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

2021 ◽  
pp. 146808742110656
Author(s):  
Fatma Bayata ◽  
Cengiz Yildiz
Keyword(s):  
Diesel Engine ◽  
Coating Thickness ◽  
Thermal Stresses ◽  
Equivalent Stress ◽  
Simulation Method ◽  
Thermal Barrier ◽  
Barrier Films ◽  
Engine Piston ◽  
Frictional Performance ◽  
Artificial Neural Network Ann

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.

Study on the Simulation of Temperature Field of Diesel Engine

2014 ◽  
Vol 971-973 ◽  
pp. 752-754
Author(s):  
Ya Nan Wang
Keyword(s):  
Heat Transfer ◽  
Temperature Field ◽  
Steady State ◽  
Diesel Engine ◽  
Simulation Method ◽  
Engine Piston ◽  
Calculation Process

In the case of each parameter Pistons have been basically provided ,to simulate the temperature field of Diesel Engine Piston, detailing the analysis of diesel engine piston transient heat steady state and heat transfer transient of the calculation process, providing a general simulation method of temperature field in general diesel engine piston.

Thermal-Mechanical Effects of Ceramic Thermal Barrier Coatings on Diesel Engine Piston

2001 ◽  
Vol 697 ◽  
Author(s):  
Jesse G. Muchai ◽  
Ajit D. Kelkar ◽  
David E. Klett ◽  
Jagannathan Sankar
Keyword(s):  
Diesel Engine ◽  
Thermal Barrier Coatings ◽  
Coating Thickness ◽  
High Stress ◽  
Thermal Barrier ◽  
Element Analysis ◽  
Simulation Code ◽  
Barrier Coatings ◽  
Thermal Boundary Conditions ◽  
Cycle Simulation

AbstractThe purpose of this paper is to investigate the piston temperature and stress distribution resulting from varying coating thicknesses of Partially Stabilized Zirconia (PSZ) thermal barrier coatings for the performance in diesel engine applications. This analysis is based on the premise that coating thickness affects the heat transfer and temperature distribution in the piston. A gas dynamic engine cycle simulation code was used to obtain thermal boundary conditions on the piston then, a 2-D axisymmetric Finite Element Analysis (FEA) using ANSYS was performed to evaluate the temperature and stress distributions in the piston as a function of coating thickness. Coating thicknesses studied include 0.1, 0.2, 0.3, 0.5, 1.0, 1.5, and 2.0mm. The results indicate increased piston surface temperature with increasing coating thickness. The maximum stress on the coated piston surface was high while the substrate stress was less than the coating yield stress for all coating thicknesses. Further, the analysis showed that the interface stress at all coated conditions is low enough such that no separation of the coating is expected. The FEA results suggest an optimum coating thickness of 0.1 to 1.5 mm for diesel engine application to avoid unduly high stress in the ceramic.

scholarly journals Artificial Intelligence to Analyze the Performance of the Ceramic-Coated Diesel Engine Using Digital Filter Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
P. Nirmala ◽  
G. Ramkumar ◽  
Satyajeet Sahoo ◽  
G. Anitha ◽  
S. Ramesh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diesel Engine ◽  
Polymer Coating ◽  
Alternative Fuels ◽  
Thermal Barrier ◽  
Time Cost ◽  
Swarm Optimization ◽  
Barrier Coating ◽  
Oxygenated Fuel ◽  
Artificial Neural Network Ann

The completeness of oil goods activates the barriers of lack of goods, inequality in the society, and surroundings impoverishment. Avoiding their use overnight and switching to clean electric motors are a challenge. Under all these conditions, researchers can launch their research on alternative fuels for a preeminent solution. Oxygenated fuel additives and thermal barrier coating (TBC) applications are essential to decrease the emission levels of exhaust and improve the performance of the vehicle. The main objective of this research is to analyze the performance of the ceramic-coated diesel engine. The ceramic particles use polymer coating to enhance the functionality and durability. Optimum outcomes are determined using Taguchi method. The impacts of various casting parameters of composites have been examined in detail. PSO-GA (Particle Swarm Optimization and Genetic Algorithm) is utilized to analyze the performance. Using an artificial neural network (ANN), the performance of diesel engine is examined to reduce time, cost, and experimental repetition. Thus, by using the artificial intelligence, the performance of the ceramic-coated diesel engine is analyzed and the polymeric substance and condition in coating ceramic engine is discussed.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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