior of aluminum-matrix nano-composite have been investigated. The fretting fatigue test was performed in fully-reversed loading condition and at the room temperature. In addition, the S-N curve and fretting fatigue properties of piston alloys were obtained. The microstructure and the fracture surface were examined by the optical and microscopy and the field emission scanning electron microscopy. The results showed that the failure behavior was brittle. In addition, the lubrication and the heat treatment improved the fretting fatigue lifetime of the piston alloy.

Composites usage according to their properties such as high strength to weight ratio, high resistance to corrosion and their ability to build complex shapes in different industries are increased, but due to their Vulnerability against unwanted impact loads, their usage has been limited. Relatively higher costs of carrying out low velocity impact (LVI) test and data sampling limit due to short experiment time in one side and adaptation of quasi-static impact (QSI) test results with LVI on the other, convinced researchers to use QSI instead of LVI. This research investigated the effect of different percentage of nanoclay (1%, 3%, 5% and 7%) on impact properties of glass-epoxy composite. For this purpose, QSI test was used to forecast this nano-composite’s behavior. To disperse and distribute nanoclay homogeneously inside the resin, mechanical and ultrasonic mixers have been used; EDAX photograph token from nano-resin section confirmed the success of this process. QSI test results showed that adding 3% nanoclay to glass-epoxy composite, increases absorbed energy up to 16% and stiffness up to 12%. It was also determined by perusing SEM photographs that specimens containing 7% nanoclay had a decreased in mechanical properties due to adhesion of nanoparticles.

New generation of wind turbines, in comparison to the old versions, have been designed with colossal blades to produce larger amount of power output. However, this has led into some unpredictable challenges including their construction procedure and expenses and particularly blades’ transportation. To overcome these issues, multi-rotor wind turbines have been suggested. Aerodynamic performances of such turbines have been previously assessed by other investigators. However, the wake characteristics of these turbines have been less studied. The focus of the present research is on the assessment of these characteristics, which are crucial in the process of any wind farm design. For this purpose, wake flow of a small three-rotor wind turbine is numerically simulated using computational fluid dynamics. A numerical simulation has been conducted for a single-rotor wind turbine and three-rotor small horizontal axis wind turbine with the angle of 180⸰ arrangement. The results of single rotor wind turbine indicated that far downstream wake extended up to 8D, with Jensen-Gaussian model can be better predicted. The comparison between three bladed wind turbine and the results of wake models for the equivalent turbine showed that because of wake interactions in the downstream of the rotor, the loss of turbulent kinetic energy and recovery of the stream speed will be faster. As a result, in the wind farms, the turbines in closer distances around 4D of the equivalent signle-rotor wind turbine can be installed.

To minimize the cost of maintenance and repair of rotating industrial equipment, one of the methods used is condition monitoring by sound analysis. This study was performed to diagnose the fault of a single-phase electric motor through machine learning method aiming to monitor its situation by sound analysis. Test conditions included healthy state, bearing failure, shaft imbalance and shaft wear at two speeds of 500 and 1400 rpm. A microphone was installed on the electric motor to record data. After data acquisition, signal processing and statistical analysis, the best characteristics were selected by PCA method and then the data were clustered by machine learning method and K mean algorithm. These features used in the ANFIS modeling process were common features selected in both electromotor speed situations. After evaluating the models, the best model had the highest accuracy value of 96.82%. The average accuracy was 96.71% for overall fault classification. The results showed that the analysis of acoustic signals and modeling process can be used to diagnose electromotor defects by machine learning method. Based on the obtained results, condition monitoring of the electromotor through acoustic analysis reduces its stop and continues its work process in the industry. The repair costs of the electromotor are reduced by its proper condition monitoring.