Explore the vast range of titles available.

The lubrication system supplies lubrication and cleans the rotating parts and contacting machinery during the operation of an aero-engine. It is crucial to maintain an adequate amount of lubricant by predicting and analyzing the consumption rate to ensure endurance and maintenance programs are effective. This paper examines the combination of temporal and non-temporal data that impact the characteristic parameters of lubricant consumption rate in aero-engines. Our study focuses on the merging of LSTM (Long Short-Term Memory) + LightGBM (Light Gradient Boosting Machine) + CatBoost, and uses KPCA dimensionality reduction optimization, along with Stacking for the fusion of a multi-feature regression prediction algorithm. On the one hand, this study utilizes integrated learning to fuse feature extractions from LSTM for temporal information and non-temporal information by GDBT (Gradient Boosting Decision Tree). This approach considers the trend and distribution of feature samples to develop a more robust feature extraction method. On the other hand, the integrated learning framework incorporates multi-decision making and feature importance extraction to strengthen the mapping relationship with the predicted output of lubrication oil consumption rate, enabling regression prediction. The algorithm for regression prediction has been executed and the results indicate a final regression prediction MAPE (Mean Absolute Percentage Error) of less than 3%. MSE and RMSE reached 1.28% and 1.33%, the results are in an ideal state. The algorithms used in this paper will be applied in the future to aero-engine lubricant systems and eventually to engines in general.

Accurate prediction of oil consumption plays a dominant role in oil supply chain management. However, because of the effects of the coronavirus disease 2019 (COVID-19) pandemic, oil consumption has exhibited an uncertain and volatile trend, which leads to a huge challenge to accurate predictions. The rapid development of the Internet provides countless online information (e.g., online news) that can benefit predict oil consumption. This study adopts a novel news-based oil consumption prediction methodology–convolutional neural network (CNN) to fetch online news information automatically, thereby illustrating the contribution of text features for oil consumption prediction. This study also proposes a new approach called attention-based JADE-IndRNN that combines adaptive differential evolution (adaptive differential evolution with optional external archive, JADE) with an attention-based independent recurrent neural network (IndRNN) to forecast monthly oil consumption. Experimental results further indicate that the proposed news-based oil consumption prediction methodology improves on the traditional techniques without online oil news significantly, as the news might contain some explanations of the relevant confinement or reopen policies during the COVID-19 period.

Since the development of diesel engine, maritime industry shows an upward trend. As the consequence there is an increasing amount of air pollutant, specifically CO2. As the global authorities, IMO take an action written in MEPC 304(72) to reduce greenhouse gas emission. This research aims to find out Energy Efficiency Operational Indicator (EEOI), in Indonesian flagged containership MV 01 and MV 02 and benchmark the result. Method proposed by Trozzi, Jalkanen, Wang and Mersin were used to estimate the fuel oil consumption then the obtained results were compared with fuel monitoring from ship’s bunker report. The resulted error for each method consecutively are 100,53%, 24,3%, 18,44%, and 75889%. Mersin method then modified and resulted error 36,22%. Average EEOI index for MV 01 is 0,000473 tonCO2/TEUS.nm and MV 02 0,000630 tonCO2/TEUS.nm. The result from benchmarking using EEOI variables indicated that in MV 02 has lower operational efficiency than MV 01. MV 01 carried more average cargo in voyage route Surabaya-Semarang and Surabaya-Sampit. From average total fuel oil consumption, MV 02 consumed more fuel oil in voyage route Surabaya-Sampit, Surabaya-Kumai, and Semarang-Kumai. The proposed improvement in EEOI for both vessels is by improving ship cargo management for shipping efficiency. While for MV 02, checking for technical operational aspect is needed especially on machinery system and hull conditions.

In the maritime industry, optimizing vessel fuel oil consumption is crucial for improving energy efficiency and reducing shipping emissions. However, effectively utilizing operational data to advance performance monitoring and optimization remains a challenge. An XGBoost Regressor model was developed using a comprehensive dataset, delivering strong predictive performance (R2 = 0.95, MAE = 10.78 kg/h). This predictive model considers operational (controllable) and environmental (uncontrollable) variables, offering insights into complex FOC factors. To enhance interpretability, SHAP analysis is employed, revealing ‘Average Draught (Aft and Fore)’ as the key controllable factor and emphasizing ‘Relative Wind Speed’ as the dominant uncontrollable factor impacting vessel FOC. This research extends to further analysis of the extremely high FOC point, identifying patterns in the Strait of Malacca and the South China Sea. These findings provide region-specific insights, guiding energy efficiency improvement, operational strategy refinement, and sea resistance mitigation. In summary, our study introduces a groundbreaking framework leveraging machine learning and SHAP analysis to advance FOC understanding and enhance maritime decision making, contributing significantly to energy efficiency and operational strategies—a substantial contribution to a responsible shipping performance assessment under tightening regulations.

As per pieces of literature, 40 to 60 % of friction losses of Internal combustion engines occur in their piston-piston rings-liner assemblies and, there is a significant supportive role of simulation in improving this assembly. Literature is also available which tells, how changes in pistons affect oil consumption. Thus, piston dynamics is also important for oil consumption. Furthermore, the results from the simulation module of piston movement also serve as a significant input for postprocessing to calculate piston ring dynamics. This research is conducted to understand the piston secondary motion effect on oil consumption, friction, and blow-by. In this work, the results of ring dynamics and oil consumption simulation modules are studied with consideration and non-consideration of piston secondary motion results. The results like minimum oil film thickness, lubricating oil consumption, friction, friction power loss, and blow-by are investigated. Results indicate that oil throw-off and the top ring oil scraping occur when piston secondary motion is considered. Moreover, with piston secondary motion consideration, there is a significant rise in blow-by gases during the compression stroke and a marginal effect on friction power loss.

Route planning procedures for ocean-going vessels depend significantly on prevailing weather conditions, the ship’s design characteristics and the current operational state of the vessel. The operational status considers hull and propeller fouling, which significantly affects fuel oil consumption coupled with route selection. The current paper examines the effect of the fouling level on the selection of the optimized route compared with the clean hull/propeller as well as the orthodrome/loxodrome route. A developed weather routing tool is utilized, which is based on a physics-based model for the calculation of the main engine’s fuel oil consumption enriched to account for different fouling levels of the hull and the propeller. A genetic algorithm is employed to solve the optimization problem. A case regarding a containership in trans-Atlantic transit using forecasted weather data is presented. The effect of ocean currents is also examined as it was derived that they greatly affect route selection, revealing a strong dependence on the level of fouling. Ignoring the fouling impact can result in miscalculations regarding the estimated fuel oil consumption for a transit. Similarly, when ocean currents are ignored in the route planning process, the resulting optimal paths do not ensure energy saving.

Recently, interest in the design and construction of smart ships has been widely increasing. Optimal route planning is a widely studied essential aspect of smart ship technology. Planning an optimal route requires an accurate estimation of the fuel oil consumption of a ship. Various studies have suggested methods for theoretically estimating the fuel oil consumption. However, the calculation methodology and accuracy are different for each method. In addition, in commercial software, a statistical model based mainly on operating data has been used. Therefore, in this study, we propose a method based on ISO 15016:2015 for estimating the fuel oil consumption of a ship by improving the ISO 15016:2002 method—which has been predominantly used in existing studies. Moreover, the accuracy of the proposed method is examined by comparing it with a gray box model based on operating data. The results confirm that the proposed method can be used for estimating the fuel oil consumption of a ship.

Coastal regions worldwide face increasing air pollution due to maritime activities. This technical note focuses on assessing the air pollution in the Daesan port area, Republic of Korea, using hourly emission measurements. Leveraging Automatic Identification System (AIS) data, we estimate vessel-induced air pollutant emissions and correlate them with real-time measurements. Vessel navigational statuses are categorized from the AIS data, enabling an estimation of fuel oil consumption. Random Forest models predict specific fuel oil consumption and maximum continuous ratings for vessels with unknown engine details. Using emission factors, we calculate the emissions (CO2, NO2, SO2, PM-10, and PM-2.5) from vessels visiting the port. These estimates are compared with actual air pollutant concentrations, revealing a qualitative relationship with an average correlation coefficient of approximately 0.33.

Under the global efforts to implement strict emission regulations, higher requirements are placed on diesel engines, and a reasonable piston ring set structure can effectively reduce diesel engine lubricating oil consumption. Taking an off-road high-pressure common rail diesel engine as the research object, the numerical simulation model of piston ring assembly dynamics was built by measuring the cylinder pressure and the temperature field of the piston and cylinder liner and combined with the test results of the gas blow-by and lubricating oil consumption. The influence of piston ring assembly structure parameters on lubricating oil consumption was systematically studied, and the mathematical regression model of lubricating oil consumption was established by using the response surface method. Based on it, the optimal solution of the piston ring assembly parameters was obtained by desirability optimization. The results show that the thickness of oil ring scraping edge will have the greatest influence on lubricating oil consumption and the lowest significance of the thickness of second ring scraping edge. With the increase of the upper end reduction of the top ring and the thickness of the oil ring scraping edge, the lubricating oil consumption gradually decreases. The optimal solution with the desirability of 1 is 0.02 mm reduction of the upper end of the top ring, 0.40 mm thickness of the oil ring scraping edge and 0 mm thickness of the second ring scraping edge. And the lubricating oil consumption is 3.07 g/h, which is 15.89% less than that of the original machine.

The three-piece oil control ring (TPOCR), traditionally used in light-duty gasoline engines, is becoming a viable option for heavy-duty gas and hydrogen engines due to its ability to control lubricating oil consumption (LOC) under throttled conditions. Understanding the distribution of oil inside the TPOCR groove, as well as the effects of rail gap and drain hole positions, is critical for optimizing TPOCR and groove designs. In this work, a one-dimensional oil distribution model was developed to simulate inertia-driven oil transport in the TPOCR groove. A novel approach was proposed by first dividing the TPOCR into units composed of a pair of expander pitches. Then, the relationship between the oil outflow rate of the unit and its oil mass was established with the help of three-dimensional two-phase computational fluid dynamics (CFD) simulations. This relationship was then used to model one-dimensional oil transport along the circumference of the TPOCR groove. Incorporating the boundary conditions at the rail gaps and drain holes, this simple model can complete computations for 10,000 cycles within a few seconds, allowing for quick the evaluation of transient behavior and design iterations. Studies on low-load conditions show that the model, with reasonable adjustment for the boundary conditions, can match the oil distribution patterns observed in visualization experiments. This is the first step toward studying oil transport in the TPOCR groove before involving the effects of gas flows.