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

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.

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Numerical Investigation of the Effect of Thermal Barrier Coating on Combustion and Emissions in a Diesel Engine

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2020.116497 ◽

2020 ◽

pp. 116497

Author(s):

Yang Wang ◽

Tianyu Ma ◽

Long Liu ◽

Mingfa Yao

Keyword(s):

Diesel Engine ◽

Numerical Investigation ◽

Thermal Barrier Coating ◽

Thermal Barrier ◽

Barrier Coating

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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

International Journal of Engine Research ◽

10.1177/14680874211065637 ◽

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.

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