Yusuf Karabacak

In this study, was focused on determining the optimum alcohol mixtures of gasoline iso-propanol and isoamyl alcohol mixtures according to minimum exhaust emission and BSFC (Brake Specific Fuel Consumption) and maximum CGP (Cylinder Gas Pressure) parameters. In the optimization study, 3 different iso-propanol (vol. 10, 20 and 30) and 3 different iso-amyl (vol. 5, 10 and 20) rate were used as input parameters at maximum power and torque speed. The experimental study, it was performed in a single-cylinder air-cooled, spark-ignition engine (SI), at full throttle position, maximum power speed (3600 min-1) and maximum torque speed (2400 min-1). In the optimization study, ANOVA supported RSM (Response Surface Methodology) and CCD (Central Composite Design) were used as the experimental design. In the results obtained, it was determined as an effective parameter for BSFC of engine speed, and for CGP were effective also alcohol types along with engine speed. As a result of the optimization, as the optimum operating parameters were determined as 3600 min-1 engine speed, 27.7778% iso-propanol and 13.9394% iso-amyl alcohol. In the confirmation tests, the error rates were obtained as 3.36%, 3.45%, 9.81%, 4.76% and 4.67% for BSFC, CGP, HC, CO and NOX, respectively.

Effectively engaging learners in an online learning environment is a crucial component of instructional design that contributes to improving performance. Gamification, a contemporary instructional strategy, seeks to integrate game elements such as leaderboards into non-game contexts with the aim of increasing learner engagement and performance. This quasi-experimental study explores the impact of integrating leaderboards as a gamification element into formative assessment on learner achievement and engagement in an engineering course. Conducted over eight weeks in the Mechatronics Technology Department of a public university in Türkiye, the study involved 159 s-year engineering students. Using a pretest-posttest control group design, the intervention included a pre-test in the first week, six weeks of instruction and formative online assessment, and a post-test in the final week. Analyzing the data using descriptive and inferential statistics, the results of the study show a positive correlation between the incorporation of a leaderboard as a gamification element into formative assessment procedures within an online platform and improved learner achievement and engagement. However, it is noted that gamification may not sustain learners’ long-term attention. Therefore, instructors are advised to carefully consider time and retention concerns when designing or adopting gamified learning opportunities.

Because of the Magnetic Levitation System's (MLS) low energy consumption and little friction two factors that are deemed crucial for unstable and nonlinear systems MLS research is now being conducted in the engineering area. This article discusses the comparison of the performance of control theories used by applying advanced control theories to cope with the complexity of the structure and controllability difficulties of MLS. The control methods compared are Proportional–Integral–Derivative (PID) and Self-Tuning Fuzzy PID (STFPID) methods. These methods were developed in the MATLAB environment. The MLS model created in the MATLAB environment was subjected to the suggested control methods, and the outcomes were compared. The outcomes unequivocally demonstrate that MLS location control may make use of PID and STFPID techniques. Four criteria were used to compare the developed control approaches performances. These are the criteria; rise time, settling time, percent maximum overshoot and overshoot value. It is clearly seen in the results that the STFPID control method provides control of the MLS with greater stability than the PID control method.

In this study, an artificial neural network (ANN) was used to estimated the performance and exhaust emission parameters of a diesel engine running on diesel, biodiesel, and propanol fuel mixtures. In addition, the parameters estimated by ANN were tried determining the optimum operating parameter by using Response Surface Methodology (RSM). In the experimental study, propanol was added in 3 different ratios (5%, 10% and 20%) into 100% diesel, 80% diesel and 20% biodiesel fuel blends. In addition, engine tests, were made at 5 different engine speeds with 400 min-1 intervals between 1000 min-1 and 2600 min-1 revolutions at full load. In addition, HC (Hydrocarbon), CO (Carbon Monoxide), NOX (Nitrogen oxides) and Smoke emissions were measured during in the working. ANN model was developed for estimation of engine output parameters depending on fuel mixture ratios and engine speed. In the ANN results, the regression coefficients (R2) of the proposed model were found to be between 0.924 and 0.99. When the obtained ANN results were compared with the experimental results, it was seen that the maximum mean relative error (MRE) was 6.895%. It has been shown that the applied model can predict with a low error rate. The RSM results showed that the optimum operating parameters were 2034-min-1 engine speed, 74.667% diesel, 11.36% biodiesel and 15% propanol fuel mixture. In addition, in the validation tests of the model where the desirability was 0.7833%, the highest error rate was obtained as 7.37% as a result of NOX. As a result of the study, it was seen that RSM supported ANN is a good method for estimating diesel engine parameters working with diesel/biodiesel/propanol mixtures and determining optimum operating parameters.

Magnetic Levitation System (MLS) has become a current study in the field of engineering due to its advantages such as low energy consumption and minimum friction. MLSs are nonlinear unstable systems. Due to the complexity of the structure and the difficulty of the controls, many advanced control theories can be applied on these systems and the performance of the controllers can be evaluated. In this article, Proportional-Integral-Derivative (PID) and Linear-Quadratic Regulator (LQR) controller methods are applied on MLS mathematically modeled in MATLAB environment. Controller performances were compared in the results found. The results obtained on the applicability of PID and LQR control methods for MLS were evaluated. In addition, the system performance of the controllers was compared with five parameters. These are rise time, settling time, maximum overshoot, overshoot and steady-state error. LQR controller produced great stability and homogeneous response compared to PID controller.

Thanks to their electrochemical structure, batteries are the elements that can store electrical energy and spend on a load when the electrical energy they store is needed. Today, with the widespread use of electrically powered mobile devices, rechargeable batteries have become widespread and battery technologies have developed. With the idea that the latest technology systems and electric vehicles will become widespread in the future, the studies on batteries are increasing day by day. In this study, charge state estimation of Li-ion battery cell used to provide power in many applications was realized by using adaptive neural fuzzy inference system (ANFIS). A Li-ion battery was discharged using variable electrical loads with a battery discharge circuit modeled on MATLAB Simulink and current, voltage, temperature and current power parameters of the battery were selected as input variables. Battery parameters and charge status data obtained from discharge tests using different electrical loads on MATLAB Simulink were used as training and test parameters of neural network. Using the MATLAB ANFIS toolbox, the system was trained with 80% of the battery parameters obtained in the battery discharge experiments and with 20% as testing data, the success performance was interpreted by applying the adaptive neural fuzzy inference system.