Search Results (65)

The inlet operating conditions of centrifugal compressors in natural gas storage reservoirs, as well as the natural gas composition, continuously vary over time, significantly impacting compressor performance. To analyze the influence of these factors on centrifugal compressors, a method for converting the performance curves of centrifugal compressors under actual operating conditions has been established. This performance conversion process is implemented through a custom-developed program, which incorporates the polytropic index and exhaust temperature calculations. Verification results show that the conversion error of this method is within 2%. Based on the proposed performance prediction method for non-similar operating conditions, the effects of varying inlet temperatures, pressures, and natural gas compositions on compressor performance are investigated. It is observed that an increase in inlet temperature results in a decrease in compressor power and pressure ratio; an increase in inlet pressure leads to higher power consumption, while the pressure ratio varies with the flow rate at the operating point; and as the average molar mass of natural gas decreases, both the pressure ratio and power exhibit a certain degree of reduction. Full article

The performance of the turbocharger nozzle ring is a key factor in the overall operation of the main engine of the ship. Minimizing failure and damage caused by high exhaust gas temperature and pressure is essential. As a first step toward improving turbocharger safety, this study performed 3D scanning of an aged nozzle ring to obtain its precise geometry and developed a corresponding numerical model. The boundary conditions of the numerical model were defined by the exhaust gas temperature and pressure at various engine output loads. Structural safety was assessed using static structural and stress-life fatigue analyses. A sharp increase in maximum equivalent stress and strain was observed at output loads of 85% and higher. At 25% load, the maximum fatigue life indicated $1.76{ \times 10}^8$ cycles, while at 100% load, the maximum damage index reached 1. A field performance test conducted at 85% of the main engine’s output load revealed severe damage under high-load conditions. Specifically, damage occurred at the contact area between the outer hoop and the tip of the blade’s trailing edge. This observed damage pattern closely aligned with the results predicted by the fatigue life analysis. The validity of the present study was confirmed through a comparative analysis of the fatigue life predictions and the field test results. Full article

Highly over-mature marine shales are distributed worldwide with substantial resource potential, yet their pore structure characteristics and controlling mechanisms remain poorly understood, hindering accurate shale gas resource prediction and efficient development. This study focuses on the Cambrian Niutitang Formation shales in the Upper Yangtze region of South China. To decipher the multiscale pore network architecture and its genetic constraints, we employ scanning electron microscopy (SEM) pore extraction and fluid intrusion methods (CO₂ and N₂ adsorption, and high-pressure mercury intrusion porosimetry) to systematically characterize pore structures in these reservoirs. The results demonstrate that the shales exhibit high TOC contents (average 4.78%) and high thermal maturity (average Ro 3.64%). Three dominant pore types were identified: organic pores, intragranular pores, and intergranular pores. Organic pores are sparsely developed with diameters predominantly below 50 nm, displaying honeycomb, slit-like, or linear morphologies. Intragranular pores are primarily feldspar dissolution voids, while intergranular pores exhibit triangular or polygonal shapes with larger particle sizes. CO₂ adsorption isotherms (Type I) and low-temperature N₂ adsorption curves (H3-H4 hysteresis) indicate wedge-shaped and slit-like pores, with pore size distributions concentrated in the 0.5–50 nm range, showing strong heterogeneity. Pore structure shows weak correlations with TOC and quartz content but a strong correlation with feldspar abundance. This pattern arises from hydrocarbon generation exhaustion and graphitization-enhanced organic pore collapse under high compaction stress, which reduces pore preservation capacity. The aulacogen tectonic setting engenders proximal sediment provenance regimes that preferentially preserve labile minerals such as feldspars. This geological configuration establishes optimal diagenetic conditions for the subsequent development of meso- and macro-scale of dissolution pores. Our findings demonstrate that feldspar-rich shales, formed in a proximal depositional system with well-developed inorganic pores, serve as favorable reservoirs for the exploration of highly over-mature marine shale gas. Full article

Gas turbines play a crucial role in power generation and aviation, where effective maintenance strategies are essential to ensure reliability. Traditional condition monitoring methods often rely on scheduled inspections, leading to potential downtime and increased maintenance costs. This study presents an AI-driven approach for thermal condition monitoring and the predictive maintenance of gas turbines using machine learning. An Extreme Gradient Boosting (XGBoost)-based classification model was developed to distinguish between healthy and faulty operating conditions based on thermal load data. The dataset, collected over six months from strategically placed thermocouples in the exhaust gas section, was processed to extract key statistical features such as mean temperature, standard deviation, and skewness. The proposed XGBoost model achieved a classification accuracy (CA) of 97.2%, with an F1-score of 96.8%, precision of 97.5%, and recall of 96.1%, demonstrating its effectiveness in detecting anomalies. The results indicate that the integration of machine learning in gas turbine monitoring significantly enhances fault detection capabilities, enabling proactive maintenance strategies and reducing the risk of critical failures. This study provides valuable insights for data-driven maintenance strategies, optimizing operational efficiency and extending the lifespan of gas turbine components. Future work will focus on real-time deployment and further validation with extended datasets. Full article

Ships carrying large amounts of cargo and passengers are larger and slower than other modes of transportation. They are mostly foreign flagged and operate at sea far from coasts for 20 years or more, incurring more operating costs than construction costs. Therefore, an efficient maintenance system is necessary for stable, economical ship operation. Researchers are attempting to equip ships with predictive maintenance technology, which is used proactively in other modes of transportation to predict the maintenance time of machines through data monitoring and analysis. However, due to the nature of ship operation, data collection is difficult, and most studies focus on fault detection, hindering the application of predictive maintenance to ships. In this study, we developed a maintenance time prediction algorithm using the revision generator engine condition criterion (RGCCV) value and the cylinder exhaust gas temperature, as developed in a previous study for marine generator engines. And through comparison and verification using machine learning, the average mean absolute error (MAE) across all cylinders was 2.916 for the RGCCV-based method and 8.138 for the temperature-based method, demonstrating a 64% improvement. These findings establish a practical foundation for implementing predictive maintenance in ship engines by enabling more reliable and condition-based maintenance. Full article

The accurate estimation of exhaust gas temperature across the turbine is always more important for the optimization of engine performance, ensuring durability of the turbine impeller and catalyst, and reducing and calculating emissions concentration. Traditional physical modeling approaches, based on thermodynamic and fluid dynamics features of gas expansion, can be used for this purpose. However, recent advancements in machine learning, particularly neural networks, offer a data-driven alternative that may enhance prediction accuracy and computational efficiency. This study presents a methodology that integrates a semi-physical turbine model for estimating the exhaust gas temperature at the turbine outlet with a neural network-based approach for predicting the pressure at the turbine inlet. The model utilizes the exhaust gas temperature upstream of the turbine, a model for which was developed in a previous work of the authors. The models are calibrated with steady-state data and then evaluated based on accuracy and robustness under transient operating conditions on six driving cycles with different features. In this way, robust and reliable validation of the models is presented, since the testing is performed on various conditions not used for model development and calibration. Results show an average root mean square error of 14%, including the initial portions of driving cycles performed with a cold engine. Thus, the developed approach that includes multiple modeling methods shows a good predictivity, which is the main objective of this research activity. Full article

The health monitoring system has been the main technological approach to extending the life of gas turbine engines and reducing maintenance costs resulting from performance degradation caused by asymmetric factors like carbon deposition, damage, or deformation. One of the most critical techniques within the health monitoring system is performance degradation prediction. At present, most research on degradation prediction is carried out using NASA’s open dataset, C-MAPSS, without considering that monitoring measurements are not always available, as in the ideal dataset. This limitation makes fault diagnosis algorithms and remaining useful life prediction methods difficult to apply to real gas turbine engines. Therefore, to solve the problem of performance degradation prediction in light-duty gas turbine engines, a prediction diagram is proposed based on Long Short-Term Memory (LSTM). Various types of onboard signals are taken into consideration among the experimental data. Only accumulated usage time, total temperature and total pressure before the inlet, low-pressure rotor speed, high-pressure rotor speed, fuel flow rate, exhaust temperature, and thrust are used in the training process, which is indispensable for an aero-engine. A genetic algorithm (GA) is introduced into the training process to optimize the hyperparameters of LSTM. The performance degradation prediction modeled with the GA-LSTM method is validated using experimental data. The maximum prediction error of thrust is 70 daN, and the mean absolute percentage error (MAPE) is less than 0.04. This study provides a practical approach to implementing performance degradation prediction in health monitoring systems to improve gas turbine engine reliability, economy, and environmental performance. Full article

FT blends derived from biomass have been confirmed to benefit reductions in GHG and particulate matter (PM). An improvement in combustibility is predicted to reduce fuel consumption and lead to further emission reduction. Various FT fuel blends (7%, 10%, 23%, and 50%) were assessed in terms of their potential for energy savings and emission reduction in a ZF850 jet engine. The engine performance, including the thrust, fuel consumption, emissions, exhaust gas temperature (EGT), acceleration, and deceleration, was investigated in terms of the whole thrust output, while combustor performance parameters, including EI_UHC, EI_PM2.5, EI_CO, EI_Nox, and combustion efficiency, were also discussed. The benefit gained in engine performance was nonlinearly related to the blend ratio, which indicated that the available FT blends required appropriate fuel properties coupled with the engine design. According to the superior improvements derived from the 7% FT fuel blend and 23% FT fuel blend, an appropriate lower C/H ratio and higher combustion efficiency with low PM emissions led to a reduction in fuel consumption. Through global sensitivity analysis, changes in the thrust-specific fuel consumption (TSFC) and the thrust and combustion efficiency with various fuel properties were captured. These can be classified as engine-influenced and fuel-influenced (EIFI) parameters. EI_CO and EI_NOx are mainly dependent on the combustor and engine design and can be categorized as engine-influenced and fuel-less-influenced parameters (EIFLI), while EI_UHC and EI_PM2.5 can be categorized as EIFI parameters. The results of this work could extend our understanding of the impact of FT blends on engine performance and GHG reduction. Full article

Combined Cycle Power Plants (CCPPs) generate electrical power through gas turbines and use the exhaust heat from those turbines to power steam turbines, resulting in 50% more power output compared to traditional simple cycle power plants. Predicting the full-load electrical power output ($P_E$) of a CCPP is crucial for efficient operation and sustainable development. Previous studies have used machine learning models, such as the Bagging and Boosting models to predict $P_E$. In this study, we propose employing Super Learner (SL), an ensemble machine learning algorithm, to enhance the accuracy and robustness of predictions. SL utilizes cross-validation to estimate the performance of diverse machine learning models and generates an optimal weighted average based on their respective predictions. It may provide information on the relative contributions of each base learner to the overall prediction skill. For constructing the SL, we consider six individual and ensemble machine learning models as base learners and assess their performances compared to the SL. The dataset used in this study was collected over six years from an operational CCPP. It contains one output variable and four input variables: ambient temperature, atmospheric pressure, relative humidity, and vacuum. The results show that the Boosting algorithms significantly influence the performance of the SL in comparison to the other base learners. The SL outperforms the six individual and ensemble machine learning models used as base learners. It indicates that the SL improves the generalization performance of predictions by combining the predictions of various machine learning models. Full article

In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine’s performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits. Full article

The objective of this study is to implement a more realistic integrated analysis model for micro gas turbines by incorporating secondary flow and combustion efficiency into the existing model, which includes main engine components such as the compressor and turbine, and to validate this model by comparing it with test results. The study was based on the JetCat P300-RX, which has a maximum thrust level of 300 N. Simulations were performed using ANSYS CFX, employing the κ-ω SST turbulence model and a mixing plane interface between individual components. The eddy dissipation model (EDM), with a combustion efficiency of 90%, was used as the combustion model. A user subroutine was also applied for the power matching of the compressor and turbine to calculate the fuel flow rate in each iteration. For secondary flow, it was assumed that 3% of the total air flow rate would flow through the secondary path and be applied to the compressor and turbine. Simulations were conducted over a range of 30,000 to 104,000 RPM, with ground conditions evaluated, including altitude-simulated conditions. To validate the analysis model, engine performance metrics such as pressure ratio, air flow rate, fuel flow rate, and exhaust gas temperature (EGT) were compared with test results. The results demonstrated that errors were less than 5% for most engine performance metrics, except for EGT and fuel flow. The discrepancy in EGT was attributed to differences in the sensing methods, while the variation in fuel flow was found to be due to the lubrication system and losses due to the secondary air flow. Consequently, this study confirmed that the integrated simulation model accurately predicts engine performance. The results indicate that the integrated simulation model provides a more realistic prediction of overall engine performance compared to previous studies. Therefore, it can evaluate detailed thermo-fluid properties without the need for component performance maps, enhancing performance evaluation and analysis. Full article

This study investigates the thermal–hydraulic behavior and deposition characteristics of a shell and tube exhaust heat exchanger using a CFD-based predictive model of soot deposition. Firstly, considering the influences of thermophoretic, wall shear stress, and other deposition and removal mechanisms, a predictive model is developed for long-term performance of heat exchangers under soot deposition. Then, the variations in exhaust heat exchanger performance during a 4 h deposition period are simulated based on the model. Subsequently, the variation of deposition distribution and different deposition velocities are also evaluated. Finally, an analysis of the long-term performance of the exhaust heat exchanger under varying gas velocities and temperature gradients is conducted, revealing the performance variations under all engine-operating conditions. Results show that the deterioration in normalized relative ${j/f}^{1/2}$ varies from 5.26% to 24.91% under different work conditions, and the exhaust heat exchanger with high gas velocity and low temperature gradient exhibits optimal long-term performance. Full article

The global energy crisis, forced by fossil fuel shortages and supply chain disruption, stimulates EU policymakers to find alternative energy replacement. Modifying the present polyisoprene footwear production plant into a hybrid system by combining different energy sources raises energy efficiency. The proposed hybrid system incorporates classical and solar-based technology, resulting in energy optimization by utilizing waste heat recovery. By installing an economizer for feeding water preheating using flue gas recovery, it results in the volume of the flue gases lowering from ${\mathrm{v}}_{{\mathrm{FG}}_{\mathrm{P}}}=1.7969{{ \mathrm{m}}^3}_{\mathrm{FG}}/{\mathrm{kg}}_{\mathrm{P}}$ to ${\mathrm{v}}_{{\mathrm{FG}}_{{\mathrm{ECO}}_{\mathrm{P}}}}=1.597 {{\mathrm{m}}^3}_{\mathrm{FG}}/{\mathrm{kg}}_{\mathrm{P}}$, or by 11.13%, while the flue gases’ temperature is lowered from 204 °C (477.15 K) to 50.99 °C (324.14 K). Further improvement in combining feed water and air preheating results in natural gas savings of 12.05%, while the flue gases’ exhaust temperature is decreased to 30.44 °C (303.59 K). The third option, using condensate heat recovery and feeding water preheating using flue gases, showed natural gas savings as much as 17.41% and exhaust flue gases cooling to 112.49 °C (385.64 K). The combination of condensate heat recovery, combustion air and feed water preheating results in the volume of the flue gases being lowered by 20.42% and natural gas savings by 20.24%, while the flue gases’ temperature is reduced to 45.11 °C (318.26 K). The proposed solar application in polyisoprene production predicts the hybrid system showing fuel savings ranging from 77.96% to 87.08% in comparison to the basic process. The greatest fuel savings of 87.08% is shown in a solarized polyisoprene footwear production plant with combustion air and feed water preheating combined with the condensate return system. Integrating the solar heat into the regular industrial process of polyisoprene production showed great potential and showed environmental sustainability through energy optimization in polyisoprene production. Full article

The transition to sustainable energy sources is critical for addressing global environmental challenges. In 2017, Egypt produced about 500,000 tons of waste cooking oil from various sources including food industries, restaurants and hotels. Sadly, 90% of households choose to dispose of their used cooking oil by pouring it down the drain or into their village’s sewers instead of using proper disposal methods. The process involves converting waste cooking oil (WCO) into biodiesel.This study introduces a multi-criteria decision-making approach to identify the optimal biodiesel blend from waste cooking oils in Egypt. By leveraging the grey relational analysis (GRA) combined with the technique for order preference by similarity to the ideal solution (TOPSIS), we evaluate eight biodiesel blends (diesel, B5, B10, B20, B30, B50, B75, B100) against various performance metrics, including carbon monoxide, carbon dioxide, nitrogen oxides, hydrocarbons, particulate matter, engine power, fuel consumption, engine noise, and exhaust gas temperature. The experimental analysis used a single-cylinder, constant-speed, direct-injection eight cylinder diesel engine under varying load conditions. Our methodology involved feature engineering and model building to enhance predictive accuracy. The results demonstrated significant improvements in monitoring accuracy, with diesel, B5, and B20 emerging as the top-performing blends. Notably, the B5 blend showed the best overall performance, balancing efficiency and emissions. This study highlights the potential of integrating advanced AI-driven decision-making frameworks into biodiesel blend selection, promoting cleaner energy solutions and optimizing engine performance. Our findings underscore the substantial benefits of waste cooking oils for biodiesel production, contributing to environmental sustainability and energy efficiency. Full article

The present work aims to investigate the newly modified transfer matrix method (MTMM) to predict an automotive exhaust muffler’s transmission loss (AEMTL). The MTMM is a mixed method between a 3D-CFD (Computation Fluid Dynamics in 3D), namely AVL FIRE^TM M Engine (process-safe 3D-CFD Simulations of Internal Combustions Engines), and the classic TMM for the exhaust muffler. For all the continuous and discontinuous sections of the exhaust muffler, the Mach number of the cross-section, the temperature, and the type of discontinuity of the exhaust gas flow were taken into consideration to evaluate the specific elements of the acoustic quadrupole that define the MTMM coupled with AVL FIRE^TM M Engine for one given muffler exhaust. Also, the perforations of intermediary ducts were considered in the new MTMM (AVL FIRE^TM M Engine linked with TMM) to predict the TL (transmission loss) of an automotive exhaust muffler with three expansion chambers. The results obtained for the TL in the frequency range 0.1-4 kHz agree with the experimental results published in the literature. The TMM was improved by adding the AVL FIRE^TM M Engine as a valuable tool in designing the automotive exhaust muffler (AEM). Full article