Engine Output Research Articles

Dynamic simulation analysis of aircraft engine planetary gear system

The Geared Turbofan (GTF) engine is used for aircraft propulsion with fan drive, characterized by its complex structure and operation in challenging environmental conditions. During its operation, variations in gear meshing and loads on the transmission shaft exacerbate vibration within the transmission system, leading to a decline in engine performance. This paper established three-dimensional solid models of the X-type aviation engine planetary gear system, input shaft, and output shaft, and employed ADAMS for multibody dynamics analysis, achieving simulation of engine operational vibrations. Modal and harmonic response analyses were conducted using ANSYS to observe and analyze critical parameters such as vibration displacement, vibration acceleration, gear meshing forces, modal resonance, and harmonic resonance, evaluating the impact of vibration amplitude, frequency, and their effects on system performance and longevity, providing guidance for vibration control and optimization of aviation engine systems.

Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks

This research is devoted to the development of a method for helicopter turboshaft engine energy characteristics control by regulating the free turbine rotor speed and fuel consumption using neural network technologies. A mathematical model was created that links the main rotor and free turbine rotor speed parameters, based on which a relation with the engine output power was established. In this research, a differential equation was obtained that links fuel consumption, output power, and rotor speed, which makes it possible to monitor engine dynamics in various operating modes. A fuel consumption controller was developed based on a neuro-fuzzy network that processes input data, including the desired and current rotor speed, which allows real-time adjustments to improve the operational efficiency. In the research, based on the flight data analysis obtained during the Mi-8MTV helicopter with a TV3-117 turboshaft engine flight test, improved signal processing quality was obtained due to time sampling and adaptive quantisation methods (this is confirmed by assessing the homogeneity and representativeness of the training and test datasets). A comparative analysis of the developed and traditional controllers showed that the neuro-fuzzy network use reduces the transient fuel consumption process time by 8.92% while increasing the accuracy and F1 score by 18.28% and 21.32%, respectively.

Geothermo-mechanical energy conversion using shape memory alloy heat engine

The shift towards renewable energy sources like geothermal energy has become desirable due to the recurrent energy crisis and global warming challenges influenced by fossil fuels. Geothermo-mechanical energy conversion using shape memory alloy (SMA) heat engines presents a novel and sustainable approach for harnessing geothermal energy. Shape memory alloys, known for their ability to undergo reversible phase transformations driven by temperature changes, are ideal for thermal-to-mechanical energy conversion. This paper explores the design and performance of an SMA heat engine that utilizes geothermal heat sources to drive mechanical work. The engine operates by cycling between the high-temperature geothermal environment and a cooler sink, exploiting the shape memory effect to generate mechanical motion. By integrating geothermal energy and SMA technology, this system offers a potential solution for renewable energy generation, with applications in remote or off-grid locations. The paper also investigates output power and the thermodynamic efficiency. A model is formulated and the engine behavior is simulated. A series of experiments are conducted for engine output power and efficiency. The model is compared to the experimental data for validation. The engine developed a maximum power of 3.5, 8.5, and 11.5 watts at 60, 80, and 90 °C respectively. The proposed SMA-based geothermo-mechanical energy conversion system offers a promising solution for efficient, reliable, and scalable geothermal energy harvesting. This research contributes to the development of innovative, efficient geothermal energy conversion technologies, supporting global renewable energy goals and reducing greenhouse gas emissions. This innovative energy conversion mechanism could play a key role in the future of sustainable power generation.

A multivariable adaptive control method for aeroengine with H∞ performance considering engine output limitation protection based on fully adjustable Neural Network

Aeroengines, being highly nonlinear systems subject to external disturbances, present a challenge for conventional control methods in achieving satisfactory performance. The radial basis function neural networks possess the capability to effectively model highly approximate nonlinear functions, making them suitable for implementation in aeroengine control. However, the radial basis function (RBF) neural network is a local approximation model that inadequately incorporates historical information. Additionally, the tracking error of neural networks can also impact control effectiveness. To address this issue, this paper proposes a multivariable control method for aeroengines with H∞ performance based on fully adjustable Long Short-term Memory-Radial Basis Function Neural Network (LSTM-RBFNN). Firstly, a factual mathematical model of an aeroengine is established based on the principles of aerodynamics and thermodynamics. Secondly, the paper proposed an LSTM-RBF neural network architecture to enhance prediction performance and proposed using a finite time perturbation estimator to gauge the approximate error of neural networks, thereby acquiring immeasurable approximative error information. Then, in order to mitigate the influence of neural network tracking error on the loop, the estimated errors are incorporated into the primary loop controller, and a static state disturbance full feedback H∞ controller is proposed. Then, leveraging the LSTM-RBF architecture proposed in this study, we further employ Taylor expansion of nodes to derive the adaptive law of the neural network and propose a novel neural network adaptive law that incorporates tracking error estimated by the disturbance observer into the adjustment mechanism to enhance tracking performance. The stability of this adaptive law has been proven based on Lyapunov function analysis. And considering the output limitation protection problem of aeroengine, a virtual command value based limitation protection method is proposed for the above control method, and it is proved that this limitation method does not damage the stability and design process of the original control method. Finally, numerical simulation and hardware in loop simulation experiments were conducted, and the results showed that under the same adaptive gain of the neural network, the performance indicators (IAE, IATE, σ) could be reduced by more than 50% compared to other methods, which demonstrates superior tracking and disturbance rejection performance achieved by our proposed method.

Optimization of power performance and combustion stability of ultra-lean combustion in hydrogen fuel engines through combined turbulent jet ignition and variable valve timing

Although pure hydrogen engines can achieve zero carbon and extremely low NOx emissions under ultra-lean combustion conditions, there are limitations with combustion stability and power performance. This paper combines turbulent jet ignition (TJI) and variable valve timing (VVT) technology, which not only improves the power output of pure hydrogen engines under ultra-lean combustion conditions but also ensures the engine’s stable operation. Therefore, this research reveals the working characteristics of TJI engines under lean conditions through numerical methods and explores the optimization characteristics of VVT on engine power performance and stability through experiments. The results indicate that TJI utilizes strong turbulence and multiple-point ignition to improve the efficiency of the mixture and combustion speed, ensuring reliable ignition capability under ultra-lean operating and achieving a stable and effective combustion process. According to the experimental results, the combination of TJI with VVT technology can ensure engine cyclic-variability of less than 1.5 % and the maximum values of Brake mean effective pressure (BMEP) and Brake thermal efficiency (BTE) are 4.5 bar and 41.6 %, respectively. This innovative technology combination not only enables the efficient and eco-friendly development of the transportation industry but also holds significant importance for promoting carbon-free fuels and environmental protection in the future.

EVALUATING PERFORMANCE, COMBUSTION, AND EMISSION CHARACTERISTICS OF WASTE PLASTIC OIL BLENDS IN CRDI DIESEL ENGINES USING DATA ENVELOPMENT ANALYSIS

As fossil fuels run out quickly, researchers studying engines are increasingly interested in alternative fuels. This paper covers the use of plastic paralysis oil as biodiesel in diesel engines. This was done using a four-stroke, multi-cylinder CRDI diesel engine running between 2000 and 2500 rpm. From zero loads to fifty percent load, the performance, combustion, and emission characteristics were computed for a variety of load scenarios. The analysis revealed that the blend D80WPO20 at 2000 rpm reduced hydrocarbon emissions from 0.059 g/kW to 0.045 g/kW, which is lower than that of 25% diesel at 26 kW. At high speed, the blend D80WPO20 at 2000 rpm gave the maximum BTE of 34.53%. The nitric oxide (NOx) emissions from D50WPO50 and diesel at a 26 kW load are 6.48 g/kWh and 5.37 g/kWh, respectively, according to reports. Data envelopment analysis, a multi-response linear programming optimization tool, was used to evaluate the output and emissions of DI diesel engines using waste plastic oil mixtures.

Analysis of hybrid electric vehicle performance and emission applied to LPG fuel system

Global carbon neutrality policies have intensified regulations on automotive emissions. CO2 and particulate number (PN) are major pollutants, with CO2 contributing to global warming and particulates affecting human health. Despite the increase of electric vehicles, internal combustion engines remain prevalent owing to their mature technology, infrastructure and economic efficiency. Liquefied petroleum gas (LPG) is a promising alternative fuel with lower CO2 and PN emissions. LPG direct injection (LPDI) technology improves fuel efficiency and reduces emissions compared to current LPG vehicles. However, vapor-lock issues at high temperatures must be addressed. Hybrid electric vehicles (HEV), combining internal combustion and electric motors, improve fuel efficiency, reduce emissions and serve as a transitional technology prior to electric vehicle (EV). In this study, a conventional 2-liter gasoline hybrid electric vehicle is converted to an LPDI-HEV using LPG. The experimental results showed that the engine output was within 5 % of an equivalent level. In real vehicle tests with the hybrid system applied, CO2 emissions were confirmed to be within a 3% difference, indicating an equivalent level, while PN emissions were significantly reduced. Although the LPDI HEV has lower fuel efficiency compared to the gasoline direct injection hybrid electric vehicle (GDI) HEV, it was found to be 12 % more economical when considering costs. This study also assesses the economic viability and environmental benefits of converting GDI HEV to LPDI HEV in urban taxi applications, providing policy insights for sustainable transportation.

Effect of different configurations of hybrid nano additives blended with biodiesel on CI engine performance and emissions

The use of nano additives to improve the cold properties of biodiesel is encouraged by its drawbacks and incompatibility in cold climate. Waste cooking oil (WCO) was transesterified to create biodiesel. A 20% by volume was used for combination of diesel and methyl ester. Current study aims to evaluate diesel engine emissions and performance. TiO2, alumina, and hybrid TiO2 + Al2O3 nanoparticles are added to WCO biodiesel mixture at 25 mg/liter. When B20 combined with nano materials such as TiO2, Al2O3, and hybrid nano, the highest declines in brake specific fuel consumption were 4, 6, and 11%, respectively. As compared to biodiesel blend, the largest gains in thermal efficiency were 4.5, 6.5, and 12.5%, respectively, at maximum engine output power. Introduction of TiO2, Al2O3, and hybrid nano particles to B20 at 100% load resulted in the highest decreases in HC concentrations up to 7, 13, and 20%, and the biggest reductions in CO emissions, up to 6, 12, and 16%. Largest increases in NOx concentrations at full load were about 7, 15, and 23% for B20 + 25TiO2, B20 + 25 Al2O3, and B20 + 25TiO2 + 25 Al2O3, respectively. Up to 8, 15, and 21% less smoke was released, correspondingly, which were the largest reductions. Recommended dosage of 25 ppm alumina and 25 ppm TiO2 achieved noticeable improvements in diesel engine performance, combustion and emissions about B20.

Research on engine dynamic torque modeling for torsional vibration analysis of vehicle powertrain

In order to improve the accuracy of dynamic torque model of engine, a simulation model of quasi-transient engine dynamic torque under all operating conditions was established considering the influence of throttle opening. Firstly, the calculation model of engine output torque and cylinder pressure is established according to the kinematic relation of crank linkage mechanism. Secondly, BP neural network is used to establish the mapping relationship model of throttle opening, rotational speed, and cylinder pressure ratio data. In order to solve the contradiction between calculation accuracy and efficiency of engine cylinder pressure interpolation, a quadratic interpolation method is proposed, which refines data to improve interpolation accuracy, and improves calculation efficiency by online adjacent interpolation, and completes the construction of dynamic torque simulation model of engine in all working conditions. Finally, the experimental data of dynamic torque characteristics of the engine at different speeds are used to verify the accuracy of the model calculation. By using the verified engine model, the dynamic operating characteristics of variable valve opening and variable speed are simulated and analyzed. The results show that the model established in this paper can effectively reflect the dynamic characteristics of the engine, and the model is suitable for the simulation of the torsional vibration of the vehicle powertrain under steady and unsteady conditions.

Experimental studies of thermal behavior, engine performance and emission characteristics of biodiesel / diesel / 1 pentanol blend in diesel engine

The present study investigates the significance of the utilization of biodiesel derived from waste cooking oil in diesel engines and its impact on engine output and environmental pollution after blending with diesel and 1-pentanol. The higher values of the ignition index and comprehensive performance index of 4.34 ×10−4mass/min°C2 and 13.71×10−6 mass2/min2 °C3, respectively, for D70B20P10 (70 % diesel, 20 % biodiesel & 10 % 1-pentanol by volume) indicate superior combustion performance. At full load, carbon monoxide and unburned hydrocarbon emissions from 1-pentanol blended fuels decrease significantly, ranging from 40 % to 52 % and 30.76–46.15 % compared to diesel. The D70B20P10 also showed an improved thermal efficiency of 28.68 % among all tested fuels at full load but slightly lower than diesel (29.56 %). Diesel demonstrated superior in-cylinder pressure (ICP) of 77 bar and heat release rate (HRR) of 41.1 J/ºCA owing to its excellent combustion characteristics. Introducing 5–10 % 1-pentanol in blend improved combustion, elevating ICP and HRR, attributed to enhanced fuel atomization and oxygen content. The sustainable process index value obtained for a global index per person is 0.002×10−4cap Litre-1, which lies between 0 and 1, indicates sustainability and compatibility of biodiesel production demonstration by universal biofuels (Andhra Pradesh).

Aero-engine performance evaluation model in the entire flight envelope

Abstract An aero-engine performance characteristics mathematical model in the entire flight envelope is proposed based on the stratification analysis method and multi-condition test data. The thrust, fuel flow, low/high-pressure rotor speed, exhaust temperature, and other engine output parameters can be calculated in the entire flight envelope, according to the engine operating characteristic model and input parameters of aircraft flight altitude, velocity, ambient temperature, throttle angle, etc. Based on the evaluation model proposed in this paper, the evaluation data of an aero-engine is consistent with the test data, and the error is less than 5%. The research and model can support flight performance calculation and mission planning and can be applied to flight simulator engine modules.

Analisis Penyebab Kerusakan Mesin Diesel pada Generator Set untuk Tindakan Perawatan di Kapal Tanker MT. Sea Serenity

The generator set on the ship has the function of supplying electrical power needs. MT Tanker Ship. Sea Serenity is one of the ships that delivers goods between islands. In operation, the diesel generator engine on the MT. Sea Serenity sometimes experiences problems resulting in blackouts. This research aims to determine the factors that cause damage as well as appropriate maintenance actions and the method used is trouble shooting. Based on the research results, it is found that the causes of damage are: Engine knock, engine exhaust smoke, problems with the injection pump, worn plunger, damage to delivery valve seat, injection nozzle interference, valve opening pressure too low, engine output unstable, worn piston rings. Maintenance carried out: Injection timing must be adjusted according to ignition sequence, set injection timing, replace plunger and delivery valve seat, set valve opening pressure, replace spring, and clean injection orifice. On unstable engine output, the control rack stroke must be checked. Perform regular oil changes and regular tune ups

Theoretical and experimental study of unsteady behavior of marine diesel engine under fluctuating back pressure condition

In order to meet the increasingly urgent emission reduction requirements, underwater exhaust has emerged as a promising technology for marine diesel engines and has received widespread attention in recent years. However, under fluctuating high back pressure conditions, the diesel engine as a whole operates in an unsteady state, with significant fluctuations in output power and exhaust temperature, posing a serious threat to their stability and safety. This paper focuses on the unsteady operating characteristics of diesel engines and its impact on the engine output power, efficiency and safety under fluctuating high back pressure conditions. Firstly, based on model order reduction and the first Lyapunov approximation theorem, the theoretical derivation reveals that the filling and emptying effects in the intake and exhaust pipes and the shaft moment of inertia of the turbocharger are the main causes of the unsteady of the engine under fluctuating high back pressure conditions. The factors influencing the strength of engine unsteadiness, including wave period, wave amplitude, mean back pressure and engine properties, are identified. Then, diesel engine experiments under different fluctuating high back pressure conditions are performed and the conclusions derived from the theoretical analysis are validated through experiments. Based on the research results mentioned above, this paper proposes a criterion to determine the unsteadiness of diesel engines. Through comparison with experimental data, it is found that this criterion effectively reflects the strength of engine unsteadiness, with a correlation coefficient of 0.9169 with the unsteady parameter.

Impact of diesel/renewable fuels blend on gaseous and particle emissions of a light-duty vehicle under real driving emissions

In this study, the environmental impact of Bio-butanol (BB) and Hydrotreated Vegetable Oil (HVO) use, each blended with diesel fuel, was investigated. To this aim, pollutant emissions were measured at the exhaust of an Euro 5 commercial light-duty vehicle, fueled with diesel, BB/diesel blend (10 %v BB) and HVO/diesel blend (15 %v HVO). The experimental activity was realized by performing Real Driving Emission (RDE) tests in Napoli (Italy), which allowed to measure total hydrocarbons (THC), carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOX), and particle emissions during the real use of vehicle by using a portable emission measurement systems (PEMS). The results show that the differences between fuels are small and not statistically significant for all emission parameters. CO and THC emissions increase with the renewable fuel blends, mainly with the BB blend in the urban environment due to the increasing of cold start contribution. NOX emissions increased with BB (by approximately 4 %) and decreased with the HVO (by approximately 13 %). With both renewable fuel blends, particle emissions at engine output were reduced by 25 %–41 % and fuel consumption by 6 %–10 % compared with diesel.

Optimizing the model-prediction of date palm fronds-derived producer gas and third generation biodiesel powered dual-fuel engine by employing Bayesian-optimized Boosted Regression Trees for enhanced prognostics

The global issue concerning the dual problem of economic energy shortage as well as greenhouse gas emission needs urgent attention. Due to its abundance and carbon neutrality, waste biomass is a good energy conversion feedstock. This research suggests a unique method for using date palm frond waste biomass-derived producer gas and algal biodiesel as sustainable diesel engine fuels. The Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees were employed to optimize engine combustion and emission performance. This was further enhanced with the application of k-cross fold to prevent the model from overfitting during training. The working of dual-fuel engines is a complex phenomenon owing to the employment of two different types of fuel one is liquid (biodiesel + diesel) while the other one is gaseous (Producer gas). The present endeavor was undertaken to handle this with the application of advanced data learning methods. The engine was tested at different compression ratios, engine loads, and fuel injection pressures for brake thermal efficiency, diesel fuel substitution, and emission. Both the Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees could predict the engine output with good prediction efficiency with comparable performance, however, the Bayesian-optimized Boosted Regression Trees-based models were more robust with a coefficient of determination value in the range of 0.9617–0.9999 and mean squared errors as low as 0.001. The mean absolute percentage error was very low in the range of 0.0316–5.231. The Theil’s U2 values were in the range of 0.0029–0.0126.

Research on Algorithmic Gender Bias under the Paradigm of Machine Behavior Studies

In the field of communication studies, machine behavior specifically refers to information dissemination activities involving artificial intelligence technology. As algorithms increasingly become the primary force in information dissemination, their potential gender bias becomes increasingly apparent. This paper, based on three research scopes in machine behavior studies: individual behavior, collective behavior, and human-machine interaction behavior, examines the gender bias exhibited by artificial intelligence entities in algorithms at these three levels. At the individual behavior level, the tendency of algorithm development to simplify features overlooks the diversity present in female society. The inherited data bias and human bias make it difficult to avoid gender discrimination. At the collective behavior level, the creation of opinion leader-type social robots expands the subject of information fog, making the concealed gender discrimination against women more covert. The use of large-scale machine armies manipulates search engine results, leading to severe gender bias in search engine outputs. At the hybrid human-machine behavior level, artificial intelligence shapes female images to construct female cognitive thinking. Algorithms acquire human bias during interaction with users, and social robots amplify gender bias issues through mixed human-machine behavior.

Autonomous quantum heat engine based on non-Markovian dynamics of an optomechanical Hamiltonian

We propose a recipe for demonstrating an autonomous quantum heat engine where the working fluid consists of a harmonic oscillator, the frequency of which is tuned by a driving mode. The working fluid is coupled two heat reservoirs each exhibiting a peaked power spectrum, a hot reservoir peaked at a higher frequency than the cold reservoir. Provided that the driving mode is initialized in a coherent state with a high enough amplitude and the parameters of the utilized optomechanical Hamiltonian and the reservoirs are appropriate, the driving mode induces an approximate Otto cycle for the working fluid and consequently its oscillation amplitude begins to increase in time. We build both an analytical and a non-Markovian quasiclassical model for this quantum heat engine and show that reasonably powerful coherent fields can be generated as the output of the quantum heat engine. This general theoretical proposal heralds the in-depth studies of quantum heat engines in the non-Markovian regime. Further, it paves the way for specific physical realizations, such as those in optomechanical systems, and for the subsequent experimental realization of an autonomous quantum heat engine.

The Problem Statement of Optimal Petroleum Engineering Output with Respect to Various Techniques and Materials Used

The Problem Statement of Optimal Petroleum Engineering Output with Respect to Various Techniques and Materials Used

Fuel consumption estimation in heavy-duty trucks: Integrating vehicle weight into deep-learning frameworks

Insufficient consideration of vehicle weight dynamics during real-world driving could lead to inaccurate fuel consumption estimates. This study examined the impact of vehicle weight on fuel consumption rate (FCR) by analyzing extensive, high-resolution operating data obtained from 162 heavy-duty trucks (HDTs). An engine output power-based (EOP) model, an artificial neural network (ANN) model, and a long short-term memory-convolutional (LSTM-Conv) model were developed and contrasted with conventional vehicle specific power (VSP) and Virginia Tech Microscopic (VT-Micro) models. The results indicated a significant, non-linear relationship between weight and FCR. Compared to 5-ton trucks, FCR for trucks weighing 15–25 tons and 45–55 tons increased by 290% and 755%, respectively, under low-speed and positive acceleration conditions. The LSTM-Conv model outperformed the VSP, VT-Micro, and EOP models, achieving MAPEs of 9.81% for FCR and 1.49% for trip fuel economy estimation. The deep-learning models exhibited enhanced stability across varying speeds, accelerations, and vehicle weights.

Research on Adaptive Cycle Engine Mode Transition Control Method

Mode transition is an important dynamic process of an adaptive cycle engine (ACE). In order to obtain a smooth mode transition process, the closed-loop controller is designed based on the strong robust augmented linear quadric regulator (ALQR) method, and with the objective of minimizing the thrust fluctuation in the process of mode transition, an open-loop geometrical mechanism control schedules optimization method based on Bézier curves is proposed, so that the closed-loop control and the open-loop control can work in coordination. The simulation results at the subsonic cruise operating point and supersonic cruise operating point show that based on the optimized open-loop geometrical mechanism control schedules and the designed closed-loop ALQR control system, the ACE achieves fast and smooth geometrical mechanisms and engine output transition during the mode transition process with a maximum thrust fluctuation of 2.58%, which is much smaller than that of the traditional linear variation geometric mechanisms with a maximum 4.64% thrust fluctuation, which verifies the effectiveness of the proposed control method.