Lubrication Engineers Research Articles

Calculation of the required minimum of a motor oil level on cylinder-piston group moving surfaces of the automotive diesel

BACKGROUND: Theme of this article is actual because issues of the reliable and cost-effective opera-tion of augmented automotive piston engines are still not solved. An important aspect of solving the problem is rational oil supply of the cylinder-piston group.
 AIMS: The purpose of this work is the required minimum motor oil level estimation for the hydrodynamic lubrication of the cylinder-piston skirt pair. There were set and solved the tasks of determining the volume of motor oil capable of filling: 1) cavities of the flat-topped surface of the cylinder; 2) a raised surface of the piston skirt and 3) the piston skirt clearance. The object of study is an automotive diesel.
 METHODS: Three types of analytical determination of the total volume of the cavities of the rough surface and the piston skirt clearance were used as methods for estimation of a motor oil level.
 RESULTS: It was found, that the estimation with the well known formula for oil volume (a method 1) gives the overevaluated result of oil level estimation, in comparison with methods 2 and 3, which taking into account the real geometry of cavities and roughness parameters. The advantage of method 3 was determened by criteria of generalization and of simplicity to apply. Using the method 3, the estimated volume of motor oil ensured the hydrodynamic lubrication mode of the cylinder piston skirt pair of the object of study was obtained.
 CONCLUSION: The analytical expressions obtained by method 3 can be recommended to optimize the piston engine lubrication systems for reducing the friction and wear losses as well as to minimize a risk of oil starvation and increased oil consumption through burning.

Monitoring Metal Wear Particles of Friction Pairs in the Oil Systems of Gas Turbine Power Plants

In the example of the aviation gas turbine engine the problem of monitoring metal wear particles of friction pairs in the oil systems of gas turbine power plants is considered. The solution based on using the multi-channel cluster single-coil eddy current sensor (CSCECS) with sensitive elements in the form of single circuits is proposed. The CSCECS provides the detection of ferromagnetic and non-ferromagnetic particles and their ranking by several size groups. The sensor is invariant to the size (inner diameter) of the monitored oil pipeline and has high throughput and identical sensitivity across all channels. Two variants of the hardware structure of the debris continuous monitoring system (DCMS) prototype implementing the proposed approach are suggested. The first variant is intended for engine bench tests and contains the CSCECS with integrated preamplifiers and forced air cooling of the electronic modules. The second variant of the DCMS prototype involves the use of the uncooled sensors without built-in electronics and it focuses on operation in autonomous mode not only in bench tests but also during the engine normal operation. A brief description of the DCMS operational algorithm is given. The algorithm is the same for both hardware versions but differs at the software implementation level. The correctness of the algorithm for the detection and size identification of the wear metal particles was verified during the laboratory experiments with a total duration of 5 h and 30 min. The DCMS prototype was also examined during the full-scale engine bench tests. The experiments indicated that the number, size, and magnetic properties of the particles detected by DCMS generally corresponded to the number, size, and magnetic properties of the particles fixed by the MetalSCAN oil debris monitoring system which was used for verification of the DCMS functional capability. The results were also confirmed through laboratory analysis of the wipe samples on the debris filters. However, unlike the existing approaches, the design of the CSCECS additionally made it possible to evaluate the oil flow features in the pipeline of the engine lubrication system.

A large contribution of methylsiloxanes to particulate matter from ship emissions.

The chemical and stable carbon isotopic composition of the organic aerosol particles (OA) emitted by a shuttle passenger ship between mainland Naples and island Capri in Italy were investigated. Various methylsiloxanes and derivatives were found in particulate ship emissions for the first time, as identified in the mass spectra of a thermal desorption - proton transfer reaction - mass spectrometer (TD-PTR-MS) based on the natural abundance of silicon isotopes. Large contributions of methylsiloxanes to OA (up to 59.3%) were found under inefficient combustion conditions, and considerably lower methylsiloxane emissions were observed under cruise conditions (1.2% of OA). Furthermore, the stable carbon isotopic composition can provide a fingerprint for methylsiloxanes, as they have low δ13C values in the range of-44.91‰ ± 4.29‰. The occurrence of methylsiloxanes was therefore further supported by low δ13C values of particulate organic carbon (OC), ranging from-34.7‰ to-39.4‰, when carbon fractions of methylsiloxanes in OC were high. The δ13C values of OC increased up to around-26.7‰ under cruise conditions, when carbon fractions of methylsiloxanes in OC were low. Overall, the δ13C value of OC decreased linearly with increasing carbon fraction of methylsiloxanes in OC, and the slope is consistent with a mixture of methylsiloxanes and fuel combustion products. The methylsiloxanes in ship emissions may come from engine lubricants.

Online Monitoring of Automotive Engine Lubricating Oil Based on Internet of Things Technology

In order to realize the online monitoring of automotive engine lubricating oil, a method based on Internet of Things technology is proposed. This method uses the self-organizing neural network of the Internet of Things to fuse the original multidimensional feature data to obtain the fusion value. The Parzen window method is used to formulate the limit value of fusion value, and the samples are divided into three states: normal, warning, and abnormal. Weka software is used to extract rules from oil data. This method can identify different wear state information from oil spectral data, extract knowledge rules, and use them to build the knowledge base of automobile engine wear diagnosis system, so as to realize the automation and intelligence of automobile engine fault diagnosis based on lubricating oil spectral wear data. The measurement method of the lubricating oil sensor is mainly to comprehensively reflect the relationship between oil quality and electrical signal, so as to effectively provide users with reliable information. After many studies, it is concluded that the conductivity of lubricating oil has a good linear relationship with its acid value, metal particles, moisture content, and the change of additive content. Measuring the change of conductivity is an effective means to detect the change of lubricating oil quality. The experimental results show that using the extracted knowledge rules to verify the state of samples, the recognition rate is 97.47%. In order to more fully explain the difference between important element fusion and all feature fusion, all features are extracted. At the same time, the fault diagnosis and recognition rate of all features is not high, only 62.39%. It is proved that the Internet of Things technology can effectively realize online monitoring of the automotive engine lubricating oil.

OPERATING TESTS OF THE DEVICE FOR ELECTROSTATIC ENGINE OIL TREATMENT

The article presents the materials of operational tests of the improved device forelectrostatic treatment of semi-synthetic motor oil EXTRA DI 10W-40 during their operation in thelubrication system of the diesel engine D-240 wheeled tractor MTZ-82. Since the testing of the deviceconsisted of two stages, the main part of the article consists of two interrelated parts, which show themain results for each stage. The first part presents the design features and technical characteristicsof the advanced device, as well as the place of its installation in the engine lubrication system andconnection to the onboard network of the machine. Particular attention in describing the design ofthe device is paid to its differences from previous counterparts, namely the presence of stepwiseregulation of the output voltage level and the presence of protection of the oil from electricbreakdown. The second part of the article presents the test results of the device according to thecriteria of the influence of electrostatic treatment on the characteristics of the diesel engine and someperformance properties of engine oil. When disclosing the effect of electrostatic oil treatment onengine characteristics, the article presents data on changes in oil pressure in the main line of thelubrication system and changes in compression in the engine cylinders as the engine speed increases.When disclosing the effect of electrostatic treatment on the performance properties of engine oil, dataon changes in the tribological characteristics of the oil, namely the diameter of the wear spot, withits different operating time in the engine lubrication system. The diameter of the wear spot wasinstalled on a four-ball friction machine according to the method described in GOST 9490-75.According to this method, the values of the diameter of the wear spot for the engine oil during itsoperation in the engine lubrication system without the use of electrostatic treatment, as well as withthe use of such treatment in the built-in device. The final part of the article presents an analysis of the results, conclusions and practical recommendations for the application of the proposed improveddevice in the lubrication systems of diesel engines of self-propelled vehicles.

A microsensor array for diesel engine lubricant monitoring using deep learning with stochastic global optimization

Measurement of concentrations of the critical properties in a diesel powered machine’s lubricant oil is an important task in preventing machine failure and excessive damage. While there are several existing sensors for detecting these properties individually, they suffer from cross sensitivity issues and tedious calibrations for varying operating temperatures. We developed an interdigital sensor array based on an artificial neural network (ANN) automatically tuned with a stochastic global optimization (SGO) method for measuring water, base, soot, and diesel fuel contaminant concentrations in a lubricant oil. The temperature effect was compensated with the neural network. The neural network architecture was automatically selected through a unique simulated annealing process which resulted in an increased prediction accuracy when compared to the ANN with traditionally selected architecture. Dropout and data augmentation techniques were used during training to prevent overfitting. Experiment results demonstrated the ANN’s ability in accurately determining the oil properties from the overlapped sensor responses as well as removing the need to calibrate for a variety of operating temperatures. The sensor array is able to provide comprehensive information about a diesel powered machine’s health status.

Frictional Power Evaluation of Additive-Mixture in Trimethylolpropane Trioleate Oil using Single Cylinder Diesel Engine

Rapid depletion of petroleum sources and increase of demand for environmentally friendly products has urged the need for biodegradable lubricants to be utilized in various applications, especially for internal combustion (IC) engine lubrication. To perform on par with the commercial engine oils, bio-based lubricants need to be further improved through the addition of appropriate additives. This study investigates the synergistic performance of additive-mixture comprising of ionic liquids (IL), glycerol monooleate (GMO), molybdenum dithiocarbamate (MoDTC) and titanium dioxide (TiO2) nanoparticle in trimethylolpropane trioleate (TMPTO) ester base oil. Lubricity performances of the prepared sample were evaluated in the context of tribological improvement using laboratory tribotester as well as friction power reduction in actual single-cylinder diesel engine. From a prior study, it was found that additive mixture containing 0.93 wt.% IL, 1.49 wt.% GMO, 0.52 wt.% MoDTC, and 1.25wt.% TiO2 was able to provide substantial friction and wear as well as extreme pressure improvements. The present study was conducted to evaluate whether the formulation can offer the same performance on the actual engine in conjunction to the laboratory bench test. To do so, two different types of engine tests were conducted which are that standard engine performance test and also the Willans line test to evaluate the engine frictional power. The performance of the additives-TMPTO mixture was compared directly to those of commercial SAE 0W-30 engine lubricant. In the standard engine performance test, it was found that the additives-TMPTO mixture performed better than commercial SAE 0W-30 engine lubricant in terms of 2.5% higher maximum torque value, 3% higher overall brake power output, 10% lower brake specific fuel consumption and 12% higher brake thermal efficiency throughout the conducted test speed of 800 to 2300 rpm. Through the Willans line test, it was found that the additives-TMPTO mixture showed better results than the commercial SAE 0W-30 engine lubricant in terms of 20-25% lower frictional power at 900, 1200 and 1500 rpm. Overall, these results further justify the enhanced lubrication properties of the formulated bio-based oil in addition to the prior conducted tribological analyses.

Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach

The lubricating oil analysis may be used to verify an assessment of the engine's health and operational conditions, as well as the need for oil changes. The wide sight of oil characteristics allows for the detection of elemental contaminations, additives, and wear for maintenance monitoring. The goal of this work is to investigate the in-operando effects of oil metal pollution on diesel engine conditions using large datasets and Support vector machine (SVM) and Radial basis function (RBF) models. To do this, 1948 datasets from engine lubricant spectral analysis were used. The greatest accuracy for forecasting engine conditions was obtained using cubic Polynomial (poly 3) and rbf kernel function in SVM; and RBF-NN classifiers used two training methods, trainbr, and trainlm. For engine condition predictions, RBF and SVM classifiers had an average accuracy of 99 percent and 97 percent, respectively. The capacity of models to generalize was tested by varying the dataset size (10% to 90%), with RBF demonstrating the greatest performance. Following that, RBF-NN used the confusion matrix approach to diagnose the engine's critical state with a 99.38 percent accuracy. The presence of metal contaminations such as Cr, Si, and Fe has a significant impact on the accuracy of engine identification in normal, critical, and numerous classes, according to the RBF-NN sensitivity study. Finally, RBF-NN has been chosen to define and monitor diesel engine conditions since it outperforms the SVM model.

Use of methanol-gasoline blend: a comparison of SI engine characteristics and lubricant oil condition

ABSTRACT The quest for alternatives to gasoline becomes more urgent day by day because of environmental concerns and exhaustion of fossil fuel reserves. Methanol has proved to be an attractive renewable, alternative fuel due to improvement in engine performance. This research not only addresses performance and emissions from engine but also examines the importance of lubricant oil in engine by monitoring variation in physicochemical properties after 100 hours of operations on gasoline with octane rating 92 (G-92) and also 6% methanol by volume blended with 94% gasoline by volume (M-6). The performance and emission parameters were evaluated for nine different speeds. The accuracy of emission data was then calculated using Weibull distribution within 75% and 95% confidence intervals. On average, M-6 produced 3.72% more brake power than G-92. But M-6 produced 3.40% lower HC emission, and 5.54% lower CO emission in comparison with G-92. The lubricant oil exhibited 4.76%, 1.28%, 6.03%, 0.48%, 0.045%, and 3.49% rise in (KV)40°C, (KV)100°C, TAN, ash content, specific gravity, and flashpoint for M-6, respectively. Although M-6 improves engine performance and reduces carbon emissions, it is also responsible for early degradation of lubricant oil. Therefore, efforts for reducing the degradation rate of lubricant oil will open new avenues for future research.

Low friction tribofilm formation and distribution on an engine cylinder tested with MoDTC-containing low viscosity engine lubricants

The piston-liner tribological contact has a key role in controlling friction caused energy losses in a passenger car engine. This study analyses the MoDTC-containing low viscosity lubricant performance and the resulting low-friction MoS2 tribofilm distribution on the liner surface. The engine liners were tested in a floating liner measurement system, where fully-formulated lubricants with different MoDTC concentrations were utilised to assess the impact of friction modifiers on the friction performance. Tested engine liners were dismantled from the measurement system, and MoS2 tribofilm formation on the liner was quantitatively characterised by Raman Microscopy mapping. The MoS2 tribofilm chemical composition and lamellar structure were further investigated by Transmission Electron Microscopy (TEM), High-Angle Annular Dark-Field Scanning (HAADF) and Energy Dispersive X-ray (STEM-EDX) Microscopy. The findings presented in this paper confirm that MoS2 tribofilm spatial distribution plays a pivotal role in reducing friction in piston-liner contact. 60% friction torque reduction was achieved at BDC area with 0.7% MoDTC concentration in the fully formulated lubricant. Moreover, MoS2 presence in the middle of the liner indicates MoDTC effectiveness throughout the whole engine cycle. Therefore, contributing to the overall energy savings in passenger cars.

Modification of Control Oil Feeding with PLC Using Simulation Visual Basic and Neural Network Analysis

The oil feeding system is an oil distribution system used in engine lubrication by flowing it directly to the engine parts to be lubricated through pipes. In addition, it is also a raw material for the production process by collecting the oil first in the storage tank, then weighing it on the oil scale before use in the production process. The current control is still using the conventional model. The operating system is still manual, and the absence of identity and damage information makes it difficult for the engineer to troubleshoot. The research method is to modify the oil feeding system control using PLC (Programmable Logic Controller) and Visual Basic to display process information. This process uses the Neural Network (NN) method. The simulation results show that the PLC program and visual basic software can be connected properly. The speed of the data transfer test connection that can be obtained is 32 ms. The prediction process of the oil feeding system using the backpropagation algorithm Neural Network and the activation function, which uses the binary sigmoid function (logsig) with the 17-10-1 architecture having very good performance getting the MSE value below the error value of 0.001 maximum epoch 961 and hidden layer 10 with an MSE value of 0.00099915.

An integrated methodology for system-level early fault detection and isolation

Fault diagnosis is an indispensable technique to ensure the high-performance operation of a mechanical system during its life-cycle. Existing research works mainly limits in the diagnosis of single component or some certain components of interest. The systematic investigation of fault diagnosis for a full system is rare, making it inefficient to implement the method developed in the practice of a complete engineering system. In this paper, a system-level fault diagnosis methodology is proposed based on fault behaviour analysis, optimal sensor placement and intelligent data analytics for multiple fault detection and isolation in a complex machine system. A dynamical model of a mechanical system is firstly constructed using Bond graph, and the response characteristics of observable parameters under different faults are derived by analysing the functional relationships of system variables. A set partitioning theory-based sensor placement approach is then presented for designing a condition monitoring system with a quantity-optimal set of sensors for a desired performance of fault isolatability. Two multivariate statistic measures, i.e. Hotelling’s T2 and Q are calculated from the system operation parameters as the indices to detect the potential faults. The abnormal parameters are separated by constructing a contribution plot once a fault is reported. The separated abnormal parameters are then input to a Bayesian network model, and the real root cause of the abnormity is isolated by blending the observations with expert knowledge of diagnosis. The diesel engine lubrication system, which involves three common domains: mechanical, hydraulic and thermodynamic processes, is taken as an example to show the implementation and performance of proposed approach. This approach can be used to design a system-level fault diagnosis scheme that including various tasks such as fault behaviour analysis, sensor placement, data collection, data processing, information fusion in order to achieve an accurate fault isolation ultimately.

Improving the Engine Lubrication and Friction with Spherical Dimples of Partial Textures Optimized on Crankpin Bearing Surface

Improving the Engine Lubrication and Friction with Spherical Dimples of Partial Textures Optimized on Crankpin Bearing Surface

Tribological study of iron infused carbon tubes additive in gearbox, engine, and vegetable-based lubricants

Tribo-film strength in the boundary layer regime depends predominantly on the type of additives present in lubricant. Here, we have synthesized iron-infused carbon tubes (Fe-CTs) that can be used as an additive to improve the tribo-film strength of the lubricants (gearbox, engine, and vegetable oil) under the boundary layer regime. Additive synthesis was done by adopting the thermal decomposition of carbon and iron precursors at 800 °C in inert atmosphere. Small amount of additive insertion into the lubricant are found to reduce the coefficient of friction of the lubricant by maximum ∼54% for gearbox oil and ∼22%, ∼15% for vegetable and engine oils respectively. Reduction in friction showed direct relation with the scar diameter and depth profiles. Tribo-surface analysis revealed, the Fe-CTs formulated lubricants provide strength to the tribo-film and protect the surface from severe damage by filling the asperities.

Effects of Biomass Fast Pyrolysis Fuel on the Tribological Behaviour of Heavy-Duty Diesel Engine Lubricating Oil

The fuel type not only influences the engine power and exhaust emissions, but dilutes the lubricating oil. We studied the effects of biomass fast pyrolysis fuel, or biofuel, on the tribological behaviour of a fully formulated engine oil (FFEO) used for heavy-duty diesel engines by reciprocating a sliding tribometer, which simulated the tribological conditions of an engine cylinder liner and piston ring. We analysed the surface morphology, surface roughness, and elemental contents of countersurfaces through scanning electron microscopy/energy dispersant spectroscopy and surface roughness measurements. The wear mechanism was studied by analysing the compositions and kinematic viscosities of the oil samples. The results indicate that the friction coefficient increased along with the emulsified biomass fuel (EBF) content in FFEO. The wear mass loss and EBF content were simultaneously increased. The wear mechanism was mainly attributed to the corrosion function of the biofuel.

Tribological Properties of the Nanoscale Spherical Y2O3 Particles as Lubricant Additives in Automotive Application

The continuous tribological development of engine lubricants is becoming more and more vital due to its fuel efficiency improvement and lifetime increasing potential. The antiwear additives play a high role in the lubricants to protect the contacting surfaces even in the presence of thinner oil film. Nanoscale spherical particles in the lubricant may increase the necessary protecting effect. This paper presents the results of the experimental tribological investigation of nanoscale spherical Y2O3 (yttria) ceramic particles as an engine lubricant additive. The ball-on-disc tribological measurements have revealed an optimum concentration at 0.5 wt% with about 45% wear scar diameter and 90% wear volume decrease, compared to the reference, neat Group III base oil. The high-magnitude SEM analysis revealed the working mechanisms of yttria: the particles collected in the roughness valleys resulted in a smoother contacting surface, they were tribo-sintered and they have also caused slight plastic deformation of the outer layer of the metallic surface.

Determination of phenyl(2-naphthyl)amine concentrations in the working environment air using HPLC-FLD technique

Phenyl(2-naphthyl)amine (FNA) is a flammable solid with a characteristic odor. FNA is used, among others, in the production of paints and dyes, and as an antioxidant in rubber processing, lubricant for automobile engines, in brake fluids, oils, etc. In the European Union, FNA is classified as a category 2 carcinogen. In Poland, the value of the maximum admissible concentration (MAC) for FNA is 0.02 mg/m3. The aim of this study was to develop a new method for the determination of FNA, which will allow for the determination of this substance in the working environment within the concentration range of 0.1-2 of MAC value. The method is based on the retention of FNA on a cellulose filter, recovery of the substance with methanol and analysis of the solution thus obtained using a high-performance liquid chromatograph with a fluorescence detector. The developed method enables the determination of FNA in the concentration range 2-40 μg/m3. The limit of detection (LOD) is 0.23 ng/l and the limit of quantification (LOQ) is 0.69 ng/l. The method complies with the requirements of the European Standard PN-EN 482 and can be used to determine FNA concentrations in the workplaces air. Med Pr. 2022;73(1):25-31.

A novel analysis of n-butanol–gasoline blends impact on spark ignition engine characteristics and lubricant oil degradation

ABSTRACT Butanol emerges as a potential biofuel for energizing modern automobiles. Its impact on engine performance and emission characteristics was investigated by many researchers. However, a research gap exists for its effect on lubricating oil deterioration. Therefore, n-butanol–gasoline blends were prepared with 6% and 12% by volume of alcohol (B6 and B12) and compared with pure gasoline (B0) in terms of engine performance, emissions, and lube oil condition. The engine performance along with exhaust gas temperature increased with an increasing ratio of butanol in the fuel blend. CO and HC emissions were the lowest in the case of B12 followed by B6 and B0. Furthermore, lubricating oil properties were determined to monitor its deterioration rate. The kinematic viscosity at 100°C and flash point temperature showed a minimum reduction in the case of B12, i.e., 22% and 16%, respectively, as compared to fresh oil. The highest concentration of wear elements in lube oil was detected for B12 fuel, which included Al (11 × 10−6), Fe (27 × 10−6), Cr (4 × 10−6), and Cu (13 × 10−6). Similarly, the additive counts for Ca, Mg, and Zn were found to be 1665 × 10−6, 7.3 × 10−6, and 730 × 10−6, respectively, in lubricating oil for B12. Therefore, advanced lubricating oil should be developed for alternative fuels to improve its lifecycle.

Interaction of anti‐wear additiveTCPwith advanced bearing steels

AbstractThe quest for high‐performance energy efficient aircraft turbine engines has led to the development of a number of high‐performance rolling element bearing materials and engine lubricants with the aim of providing superior mechanical component durability. The heat treatments/surface treatments used to achieve the desired physical and mechanical properties for these newer alloys can result in altered surface chemistry from currently used materials. Surface chemistry plays an important role during lubricant‐bearing material interactions and the formation of beneficial tribological films during component operation. The objective of this study was to analyse the tribo‐films formed on bearing surfaces and investigate the interaction of lubricant additives, specifically the phosphorus‐based anti‐wear additive tricresyl phosphate (TCP), with different bearing materials under relevant bearing operating conditions. Bearing tests were conducted on 208‐size (40 mm bore) angular contact bearings at 127°C and 154°C using gas turbine engine lubricants conforming to MIL‐PRF‐23699G at maximum Hertzian contact stresses of 3.1 GPa and 3.55 GPa. Bearing materials evaluated included AISI M50, M50NiL, nitrided M50NiL (N) and three variants of Pyrowear 675 with silicon nitride rolling elements. Tribo‐films were analysed using Energy dispersive X‐ray spectroscopy and Auger Electron spectroscopy. Results indicate that phosphorus‐rich anti‐wear tribo‐films form on all of the bearing materials studied. The applied thrust load and heat treatment had a significant effect on tribo‐film thickness. The study also suggests that current gas turbine engine lubricants formulated with TCP should form beneficial tribo‐films that enhance bearing fatigue life and performance.

Monitoring Fuel Dilution of Diesel Engine Lubricant Oil by Ultraviolet Fluorescence Spectroscopy

Fuel contamination is a common problem in lubricating oil contamination. Generally, methods such as gas chromatography can be used, but the experimental process is more complicated. Fluorescence spectroscopy analysis has the advantages of fast detection speed and high sensitivity. Applying it to fuel dilution analysis can simplify the analysis process and enable rapid and accurate quantitative analysis. A certain type of fluorescence spectrophotometer was used to analyze and monitor lubricating oil containing different proportions of diesel oil. The fluorescence sensitive band was found, and the corresponding relationship between different lubricating oil content and the height and peak area of the fluorescence characteristic peak was analyzed.