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    Multi-objective optimum design of an alpha type Stirling engine using meta-models and co-simulation approach
    (Pergamon-Elsevier Science Ltd, 2021) Yildiz, Cengiz; Bayata, Fatma; Mugan, Ata
    An alpha type Stirling engine was optimized using meta-models considering uninterrupted electric power supply concurrently with natural gas combi boilers at homes during electricity interruptions. To predict and optimize the power and efficiency of the designed Stirling engine, an artificial neural network (ANN) model was trained as a meta-model. The ANN modeling method was used in solving a multi-objective Pareto optimization problem under some constraints to determine the optimum engine parameters. The design parameters were swept volume, hot and cold cylinder temperatures, gas constant, charge pressure and engine operation speed. Feed forward and Levenberg-Marquardt back propagation algorithms were evaluated to determine the best resulting network architecture that was found as 6-12-8-1. Subsequently, the fraction of variance (R-f) value was calculated close to 1 and the absolute mean error percentage (AMEP) was calculated as 6.07%. Trained ANN model was used in solving the multi-objective optimization problem. Using the optimum design parameters, the meta model predicted the power as 73.3 W and efficiency as 32.2%. Co-simulation approach was followed to verify the optimization results, and the nominal power output and corresponding efficiency were calculated using the Schmidt theory and the calibrated 1-D model created by the GT-Suite software that yield respectively, 144.6 W and 85.8 W for the power and 35% and 35.1% for the cycle efficiency. Consequently, the use of an ANN model in solving the associated optimization problem proved itself as a fast, accurate enough and powerful method to find the optimum design parameters and predict the engine performance.
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    The analyses of frictional losses and thermal stresses in a diesel engine piston coated with different thicknesses of thermal barrier films using co-simulation method
    (Sage Publications Ltd, 2023) Bayata, Fatma; Yildiz, Cengiz
    This study comparatively presents the thermal and mechanical effects of different Thermal Barrier Coatings (TBCs) and their thicknesses on the performance of aluminum diesel engine piston by combining Finite Element Analyses (FEA) and Artificial Neural Network (ANN) methods. The piston structure of MWM TbRHS 518S indirect injection six-cylinder diesel engine was modeled. The clustered TBCs (NiCrAlY-Gd2Zr2O7, NiCrAlY-MgO-ZrO2, NiCrAl-Yttria Partially Stabilized Zirconia (YPSZ), and NiCrAlY-La2Zr2O7) were implemented to the related surface of aluminum alloy piston and then static, thermal, and transient structural FEA were conducted for each model. Based on both of the temperature and equivalent stress distributions, NiCrAlY-Gd2Zr2O7 coated model displayed the best performance. Additionally, the effects of top coating thicknesses of TBCs were investigated in the range of 0.1-1.0 mm with 0.1 mm increments in FEAs. The thermally effective top coating thickness was predicted as 0.95 mm for the selected TBC using ANN method. Then the effects of coating thickness on frictional performance were revealed by generating transient structural FE models and utilizing stribeck diagram. The uncoated and 0.95 mm NiCrAlY-Gd2Zr2O7 coated models were adjusted as transient and the related crank angle - dependent in-cylinder combustion pressure data was implemented. The friction force was reduced by at least 15% in NiCrAlY-Gd2Zr2O7 coated model.
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    The effects of design parameters on mechanical failure of Ti-6Al-4V implants using finite element analysis
    (Pergamon-Elsevier Science Ltd, 2020) Bayata, Fatma; Yildiz, Cengiz
    The failure of dental implant is frequently occurred as the result of mechanical complications under daily biting forces. The long-term stability and the success of a dental implant are highly depended upon developing an implant system having high fracture resistance under cyclic biting forces. The design of implant has a crucial role in improving the fracture resistance of implant by decreasing stress concentration around the implant system. In the present study, three-dimensional (3D) models of the internally connected Ti-6Al-4V implant systems were developed in different dimensions and the failure analysis of these implants was performed under real biting forces according to ISO 14801 using Finite Element Analysis (FEA). In these models, the design parameters (implant diameter, abutment diameter, taper angle of abutment, implant wall thickness, etc.) were optimized in order to prevent the failure of implants in long-term use. The implant model exhibiting the best biomechanical behavior was determined and the stress value was decreased to 170.34 MPa (von-Mises stress) in this implant under cyclic biting loads. Besides the high fracture resistance of this model, longer service life was predicted and relatively smoother stress transmission was achieved from the implant to the bone tissues under biting forces. The implant stability was also increased by optimizing implant tightening torques. Additionally, the mechanical behavior of this implant model was investigated under different biting conditions.