Search Results (270)

Studies on the use of LPG in maritime applications mostly evaluate emissions, engine performance, or system safety separately; approaches that integrate experimental findings with ship-level risks remain limited. This study aims to evaluate the trade-offs between the environmental advantages of LPG and energy performance and safety requirements within a common decision support framework. In the experimental phase, a single-cylinder gasoline–LPG spark-ignition engine was tested at five LPG mixture ratios and six load levels between 500–3000 W; specific fuel consumption, thermal efficiency, CO, CO₂, and HC were measured. Using legislation, the literature, and engineering evaluation, 38 failure types were identified from the experimental findings and prioritized using FMEA and entropy-weighted multi-criteria decision-making methods. The final ranking was obtained using the Borda method, and inter-method agreement and ranking stability were validated with sensitivity analyses. The results showed that increasing the LPG ratio reduced CO, CO₂, and HC emissions, but higher ratios increased fuel consumption and decreased thermal efficiency. Specific fuel consumption, gas detection error, and thermal efficiency were identified as the three most prioritized risks. The findings reveal that the emission benefits of LPG in maritime applications should be evaluated in conjunction with sensing, insulation, emergency stop reliability, and energy performance. This integrated approach provides a scientific basis for balanced and transparent fuel decisions. Full article

Reactivity-controlled compression ignition (RCCI) enhances engine performance while mitigating the diesel soot–NO_x tradeoff. In this work, natural gas (NG) RCCI combustion was investigated on a heavy-duty single-cylinder research engine with three different pilot fuels: diesel, an 80/20 (% v/v) blend of n-butanol and diesel (80B20D), and dipropyl oxymethylene ether (P1P). The experiments were performed at a constant speed of 1339 rev/min, a fixed load (IMEP_g = 5 bar), and 1.5 bar boost pressure. Initially, NG RCCI combustion was studied under identical operating conditions, and subsequently with constant combustion phasing (CA50) for all pilot fuel–NG combinations for a levelized comparison. The results revealed that CA50 profoundly impacted the efficiency and unburned hydrocarbon (HC) emissions for all pilot fuels. Maintaining an optimal CA50 of 363 ± 1 CAD, high fuel conversion efficiencies (~40%) and HC emission reductions (~38–49%) were achieved across pilot fuels. The pilot fuel reactivity significantly affected combustion and emissions. The apparent heat release histories transformed from a two-stage to a single-stage Gaussian profile at a much-retarded start of injection (SOI~30 bTDC) for 80B20D-NG compared to diesel–NG and P1P-NG (40 bTDC), leading to significantly lower NO_x emissions. More advanced SOIs and lower NO_x emissions were possible with diesel–NG and P1P-NG compared to 80B20D-NG. Full article

In recent years, hydrogen-fueled internal combustion engines offer significant potential for achieving high efficiency and near-zero carbon emissions. However, stable combustion remains challenging due to the limited time available for fuel–air mixing, particularly in direct-injection concepts. This study investigates the influence of injector orientation on in-cylinder charge preparation in a heavy-duty spark-ignition engine operating with a side-mounted hydrogen direct-injection strategy. Three-dimensional computational fluid dynamics (CFD) simulations are performed to evaluate the effects of injector blow-cap inclination and azimuthal alignment on hydrogen jet evolution, flow-field development, and mixture formation. Under high-pressure injection conditions, hydrogen enters the cylinder as a highly under-expanded jet with strong momentum, resulting in significant interaction with the in-cylinder flow field. The results show that injector inclination influences jet impingement behavior, wall-guided flow development, and subsequent vortex evolution, while injector rotation modifies the interaction between the jet trajectory and in-cylinder swirl motion, affecting aerodynamic shear and flow-field complexity. The resulting mixture formation is evaluated through local air–fuel ratio distribution together with flow-field analysis and streamline evolution, demonstrating strong sensitivity to injector orientation and its coupling with in-cylinder aerodynamic structures. Quantitatively, injector orientation produces significant changes in the local air–fuel ratio distribution, with up to 25% reduction in the standard deviation of local air–fuel ratio for inclination variations and up to 35% for azimuthal variations between the extreme configurations, indicating improved mixture uniformity. Configurations promoting earlier jet disruption and enhanced spatial dispersion achieve more homogeneous charge preparation, whereas stronger wall-guided jet attachment results in localized fuel-rich regions. The findings provide physical insight into the role of jet–wall interaction, aerodynamic shear, and vortex restructuring in governing hydrogen mixing processes. The simulation framework captures the relevant in-cylinder flow physics and provides trends consistent with available experimental observations in the literature, which report improved efficiency and reduced NOx emissions under enhanced mixture homogeneity conditions. Full article

This study evaluates the effects of novel metal-based fuel additives on the combustion, thermal behavior, and emissions of a common-rail diesel engine. Two transition metal complexes, Bis-[N-(1,1′-biphenyl)-2-chlorobenzoylthioureato]nickel(II) (NiL₂) and cobalt(II) (CoL₂), were synthesized and utilized as diesel additives for the first time. Experiments were conducted on a 1.5-L, four-cylinder engine at 1750 rpm under three load conditions: 50, 75, and 100 Nm. The results demonstrated that 25 ppm of NiL₂ and CoL₂ altered the combustion kinetics. At medium loads, the additives increased maximum cylinder pressure by 3% and shortened ignition delay at low loads. Peak heat release and heat transfer rates improved by 4% and 7%, respectively. CoL₂ exhibited the most pronounced thermal effect, raising average in-cylinder gas temperatures by up to 4% at high loads. However, despite these thermodynamic changes, the additives did not yield any reductions in NO, HC, or CO emissions; in fact, emission levels were generally similar to or slightly higher than those of neat diesel. These findings suggest that while these specific complexes act as combustion modifiers that enhance in-cylinder thermal parameters, they do not offer significant advantages regarding emissions under the tested configurations. Full article

Methanol is a promising alternative fuel for internal combustion engines; however, formic acid (methanoic acid, HCOOH) formed or enriched during engine operation can corrode cylinder liners and reduce cylinder liner–piston ring reliability. In this study, high-alloy bainitic gray cast iron cylinder liners were investigated in formic acid–NaCl media under three surface states: untreated substrate, quench–polish–quench (QPQ) treatment, and nitriding–oxidizing treatment. Static full-immersion tests were conducted in 5 and 10 vol% formic acid–NaCl solutions, and corrosion damage was evaluated by mass loss, mass-loss-equivalent average corrosion depth, and cross-sectional SEM-BSE observation. With increasing solution aggressiveness, all specimens showed increased corrosion depth and mass loss. For the untreated substrate, these values increased from 97.65 to 146.05 μm and from 929.8 to 1391.2 mg, respectively; the corresponding changes were 61.45–112.55 μm and 545.0–997.3 mg for QPQ-treated specimens and 55.25–119.00 μm and 488.7–1054.6 mg for nitriding–oxidizing-treated specimens. Under the lower-severity condition, nitriding–oxidizing and QPQ treatments reduced the mass-loss-equivalent corrosion depth by 43.42% and 37.07%, respectively. Cross-sectional observations indicate that flake graphite/bainitic-matrix microgalvanic coupling, formate-assisted dissolution, local degradation of modified surface regions, and defect-assisted electrolyte penetration jointly promoted inward corrosion. These results provide guidance for corrosion-resistant surface design of methanol-engine cylinder liners. Full article

The development of a marine ammonia–natural gas co-combustion engine aims to achieve high power with low carbon and low nitrogen oxide (NO_x) emissions. Using AVL Fire software, a numerical model of a marine dual-fuel engine with a large cylinder diameter and diesel ignition for ammonia and natural gas co-combustion was constructed. The effects of ammonia injection timing (AI) and natural gas injection timing (NGI) on the combustion process, as well as on NO_x and carbon dioxide (CO₂) emissions, were investigated. The results indicate that, under full-load, low-speed engine conditions, when the energy fractions of three fuels are constant, the heat release rate and pressure are more sensitive to AI. Advancing the injection timing of NH₃ and CH₄ can achieve higher indicated mean effective pressure and indicated thermal efficiency. Under various ammonia and natural gas injection strategies, the marine engine meets the NO_x emission requirements of International Maritime Organization’s Tier III. Furthermore, advancing AI or NGI reduces greenhouse gas emissions. Specifically, when ammonia is injected at a high pressure before the top dead center (TDC), advancing injection by every 2 degrees of the crank angle (°CA) reduces equivalent CO₂ emissions by 1.3%. Similarly, when natural gas is injected at a high pressure before the TDC, advancing injection by every 2 °CA reduces equivalent CO₂ emissions by 1.7%. Full article

With the rapid development of the hydrogen economy, Type IV composite pressure vessels have emerged as the core components of on-board hydrogen storage systems. However, accurate fatigue life prediction remains a critical bottleneck limiting their design optimization and safe operation. Existing methods often exhibit prediction errors exceeding ±50% due to the inherent scatter, anisotropy, and complex service environments of composites. This study proposes an improved simulation method for fatigue life prediction of Type IV cylinders. Systematic tension–tension fatigue tests were conducted on carbon fiber-reinforced polymer (CFRP) laminates at four ply angles (0°, ±15°, ±30°, ±45°) and PA6 liner at three temperatures (−30 °C, 25 °C, 82 °C) to establish comprehensive S-N curve databases. The results reveal that ply angle is the predominant factor governing CFRP fatigue performance, while temperature significantly influences PA6 behavior, and failure mode transitions from fiber fracture to matrix-dominated damage as ply angle increases. A fatigue analysis model was developed in nCode, incorporating the ply fatigue Algorithm to characterize the anisotropic fatigue behavior of CFRP overwraps. Full-scale validation on Type IV cylinders under cyclic pressure (2–87.5 MPa) confirmed the method’s effectiveness, achieving prediction errors of 11.5% and 35.3% for the two failed specimens, with failure locations well predicted. This study provides a rapid and reliable engineering calculation method and data support for the anti-fatigue design, safety assessment, and life management of Type IV cylinders. Full article

To address the bottlenecks of conventional valve-controlled marine steering systems—characterized by high throttling losses, low efficiency, and high leakage risk—as well as the insufficient power density and impact resistance of electro-mechanical actuators (EMAs) for high-load steering of large vessels, this paper proposes and validates a high-performance integrated solution for an electro-hydrostatic actuator (EHA) for ship steering. First, a fifth-order electro–hydraulic–mechanical coupled dynamic model comprising a permanent magnet synchronous motor, hydraulic pump, hydraulic cylinder, and load is established. The validity and applicability boundaries of three simplifying assumptions—neglecting leakage, pipeline pressure losses, and steady-state fluid compressibility effects—are quantitatively analysed, with a total introduced error ≤3%. These assumptions are justified under medium-pressure, short-pipeline, and well-sealed conditions typical of marine EHA systems. Second, a composite control architecture combining outer-loop sliding mode control with inner-loop motor PID dual-loop control is proposed. Parameter tuning is performed using pole placement for the sliding surface and the Ziegler–Nichols critical ratio method for the inner loops, effectively suppressing hydraulic system parameter perturbations and random wave-induced load disturbances. Quantitative comparisons show that the proposed method reduces overshoot by 11.63% and improves sinusoidal tracking accuracy by 90.13% compared to conventional single-loop PID control. An integrated drive-control structure is designed, and a three-phase full-bridge inverter main circuit with wide-voltage input capability—including EMI filtering, soft-start, and LC filtering—is developed to accommodate the ±20% voltage fluctuations typical of ship power grids, thereby enhancing system integration and grid adaptability. Phased bench tests demonstrate that the settling time from no-load start-up to 200 r/min is only 0.01 s. When a sudden 20 N·m load is applied, the speed drop is less than 3%, and the recovery time is less than 0.025 s. The steady-state steering angle error does not exceed 0.12°, the maximum average steering rate reaches 3.33°/s, and the steering response time is within 0.3 s. All core performance indicators exceed the general technical standards for marine steering systems, with a 65.7% improvement in steady-state accuracy and a 62.5% improvement in response speed over conventional PID control. The research findings provide an effective general technical solution and experimental data support for the performance optimization and engineering application of marine EHA systems. Full article

Local scour and vortex-induced vibrations around cylindrical structures in curved channels pose significant risks to the safety and stability of critical hydraulic infrastructure, such as bridge piers. To address these engineering challenges and elucidate the underlying flow mechanisms, this study conducts numerical simulations of flow past two side-by-side circular cylinders of equal diameter in a curved channel under subcritical conditions at Re = 3900, using the Realizable kε turbulence model. Spectral Proper Orthogonal Decomposition (SPOD) is introduced to quantitatively characterize the energy distribution and dominant coherent structures. Taking the spacing ratio L/D and the placement angle α as key design parameters, the flow field characteristics, modal energy distribution, and coherent structure evolution are systematically investigated for two side-by-side cylinders in three-dimensional straight and curved channels. The numerical results show that, in the straight channel, as L/D increases from 2 to 4, the flow field evolves from strong coupled interference to weak interaction. The vortex shedding frequency structure evolves from a single dominant frequency to a multi-frequency distribution with rich harmonic components, indicating a transition in wake dynamics from energy concentration to multimodal dispersion, accompanied by a significant improvement in flow stability. Under curved channel conditions, the results reveal an asymmetric flow field caused by pronounced energy concentration on the inner side of the channel. SPOD analysis further indicates that as the placement angle α increases from 30° to 90°, the modal energy distribution changes from concentrated to dispersed, the frequency spectrum broadens with enhanced harmonic components, and flow instability gradually intensifies. Overall, the spacing ratio L/D mainly governs the wake-interference pattern, whereas the placement angle α regulates the frequency structure and energy distribution. Among all the cases investigated, relatively favorable flow stability is achieved at L/D = 4 and α = 30°. The SPOD-derived modal energy distributions show that the streamwise fluctuation length of the dominant-mode energy is approximately 0.25 m at α = 30°, compared with 0.5 m at α = 90°, with the energy bandwidth nearly doubling. The combined CFD-SPOD approach effectively captures energy evolution and coherent structure characteristics of complex flows across spatial, temporal, and spectral dimensions. This enables a shift from conventional flow-field description to frequency-based mechanism analysis and provides a theoretical basis for structural layout optimization and scour protection in hydraulic engineering. Full article

The transportation sector is the primary consumer of vehicle fuel worldwide and is thus a major contributor to climate change via carbon dioxide (CO₂) emissions. In addition to severe environmental impacts, such as global warming, droughts, floods, and rising sea levels, these emissions have a negative effect on public health by increasing the prevalence of respiratory disease. Achieving environmental sustainability through regulatory oversight requires a strong understanding of vehicular fuel consumption and CO₂ emissions. However, accurate modeling of these remains challenging due to the complex non-linear relationships between various vehicular characteristics and the lack of interpretability of many predictive models. Traditional linear models often fail to capture high-dimensional data complexities, while black-box methods provide few actionable insights for policymaking. To address these gaps, we developed a robust and data-driven two-stage machine-learning (ML) framework designed to enhance model performance and reliability. First, we implemented standard data preprocessing, enhanced feature engineering, and hyperparameter tuning for 14 cutting-edge ML algorithms and three advanced modeling techniques to explore their predictive performance. Second, we introduced three interpretable explainable AI (XAI) approaches. These were evaluated on a publicly available Kaggle static dataset of 550 vehicles, dominated by gasoline-powered vehicles, with only two diesels and two electric vehicles. The tuned CatBoost model demonstrated strong predictive performance, achieving an impressive R² of 0.9260, a root mean square error (RMSE) of 1.1759, and a mean absolute error (MAE) of 0.8147. In parallel, we deterministically estimated CO₂ emissions from fuel consumption, which provide direct estimates of tailpipe emissions. To ensure transparency and model interpretability, we employed Shapley additive explanations, local interpretable model-agnostic explanations, and permutation importance to identify the key factors contributing to the model predictions. Across the explainability analyses, cylinder count, front-wheel drive (drive_fwd), and the displacement–year interaction were the primary contributors to the predicted combined miles per gallon; in other words, they strongly affected fuel consumption. Collectively, these findings demonstrate the ability of the proposed model to capture complex feature relationships; thus, it offers a valuable tool for researchers and policymakers in sustainability planning and emission control. Future research should focus on real-time driving or dynamic measurements data and enhancing practical applications to further reduce emissions and promote environmental sustainability. Full article

This study presents an integrated experimental–numerical approach for evaluating the wear behavior of three non-standardized hypereutectic aluminum–silicon (Al–Si) piston alloys based on the AlSi25CuCr system, namely AlSi25Cu4Cr (M1), AlSi25Cu5Cr (M3), and AlSi25Cu5Cr (M5). The wear coefficient was determined experimentally under boundary-lubrication conditions, while the contact conditions in the piston–cylinder system were evaluated using Finite Element Analysis (FEA) and implemented within the Archard wear model. The results reveal a pronounced inconsistency between hardness and wear resistance. Although hardness increases from 1363 MPa (M1) to 1677 MPa (M5), the corresponding wear depth increases from 13.94 nm to 27.61 nm per engine cycle. This behavior is attributed to differences in microstructural characteristics, particularly the morphology and distribution of silicon particles and intermetallic phases, which significantly influence the tribological performance of hypereutectic Al–Si alloys. The experimentally determined wear coefficient K also shows a significant increase, rising from 12.14 × 10⁻⁵ (M1) to 29.59 × 10⁻⁵ (M5). The lowest wear is observed for alloy M1, whereas M5 exhibits the poorest tribological performance. These findings demonstrate that microstructural characteristics, particularly the morphology and distribution of silicon particles and intermetallic phases, have a dominant influence over hardness in governing wear behavior. The main scientific contribution lies in the direct coupling of experimentally determined material properties with realistically simulated contact conditions, enabling a quantitative and physically consistent comparison of piston alloys under identical operating regimes. The proposed methodology provides a reliable framework for material selection and optimization of piston alloys with enhanced wear resistance. Full article

We present a unified GPU-accelerated framework for real-time Smoothed Particle Hydrodynamics (SPH) fluid simulation with two-way rigid body coupling, secondary particle effects, and volumetric rendering, implemented entirely within the Unity game engine. The framework employs a weakly compressible SPH formulation with O(n) count sort-based spatial hashing and introduces a signed distance field (SDF) coupling system that evaluates three representative geometric primitives, sphere, cylinder, and torus, of increasing topological complexity directly on the GPU. Bidirectional force exchange is achieved through lock-free atomic compare-and-swap impulse accumulation, enabling thousands of fluid particles to interact simultaneously with each rigid body without serialization. A GPU stream compaction–based secondary particle system generates and classifies foam, spray, and bubble effects in real time, while a volumetric rendering pipeline samples fluid density into a 3D texture for SDF-composited volume rendering without surface mesh extraction. A conditional kernel dispatch strategy eliminates GPU cycles for disabled subsystems, and dynamic buffer management reduces memory pressure through runtime allocation. The system sustains above 54 frames per second at four million particles on a consumer-grade GPU, with sub-linear frame time scaling and a 1.70× speedup from dynamic buffer allocation over static pre-allocation. Full article

Dimethyl ether (DME) is a promising alternative fuel for compression ignition (CI) engines due to its potential to simultaneously reduce nitrogen oxides (NO_x) and particulate matter (PM) emissions while maintaining diesel-equivalent power. However, its combustion behavior under varying injection timing and exhaust gas recirculation (EGR) conditions remains insufficiently characterized for practical calibration. This study investigates the combustion, emissions, and performance of DME relative to diesel using a fully instrumented John Deere 6068CI550 single-cylinder research engine modified for high-pressure common-rail DME operation. Baseline tests were conducted at three ISO 8178 C1 steady-state modes with matched combustion phasing, load, and EGR to isolate fuel property effects. Injection timing and EGR sweeps were then performed at 1600 rpm and 50% load. Results show that DME produces 10–35% lower NO_x and orders-of-magnitude lower PM than diesel while maintaining comparable thermal efficiency. DME exhibits a single-stage premixed heat release structure with reduced peak apparent heat release rates and 4–5° shorter combustion durations than diesel. Stable combustion was sustained up to 55% EGR, beyond which incomplete combustion increased carbon monoxide (CO), total hydrocarbons (THC), and fuel consumption. Optimal low-emission operation occurred near CA50 ≈ 16° ATDC and EGR levels of 30–40%. These findings demonstrate DME’s ability to mitigate the traditional diesel NO_x–PM tradeoff and support its viability as a low-emission CI fuel. Full article

Accurate classification of seal wear is essential for condition-based and predictive maintenance of hydraulic cylinders, where seal degradation can cause fluid leakage and impair normal system operation. This study investigates the adaptation of a Transformer-based audio model for classifying seal wear conditions using acoustic emission (AE) signals. Specifically, we adapt the Audio Spectrogram Transformer (AST), a convolution-free, purely attention-based model that operates directly on audio spectrograms. The Transformer architecture enables the modeling of long-range dependencies, while the model learns discriminative representations directly from AE data without relying on manually engineered features. A selective fine-tuning strategy was implemented by adding layer-freezing functionality to the AST training pipeline, enabling different freezing configurations during fine-tuning. This allowed earlier pretrained representations to be preserved while adapting the later layers to the target AE signals, thereby reducing the risk of overfitting in the small-data setting. In addition, validation-driven early stopping was implemented to further improve generalization during fine-tuning. The model was initialized with ImageNet and AudioSet pretrained weights to exploit general-purpose representations learned from large-scale datasets. The AE data were acquired under varying pressure conditions on a hydraulic test rig designed to simulate hydraulic cylinder leakage. The datasets were partitioned into fine-tuning, validation, and evaluation subsets and labeled into three wear states: unworn, semi-worn, and worn. In addition, data augmentation techniques were applied to the fine-tuning data to increase diversity and mitigate class imbalance. The adapted model achieved 97.92% classification accuracy across all wear conditions and pressure settings, demonstrating its ability to learn discriminative wear-related patterns directly from AE data. Furthermore, the framework’s versatility was further assessed on a bearing strip dataset acquired from the same hydraulic test rig. Using the same fine-tuning configuration, the model achieved 95.65% accuracy and 100% recall for the worn state. These findings highlight the potential of transformer-based architectures for data-efficient, end-to-end AE-based diagnostics across hydraulic system components. Full article

Continuous monitoring of vehicle engine vibration is a key enabler of real-time diagnostics, yet conventional accelerometers require an external power supply and fit poorly into the distributed sensor networks envisioned for next-generation vehicles. Triboelectric nanogenerators offer an attractive self-powered alternative, but their direct application to the vibration of a running passenger vehicle engine, and the explicit link between sensor design parameters and individual engine operating states, remains largely unexplored. Here, we address this gap by co-tuning the air gap and the substrate rigidity of a contact-separation triboelectric vibration sensor to the vibration spectrum of an automotive engine. A systematic 3 × 3 design sweep across three gap distances and three substrate types identifies a single configuration that simultaneously resolves the low-frequency idle band and the higher-frequency acceleration band of a four-cylinder gasoline engine. A frequency-amplitude response map confirms that the real engine operating points fall within the sensitive region of the optimized device, and an on-vehicle test demonstrates clean discrimination of all seven operating states, from ready to shut-down, without any external power. The results establish design guidelines for source-matched triboelectric vibration sensors and outline a practical path toward self-powered, wireless-ready engine health monitoring in future vehicles. Full article