Search Results (2,389)

Biodiesel fuel produced through transesterification is mainly used in blends with conventional diesel fuel (D100). The analysis of the combustion process parameters for each specific biodiesel fuel represents the basis for a rational approach to the utilization of available motor fuel quantities. In this study, the differential and cumulative heat release laws during the combustion of D100 and blends of biodiesel fuel made from waste grape seed oil and D100 were analyzed. In addition, the engine efficiency and economy for the cases of using the aforementioned fuels were analyzed. The tests were conducted on a single-cylinder, air-cooled diesel engine with direct fuel injection. The engine testing was conducted for two engine loads; that for which the brake was a mean effective pressure of 4.2 bar, and for the full load, that for the brake was a mean effective pressure of 5.6 bar at engine speeds of 1635 rpm, 1937 rpm, and 2239 rpm. All experimental work was conducted for conventional diesel fuel D100 and for biodiesel diesel blends B7 and B14. The combustion rates of D100, a blend containing 7% of biodiesel by volume (B7), and a blend containing 14% of biodiesel by volume (B14) were examined. However, the higher combustion rate of the B14 blend, particularly during the combustion of the first 50% of the fuel mass per cycle, could have a positive impact on the fuel economy of the working cycle and the brake thermal efficiency (BTE). The maximum heat release rates for D100, B7, and B14 at full load and an engine speed of 2239 rpm are 115.65 J/deg, 148.01 J/deg, and 152.99 J/deg, respectively. At full load and engine speeds of 1635 rpm and 2239 rpm, the brake thermal efficiencies (BTEs) for D100, B7, and B14 were 0.301, 0.285, and 0.296 and 0.281, 0.273, and 0.277, respectively. Under other tests, the highest BTE was observed for the B14 blend. Therefore, from the perspective of brake thermal efficiency (BTE), the most favorable blend for application is B14. Full article

Syngas (synthesis gas) is a promising gaseous biofuel for small-scale power generation, but its highly variable composition, which depends on the biomass source and gasification process, poses challenges for engine optimization. This study investigates syngas–diesel dual-fuel combustion in an optically accessible engine using chemiluminescence imaging of OH*, CH*, and CH₂O* to characterize ignition and flame development. Three representative syngas compositions—Downdraft, Updraft, and Fluidbed—were examined. The Fluidbed composition exhibited the weakest OH* signal, approximately one-third of that observed for the other two, primarily due to its higher CO₂ dilution and lower H₂ content. Ignition delay trends were strongly correlated with dilution level: Downdraft and Updraft showed similar delays despite different H₂/CO ratios, while larger CO₂ shares led to longer delays and flattened heat-release rates. CH* and CH₂O* chemiluminescence showed better agreement with combustion timing than OH*. Methane enrichment enhanced flame propagation and reduced ignition delay, partially offsetting CO₂ dilution effects. Full article

This paper proposes a formal framework for synthesizing multi-stage condition-based maintenance (CBM) decision logic for marine diesel monitoring systems. The design object is treated not as a single threshold or classifier output, but as an implementable decision logic with explicit stages of data-quality gating, thresholding, confirmation, fusion, and temporal filtering. Decision quality is evaluated using unconditional control-reliability indicators (CRIs) under a prescribed prior probability of rare abnormal events within a unified Monte Carlo verification protocol. Within a simplified Gaussian surrogate model, we compare baseline thresholding, repeated-measurement averaging, within-path confirmation, and measurement-level fusion. For the reported reference configuration, averaging five repeated measurements yields the largest reduction in the raw error criterion, “2 out of 3” confirmation provides a smaller but consistent improvement, and two-path multi-fidelity fusion is beneficial only after calibration toward the more informative path. The results show that, under rare abnormal events and limited measurement accuracy, decision quality is determined primarily by calibration of the multi-stage channel-level logic rather than by thresholding alone. Full article

Although bio-hydrogenated diesel (BHD) offers drop-in compatibility and high oxidative stability, its poor lubricity remains a critical barrier to long-term engine deployment. Previous studies have primarily relied on short-term tribological assessments, leaving insufficient empirical data on sustained wear behavior under realistic conditions. This study addresses that gap through a 200 h durability evaluation of BHD–biodiesel blends in a single-cylinder diesel engine under constant load conditions per Thai Industrial Standard TIS 2618-2557. Five fuels, namely diesel, pure BHD, BHD90, BHD70, and pure biodiesel, were tested to identify the critical biodiesel threshold for optimal tribological and oxidative performance. BHD90 (90% BHD + 10% biodiesel) emerged as the optimal formulation, delivering the lowest torque reduction (11.2%) and minimal iron wear particles (101 ppm), while preserving oxidation stability. Biodiesel concentrations exceeding 10% induced accelerated lubricant oxidation through hygroscopic effects, negating the lubricity benefits. Fourier-transform infrared spectroscopy (FTIR) analysis of piston carbon deposits further revealed that higher biodiesel blends produced more oxygenated compounds, whereas pure BHD and diesel generated predominantly aliphatic hydrocarbons. These findings establish a mechanistic relationship between fuel composition, oxidation, and wear under endurance conditions, providing a practical guideline for renewable diesel formulation that balances lubrication performance, oxidation control, and long-term engine durability. Full article

The development of modern society has intensified fossil fuel consumption, resulting in the depletion of oil resources and rising greenhouse gas emissions. In this context, the promotion of renewable alternatives in the transport sector has become essential, with Hydrotreated Vegetable Oil (HVO) emerging as a promising transitional fuel due to its compatibility with conventional diesel engines. To ensure proper engine operation and performance, the physical properties and chemical structure of HVO must be accurately characterized. Gas chromatography is commonly used for this purpose. While dedicated gas chromatography methods for HVO are available on specialized equipment, this study proposes a chromatographic method applicable to conventional gas chromatograph systems equipped with a flame ionization detector, enabling the analysis of HVO using commonly available laboratory equipment. The method was developed using commercially available HVO and pure n-alkanes (C₅–C₁₈) as reference compounds for component identification. The proposed approach enabled the estimation of carbon and hydrogen atom numbers in the analyzed fuel fractions and the determination of the stoichiometric air. The calculated values show good agreement with the literature data, confirming the reliability and applicability of the proposed boiling-point-based chromatographic method. Full article

A capacitive micromachined ultrasonic transducer (CMUT) was engineered and functionalized with either zeolitic imidazolate framework-8 (ZIF-8) dispersed in an AZ1512HS photoresist matrix or with graphene oxide (GOx) to operate as a gravimetric sensor for organic vapors. The sensor response was investigated under controlled humidity conditions during pulsed exposure to acetone, ethyl methyl ketone, isopropanol, kerosene, and diesel vapors. The impedance of the device was monitored by observing and tracking the resonance frequency shift as well as the resistance maximum shift, giving us the possibility to track two response parameters simultaneously. Different combinations of shifts in the sensor resonance frequency and the resistance maximum values were observed for the ZIF-8 functionalized device when exposed to the selected vapors, ranging from 12.4 kHz for ethyl methyl ketone to 2.4 kHz for diesel, and from 580 Ω for acetone to 20 Ω for isopropanol. Sensors functionalized with GOx did not demonstrate any significant response to either ethyl methyl ketone or isopropanol in the frequency domain. GOx-functionalized sensors were used for relative humidity monitoring in test gases. Besides the conventional response of the produced gravimetric sensing system, we also observed a strong relationship between the humidity of the gas mixture and the strength of the interaction of target gases with the functional film of the sensor. The results highlight the multidimensional nature of the sensor response and demonstrate how humidity influences the interaction between vapor molecules and the functional coating. This paper focuses on the characterization of the coupled behavior of resonance frequency and resistance shifts under controlled operating conditions. The presented experimental setup provides a basis for future concentration-dependent investigations and functional material comparisons in CMUT-based gravimetric sensing systems and provides a necessary foundation for accurate interpretation of future concentration-resolved measurements. Full article

This study aims to show a detailed life cycle assessment (LCA) approach of battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), with an emphasis on determining the electrical carbon intensity at which these vehicles reach life-cycle greenhouse gas (GHG) parity. The analysis was conducted in openLCA v2.0.3 using the Ecoinvent v3.9.1 database under a European use-phase context, with a functional unit of 150,000 km. BEVs were evaluated for two representative lithium-ion battery chemistries (NMC622 and LFP) under three electricity carbon intensity scenarios (50, 400, and 850 g CO₂/kWh), while ICEVs were modeled for both gasoline and diesel pathways. Results show that BEV life-cycle GHG emissions vary between 91 and 221 g CO₂-eq/km across different combinations of electricity mix, battery chemistry, and end-of-life conditions. When isolating electricity carbon intensity as the primary variable under a fixed BEV configuration, emissions increase approximately linearly with grid emission factor. Under average European electricity conditions (400 g CO₂/kWh), BEVs exhibit lower life-cycle GHG emissions than gasoline ICEVs, whereas under coal-intensive electricity conditions (850 g CO₂/kWh) this advantage may be reduced or reversed. The break-even electricity carbon intensity is derived by linear interpolation under a fixed BEV configuration (NMC622, 60 kWh, constant lifetime and EoL conditions), yielding a threshold of approximately 600 g CO₂/kWh. The results further indicate that this threshold is influenced by battery chemistry, production-related emissions, recycling efficiency, and assumed vehicle lifetime. These findings highlight the importance of simultaneous progress in electricity decarbonization and end-of-life recycling to secure the environmental benefits of vehicle electrification, and they provide a threshold-oriented framework for policy-relevant interpretation of comparative vehicle LCA results. Full article

Diesel engines have evolved significantly over the last century while maintaining core qualities such as reliability, durability, and fuel economy. Currently, the viability of their continued use is under discussion, mainly due to the environmental impact of polluting emissions from conventional fossil fuels. An advantage of these engines is their high fuel flexibility, which includes the capability to operate with pure vegetable oils. Following the established limitations of large-scale conventional biodiesel use, this perspective explores the implementation of straight vegetable oils (SVOs) blended with low-viscosity, low-octane (LVLC) oxygenated solvents to address climate targets such as the “Fit for 55” agenda. The discussion examines the potential of these advanced biofuels to contribute to the 2050 carbon neutrality goals while addressing the technical and economic requirements of the transport sector. Full article

In the fault diagnosis of complex industrial systems, labeled samples are expensive to obtain, which leads to insufficient training data for the belief rule base (BRB) model. Although unlabeled samples are abundant, the uncertainty of their pseudo-labels may undermine semi-supervised learning and hinder accurate parameter optimization of the BRB model. To address these issues, a dual-source evidence-driven semi-supervised BRB method (SS-BRB) is proposed for fault diagnosis. The proposed method makes effective use of unlabeled samples while preserving the interpretability and inference transparency of the BRB model. To improve the reliability of pseudo-labels in semi-supervised learning, a dual-source evidence-driven pseudo-labeling mechanism is designed. In this mechanism, local similarity information is combined with the global inference results of the BRB model. An entropy factor and a feature distance factor are introduced to adaptively adjust the confidence of pseudo-labels. In this way, the quality of pseudo-labels is improved, and the influence of noisy samples is reduced. Based on this mechanism, high-confidence pseudo-labeled samples are incorporated into the training set to further optimize the model. Experimental results show that the proposed method achieves good diagnostic performance on both the gearbox dataset and the WD615 diesel engine dataset. Even with limited labeled data, the proposed method still achieves high accuracy, robustness, and good generalization performance. Full article

Hydrogen is recognized as a promising energy vector for the decarbonization of energy production. Besides the undoubted benefits, its utilization poses some technological challenges in the generation, transportation, storage and utilization phases, which must be carefully assessed. The aim of this work is to assess the effect of methane substitution with hydrogen in a dual-fuel diesel/methane engine on fuel conversion efficiency and pollutant emission levels. Therefore, an extensive experimental campaign has been designed in which a hydrogen/methane mixture with variable composition is ignited with a pilot injection of diesel fuel. The engine was operated in naturally aspirated or supercharged conditions, and conventional or alternative combustion strategies were implemented, spanning a pilot injection timing over a broad range of values. The results show that the effect of a variation in H₂ percentage of up to 20% strongly depends on air intake pressure and pilot injection timing. In particular, engine efficiency and HC and CO emissions are penalized as H₂ percentage increases; however, this penalty can be mitigated in naturally aspirated conditions if a late pilot SOI strategy is adopted. In terms of NO_x, a reduction is observed as H₂ percentage increases. Late SOIs determine the lowest levels of NO_x emissions in both naturally aspirated and supercharged conditions. Full article

Fossil fuel depletion has increased interest in renewable alternatives such as biodiesel derived from non-edible plant oils. Droplet evaporation is a key process influencing fuel–air mixing and combustion efficiency in diesel engines. In this study, the evaporation characteristics of diesel and two non-edible biofuels, Jatropha and Castor, are investigated using computational fluid dynamics (CFD) under high-temperature and high-pressure conditions representative of engine environments. The numerical model incorporates the conservation equations of mass, momentum, and energy, together with the k–$\epsilon$ turbulence model and a discrete phase model to simulate droplet heating, motion, and mass transfer during evaporation. A comparative CFD analysis is performed to examine how fuel properties, ambient temperature, and droplet size affect the evaporation behaviour of diesel, Jatropha, and Castor droplets under identical engine-like conditions. The evolution of droplet diameter, temperature, velocity, and lifetime is analysed, and the applicability of the classical $D^2$-law is evaluated under different operating conditions. The results indicate that biofuel droplets generally evaporate faster than diesel droplets at lower temperatures, while evaporation trends become similar at higher temperatures. These findings provide insight into the evaporation behaviour of Jatropha and Castor fuels and their potential application in diesel engines. Full article

As part of efforts to support the transition toward a zero-carbon future, this research evaluates how the use of natural gas and liquefied petroleum gas under lean burn conditions affects the energy efficiency and environmental outcomes of a diesel engine that has been retrofitted to operate with spark ignition. The assessment of the ecological potential of these low-carbon gaseous fuels was performed at the engine test bed at optimum spark advance set from the condition of achieving maximum brake thermal efficiency (i.e., lowest carbon dioxide emission, CO₂). The results found with lean mixtures are compared to those obtained under stoichiometric conditions, as well as to those from a commercial gasoline engine of comparable size, equally operated at stoichiometry. With lean burning, a clear improvement is observed for all operating points in terms of brake thermal efficiency with respect to the stoichiometric operation. The results highlight a slightly greater improvement when operating with natural gas lean mixtures: between (1.35 and 2.35) percentage points gained in this case, compared to (1.15–2.10) percentage points gained in the case of liquefied petroleum gas. As for CO₂, a maximum 28% reduction when using natural gas is achieved with lean operation with respect to the commercial gasoline engine. Using lean mixtures also brings an important reduction in the engine-out pollutants (carbon monoxide, nitric oxides and particulate number). However, with respect to stoichiometric operation, cyclic variability of the prototype degrades with lean burning but remains lower than one of the baseline commercial gasoline engines. Full article

The primary objective of this research is to engineer a high-performance, sustainable material for aquatic remediation by repurposing low-cost biogenic silica into a selective hydrophobic adsorbent. By integrating the natural hierarchical porosity of Diatomaceous Earth (DE) with a tailored silanization strategy, this work aims to provide a scalable and eco-friendly solution for the efficient encapsulation and mechanical recovery of hydrocarbons from contaminated water. To overcome the inherent hydrophilicity of DE, a two-step functionalization process was developed, involving alkaline activation followed by the covalent grafting of hexadecyltrimethoxysilane (C16) in different concentrations. The resulting C16@DE hybrid materials underwent a dramatic surface energy transformation, shifting from hydrophilic behavior to robust hydrophobicity, with static contact angles reaching up to 134.8°. Optical analysis revealed a unique remediation mechanism: while pristine DE disperses homogeneously in the aqueous phase, functionalized C16@DE spontaneously organizes into discrete pellets upon contact with diesel, effectively encapsulating the fuel. Quantitative UV/vis spectrophotometry confirmed that these composites sequester approximately 55–56% of the diesel phase. Together, these results demonstrate that C16@DE materials couple intrinsic biosilica porosity with tailored hydrophobicity to achieve efficient hydrocarbon capture. By combining the natural hierarchical porosity of diatoms with engineered surface selectivity, this research positions functionalized DE as a scalable, low-cost, and eco-friendly promising solution for marine oil spill recovery and industrial wastewater treatment. Full article

As the core power unit of complex electromechanical systems, accurate health assessment of diesel engines is essential for safe operation. The Interval Belief Rule Base (IBRB) method integrates observed data with expert knowledge to support system assessment. However, engine operating parameters change over time because of wear and aging. Additionally, traditional optimization methods struggle to balance global search speed with local convergence efficiency. To address these issues, this paper proposes an Interval Belief Rule Base method based on Hybrid Optimization and Adaptive Intervals (IBRB-HOAI). First, an adaptive reference interval is introduced by combining K-means clustering and quantile interval estimation, dynamically generated based on the actual operating state of the engine. The health assessment baseline is optimized. The applicability of the model is enhanced. Second, the global exploration ability of particle swarm optimization is combined with the local refinement ability of the projected covariance matrix adaptation evolution strategy. The model parameters are collaboratively optimized. Finally, experimental verification is conducted on a diesel engine dataset containing 2700 sample points. Compared with the traditional IBRB method, the proposed method achieves a significant reduction in MSE of 97.5%. It outperforms other machine learning methods. The effectiveness of the proposed method is verified. Full article

Fuel cell technologies are increasingly investigated as alternatives to conventional auxiliary diesel generators in order to enhance shipboard energy efficiency and reduce greenhouse gas emissions. This study presents a unified and uncertainty-driven system-level assessment of solid oxide fuel cell (SOFC) and proton exchange membrane fuel cell (PEMFC) systems operating as auxiliary power sources on a 200 m bulk carrier. Both technologies are evaluated under identical vessel characteristics, operating profiles, auxiliary load levels (360–600 kW), and cost assumptions, and are benchmarked directly against a conventional three–diesel-generator configuration. A modular numerical framework is developed to model propulsion–auxiliary interactions for ship speeds between 10 and 14 knots. SOFC systems are assessed using grey, bio-derived, and green natural gas pathways, while PEMFC systems are examined under grey, blue, and green hydrogen supply routes. Performance indicators include annual fuel consumption, carbon dioxide (CO₂) emission reduction, net present value (NPV), internal rate of return (IRR), payback period (PBP), and marginal abatement cost (MAC). Economic uncertainty is explicitly embedded in the framework through Monte Carlo simulation, where fuel prices (±20%) and capital costs are sampled across defined ranges, generating probabilistic distributions rather than single deterministic estimates. This uncertainty-centred approach enables assessment of robustness, downside risk, and probability of profitability. Results show that replacing a single operating 600 kW diesel generator with fuel cell systems reduces auxiliary fuel energy demand by 25–35% for SOFC and approximately 15–25% for PEMFC relative to the diesel benchmark. Annual CO₂ reductions range from 1.1 to 1.3 kt for SOFC systems and 1.8–2.8 kt for PEMFC configurations. Under grey fuel pathways, median NPVs reach approximately 2–4.5 M$ for SOFC and 9–17 M$ for PEMFC as load increases, with IRRs exceeding 15% and 30%, respectively. Transitional pathways exhibit narrower margins, while renewable pathways remain more sensitive to fuel price variability. The findings demonstrate that fuel pathway cost dominates lifecycle outcomes under uncertainty and that hydrogen-based PEMFC systems exhibit the strongest economic resilience within the examined market ranges. The framework provides structured, uncertainty-aware decision support and establishes a foundation for integration into model-based systems engineering (MBSE) environments for early stage ship energy system design. Full article