Engine Output Research Articles

Study on interactive effects of CRDi engine operating parameters through RSM based multi-objective optimization technique for biofuel application

The current research focuses on the optimization of D+LPO (20% waste-based lemon peel oil and 80% diesel) fueled Common Rail Direct Injection (CRDi) engine fuel injection parameters for the optimum performance and emission characteristics. The foremost influential fuel injection parameters such as Injection Pressure (IP), Dwell Time (DT), Pilot Mass (PM) and Pilot Timing (PT) are considered to enhance the performance and decrease the emissions. Engine output responses like Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC), Hydrocarbons (HC), Carbon monoxide (CO), nitrogen oxides (NOx) and smoke emissions are modeled by using Response Surface Methodology (RSM). Analysis of Variance (ANOVA) disclosed that all the developed models are statistically significant with an R2 value of 0.9735 for BTE, 0.9719 for BSFC, 0.8517 for CO, 0.956 for HC, 0.9282 for NOx and 0.927 for smoke. Multi-objective optimization is performed using the desirability function approach to minimize the BSFC, HC, CO, NOx and smoke with maximum BTE. The D+LPO blend at IP 700 bar, PM 20%, PT 27°bTDC and 12° DT was predicted to be an optimum operating condition for this particular engine. Confirmatory tests were used to validate the predicted combination, and the prediction error was determined to be less than 5%.

Development of a new technique of waste heat recovery in cement plants based on Stirling engine technology

Cement plants are one of the most energy consuming industries in which a large amount of heat energy is lost into the atmosphere causing the increase of environmental issues and leading to the rise of cement production energy cost. Recently, waste heat recovery systems such as steam and Rankine cycles have received much attention by researchers and industrial sectors for power production application to enhance the system efficiency. However, the existing waste heat recovery systems present different drawbacks. This paper proposes, for the first time, a new technique of recovering cement plants waste heat based on Stirling engine technology. The modelling of a Gamma type Stirling engine was conducted for recovering the waste heat released from the clinker cooling process. Furthermore, the non-ideal adiabatic model was used for engine designing in MATLAB software. For model validation step, the results obtained from the present study have been compared with experimental data of Lewis Research Center in the National Aeronautics and Space Administration and previous numerical models. The comparison findings have shown a high accuracy of the current model. The effect of several geometrical and physical specifications on Stirling engine performances was studied to obtain the best engine design. The results showed that maximum efficiency can be achieved by increasing the regenerator porosity up to 79 % and by decreasing the regenerator length to 0.015 m. A matrix wire diameter of 50 μm has been found as an optimum value regarding the high output power generated and acceptable heat input required. The parametric analysis of heat exchangers geometries has revealed that the increase of the heater and cooler tube lengths reduces the engine performances. However, the rise of heater and cooler tube inner diameters could enhance the power and efficiency. Furthermore, it was found that the variation of phase angle has an important influence on Stirling engine outputs, thus the consideration of 90 degree as phase angle is more suitable for engine design which will be able to produce 1641.36 W with a thermal efficiency of 21.29 %. Additionally, it was concluded that the augmentation of pressure and rotational speed increases the engine power. Finally, it was shown that the rise of working gas mass enhances the engine output power but increases the heat input requirement. This work demonstrates the potential of using Stirling engine technology for cement plants waste heat recovery applications and highlights its ability to produce high output performances.

DESIGN AND ANALYSIS OF SPROCKET FOR FORMULA STUDENT VEHICLE

In a Formula student vehicle, the sprocket is one of the important components in the chain drive which transmits power from the engine output shaft to the drive shaft. To ensure efficient power transmission, sprocket should be designed and manufactured properly. In this study the driven sprocket of the formula student vehicle has been designed in Solid works and analysed using FEA. ANSYS software has been used to perform Static analysis to calculate the stresses induced and deformations in the sprocket. And then the Fatigue analysis has been done to determine the Fatigue life and damage. The sprocket is then optimized for weight reduction and re-analysed in ANSYS for safety and reliability. Chain sprocket is one of the most important parts used in transmitting power from the engine to the drive shafts. This study mainly deals with the design and analysis of rear sprocket of a Formula Student car. This sprocket is designed such that it is light in weight and functions efficiently. The type of chain used is roller type chain. The main challenge faced is determining the proper material and number of teeth for chain sprocket. Many iterations were carried mathematically to determine the total number of teeth and final drive ratio. The CAD model was created in SolidWorks and Finite Element Analysis was carried out in ANSYS Static Structural module. Initially a solid sprocket was created without any slots for different materials and Static structural analysis was performed. For doing static analysis, the forces and torques acting on the teeth of sprocket were calculated. Boundary conditions

Digital Twins-Based Online Monitoring of TFE-731 Turbofan Engine Using Fast Orthogonal Search

Due to the complicated structure of the aircraft engine, it is hard to observe the damage and determine its status with the traditional diagnosis method, which sets a fixed threshold for some specific parameters. To accurately reflect the condition of the engine, this article provides a novel fault diagnosis method for the TFE-731 turbofan engine and develops an online diagnosis system. With the combinations of model-based and data-driven approaches, models are constructed to create the so-called digital twins of the engine parameters. For the model-based approach, the physical isentropic compression is used as the basis of the model. For the data-driven approach, the fast orthogonal search is utilized to ensure that the model outputs close to the real engine data. Based on the model prediction-based monitoring strategy, the status of the engine can be identified online. By using the proposed method, an alarm will be triggered once the tested engine outputs cross the boundaries generated by the digital twins. This method can be further applied in quality control to examine the abnormal parts of an aircraft engine. Finally, the digital twins’ online diagnosis system is realized in the practical TFE-731 test facility, verifying the effectiveness of the proposed method.

Effect of fuel injection timing and injection pressure on performance in a hydrogen direct injection engine

The in-cylinder hydrogen fuel injection method (diesel engine) induces air during the intake stroke and injects hydrogen gas directly into the cylinder during the compression stroke. Fundamentally, because hydrogen gas does not exist in the intake pipe, backfire, which is the most significant challenge to increasing the torque of the hydrogen port fuel injection engine, does not occur. In this study, using the gasoline fuel injector of a gasoline direct-injection engine for passenger vehicles, hydrogen fuel was injected at high pressures of 5 MPa and 7 MPa into the cylinder, and the effects of the fuel injection timing, including the injection pressure on the output performance and efficiency of the engine, were investigated. Strategies for maximizing engine output performance were analyzed.The fuel injection timing was retarded from before top dead center (BTDC) 350 crank angle degrees (CAD) toward top dead center (TDC). The minimum increase in the best torque ignition timing improved, and the efficiency and excess air ratio increased, resulting in an increase in torque and decrease in NOx emissions. However, the retardation of the fuel injection timing is limited by an increase in the in-cylinder pressure. By increasing the fuel injection pressure, the torque performance can be improved by further retarding the fuel injection timing or increasing the fuel injection period. The maximum torque of 142.7 Nm is achieved when burning under rich conditions at the stoichiometric air-fuel ratio.

Development of artificial neural network to predict the performance of spark ignition engine fuelled with waste pomegranate ethanol blends

In this study, an artificial neural network (ANN) is developed to predict the performance of a spark-ignition engine using waste pomegranate ethanol blends. A series of experiments on a single-cylinder, four-stroke spark-ignition engine yielded the data needed for neural network training and validation. 70 percent of the experimental data was used to train the network using the feed-forward back propagation (FFBP) algorithm. The developed network model's performance was evaluated by contrasting its output with experimental results. Input parameters included engine speed, ethanol blends, and output parameters included indicated and brake power, thermal, volumetric, and mechanical efficiencies. Training and testing data had regression coefficients that were almost identical to one. The research revealed that the ANN model can be a better option for predicting engine performance with a higher level of accuracy.

An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine

The present study investigates the possibility of increasing efficiency and lowering pollutant emissions from a syngas-powered engine by modulating operational parameters such as engine load and syngas composition. Although artificial intelligence-based technologies are becoming more common for modeling single-fuel mode engines, they are rarely employed to simulate a dual-fuel syngas/diesel engine. In the first of its kind Endeavor in the area of syngas fueled engines, a hybrid adaptive neuro-fuzzy inference system (ANFIS)-response surface methodology (RSM) is investigated. The performance of syngas (H2+CO), a novel synthetic gaseous fuel, was studied in four different combinations. ANFIS and RSM-based prediction models were developed using engine performance and emissions data collected over the whole load range. While ANFIS surpassed RSM in model prediction, RSM was useful in establishing mathematical links between engine input and output. The ANFIS model developed had a good correlation between R (0.996–0.9998) and R2 (0.992–0.9972), as well as low model errors as determined by the root means square error range (0.0086–5.936) and mean absolute percentage error range (0.0028–0.0194). Theil's U2 was used to calculate the model's uncertainty, which was estimated to be 0.0065–0.0439. The superior forecasting abilities of ANFIS models were proved by low errors and uncertainty. The performance of the syngas-powered engine was optimized using the desirability approach to achieve optimum efficiency while emitting the least amount of pollution. The ideal engine load and syngas compositions for maximum production were 67.99% engine load and 72.4:27.6 as H2:CO syngas mix.

RETRACTED: Optimization of variable compression ratio diesel engine fueled with Zinc oxide nanoparticles and biodiesel emulsion using response surface methodology

In the current work, the optimization of engine parameters like compression ratio and ZnO nano-particles along with diesel-biodiesel emulsion have been explored to improve the performance, combustion, and emissions of diesel engines. The novelty of current work is no studies on optimization of engine input variables such as compression ratio, biodiesel volume fraction, water percentage, and nanoparticle concentration. The engine's input parameters were chosen to be compression ratio, biodiesel-blend ratio, water percentage, and ZnO nanoparticle concentration for optimization using response surface methodology. In accordance with Central Composite Rotating Design matrix, the experiments on the diesel engine were carried out, while multi-objective optimization was also carried out for the optimum engine responses. The optimum output engine responses of brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbons, oxides of nitrogen, in-cylinder pressure, and heat release rate were 30.75%, 13.92 MJ/kW.hr, 0.057%, 34.68 ppm, 717.28 ppm, 57.61 bar, and 51.26 J/°A, respectively. At optimal running conditions, it yielded a composite desirability of 0.8594. Furthermore, optimized engine parameters received 18.84 of compression ratio, 18.98% of biodiesel blend ratio, 5.71% of water emulsion, and 90.9 ppm ZnO nanoparticles concentration. Confirmation experiments were carried out to validate optimization results by engine input parameters to optimal values and found satisfactory. After the optimization engine output responses are evaluated experimentally, the acceptable range of error (<5%) was reported.

The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines

The indicated mean effective pressure (IMEP) is a key parameter for measuring the power output of an internal combustion engine (ICE). This indicator can be used to locate the high efficiency regions of engines. Therefore, it makes sense to predict the IMEP based on the machine learning (ML) approaches. However, different ML models are applicable to different scenarios, so it is important to choose the right model for prediction. The objective of this paper was to compare three ML models’ (ANN, SVR, RF) predictive performance in forecasting IMEP indicator with the input parameters spark timing (ST), speed and load. A validated one-dimensional (1D) computational fluid dynamics (CFD) model was employed to provide 756 sets of data for the training, validation, and testing of the model. The results indicated that the random forest (RF) model had the worst prediction performance, and support vector regression (SVR) had a slightly better prediction performance than the artificial neural network (ANN), at least for the investigations in this study. Overall, the ANN and SVR models showed good predictive performance for IMEP, as the coefficient of determination (R2) was close to unity, and the root mean squared error (RMSE) was close to zero. Whereas the overall prediction results of the RF model are acceptable, the RF model does not learn well for some internal engine laws.

Performance and emissions analysis of diesel engine fuelled with exhaust gas energy preheated ROME biodiesel

The crude oil resources are depleting as the days progress and biodiesel is an emerging substitute. But the usage of biodiesel is associated with fuel atomization problems. The prime cause is the high viscous nature of oils obtained from green matter. As we know that with increase in temperature of oil viscosity decreases. Current work deliberates the influence of preheating of biofuel on the engine output parameters. DI diesel engine fuelled with preheated biofuel blends result in increase in thermal efficiency and drop in HC, CO emissions, but the rise in peak cylinder temperature resulted in increase in NO&lt;sub&gt;x&lt;/sub&gt; emissions.

Power enhancement of a turbo-charged industrial diesel engine by using of a waste heat recovery system based on inverted Brayton and organic Rankine cycles

In this study, energy assessment is performed for an industrial turbocharged diesel engine integrated with a novel waste heat recovery (WHR) system. The exhaust energy is used in inverted Brayton cycle (IBC) for waste heat recovery purpose. Also, the heat energy from IBC heat exchanger is used as the heat source for the organic Rankine cycle (ORC) to produce extra power. The case study engine is modelled in AVL BOOST software and the model is validated against real engine performance data. For studying the performance of the proposed waste energy recovery system, IBC is added to the engine model in AVL BOOST software and the thermodynamic model of the ORC is developed in MATLAB and it is linked to AVL BOOST. Then, the model is solved, and the main engine output parameters are studied at 1800 RPM and various engine loads. The results show that employment of the proposed WHR system leads to enhancement of the system power by about 18%. However, the backpressure produced by installing the WHR system can result in increase of the BSFC up to 3% and reduction of the total thermal efficiency by almost 1% at engine full load condition. The results of this work contribute to determine the interactions between the proposed novel waste heat recovery system (IBC-ORC) and the engine. The proposed bottoming cycle based on IBC-ORC can be installed on existing industrial stationary engines for enhancement of power generation without imposing a new source of power generation.

A detail study of a RCCI engine performance fueled with diesel fuel and natural gas blended with syngas with different compositions

The catalytic reforming is an applicable method to generate hydrogen as an alliterative fuel directly that is of prime interest to replace hydrocarbon fuels. Although, the use of this type of catalyst has the potential to solve the problem of safe storage of hydrogen in ICEs, but, this method suffers from the simultaneous production of carbon monoxide with hydrogen known as syngas. Depending on the engine operating conditions, different syngas composition in terms of H2/CO volumetric ratio can be produced through the mentioned catalyst. An engine performance which uses onboard hydrogen produced is completely affected by syngas composition. Therefore, the aim of the current simulation study is to evaluate the performance of a heavy-duty diesel engine under RCCI combustion fueled with diesel fuel/natural gas blended with syngas with different compositions. For this purpose, at 9.4 bar gross IMEP, natural gas is gradually replaced by five different compositions of syngas that is H2/CO volumetric ratios of 33.33/66.67, 50/50, 66.67/33.33, 80/20, and 100/0. The simulation results show that not only the engine output power can be improved up to 27.7% by simply increasing of the CO/H2 volumetric ratio in syngas composition to 66.67/33.33, but also the GIE is reduced by less than 9%. In contrast, the risk of the diesel knock occurrence may increase only in higher CO/H2 ratios. Although, the NOx level can be achieved closer to the EURO VI level, but, same level for UHC and CO and also the level of EPA 2007 for formaldehyde are not achievable for syngas with the higher CO/H2 volumetric ratio.

Reducing NOx Emissions through Microwave Heating of Aftertreatment Systems for Sustainable Transport in the Inland Waterway Sector

The inland navigation sector makes a significant contribution to the growth of the global economy as well as to climate change due to pollutants emitted by diesel engines. NOx emissions are very high in port areas where, due to traffic, the ships run at idling regimes. Selective catalytic reduction (SCR) represents one of the most suitable technologies, in terms of cost effectiveness, but does not perform well if the temperature during vessel operation is lower than 180 °C. Microwave technology can support preheating of the ceramic core of SCR in order to increase the temperature towards the optimal interval for the best NOx reduction. Research has focused on coupling a magnetron head to a SCR device in order to evaluate to what extent the technology can meet the requirements of Stage V of the European Directive related to NOx emissions. Measurements of NOx emitted have been performed on engines with 603.5 kW nominal power and 1500 rpm that operate at a lower engine speed (700–1200 rpm) and output power (58–418 kW). The values recorded for emissions using microwave heating of ceramic core of SCR have decreased by 89% for a constant load of engine and idling engine speed.

Experimental investigation of the effects of hot-end and cold-end connection on the performance of a gamma type Stirling engine

External combustion Stirling engines are considered as an alternative to the solution of increasing energy and environmental problems due to their ability to work with any heat source and to be an efficient energy conversion system. In this study, the effects of hot-end and cold-end connections of power and displacer cylinders on the performance of a gamma-type Stirling engine (GTSE) were investigated experimentally. The effects of the connection type between the power and displacer cylinders on the engine performance were compared at the same operating conditions without making any structural modifications to the engine. The performance tests were conducted using helium as the working gas at different charging pressures. The experimental results show that the maximum values of the engine torque and output power were obtained at a certain charging pressure for both hot-end and cold-end connected engines. Compared to the cold-end connection (CEC) engine, the maximum torque and output power increased in the hot-end connection (HEC) engine at the same temperature range. In the HEC engine, the maximum engine torque and power were achieved as 0.5665 Nm and 23.35 W, resulting in an increase of 68.4% and 84.7% compared to those of the CEC engine, respectively.

Improvement of SI engine combustion with ammonia as fuel: Effect of ammonia dissociation prior to combustion

Although recent studies have shown the possibility of running ‘standard’ spark-ignition engines with pure ammonia, the operating range remains limited mainly due to the unfavorable characteristics of ammonia for premixed combustion and often requires the addition of a complementary fuel such as H2 to extend it. As the best way to add H2 is to crack ammonia directly on-board, this paper focuses on the impact of the upstream cracking level of ammonia on the performance and emissions of a single cylinder spark ignition engine. Experiments were performed over several equivalence ratios, dissociation rates and load conditions. It is confirmed that only a slight rate of ammonia dissociation (10%) upstream of the combustion considerably enhances the engine's operating range thanks to a better combustion stability. In terms of pollutant emissions, the partial dissociation of ammonia, especially for slightly lean mixtures induces a very clear trade-off between high NOx and high unburned ammonia level for high and low ammonia dissociation rates, respectively. Therefore, cracking NH3 does not only improve the operating range of ammonia-fueled spark ignition engines but can also help to reduce NH3. However, to reach the same engine output work, higher ammonia fuel consumption will be necessary since the global system efficiency is lower using fuel dissociation. In addition, the global warming effect is increased with dissociation level since a higher level of N2O is generated by the hydrogen contribution.

암모니아 운반 선박의 암모니아 추진 모델 적용을 위한 연료 소비량 분석

In order to analyze the fuel consumption of eco-friendly ships promoting ammonia fuel, which is currently being developed due to increasing international demand for alternatives of environmental pollution causing ships, a modeling of fuel consumption analysis has been analyzed in this study. Modeling of fuel consumption analysis has been applied to a pre-existing ammonia transporting ship to figure out HFO consumption according to engine output. The applied ship, a 16,080 kW class model crossing the United States and Europe, showed a fuel consumption of 2,714 kg/h at 100% load. In the meantime, ammonia fuel conversion rate of 2.2 was applied to confirm the maximum fuel amount of 5,909 kg/h and the consumption per output of 368.9 g/kWh based on the same engine power. Moreover, an additional analysis of the fuel consumption and storage capacity regarding the operating speed and route were analyzed. In the case of the load change rate of the diesel engine applied to the ammonia engine, the smallest SFOC of 360.0 g/kWh at 75% of the load was shown and the fuel consumption was about 4,336.9 kg/h. In the case of the route from the US to UK, with 30% portion of maximum load applied, fuel consumption of 1,837,000 kg/h and volume of 2,816.6 ㎥ were required for the fuel consumption analysis. Results of this study will be used for further fuel consumption analysis of operational characteristics in the future.

Fracture mechanism of transmission spindle system for high-speed tracked vehicle

According to the traits of engine output torque and vehicle longitudinal dynamic model, considering the influence of the vehicle, the dynamic model of the transmission spindle system is established by using the lumped mass model, and the Runge–Kutta is used to solve the model under sinusoidal excitation and step excitation in starting stage, the time–frequency of transmission spindle under two kinds of excitation is analyzed. Modal analysis is used to decouple differential equations and solve the response of a single-degree-of-freedom system. The analytical approach and numerical solution are compared to each other to verify the correctness of the analysis method, the impact of rotating shaft torsional stiffness on the natural characteristics and dynamic load are analyzed, and the fracture mechanism of transmission spindle is further explored. The analysis results show that when the spindle stiffness is asymmetric, the sinusoidal excitation causes the dynamic load on both sides of the spindle and the driving force on both sides of the vehicle track to be inconsistent, the step excitation induces the second-order low-frequency resonance. Under the small damping, the vibration attenuation is slow, the right end of the spindle is broken under two combined excitations. When the stiffness of the spindle is symmetrical, the influence of sinusoidal excitation on both sides of the spindle is undifferentiated, and the vibration caused by step excitation decays quickly. Finally, the theoretical analysis results were confirmed by crack morphology, vehicle test data.

A potent numerical model coupled with multi-objective NSGA-II algorithm for the optimal design of Stirling engine

In this article, a novel numerical model of the Stirling engine encompassing a potent loss mechanism coupled with the NSGA-II algorithm is proposed. Multi-objective optimization of GPU-3 Stirling engine was performed using a class of genetic algorithms, namely NSGA-II, with five decision variables to minimize the losses and increase the power output and efficiency of the GPU-3 engine. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Combinative Distance-based Assessment (CODAS) decision-making approaches were used to obtain the optimum solution from Pareto optimal space. Furthermore, the optimization results were compared with the experimental results of the GPU-3 Stirling engine. Results from the multi-objective optimization effort indicate that output power increases by approx. 500 W and efficiency enhances by approx. 5%, whereas losses decrease by 516 W. Later, to demonstrate the model's design capability, the developed model and optimization approach, i.e. (NSGA-II), is utilized to develop an optimal design of a beta-type free piston Stirling engine (FPSE) with an indicated power of 10 kW. After optimizing a combination of twelve operating and geometric parameters, the Stirling engine that yields a net power output of about 7.95 kW with a thermal efficiency of about 30% is developed. This work presents a novel and powerful numerical method for the optimal design of Stirling engine.

Production of Chlorella pyrenoidosa biodiesel by heterotrophic pathway to improve CI engine output characteristics using statistical approaches

An alarming rise in greenhouse gases originating from on-road vehicles and the rapid depletion of cultivable land for fuel feedstocks has led researchers to explore alternative means of biofuel production. In this context, the current study explores the heterotrophic cultivation of Chlorella pyrenoidosa microalgae by scaling-up using various combinations of stir-tank bioreactors for the sustainable synthesis of low-density biodiesel. Furthermore, statistical tools are employed to develop prediction models to incorporate 20% (vol/vol) of the low-density biodiesel with diesel fuel (CP20) to obtain optimal experimental outputs. From the study, it was found that the percentage dry cell weight of samples acquired from 100 L, 200 L and 300 L stir tank bioreactors achieved 44.1%, 46.7% and 41% of lipid content and biomass concentrations of 5.15 g/L, 4.24 g/L and 4.36 g/L on up-scaling. At an optimal reaction temperature and time of 37 °C and 12 h, the highest biodiesel conversion rate of 98% was achieved during enzymatic transesterification. The prediction engine models reveals that at the finest engine input parameter combinations, 0.74% improvement in brake thermal efficiency and a 50% drop in smoke emissions are achieved using the low-density CP20 blend. Also, superior combustion characteristics are delivered on operation as a trade-off for NOX emissions.

Using energy time\u2013frequency of Hilbert Huang transform to analyze the performance of the variable valve timing engine

In this study, a diagnosis method was successfully implemented to identify different sounds coming from individual mechanical parts within a group of engine moving parts controlled through a variable valve timing system. The novelty of this diagnosis method is in the determination of specific sounds coming from each part within this group when they are in good working condition and without any defects. This will facilitate in early detection of faults occurring on the parts, identified through changes in the sound wave energy. Through this study, this diagnosis method was validated in three ways, namely the consistency of the results with previous studies, the synchronization of sounds from mechanical parts in overlapping cases, and the cross-correlation of engine sound modes that results from analysis using the Hilbert Huang Transform. In this paper, the distribution of sound energy according to its frequencies was utilized to distinguish which of the engine combustion chambers of a Dodge Journey 2.4 was faulty. To conduct that, the noise-based test technique was selected to record the engine sound. The results show that there is a link between the RMS energy of the engine sound and the engine output torque.