Search Results (876)

Hybrid systems have recently become widespread in motorsports due to advantages such as increased power through the use of electric motors and reduced fuel consumption thanks to regenerative braking. Achieving high performance from a hybrid powertrain requires a highly efficient control system for managing power flows between the internal combustion engine (ICE) and the electric motor. The goal of this study is to develop a control algorithm for a hybrid powertrain aimed at minimizing lap times compared to traditional vehicles equipped with an ICE. To achieve this objective, a mathematical vehicle model based on the tractive balance equation was used. Lap time simulations were conducted for both a traditional ICE vehicle and a hybrid system. The results showed that the hybrid vehicle has a significant advantage in lap time; however, the energy from a fully charged battery would only be sufficient for two laps. To address this issue, a hybrid system control algorithm is proposed, which maintains the energy balance of the battery throughout the entire lap while still providing better lap times compared to a vehicle equipped with a traditional ICE. Full article

The growth of greenhouse gas emissions, driven by the use of internal combustion engines (ICE), highlights the urgent need for sustainable solutions, particularly in the shipping sector. Non-invasive predictive maintenance using acoustic signal analysis has emerged as a promising strategy for fault diagnosis in ICEs. In this context, the present study proposes a hybrid Deep Learning (DL) model and provides a novel publicly available dataset containing real operational sound samples of ICEs, labeled across 12 distinct fault subclasses. The methodology encompassed dataset construction, signal preprocessing using log-mel spectrograms, and the evaluation of several Machine Learning (ML) and DL models. Among the evaluated architectures, the proposed hybrid model, BiGRUT (Bidirectional GRU + Transformer), achieved the best performance, with an accuracy of 97.3%. This architecture leverages the multi-attention capability of Transformers and the sequential memory strength of GRUs, enhancing robustness in complex fault scenarios such as combined and mechanical anomalies. The results demonstrate the superiority of DL models over traditional ML approaches in acoustic-based ICE fault detection. Furthermore, the dataset and hybrid model introduced in this study contribute toward the development of scalable real-time diagnostic systems for sustainable and intelligent maintenance in transportation systems. Full article

The application of mechanical products in many situations involves linear motion. The cylinder head of an internal combustion engine (ICE), a mechanical product, contains intake and exhaust valves. These valves open or close using the linear motion converted by the camshafts rotated by the engine. A typical engine is operated with a single cam profile; depending on the engine rotation, there are areas where the cam profiles do not match, resulting in a poor engine performance. An intake and exhaust system with an actuator can solve this problem. In a previous study on this system, the geometry and processing during manufacturing were complex. Therefore, in response, a linear actuator operated by Lorentz force with a coil as the mover was designed in this study. Through an electromagnetic field analysis using the finite element method, a three-phase alternating current was applied to the coil, assuming that it would be used as a power source for a general inverter. Consequently, the thrust obtained in the valve-actuation direction was 56.7 N, indicating improved axial thrust over the conventional model. Full article

At the international level, new measures, policies, and technologies are being developed to reduce greenhouse gas emissions and, more broadly, air pollutants. Road transportation is one of the main contributors to such emissions, as vehicles are extensively used in logistics operations, and many fleet owners of fossil-fueled trucks are adopting new technologies such as electric, hybrid, and hydrogen-based vehicles. This paper addresses the Hybrid Fleet Capacitated Vehicle Routing Problem with Time Windows (HF-CVRPTW), with the objectives of minimizing costs and mitigating environmental impacts. A mixed-integer linear programming model is developed, incorporating split deliveries, scheduled arrival times at stores, and a carbon cap-and-trade mechanism. The model is tested on a real case study provided by Decathlon, evaluating the performance of internal combustion engine (ICE), electric (EV), and hydrogen fuel cell (HV) vehicles. Results show that when considering economic and emission trading costs, the optimal fleet deployment priority is to use ICE vehicles first, followed by EVs and then HVs, but considering only total emissions, the result is the reverse. Further analysis explores the conditions under which alternative fuel, electricity, or hydrogen prices can achieve competitiveness, and a further analysis investigates the impact of different electricity generation and hydrogen production pathways on overall indirect emissions. Full article

Syngas, a renewable fuel primarily composed of hydrogen and carbon monoxide, is emerging as a viable alternative to conventional fossil fuels in internal combustion engines (ICEs). Obtained mainly through the gasification of biomass and organic waste, syngas offers significant environmental benefits but also presents challenges due to its lower heating value and variable composition. This review establishes recent advances in understanding syngas combustion, chemical kinetics, and practical applications in spark-ignition (SI) and compression-ignition (CI) engines. Variability in syngas composition, dependent on feedstock and gasification conditions, strongly influences ignition behavior, flame stability, and emissions, demanding detailed kinetic models and adaptive engine control strategies. In SI engines, syngas can replace up to 100% of conventional fuel, typically at 20–30% reduced power output. CI engines generally require a pilot fuel representing 10–20% of total energy to start combustion, favoring dual-fuel (DF) operation for efficiency and emissions control. This work underlines the need to integrate advanced modelling approaches with experimental insights to optimize performance and meet emission targets. By addressing challenges of fuel variability and engine adaptation, syngas reveals promising potential as a clean fuel for future sustainable power generation and transport applications. Full article

Accurate lithium-ion battery state of health estimation is critical for safety and range anxiety mitigation. Existing methods often lack interpretability in the extraction of feature fragments and fail to model spatial correlations between features. To address these gaps, this paper introduces a novel framework centered on interpretable feature engineering and synergistic spatial–temporal learning. The core novelty lies in using incremental capacity (IC) analysis on charging data, captured by onboard sensors, to dynamically select a 0.1 V voltage window based on IC peaks, ensuring the extracted voltage and capacity fragments are physically meaningful. These fragments are then transformed into graph-structured data, enabling a graph attention network and a bi-directional gated recurrent unit to effectively capture both spatial dependencies and temporal degradation trends, with a residual connection optimizing the network. Validation on two public benchmark datasets demonstrates the model’s superiority, achieving an average mean absolute error of 0.561% and a root mean square error of 0.783%. Furthermore, the model exhibits a low computational footprint, requiring only 1.68 MFLOPs per inference, and its fast inference time of 17.55 ms on an embedded platform confirms its feasibility for practical deployment. Full article

To achieve the goal of climate neutrality set by the European Union, it is important to find an efficient strategy to simultaneously lower nitrogen oxide, carbon monoxide, and particle emissions. When a portion of exhaust gas is reintroduced back into the combustion chamber, it reduces the combustion temperature. This reduces NO_X emissions but has a negative impact on CO and particle emissions due to the lower concentration of O₂. Reducing the combustion temperature can also indirectly influence particle formation. By including an oxygen-rich alternative fuel, CO emissions are reduced by 28% and 33% at 60 and 90 Nm, respectively. To further reduce particle emissions, which have significant health risks, acoustic waves are introduced to achieve better filtration through conventional DPFs that filter particles with larger diameters. With 21 kHz of acoustic frequency and 0% EGR, a 6% increase in large particles is observed. With moderate rise in the recirculation percentage, a higher combined efficiency of EGR and acoustic waves is observed. With 21 kHz acoustic frequency and 10% EGR, a 73% increase in larger particles is observed at lower loads and a 32% increase at higher loads is observed. Simultaneous emission reduction can be achieved by combining the benefits of using oxygen-rich fuel, acoustics, and EGR at a moderate rate. Full article

Superhydrophobic surfaces, characterized by water contact angles greater than 150°, have attracted widespread interest due to their exceptional water repellency and multifunctional applications. However, traditional fabrication methods often rely on fluorinated compounds and petroleum-based polymers, raising environmental and health concerns. In response to growing environmental and health problems, recent research has increasingly focused on developing green superhydrophobic surfaces, employing eco-friendly materials, energy-efficient processes, and non-toxic modifiers. This review systematically summarizes recent progress in the development of green superhydrophobic materials, focusing on the use of natural substrates such as cellulose, chitosan, starch, lignin, and silk fibroin. Sustainable fabrication techniques, including spray coating, dip coating, sol–gel processing, electrospinning, laser texturing, and self-assembly, are critically discussed with regards to their environmental compatibility, scalability, and integration with biodegradable components. Furthermore, the functional performance of these coatings is explored in diverse application fields, including self-cleaning, oil–water separation, anti-corrosion, anti-icing, food packaging, and biomedical devices. Key challenges such as mechanical durability, substrate adhesion, and large-scale processing are addressed, alongside emerging strategies that combine green chemistry with surface engineering. This review provides a comprehensive perspective on the design and deployment of eco-friendly superhydrophobic surfaces, aiming to accelerate their practical implementation across sustainable technologies. Full article

Recent advances in time-series anomaly detection have leveraged artificial intelligence (AI) to improve detection performance. In industrial control systems (ICSs), however, acquiring training data is challenging due to operational constraints and the difficulty of system shutdowns. To address this, many countries are developing ICS simulators and testbeds to generate training data. This study uses a publicly available ICS testbed dataset as a benchmark for the discriminator in a Semi-Supervised Generative Adversarial Network (SGAN). The goal is to generate large volumes of synthetic time-series data through adversarial training between generator and discriminator networks, thereby mitigating data scarcity in ICS anomaly detection. Comparative experiments were conducted using this synthetic data to evaluate its impact on existing detection models. Using the HAI 22.04 dataset from the National Security Research Institute, this study performed feature engineering and preprocessing to identify correlations and remove irregularities. Various models, including One-Class SVM, VAE, CNN-GRU-Autoencoder, and CNN-LSTM-Autoencoder, were trained and tested on the dataset. A synthetic dataset was then generated via SGAN and validated using PCA and t-SNE. The results show that applying SGAN-generated data to time-series anomaly detection yielded significant performance improvements in F1 score. Additional validation using the SWaT dataset from the National University of Singapore confirmed similar gains. These findings indicate that synthetic data generated by SGANs can effectively enhance semi-supervised learning for anomaly detection, classification, and prediction in data-constrained environments such as medical, industrial, transportation, and environmental systems. Full article

In engineering design for semi-submersible drilling platforms, it is necessary to improve anti-collision performance by optimizing the platform’s column’ structure. However, collision is usually analyzed through numerical analysis methods such as finite element analysis, which comes with high calculation costs. The genetic algorithm (GA) and other traditional optimization methods require massive numerical simulations, with unacceptable computational complexity. To address the above problems, a parallel efficient global optimization (EGO) multi-objective algorithm, based on hybrid criteria for the Kriging approximate model, is put forward in this paper. The proposed algorithm was validated through six typical multi-objective optimization test functions. The results show that it is superior to classic EGO, in terms of both optimization results and computational efficiency. Lastly, the hybrid criterion-based parallel EGO algorithm proposed in this paper was employed for the anti-collision, lightweight design of the column of the first ice zone semi-submersible drilling platform in China. It was found that the anti-collision capacity of the platform column rose by 11.9% and the structural weight declined by 2.7 t in the optimized design, suggesting obvious optimization effects with respect to the original design. Full article

The decarbonization and automation of port operations are emerging as key strategies to enhance the sustainability and efficiency of maritime logistics. This study proposes a simulation-based framework to assess the operational and environmental impacts of transitioning from traditional Internal Combustion Engine (ICE) tractors to Battery Electric Tractors (BET) and Automated Electric Guided Tractors (e-AGT) in Roll-on/Roll-off (Ro–Ro) port terminal operations. The proposed framework is applied to simulate a full vessel turnaround at the Ro–Ro terminal of the Port of Ravenna (Italy). A set of Key Performance Indicators (KPIs) is defined to evaluate turnaround time, vehicle productivity, energy consumption and CO₂ emissions across three scenarios. The results indicate that both BET and e-AGT configurations significantly reduce emissions compared to ICE, with reductions up to 40%. However, the e-AGT scenario reveals operational drawbacks, including increased unloading time and reduced fleet availability due to charging constraints and routing limitations. These findings highlight the environmental potential of automation and electrification but also emphasize the need for integrated planning of fleet size, charging infrastructure and circulation specifications. The proposed framework provides a replicable decision-support tool for port authorities and logistics operators to evaluate alternative handling technologies under realistic conditions. Full article

As internal combustion engine (ICE) systems are increasingly exposed to severe thermal and oxidative environments, the oxidation resistance and structural integrity of exhaust valve materials have become critical for maintaining long-term engine reliability and efficiency. This study presents a comparative evaluation of the cyclic oxidation behavior of two candidate valve steels, 1.4718 (ferritic stainless steel) and 1.4871 (austenitic stainless steel), under service-temperature conditions. The specimens were exposed to repeated oxidation at 550 °C, 650 °C and 750 °C for 25 cycles in ambient air. The surface and cross-sectional morphologies of the oxide layers were analyzed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to investigate oxide scale composition, thickness, and growth characteristics. The oxidation behavior of both alloys proceeded in two distinct stages: an initial phase marked by accelerated oxidation, followed by a slower, more stable growth period. The extent of oxidation intensified with increasing temperature. The 1.4718 alloy developed relatively porous but compositionally stable oxide layers consisting primarily of Fe- and Cr-based spinels such as FeCr₂O₄ and Cr₂SiO₄. In contrast, the 1.4871 alloy formed a dense, adherent, dual-layered oxide scale composed of an outer Mn₂O₃-rich layer and an inner Cr₂O₃-rich layer, attributable to its high Mn and Cr content. The results underscore the critical influence of elemental composition, particularly Cr, Mn and Si, on oxide scale stability and spallation resistance, demonstrating the superior cyclic oxidation resistance of the 1.4871 alloy and its potential suitability for exhaust valve applications in thermally aggressive environments. Full article

Effective intracellular delivery for ovarian cancer therapy remains a significant challenge. We present chrysin-loaded p(MMA-co-DMAEMA)-b-(OEGMA-co-DMA), PMOD-Chr, a nanoparticle platform precisely engineered via RAFT polymerization for advanced therapeutic delivery. This multi-functional platform features a hydrophobic p(MMA) core encapsulating chrysin (Chr), a pH-responsive p(DMAEMA) segment for endosomal escape, and a hydrophilic OEGMA (Oligo(ethylene glycol) methyl ether methacrylate) shell functionalized for enhanced cellular affinity and systemic stability. The combination of OEGMA and DMA (Dopamine methacrylamide) block facilitates passive targeting of ovarian cancer cells, enhancing internalization. Nanoparticles prepared via the nanoprecipitation method exhibited ~220 nm, demonstrating effective size modulation along with high homogeneity and spherical morphology. In A2780 and OVCAR3 ovarian cancer cells, PMOD-Chr demonstrated significantly enhanced cytotoxicity, substantially lowering the effective IC₅₀ dose of Chr. Mechanistically, PMOD-Chr induced a potent G2/M cell cycle arrest, driven by the upregulation of the CDK1/Cyclin B1 complex. Furthermore, the formulation potently triggered programmed cell death by concurrently activating both the intrinsic apoptotic pathway, evidenced by the modulation of Bax, Bcl2, and caspase 9, and the extrinsic pathway involving caspase 8. These findings emphasize that precision engineering via RAFT polymerization enables the creation of sophisticated, multi-stage nanomedicines that effectively overcome key delivery barriers, offering a highly promising targeted strategy for ovarian cancer. Full article

This review comprehensively presents the development of energy-efficient pneumatic propulsion systems (PPSs) in road vehicle applications, which are classified as green vehicles. The advantages and disadvantages of PPSs were presented, and PPSs were compared with combustion propulsion systems (CPSs) and electric propulsion systems (EPSs), as well as their power-to-weight ratios (PWRs), energy densities, and CO₂ emissions. The review of compressed air vehicles (CAVs) focuses on their historical development and future prospects. This review discusses the use of PPSs with compressed air engines (CAEs) as an alternative propulsion system in green vehicles, providing a simple, energy-saving, and environmentally friendly solution. This review also discusses hybrid air propulsion, which, when combined with internal combustion engines (ICEs) or electric motors (EMs), offers innovative energy-efficient propulsion systems that are more economical than conventional hybrid propulsion systems. This review focuses on recent advances in lightweight air vehicles that improve vehicle handling, increase efficiency, and reduce propulsion energy consumption. Discussion of the study results concerns the use of PPSs in a three-wheeled rehabilitation tricycle (RTB). A comprehensive computation model of the RTB was presented, and the key performance parameters crucial to its operation were analyzed. The results of the RTB simulation were verified through field tests. Full article

As internal combustion engines (ICEs) remain dominant in maritime transport, reducing their greenhouse gas (GHG) emissions is critical to meeting IMO’s decarbonization targets. Ammonia (NH₃) has gained attention as a carbon-free fuel due to its zero CO₂ emissions and high hydrogen density. However, its low flame speed and high ignition temperature pose combustion challenges. This study investigates the combustion and emission performance of NH₃-diesel dual-fuel engines, applying Response Surface Methodology (RSM) for multi-objective optimization of key operating parameters: ammonia fraction (AF: 0–30%), engine speed (1200–1600 rpm), and altitude (0–2000 m). Experimental results reveal that increasing AF led to a reduction in Brake Thermal Efficiency (BTE) from 39.2% to 37.4%, while significantly decreasing NO_x emissions by 82%, Total hydrocarbon emissions (THC) by 61%, and CO₂ emissions by 36%. However, the ignition delay increased from 8.2 to 10.8 crank angle degrees (CAD) and unburned NH₃ exceeded 6500 ppm, indicating higher incomplete combustion risks at high AF. Analysis of variance (ANOVA) confirmed AF as the most influential factor, contributing up to 82.3% of the variability in unburned NH₃ and 53.6% in NO_x. The optimal operating point, identified via desirability analysis, was 20% AF at 1200 rpm and sea level altitude, achieving a BTE of 37.4%, NO_x of 457 ppm, and unburned NH₃ of 6386 ppm with a desirability index of 0.614. These findings suggest that controlled NH₃ addition, combined with proper speed tuning, can significantly reduce emissions while maintaining engine efficiency in dual-fuel configurations. Full article