Search Results (321)

With the increased power density of internal combustion engines (ICE) and growing demands for lightweight design, the connecting rod big-end bearings are subjected to significant alternating loads. Consequently, the interference–fit interfaces become susceptible to fretting damage, which can markedly shorten engine service life and impair reliability. In the present study, the effects of the big end manufacturing process, bolt preload, and bearing bush interference fit are considered to develop a coupled lubrication–dynamic model of the connecting rod big-end bearing. This model investigates the fretting damage issue in the bearing bush of a marine diesel engine’s connecting rod big end. The results indicate that the relatively low stiffness of the big end is the primary cause of bearing bush fretting damage. Interference fit markedly affects fretting wear on the bush back, whereas the influence of bolt preload is secondary; nevertheless, a decrease in either parameter enlarges the fretting distance. Based on these findings, an optimized design scheme is proposed. Full article

Controlling nitrogen oxide (NO_x) emissions is a critical priority for the maritime industry, driven by increasingly stringent international maritime organization (IMO) Tier III regulations and the sector’s broader decarbonization efforts. Accurate prediction and minimization of NO_x emissions require well-calibrated engine models that reflect real-world operating behavior under varied conditions. This study presents a robust calibration methodology for the NO_x emissions model of a high-speed dual-fuel marine engine, using a 1D engine simulation platform (WAVE 2025.1) integrated with a nonlinear optimization algorithm (fmincon in MATLAB R2025a). The calibration focuses on tuning the extended Zeldovich mechanism by empirically adjusting the Arrhenius equation coefficients to achieve a weighted sum of NO_x and unburned hydrocarbon (HC) emissions below the 7.2 g/kWh regulatory threshold. The proposed approach reduces the need for extensive experimental data while maintaining high predictive accuracy. Simulation results confirm compliance with IMO regulations across multiple engine loads defined by the E3 test cycle. A sensitivity analysis further revealed that while the pre-exponent multiplier (ARC1) plays a critical role in influencing NO_x emissions at high loads, the exponent multiplier (AERC1) has an even more significant impact across the full load range, making its precise calibration essential for robust emissions modeling. The calibrated NO_x emissions model not only ensures realistic emissions estimation but also provides a reliable foundation for further research, such as dual-fuel performance studies, and can be effectively integrated into future engine optimization tasks under different operating conditions. Full article

This study aimed to investigate the effects of diesel/ethanol/n-butanol mixed fuel on the marine diesel engine combustion and emissions at different ethanol blending ratios, different single injection times, and pre-injection times. In addition, this study takes the injector fault phenomenon as an example, simulates the three fault phenomena of the injector, and uses a variety of algorithms to optimize the probabilistic neural network model to achieve the fault state identification and diagnosis of the injector. The results of research showed that, with the increase in the ethanol blending ratio, the peak cylinder pressure shows a decreasing trend. The ignition delay period is extended, and the peak instantaneous heat release rate increases. Compared with D100, the nitrogen oxide (NO_x) emissions of D50E40B10 mixed fuel are reduced by 12.3%, soot emissions are reduced by 29.18%, and carbon monoxide (CO) emissions are increased by 5.7 times. With the injection time advances, the peak values of cylinder pressure and heat release rate show an increasing trend, soot emissions gradually decrease, and NO_x and CO emissions gradually increase. The peaks of the cylinder pressure and heat release rate in the pilot injection stage gradually decrease as the pilot injection time advances, while the peak heat release rate in the main injection stage increases. In terms of emissions, NO_x emissions first decrease and then increase as the pilot injection time advances, while soot emissions gradually increase. The average accuracy of the PSO-PNN neural network model reaches 90%, and the average accuracy of the WOA-PNN neural network model reaches 95%. Therefore, the WOA-PNN neural network model is determined to be the optimal injector fault diagnosis model, which can be applied to the identification and diagnosis of injector fault states of diesel engines. Full article

Algal biodiesel is a promising renewable energy source due to its high lipid productivity and environmental benefits compared to conventional diesel fuels. This study presents a SWOT technique (strengths, weaknesses, opportunities, and threats) and an analytical hierarchy process (AHP) to assess the current status and future prospects of algae-based biodiesel production. Data from the last decade on algae production was analysed, highlighting significant technological improvements such as genetic engineering, novel extraction techniques, and integration with circular economy approaches. The results show that algal biodiesel can achieve a lipid content of up to 75% of dry biomass and reduce greenhouse gas emissions by up to 90% compared to fossil diesel. Key strengths include high biomass yield and effective CO₂ sequestration, while challenges include scaling production and reducing capital costs. Opportunities lie in product diversification and policy support, while threats include competition from battery electric vehicles and regulatory barriers. The AHP analysis provides a quantitative framework for prioritising strategies to improve the economic viability and environmental sustainability of algae biodiesel. In the short term (by 2030), algae-based biodiesel is expected to be used mainly as a blend with fossil diesel and to gain traction in niche applications where electric vehicles face competitiveness challenges (marine and heavy road transport). In the long term (by 2050), algae-based biodiesel will play a role in certain sectors that are integrated into the circular economy. Full article

The maritime industry, while indispensable to global trade, is a significant contributor to greenhouse gas (GHG) emissions, accounting for approximately 3% of global emissions. As international regulatory bodies, particularly the International Maritime Organization (IMO), push for ambitious decarbonization targets, hydrogen-based technologies have emerged as promising alternatives to conventional fossil fuels. This review critically examines the potential of hydrogen fuels—including hydrogen fuel cells (HFCs) and hydrogen internal combustion engines (H2ICEs)—for maritime applications. It provides a comprehensive analysis of hydrogen production methods, storage technologies, onboard propulsion systems, and the associated techno-economic and regulatory challenges. A detailed life cycle assessment (LCA) compares the environmental impacts of hydrogen-powered vessels with conventional diesel engines, revealing significant benefits particularly when green or blue hydrogen sources are utilized. Despite notable hurdles—such as high production and retrofitting costs, storage limitations, and infrastructure gaps—hydrogen holds considerable promise in aligning maritime operations with global sustainability goals. The study underscores the importance of coordinated government policies, technological innovation, and international collaboration to realize hydrogen’s potential in decarbonizing the marine sector. Full article

Driven by stringent emission regulations, advanced combustion modes utilizing turbulent jet ignition technology are pivotal for enhancing the performance of marine low-speed natural gas dual-fuel engines. This review focuses on three novel combustion modes, yielding key conclusions: (1) Compared to the conventional DJCDC mode, the TJCDC mode exhibits a significantly higher swirl ratio and turbulence kinetic energy in the main chamber during initial combustion. This promotes natural gas jet development and combustion acceleration, leading to shorter ignition delay, reduced combustion duration, and a combustion center (CA50) positioned closer to the Top Dead Center (TDC), alongside higher peak cylinder pressure and a faster early heat release rate. Energetically, while TJCDC incurs higher heat transfer losses, it benefits from lower exhaust energy and irreversible exergy loss, indicating greater potential for useful work extraction, albeit with slightly higher indicated specific NOx emissions. (2) In the high-compression ratio TJCPC mode, the Liquid Pressurized Natural Gas (LPNG) injection parameters critically impact performance. Delaying the start of injection (SOI) or extending the injection duration degrades premixing uniformity and increases unburned methane (CH₄) slip, with the duration effects showing a load dependency. Optimizing both the injection timing and duration is, therefore, essential for emission control. (3) Increasing the excess air ratio delays the combustion phasing in TJCPC (longer ignition delay, extended combustion duration, and retarded CA50). However, this shift positions the heat release more optimally relative to the TDC, resulting in significantly improved indicated thermal efficiency. This work provides a theoretical foundation for optimizing high-efficiency, low-emission combustion strategies in marine dual-fuel engines. Full article

This study creates and tests a machine learning model that can predict fuel use and emissions (NO_x, CO₂, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel experiments. Subsequently, we evaluated its ability to transfer by employing the parameters associated with waste cooking oil (WCO) biodiesel and its 60/40 diesel mixture. The machine learning model demonstrated exceptional proficiency in forecasting diesel mode (R² > 0.95), effectively encapsulating both long-term trends and short-term fluctuations in fuel consumption and emissions across various load regimes. Upon the incorporation of WCO data, the model maintained its capacity to identify trends; however, it persistently overestimated emissions of CO, HC, and PN. This discrepancy arose primarily from the differing chemical composition of the fuel, particularly in terms of oxygen content and density. A significant correlation existed between indicators of incomplete combustion and the utilization of fuel. Nonetheless, NO_x exhibited an inverse relationship with indicators of combustion efficiency. The findings indicate that the model possesses the capability to estimate emissions in real time, requiring only a modest amount of additional training to operate effectively with alternative fuels. This approach significantly diminishes the necessity for prolonged experimental endeavors, rendering it an invaluable asset for the formulation of fuel strategies and initiatives aimed at mitigating carbon emissions in maritime operations. Full article

This study investigates the impact of different combustion control strategies on marine engine combustion and emission characteristics at a high ammonia energy ratio. Compared to the strategy of maintaining a constant fuel injection duration, the strategy of keeping the fuel injection pressure constant allows the kinetic energy of diesel to remain at a higher level. This results in an increase in combustion efficiency and indicated the thermal efficiency of the engine, while also reducing CO₂ and soot emissions. However, when the ammonia energy ratio increases to more than 50%, the indicated thermal efficiency starts to decrease along with the increase in the emissions of N₂O and unburned ammonia. To address these issues, one of the potential means is to improve the in-cylinder combustion environment by increasing the in-cylinder gas temperature. This can enhance combustion efficiency and ultimately optimize the performance and emission characteristics of dual-fuel engines, which results in an increase in the combustion efficiency to 98% and indicated thermal efficiency to 54.47% at a relatively high ammonia energy ratio of 60%. Emission results indicate that N₂O emissions decrease from 1099 ppm to 25 ppm, while unburned ammonia emissions drop from 16016 ppm to 100 ppm. Eventually, the greenhouse gas emissions were reduced by about 85.3% in comparison with the baseline case. Full article

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations. Full article

Ocean-going vessels rely on marine diesel engines, referred to as the main engine, to carry the vessel’s load and ensure safe travel. These engines play a critical role, as their operation impacts on all aspects of the vessel’s functionality. To meet increasing demands for extended run times while maintaining reliability, it is essential to address the risks of main engine failure. Previous studies have highlighted numerous accidents resulting from such failures. Consequently, the reliability of the main propulsion engine is a crucial component of safe vessel operation. This study addresses the lack of methodologies for predicting engine reliability using failure running hours (FRHs). A data-driven model was developed using FRH data collected from marine engineers during on-board maintenance operations. Additionally, fault tree analysis (FTA) was employed to calculate the reliability of individual subsystems and the overall main propulsion engine. The findings indicate that the lube oil system, freshwater cooling system, scavenge system, and fuel system reach 0% reliability at approximately 2000 h, 14,000 h, 2500 h, and 1400 h of operation, respectively. Additionally, the reliability of the main propulsion engine drops to 0% after around 900 h of operation. By incorporating this prediction model, ship operators can better schedule maintenance, significantly enhancing engine reliability and reducing maritime accidents. This approach contributes to safer and more efficient operations for commercial marine systems. This study represents a vital step toward improving the reliability of ocean-going vessels. Full article

This study investigates the degradation characteristics of turbocharger nozzle rings in marine diesel engines by conducting numerical analysis and solid particle erosion (SPE) tests to examine their structural stability and morphological surface damage trends. The fatigue analysis was conducted under a load condition corresponding to 100% output of the main engine, using ANSYS software. The SPE test was conducted in accordance with ASTM G76-05 standards, and the weight loss and erosion rate were calculated. Surface damage was closely examined through 3D analysis and scanning electron microscopy (SEM). The flow analysis revealed that the loads were highly concentrated at the nozzle ring inlet and the leading edge of the blades, with a maximum pressure coefficient of 0.07678 MPa. The load decreased toward the trailing edge of the nozzle ring, and the surface pressure coefficients of the flange, inner hoop, and outer hoop—where the nozzle ring blades are fixed—were found to be nearly identical. The fatigue life of the nozzle ring under 100% engine load was calculated as 1.377e⁺⁷ cycles, with a fatigue damage value of 1.32e⁺³². Notably, the fatigue life in the regions near the inner and outer hoops of the nozzle ring approached zero. The results of the SPE test using spherical SiO₂ particles confirmed that the surface damage of the nozzle ring material, 316L stainless steel, followed a typical ductile material damage mechanism. In addition, the surface damage characteristics were significantly influenced by SPE test parameters such as the shape of solid particles, nozzle diameter, and impact angle. Full article

Ammonia fuel is regarded as a promising zero-carbon alternative to diesel in next-generation marine engines. However, the high-temperature ammonia-rich environment poses significant corrosion challenges to hot-end components such as valves. This study investigates the corrosion behavior of Ni80A alloy marine valves under the coupled effects of a high temperature and ammonia atmosphere. Using computational fluid dynamics (CFD), the service temperature of the valve and the ammonia concentration distribution inside the engine cylinder were identified. High-temperature corrosion experiments were conducted with a custom-designed setup. Results show that corrosion kinetics accelerated markedly with temperature: the initial corrosion rate at 800 °C was four times that at 500 °C, and the maximum corrosion layer thickness reached 37 μm—double that at lower temperatures. Microstructural analysis revealed a transition from a dense, defect-free corrosion layer at 500 °C to a non-uniform layer with coarse CrN particles and aggregated nitrides at 800 °C. Notably, surface hardness increased at both temperatures, peaking at 590 HV at 500 °C, while matrix hardness at 800 °C declined due to γ′ phase coarsening and grain growth. This work provides detailed insight into the temperature-dependent ammonia corrosion mechanisms of marine Ni-based alloy valves, offering essential data for material design and durability assessment in ammonia-fueled marine engines. Full article

In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, cyberattacks have become a growing menace, and SMG systems are commonly targeted by such attacks. This study proposes a framework for the frequency management of an SMG system using an innovative combination of a smart controller (i.e., the Fuzzy Logic Controller (FLC)) with three conventional cascaded controllers, including Fractional-Order PI (FOPI), Tilt Integral Fractional Derivative (TID^μ), and Proportional Integral Derivative Acceleration (PIDA). The recently released Eel and Grouper Optimization (EGO) algorithm is used to fine-tune the parameters of the proposed controller. This algorithm was inspired by how eels and groupers work together and find food in marine ecosystems. The Integral Time Squared Error (ITSE) of the frequency fluctuation (ΔF) around the nominal value is used as an objective function for the optimization process. A diesel engine generator (DEG), renewable sources such as wind turbine generators (WTGs), solar photovoltaics (PVs), and storage components such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) are all included in the SMG system. Additionally, electric vehicles (EVs) are also installed. In the beginning, the supremacy of the adopted EGO over the Gradient-Based Optimizer (GBO) and the Smell Agent Optimizer (SAO) can be witnessed by taking into consideration the optimization process of the recommended regulator’s parameters, in addition to the optimum design of the membership functions of the fuzzy logic controller by each of these distinct algorithms. The subsequent phase showcases the superiority of the proposed EGO-based FFOPI-TID^μ-PIDA structure compared to EGO-based conventional structures like PID and EGO-based intelligent structures such as Fuzzy PID (FPID) and Fuzzy PD-(1 + PI) (FPD-(1 + PI)); this is across diverse symmetry operating conditions and in the presence of various cyberattacks that result in a denial of service (DoS) and signal transmission delays. Based on the simulation results from the MATLAB/Simulink R2024b environment, the presented control methodology improves the dynamics of the SMG system by about 99.6% when compared to the other three control methodologies. The fitness function dropped to 0.00069 for the FFOPI-TID^μ-PIDA controller, which is about 200 times lower than the other controllers that were compared. Full article

This study examines the emissions generated by a tall ship of 81.36 m length under various operating conditions, focusing particularly on carbon dioxide emissions at different navigation speeds. The main purpose of the paper is to establish theoretical and practical methods for calculating and measuring the level of CO₂ emitted by the ship engines. Additionally, this article compares the results of carbon dioxide emission calculations based on theoretical methods with the results of real measurements. The paper verifies and assesses the carbon dioxide emission calculation methods compared to the emissions measured in real conditions for diesel engines. A comparative analysis of several methods for determining CO₂ emissions leads to much more accurate and conclusive results close to reality. The results obtained through empirical and theoretical methods for determining CO₂ emissions from the main engine demonstrate that the difference between these values is more accurate at lower engine loads but shows discrepancies at higher loads due to real-world inefficiencies, combustion variations, and model simplifications. The measured CO₂ emission values for auxiliary engines at 60% load demonstrate consistency and closely reflect real operating conditions, while analytical calculations tend to be higher due to theoretical losses and model assumptions. Stoichiometric values fall in between, assuming ideal combustion but lacking adjustments for real variables. This highlights the efficiency of the diesel generator and the importance of empirical data in capturing actual emissions more accurately. The investigation aims to provide a detailed understanding of CO₂ emission variations based on the ship’s operating parameters, including the study of these emissions at the level of the main diesel propulsion engine as well as the auxiliary engines. By analyzing these methods for determining engine emissions, conclusions can be reached about aspects such as the following: engine wear condition, efficiency losses, or incomplete combustion. This analysis has the potential to guide the implementation of new policies and technologies aimed at minimizing the carbon footprint of a reference ship, considering the importance of sustainable resource management and environmental protection in a viable long-term manner. Full article

Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine load, and operating conditions; (2) most research lacks comprehensive lifecycle assessment integration with real-time operational data; (3) previous optimization models demonstrate insufficient accuracy (R² < 0.80) for practical marine applications; and (4) no adaptive algorithms exist for dynamic biodiesel ratio adjustment based on operational conditions. These limitations prevent effective biodiesel implementation in maritime operations, necessitating an integrated multi-parameter optimization approach. This study addresses this research gap by proposing an integrated optimization model for fuel efficiency and emissions of marine diesel engines using biodiesel mixtures under diverse operating conditions. Based on extensive experimental data from two representative marine engines (YANMAR 6HAL2-DTN 200 kW and Niigatta Engineering 6L34HX 2471 kW), this research analyzes correlations between biodiesel blend ratios (pure diesel, 20%, 50%, and 100% biodiesel), engine load conditions (10–100%), and operating temperature with nitrogen oxides, carbon dioxide, and carbon monoxide emissions. Multivariate regression models were developed, allowing prediction of emission levels with high accuracy (R² = 0.89–0.94). The models incorporated multiple parameters, including engine characteristics, fuel properties, and ambient conditions, to provide a comprehensive analytical framework. Life cycle assessment (LCA) results show that the B50 biodiesel ratio achieves optimal environmental efficiency, reducing greenhouse gases by 15% compared to B0 while maintaining stable engine performance across operational profiles. An adaptive optimization algorithm for operating conditions is proposed, providing detailed reference charts for ship operators on ideal biodiesel ratios based on load conditions, ambient temperature, and operational priorities in different maritime zones. The findings demonstrate significant potential for emissions reduction in the maritime sector through strategic biodiesel implementation. Full article