Wear Analysis of Top Piston Ring to Reduce Top Ring Reversal Bore Wear

Pachaiyappan, Ilanthirayan; Mohanraj, S.; Kalayarasan, M.

doi:10.24874/ti.2017.39.04.08

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The fate of oil elements of the film layer covered by the piston stroke can be obtained simply considering a shorter simulation time equal to the time at which the piston reaches a specific axial coordinate. An initial oil thickness of 4 μm according to [33] [34] [35]. In order to capture diffusion phenomena related to the oil components, the grid size has been set to 0.1 μm.…”

Section: Diffusion and Evaporation Modelmentioning

confidence: 99%

Application of a One-Dimensional Dilution and Evaporation Lubricant Oil Model to Predict Oil Evaporation under Different Engine Operative Conditions Considering a Large Hydrogen-Fuelled Engine

De Renzis,

Mariani,

Bianchi

et al. 2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">The increasing environmental concern is leading to the need for innovation in the field of internal combustion engines, in order to reduce the carbon footprint. In this context, hydrogen is a possible mid-term solution to be used both in conventional-like internal combustion engines and in fuel cells (for hybridization purposes), thus, hydrogen combustion characteristics must be considered. In particular, the flame of a hydrogen combustion is less subjected to the quenching effect caused by the engine walls in the combustion chamber. Thus, the significant heating up of the thin lubricant layer upon the cylinder liner may lead to its evaporation, possibly and negatively affecting the combustion process, soot production. The authors propose an analysis which aims to address the behavior of different typical engine oils, (SAE0W30, SAE5W30, SAE5W40) under engine thermo-physical conditions considering a large hydrogen-fuelled engine. The operative conditions are obtained by means of simulations through a zero-dimensional engine model in <i>OpenWAM</i> environment. The lubricant oils composition and properties are defined by means of a statistical interference-based optimization approach which identifies the most proper mixture of heavy hydrocarbons as a surrogate of real oils. Then, the mixture is implemented in an in-house developed heat and mass transfer one-dimensional model which accounts for the lubricant oil evaporation and the mutual diffusion between the oil surrogate components. This work aims to test and analyze the response of different lubricant oils to heating and evaporation processes during the compression and combustion stroke of a hydrogen-fuelled internal combustion engine. The behaviour and the properties evolution during the compression and part of the expansion strokes of different lubricant oils in two different engine operative conditions are captured and discussed.</div></div>

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Section: Diffusion and Evaporation Modelmentioning

confidence: 99%

Application of a One-Dimensional Dilution and Evaporation Lubricant Oil Model to Predict Oil Evaporation under Different Engine Operative Conditions Considering a Large Hydrogen-Fuelled Engine

De Renzis,

Mariani,

Bianchi

et al. 2023

SAE Technical Paper Series

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show abstract

“…There are multiple on-site and external methodologies that characterize the surface structure including stylus instruments, white light interferometry [23], scanning electron microscopes, bore measurements, thin layer activation [24] and 3D optical confocal measurements [23,25]. Various methods have been proposed for cylinder liner wear assessment including component mass losses [26], precise coordinate measurements [27], volume losses [28,29] and repeated measurements of component positions [30].…”

Section: Cylinder Liner Wear Assessmentmentioning

confidence: 99%

Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks

Angermann¹,

Haltmeier²,

Laubichler³

et al. 2021

Preprint

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Digitalization offers a large number of promising tools for large internal combustion engines such as condition monitoring or condition-based maintenance. This includes the status evaluation of key engine components such as cylinder liners, whose inner surfaces are subject to constant wear due to their movement relative to the pistons. Existing state-of-theart methods for quantifying wear require disassembly and cutting of the examined liner followed by a high-resolution microscopic surface depth measurement that quantitatively evaluates wear based on bearing load curves (also known as Abbott-Firestone curves). Such reference methods are destructive, time-consuming and costly. The goal of the research presented here is to develop simpler and nondestructive yet reliable and meaningful methods for evaluating wear condition. A deep-learning framework is proposed that allows computation of the surface-representing bearing load curves from reflection RGB images of the liner surface that can be collected with a simple handheld device, without the need to remove and destroy the investigated liner. For this purpose, a convolutional neural network is trained to estimate the bearing load curve of the corresponding depth profile, which in turn can be used for further wear evaluation.

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Monitoring of oil lubrication limits, fuel consumption, and excess CO2 production on civilian vehicles in Mexico

Carrera-Rodríguez

Villegas-Alcaraz

Salazar-Hernández

et al. 2022

Energy

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Wear Analysis of Top Piston Ring to Reduce Top Ring Reversal Bore Wear

Abstract: A B S T R A C T

Cited by 4 publications

References 19 publications

Application of a One-Dimensional Dilution and Evaporation Lubricant Oil Model to Predict Oil Evaporation under Different Engine Operative Conditions Considering a Large Hydrogen-Fuelled Engine

Application of a One-Dimensional Dilution and Evaporation Lubricant Oil Model to Predict Oil Evaporation under Different Engine Operative Conditions Considering a Large Hydrogen-Fuelled Engine

Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks

Monitoring of oil lubrication limits, fuel consumption, and excess CO2 production on civilian vehicles in Mexico

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