Diesel Engine Vibration Research Articles

Marine diesel exhaust manifold failure and life prediction under high-temperature vibration

The working environment of the diesel exhaust manifold is harsh. On one hand, exhaust gas with high temperature continues to flush on the inner wall of the manifold, and on the other hand, the manifold is also affected by some complex factors, such as diesel engine vibration. If the design is unreasonable, air leakage, cracking, and other failure phenomena are easy to appear. In this article, aiming at the aforementioned problems in the exhaust manifold design process, a high-speed diesel exhaust manifold is taken as an example and analyzed by computational fluid dynamics and finite element method technology. The temperature field distribution of the exhaust manifold is simulated by the fluid–structure interaction method, and then the thermal stress distribution is simulated based on the temperature field of the exhaust manifold. According to the thermal stress of the exhaust manifold and the vibration stress in three directions (axial, transverse, and vertical), combined with the time domain acceleration signals, the high-cycle fatigue life of the exhaust manifold is obtained. The exhaust manifold structure is optimized based on the minimum high-cycle fatigue life position. Then, the high-cycle fatigue life analysis of the optimized structure shows that the optimized structure can significantly improve the life of the exhaust manifold. Then, the optimized structure is further analyzed for low-cycle fatigue life to check whether it meets the requirements of low-cycle fatigue life. After simulation, it can be known that the low-cycle fatigue life meets the design requirements. Finally, sensitivity analysis of the fatigue life under different loading conditions (thermal load and vibration intensity) shows that the thermal load has a significant influence on the fatigue life, so the thermal load should be paid attention to in the design process.

Application of analytic hierarchy Process in abnormal vibration of Marine machinery

This paper will use analytic hierarchy process to evaluate abnormal vibration of diesel engine in operation. The possible abnormal vibration factors of diesel engine in use are further analyzed to ensure the normal operation of diesel engine in use. Combined with analytic hierarchy process, the hierarchical structure model is determined, and the problems are analyzed from different angles to reduce the influence of subjective factors.

Diagnostic Methodology of the Low Displacement Diesel Engine Vibration Signal for Identification of Operating Conditions

The power developed by an engine is one of the main indications of its operating condition since it directly reflects changes in its performance and, therefore, the possibility of faults or anomalies. Usually, this parameter is measured directly by the use of dynamometers. However, in most real operating conditions, it is impractical or impossible to install this equipment. Additionally, diagnostic techniques generally require a level of intrusion that can cause engine damage. Therefore, in this study, a diagnostic methodology based on the indirect measurement of the engine output power is presented. The experimental procedure has been carried out in a single-cylinder diesel engine, which operates in four different operating conditions: 25%, 50%, 75%, and 100%. The methodology is based on the use of the engine vibration signal as a tool to identify different operating loads. By calculating the velocity of the vibration and the implementation of an algorithm based on the Inductive Monitoring System (IMS), it has been possible to relate the changes in the speed of vibration to the percentage of engine load. This methodology has showed an accuracy between a range of 84% and 94%, which demonstrates its reliability.

Simulation Study on the Influence of Single Balance Shaft Mechanism on Noise Radiation of 3-Cylinder Engine

In this paper, simulation work was conducted to evaluate the vibration and noise of a 3-cylinder diesel engine. The finite element model of the whole engine and the dynamic model of the crankshaft system were established respectively. The boundary element method and the modal-based acoustic transfer vector (MATV) method were used to calculate the acoustic and vibration response, and the characteristic points were selected to monitor the radiated noise in the external acoustic field. Finally, through the structural optimization of the diesel engine model, the influence of adding a single balance shaft on the sound radiation of the whole machine was analyzed. The research results of this paper will provide technical support for the design of shock absorption and noise reduction of 3-cylinder engine in the application of unmanned ground vehicles (UGV), outboard engine, Unmanned Aerial Vehicles (UVA) and hybrid power system.

Analysis of Noise and Vibration on Single Cylinder Four Stroke Engine with Green Diesel Fuel Based on Simulations

One of the alternative and renewable energies is green diesel. Despite the fact that both use the same raw material, vegetable oil, the processes for producing green diesel and biodiesel are somewhat different. Green diesel is generated via the hydrotreating process, which involves injecting hydrogen gas into an oil to remove oxygen. The combustion process in the engine is influenced by the nature of the fuel. Green diesel fuel has a higher yield of material than biodiesel fuel. High pressure caused by combustion in the engine combustion chamber affects the vibrations and noises of diesel engines. By varying engine speed and load, this study compares noise and vibration produced by green diesel fuel to biodiesel B30 using a simulation process. The results showed that green diesel fuel produces more noise and vibration than biodiesel B30 fuel and that this is directly proportional to the pressure and power produced in the combustion chamber by the green diesel fuel combustion process. As the pressure in the combustion chamber rises, the explosion becomes more powerful, resulting in increased diesel engine noise and vibration.

Model-based design of dynamic firing patterns for supervisory control of diesel engine vibration

Diesel engine cylinder deactivation (CDA) has been demonstrated to provide significant efficiency and aftertreatment thermal management benefits, enabling fuel-efficient emissions reduction from modern diesel engines at low load engine operation. Dynamic cylinder activation (DCA), a variant of CDA where the set of deactivated cylinders varies on a cycle-by-cycle basis, has been demonstrated to enable greater control over driveline torsional vibration while maintaining the fuel efficiency and thermal management benefits shown by fixed CDA via appropriate design of firing patterns. A model-based algorithmic approach to designing firing patterns during DCA – to control driveline torsional vibration in a user-defined frequency range, given firing density, engine speed, and maximum length of firing pattern – is described in this article. The described algorithm is generalizable to any engine configuration including different piston–cylinder layouts and number of cylinders. The algorithm is extended to design firing patterns for constrained DCA operation when CDA hardware is installed on a subset of cylinders of the engine. The resulting optimal firing patterns using the algorithm, for various combinations of inputs, are presented through an experimentally-validated simulation framework and discussed. It is demonstrated that the weighted phase-angle approach can accurately predict the frequencies and relative amplitudes of the vibration content, and, if theoretically possible, the proposed algorithm determines firing patterns that meet the specified requirements. The presented algorithm can be easily extended in future work for simultaneous selection of firing density and firing pattern during online, real-time implementation during both steady-state and transient operating conditions.

Experimental analysis for assessing noise and vibration of the diesel engine fuelled with a butanol–diesel blend under different injection pressures and engine speeds

Because there is still insufficient information about the effects of changing the fuel injection pressure with an alcohol–diesel blend on engine vibration and noise emission, in this study, the effects of changing the fuel injection system and the diesel–alcohol blend on engine vibration and noise emissions were investigated experimentally. The butanol was homogeneously mixed with conventional diesel fuel at a concentration of 10%. This blend was called B10D90. To investigate the effect of B10D90 on vibration and noise characteristics of a single-cylinder diesel engine, petroleum-based diesel fuel (D100) was selected as a reference fuel. The experimental studies on the single-cylinder diesel engine were carried out in five different engine speeds of 1600, 2000, 2400, 2800 and 3200 rpm under a constant load mode 80% and additionally three different injection pressures of 190 bar, 210 bar and 230 bar. The results showed that the addition of butanol in conventional diesel fuel generally increased engine vibration values at 190 bar, 210 bar and 230 bar fuel injection pressures. For all injection pressures, the differences between the noise level of the B10D90 blend and D100 appears to be about 2–3 dBA. This difference showed that the B10D90 blend has little effect on the noise emission of the engine.

Artificial neural networks approach on vibration and noise parameters assessment of flaxseed oil biodiesel fuelled CI engine

The current research dwells the experimental investigation together with artificial neural networks analysis on vibration and noise intensity of VCR diesel engine energized with flaxseed oil biodiesel blends. The tests were carried out at four different loads, namely 25, 50, 75, and 100% and three compression ratios (16.5, 17.5, and 18.5). The vibration levels were measured at different locations of the engine body which accounts for the development of bulk vibration. The RMS acceleration (vibration) and RMS noise were calculated. The flaxseed biodiesel mixes have shown the positive outcome concerning vibration and noise intensities, and FSOME20 (20% blend) was revealed inferior to leftover fuel samples. Besides, the intensity of vibration and noise was seen superior at higher compression ratios and loads. The identical disparity was observed irrespective of the fuel samples. At peak load, the RMS acceleration of FSOME20 was reduced by 12.2, 12.05, and 16.74%, while the RMS noise was brought down by 6.8, 9.6, and 7.23% at CR16.5, CR17.5, and CR18.5, respectively. Finally, the experimental results were compared with artificial neural networks (ANNs) predicted outcomes. The coefficient of correlation (R2) and root mean square error (RMSE) noticed were 0.965 and 0.12% intended for vibration, whereas 0.989 and 0.56% for noise correspondingly.

Catalytic effect of metal based nanoparticles on emission and vibration analysis of diesel engine

In current study, exhaust emission, vibration and noise features of diesel engine with addition of metallic based nanoparticles into diesel fuels were investigated experimentally. Various nanoparticles called as silver (II) nitrate (AgNO3), manganese (II) nitrate hydrate (Mn(NO3)2. xH20) and nickel(II) nitrate hexahydrate (Ni(NO3)2.6H2O) were used as an additive to diesel fuel at 25 ppm and 50 ppm dosing level so as to obtain six different fuel samples. Diesel engine with air-cooled and a single cylinder were utilized for engine tests altering engine speed from 1200 to 2800 rpm at the intervals of 400 rpm. The results demonstrated that of the nanoparticle addition did not remarkably affect the physicochemical features of the diesel test blends. But, the addition of nanoparticles slightly raised viscosity, cetane number and the heating value of test fuels especially, at the higher nanoparticle concentrations. In all experiments, with nanoparticle added blend fuels, CO, HC, NOx emission values and brake specific fuel consumption (BSFC) declined in parallel with the increase in nanoparticle concentration. In addition, it was shown that the Sound Pressure Level (SPL) and vibration characteristics of the engine block also decreased with increasing nanoparticle concentration of test fuels. As a result, AgNO3 addition to diesel fuel has an effective role in both reducing emission and vibration values and reducing BSFC values.

Effect of fuel injection pressure on the diesel engine fuelled with Moringa oleifera oil biodiesel blends: vibration and noise study

The purpose of the present study is to estimate the vibration and noise intensity of a single-cylinder direct injection diesel engine. The experiment was performed by varying the fuel injection pressures (FIP) such as 200 bar, 220 bar, and 240 bar respectively with four test fuel samples of Moringa oleifera oil biodiesel blends; BD0D100, BD10D90, BD20D80, and BD30D70. The intensity of vibration and noise has come down with the blends of Moringa oleifera biodiesel than regular diesel fuel (BD0D100). Besides, the increase in FIP looked at lower levels of vibration and noise disregarding fuel blends. Both the vibration and noise intensity was seen least with the BD20D80 fuel blend than leftover samples. The slightest RMS velocity and RMS noise were seen by 0.104 m/s and 39.71 dB correspondingly for BD20D80 than ordinary diesel at a FIP of 240 bar. Hence, the compression ignition engine can be operated with the BD20D80 fuel blend devoid of any amendment in the engine for the inferior vibration and noise.

A Novel Fault Diagnosis Method for Diesel Engine Based on MVMD and Band Energy

Vibration signal, as an important means for diesel engine condition detection and fault diagnosis, has attracted attention for many years. In traditional vibration signal analysis, most processing methods are for single-channel data. However, single-channel vibration signal cannot reflect the operating information of the diesel engine comprehensively because diesel engine vibration is coupled by multiple source signals. This paper proposes the MVMD band energy method for fault diagnosis by four channels of vibration signals. First, the original multivariate signals are decomposed adaptively by MVMD, which obtains a series of components with modal alignment. Then, the band energy values of each measuring point are calculated as the fault characteristics. Finally, SVM is used to realize the diagnosis and identification of diesel engine misfire. The working conditions have a great influence on the vibration signal of the cylinder. In order to obtain the best diagnostic working conditions, six working conditions are set for testing. The result shows that the fault identification rate is highest under the 1500 rpm and 50% load working condition. The fault recognition rate of this method reaches more than 99%, which is superior to the other four common methods.

Effect of Injection Parameters on Vibration of High Pressure Common Rail Diesel Engine for Light Vehicle at Idle Speed

With the progress of science and technology and the improvement of people’s living standards, the comprehensive performance requirements of automobiles are also increasing. Reducing engine’s vibration and noise is one of the important research contents in the field of automobile research. An experimental study was performed to investigate the effect of injection parameters on engine’s vibration at idle speed by using a four-cylinder high pressure common rail diesel engine. On the premise of not changing the components, the influence of each parameter on the vibration is compared and analyzed by changing the parameters such as main injection timing, interval of pilot-main injection, pilot injection quantity and rail pressure. The test results show that the vibration of diesel engine can be reduced by delaying the main injection timing properly, and the optimal interval of pilot-main injection is related to the main injection moment and the quantity of fuel injection. The determination of the pilot injection quantity must be determined by the pilot-main injection moment and the cycle injection quantity together in order to achieve vibration reduction. The increase of rail pressure will increase the vibration of the whole machine. Thus, the proper injection strategy is conducive to improve engine vibration characteristics.

Impact of various metal-oxide based nanoparticles and biodiesel blends on the combustion, performance, emission, vibration and noise characteristics of a CI engine

With the burning of 1 L of diesel fuel, approximately 3 kg of greenhouse gas is released into the atmosphere. Therefore, it is of great importance to reduce emissions with some additives in diesel engines. This study deals with the impacts of blends of waste cooking oil methyl ester and various metal-oxide based nanoparticles on the emission, combustion, performance, vibration and noise characteristics of a single-cylinder diesel engine. The test engine was loaded at different engine loads of 2.5, 5, 7.5 and 10 Nm and a constant engine speed of 2000 rpm. In this investigation, various fuels [called as reference diesel (D100), 10 vol% of waste cooking oil methyl ester (B10), and finally the mass fractions of 100 ppm aluminium oxide (B10Al2O3), titanium oxide (B10TiO2) and silicon oxide (B10SiO2) into the B10, separately] were tested. The addition of metal-oxide based nanoparticles has firstly increased the viscosity, cetane number, and heating value of biodiesel. Higher oxygen atoms in biodiesel-nanoparticles blends have improved the quality of the combustion process. Higher peak point in CPmax and HRRmax could be reached in these nano fuels due to their lower cetane numbers than that of D100. CO, HC and NOx emissions were significantly reduced with the blending of nanoparticles and biodiesel in comparison with those of D100. The addition of nanoparticles highly improved engine performance. B10 had the lowest thermal efficiency due to its heating value, but its efficiency was converted to the highest one with the addition of nanoparticle. In conclusion, this study is suggesting that the addition of metal-oxide based nanoparticles into biodiesel blends can give better results than using biodiesel alone for diesel engines.

Combustion, vibration, and noise characteristics of direct injection VCR diesel engine fuelled with Mesua ferrea oil methyl ester blends

ABSTRACT The present study deals with the assessment of combustion, vibration, and noise characteristics of single-cylinder water-cooled VCR diesel engine running with Mesua ferrea oil methyl ester (MFOME) blends. Four test fuels were used in this analysis namely diesel, B10, B20, and B40. The testing was performed at three fuel injection pressures (IPs) of 180, 200, and 220 bar, respectively. The combustion characteristics like CP, NHRR, CHRR, and RoPR were improved with MFOME (Mesua ferrea oil methyl ester) blends and B20 has attained higher than leftover test fuel blends. Further, RMS velocity and RMS noise were found to be decreased with MFOME blends as compared to conventional diesel fuel. At higher injection pressure (220 bar), the CP, NHRR, CHRR, and RoPR were improved by 4.98%, 9.23%, 5.4%, and 11.58%, respectively, while the RMS velocity and RMS noise were reduced by 14.76% and 3.54%, respectively, for B20. Abbreviations: MFOME: Mesua ferrea oil methyl ester; B10: MFOME 10% in diesel; B20: MFOME 20% in diesel; B40: MFOME 40% in diesel; K3PO4: Tripotasium phosphate; CI: Compression ignition; ASTM: American standards for testing materials; LDV: Laser Doppler Vibrometer; DAQ: Data acquisition system; RMS: Root mean square; dB (A): Decibel units; CR/CRs: Compression ratio/Compression ratios; IP/IPs: Injection pressure/Injection pressures; CP: Cylinder pressure (bar); NHRR: Net heat release rate (J/o); CHRR: Net heat release rate (J/o); RoPR: Rate of pressure rise (bar/o); FFT: Fast Fourier transforms; PDV: Polytec Doppler vibrometer; VCR: Variable compression ratio; V rms: RMS velocity; : Net heat release rate (J/ o); : Heat transfer rate to the wall of combustion chamber (J/ o); : Volume change rate with crank angle (m3/ o); : Pressure change rate with crank angle (bar/ o); : Specific heats ratio; : Cylinder volume (Instantaneous); : Crank angle (Degree); : Cylinder pressure (Instantaneous); : Specific heat at constant pressure (J/Kg-K); : Specific heat at constant volume (J/Kg-K); t or t i: Time (sec); : Uncertainty in load sensor; : Uncertainty in speed sensor; : Uncertainty in piezo sensor; : Uncertainty in crank angle sensor

Analysis of torsional vibration effect on the diesel engine block vibration

In this paper, the coupling effect between the crankshaft torsional vibration and the low speed diesel engine block vibration is investigated. Using finite element method (FEM), a model of low speed diesel engine is established to study the vibration response. The mode results of FEM agree very well with the test. The additional torques acting on the low-speed diesel engine caused by torsional vibration are discussed in detail. The vibration response of the low-speed diesel engine considering the effect of torsional vibration is studied using FEM simulation. By analyzing the effect of torsional vibration, the results demonstrate that the crankshaft torsional vibrations have important effects on the engine block vibration. The results from this study can provide useful theoretical guidance to predict vibration of low-speed diesel engine.

The Influence of Emulsified Water Fuel Containing Fresh Water Microalgae on Diesel Engine Performance, Combustion, Vibration and Emission

Microalgae is considered as an excellent potential renewable source of fuel in many forms including powder or slurry. A high percentage of emulsified water in the fuel is reported to reduce diesel engines’ emissions such as NOx, but that will compromise the engine output power. Using microalgae powder as an additive to enhance the emulsified water fuel heating value is the main objective of this work. Diesel engine combustion, vibration, performance and emissions were evaluated for pure cottonseed biodiesel (CS-B100), emulsified water 20% (vol.) in cottonseed biodiesel (CSB-E20) and emulsified water 20% (vol.) containing Fresh Water Microalgae Chlorella Vulgaris (FWM-CV) in cottonseed biodiesel (CSB-ME20). The emulsified water fuels showed a reduction in in-cylinder pressure, vibration, brake power, torque, exhaust gas temperature, CO2 and NOx, while BSFC and O2 were higher than the pure biodiesel (CS-B100). CSB-ME20 produced higher power and torque than CSB-E20 due to the presence of microalgae in the fuel that increased the energy content of the fuel.

Study on the effect of piston skirt profile on the vibration behavior of non-road high pressure common rail diesel engine

The piston secondary motion is the most predominant excitation source for the mechanical vibration of diesel engine, which is directly affected by the piston profile. The effects of piston skirt profile on the dynamic behavior of piston and the vibration characteristics of engine block is studied synthetically by the method of a combination of experimental test and numerical simulation. The temperature distribution of the piston top surface under rated working condition is tested by a hardness plug, which is used as the boundary condition to carry out the thermodynamic simulation of diesel engine working process, to obtain the hot profile of the piston. Then, the dynamics model of piston assemblies are formally established, and the skirt profiles of different bump positions of the piston skirt are optimally designed. Based on the above analysis, a comprehensive study on the influence of bump position on the piston dynamic behavior is carried out. Then, applying the modal testing method for the engine block and crankshaft to establish the elastic multi-body dynamics model of diesel engine and load the piston slapping force at different bump positions, the vibration response of the surface structure under the action of different piston slapping forces is also studied. The results show that the mechanical vibration of engine block will become more severe with the hiking up of bump position at the piston skirt. Also, the vibration behavior of the engine block in the middle and high frequency section is very sensitive to the bump position of the piston skirt, especially in the frequency range of 1600 Hz–3150 Hz. In addition, vibration noise performance can be significantly improved by adjusting the bump position of piston skirt downwards. Particularly, the vibration noise performance can be best achieved when the bump position of piston skirt is 5 mm.

Artificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engine

ABSTRACTThe present paper investigates the prediction of vibration, noise level, and emission characteristics of a four-stroke, four-cylinder diesel engine fueled with sunflower, canola, and corn biodiesel blends while H2 injected through inlet manifold using two different artificial intelligence methods: artificial neural network (ANN) and support vector machines (SVM). The aim of using these methods is to predict vibration, noise, carbon monoxide (CO), CO2, and NOx based on the initial experimental study by varying engine speed, blends of biodiesel, and H2 energy substitution ratio. Experimental data were gathered from the literature. For the ANN method, LevenbergMarquardt backpropagation training algorithm with logarithmic sigmoid and linear transfer function for hidden and output layers, respectively, gives the best results for prediction of vibration, noise, and emission characteristics. For SVM, a regression model is implemented with Gaussian kernel function. Results show that the ANN performs better than SVM, and the best mean average percent error and R2 for the models developed are 2.03 and 0.988 for vibration acceleration, 0.39 and 0.9615 for noise, 7.27 and 0.8549 for CO, 5.09 and 0.9398 for NOx, and 2.21 and 0.993 for CO2 values, respectively. Eventually, it is found that the ANN method is a good choice for simulation and prediction of dual fueled hydrogen sunflower, canola, and corn biodiesel blends.

Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.

Multi-task Bayesian compressive sensing for vibration signals in diesel engine health monitoring

In the diesel engine health monitoring system, massive vibration signals are acquired and transmitted to the system center for failure detection and identification. To improve the efficiency of data transmission, an improved compressive sensing (CS) algorithm is developed for vibration signal processing by taking advantages of the multi-task Bayesian compressive sensing (MT-BCS) and classification theory. In this paper, the vibration signals are divided into different ‘tasks’ by rectangular window function to reduce the complexity of CS. with the effects of noises considered, the reconstruction model for the vibration signals is established based on the learned dictionary. Whereafter, Fuzzy C-means (FCM) is used to classify all the tasks into several clusters, which guarantees the existence of information sharing in each cluster. Thus multi-task Bayesian regression algorithm can be used for the tasks belonging to the same cluster to improve the reconstruction effect for diesel engine vibration signals. Finally, the effectiveness of the proposed multi-task Bayesian compressive sensing is validated by the experiments.