Search Results (11,594)

A high-sensitivity terahertz (THz) biosensor is proposed in this paper based on a multi-layer hybrid structure consisting of a defect mode and graphene with a truncation layer. This biosensor is based on symmetrical Bragg reflectors with a defect layer and graphene with a truncation layer, which effectively comprise a multi-layer hybrid resonance excitation structure. The high sensitivity of this biosensor is developed through defect mode resonance, and the resonance reflection peak is made sharper and more sensitive by using graphene with a truncation layer. After testing and analysis, the sensitivity of this biosensor structure is greatly affected by the refractive index and thickness of the sensing medium. By setting parameters appropriately, the composite structure can be used as both a liquid biosensor and a gas biosensor, the maximum sensitivity of which can surpass 2000°/RIU, while an FOM value of 22,500 RIU⁻¹ can be achieved. At the same time, when the refractive index of the liquid sensing medium changes to 0.01 relative to water (the same applies to changes in the gas sensing medium), the sensitivity of this structure still exceeds 600°/RIU, demonstrating that this biosensor has advantages including high sensitivity, a high FOM, wide applicability, and slow sensitivity attenuation. Therefore, the sensing scheme proposed in this paper has potential application prospects in the field of biosensing based on micro/nanostructures due to its simple structure, low requirements for processing conditions, and high sensitivity. Full article

Three-dimensionally ordered macroporous (3DOM) La₂O₃-supported Ni catalysts exhibit outstanding performance for methane dry reforming (DRM). The 5Ni/La₂O₃-3DOM catalyst achieves 79% CH₄ and 84% CO₂ conversions at 800 °C under the reaction conditions of atmospheric pressure, CH₄:CO₂ molar ratio of 1:1, and gas hourly space velocity (GHSV) = 36,000 mL·gcat⁻¹·h⁻¹, outperforming its counterparts (5Ni/La₂O₃-PP prepared by means of co-precipitation and 5Ni/La₂O₃-GNC prepared by means of glycine–nitrate combustion) by 15–20%. Long-term stability tests at 700 °C (same CH₄:CO₂ ratio and GHSV as above) show that the 5Ni/La₂O₃-3DOM catalyst maintains CH₄ and CO₂ conversions at approximately 80% and 85%, respectively, with zero deactivation over 50 h. Meanwhile, its carbon deposition rate plummets to 1.1 mg·g⁻¹·h⁻¹, which is 75% lower than that of the precipitation-derived 5Ni/La₂O₃-PP catalyst. This excellent performance stems from the synergy of nano-confined Ni particles (11.2 nm in crystallite size after reduction) and abundant surface oxygen species (38 μmol·g⁻¹), establishing 3DOM La₂O₃ as a superior anti-coking support platform for scalable H₂ production via DRM. Full article

In this study, two types of gas sensors—silicone-based interdigital electrode and silicon micropillar sensors based on rGO and rGO/SnO₂—were fabricated. Their gas-sensing performance was investigated at room temperature. First, interdigital electrodes of different channel widths were fabricated to investigate the impact of the channel width parameter. Subsequently, the rGO/SnO₂ doping ratio in the composite material was varied to identify the optimal composition for gas sensitivity. Additionally, triangular and square-arrayed silicon micropillar substrates were fabricated via photolithography and inductively coupled plasma etching. The rGO/SnO₂-based gas sensor on a silicon micropillar substrate exhibited an ultra-high specific surface area. The triangular micropillar arrangement of rGO/SnO₂-160 demonstrates the best performance, showing approximately 14% higher response and a 106 s reduction in response time compared with interdigital electrode sensors spray-coated with the same concentration of rGO/SnO₂ when tested at room temperature under 250 ppm NO₂. The optimized sensor achieves a detection limit as low as 5 ppm and maintains high responsiveness, even in conditions of 60% relative humidity (RH). Additionally, the repeatability, selectivity, and stability of the sensor were evaluated. Finally, structural and morphological characterization was conducted using XRD, SEM, TEM, and Raman spectroscopy, which confirmed the successful modification of rGO with SnO₂. Full article

Ever-increasing demands to improve fuel burn efficiency of aero gas turbines lead to rises in fuel system pressures and temperatures, posing challenges for the structural integrity of the pump housing and creating internal deflections that can adversely affect volumetric efficiency. Non-invasive strain and vibration measurements could allow transient effects to be quantified and considered during the design process, leading to more robust fuel pumps. Fuel pumps used on a high bypass turbofan engine were instrumented with optical fibre Bragg grating (FBG) sensors, strain gauges and thermocouples. A hydraulic hand pump was used to facilitate measurements under static conditions, while dynamic measurements were performed on a dedicated fuel pump test rig. The experimental data were compared with the outputs from a finite element (FE) model and, in general, good agreement was observed. Where differences were observed, it was concluded that they arose from the sensitivity of the model to the selection of nodes that best matched the sensor location. Strain and vibration measurements were performed over the frequency range of 0 to 2.5 kHz and demonstrated the ability of surface-mounted FBGs to characterise vibrations originating within the internal sub-components of the pump, offering potential for condition monitoring. Full article

Liquefied natural gas plays a crucial role in global energy transitions due to its high efficiency and low emissions, especially in long-distance transportation. However, the thermal management of LNG storage tanks remains a significant challenge due to temperature fluctuations, which impact both efficiency and safety. Traditional methods rely on thermodynamic models or computational fluid dynamics simulations but are computationally expensive and time-consuming. This study proposes a hybrid approach that integrates machine learning techniques with CFD data to predict temperature variations inside LNG storage tanks. Several ML models, including Random Forest, XGBoost, and deep learning-based models like CNN and TCN, were tested. Results indicate that CNN and TCN models offer the best performance in predicting temperature changes, showing superior accuracy and computational efficiency. This approach significantly enhances the real-time prediction capability, offering a promising solution for improving LNG tank thermal management, ensuring both operational safety and efficiency. Full article

The safety of offshore structures is a key topic in developing offshore oil and gas and offshore wind energy. Due to the harsh offshore environment and costly offshore field tests, offshore field trials to validate the theoretical models for offshore structures are limited, and testing results can rarely be found in the public domain. The Bayesian updating technique combines existing engineering knowledge with the observed performance data about in-service offshore structures to update model uncertainties. Hence, the Bayesian technique overcomes the shortcomings of limited offshore field trials. This paper compiles performance data on offshore jackets in hurricanes in the Gulf of Mexico (GoM) in the past two decades and calibrates the model uncertainties of the API method using the Bayesian technique. With the updated model uncertainty, this paper evaluates the reliability of generic offshore jackets in the GoM and the case study’s offshore wind substation in China. With a typical reserve strength ratio (RSR) of about 2.0 to 2.2, the reliability analysis reveals that the updated annual failure probability of a generic offshore jacket in the GoM is largely less than $1.0\times {10}^{-3}$, indicating that the extreme weather overload is not a major concern. However, the RSR of the case study platform in China is greater than 4.5, and the annual failure probability for the case study offshore wind substation is about 2–3 orders of magnitude lower than typical oil and gas jackets. Hence, from the extreme metocean condition perspective, the substation under investigation has sufficient structural capacity, and the design practice for offshore wind substations in northern China may be improved. Full article

To solve the unrelated parallel batch processing machine scheduling problem (UPBPMSP) with dynamic job arrivals, heterogeneous processing times, and machine heterogeneity, this paper presents an improved artificial bee colony (IABC) algorithm aimed at minimizing the makespan. Three improvements include the following: (1) a hybrid encoding scheme that combines machine allocation coefficients and priority weights, allowing for flexible consideration of machine capabilities and dynamic job priorities; (2) a dual-mode variable neighborhood search strategy to optimize machine allocation and job sequencing simultaneously; (3) a dynamic weight tournament selection mechanism to enhance population diversity and avoid premature convergence. Experimental results show that IABC reduces the makespan by 5% to 25% compared to traditional ABC and genetic algorithms (GAs), with the most significant advantages observed in concentrated job arrival scenarios. Statistical tests confirm that the improvements are statistically significant, validating the effectiveness of the proposed algorithm. Full article

Accurately determining the modulus of each structural layer remains a key challenge in asphalt pavement design, construction quality control, and bearing capacity assessment. This study introduces an ensemble model combining a genetic algorithm-optimized backpropagation neural network (GA-BP) and a convolutional neural network (CNN) to back-calculate the dynamic modulus of asphalt pavement layers over rubblized old cement concrete structures. Using a dynamic deflection basin database created by our research team, we built a dataset of 1,552,000 pavement structure samples with Falling Weight Deflectometer (FWD) data. Based on this dataset, we developed regression models, including a backpropagation (BP) neural network, GA-BP, and CNN, to perform the back-calculation of dynamic modulus values. Performance testing revealed that the CNN model outperformed both the GA-BP and BP models in terms of accuracy and stability, as indicated by evaluation metrics (R², MAE, RMSE, MAPE), with the following ranking: CNN > GA-BP > BP. Nonetheless, the maximum relative error across all three models remained notable. To address this, an ensemble model combining GA-BP and CNN was created, significantly enhancing the accuracy and stability of the back-calculation results. The proposed ensemble model was tested on-site with FWD data to estimate the dynamic modulus of asphalt pavement layers. The results demonstrated strong agreement with actual pavement performance and high consistency with numerical outcomes from three-dimensional (3D) dynamic finite element method simulations. These findings suggest that the GA-BP and CNN ensemble approach offers a reliable method for back-calculating the dynamic modulus of asphalt pavement layers over rubblized old cement concrete structures. Full article

Genetic Algorithms (GAs) are pillars of evolutionary computing and one of the most well-known population-based metaheuristic optimisation techniques. They are widely used in engineering and applied optimisation problems for their capabilities in finding global solutions. Standard GAs (SGAs) determine probabilities of crossover and mutation by computationally expensive trials. Adaptive Genetic Algorithms (AGAs), on the other hand, improve this process by adjusting the parameters throughout generations. This study proposes three deterministic parameter control functions, ACM1, ACM2 and ACM3, for the regulation of crossover and mutation probabilities. Using advanced test functions, comparisons between four deterministic GAs, an SGA, two fixed-parameter GAs, and an AGA have been made. The fixed-parameter configurations are called FCM1 and FCM2. The AGA is called LTA, and four deterministic methods are called HAM and ACM1–3. Results show that the SGA is mostly inadequate for complex optimisation problems. The LTA performs inconsistently by failing on some functions and succeeding on others. The methods, ACM2, HAM, and FCM2, are highly robust and effective. Unexpectedly, the FCM2 performs the best for smaller population sizes. However, in higher-dimensional problems, the proposed method, ACM2, is superior and shows less variability in finding optimal solutions. The methods are also evaluated using a boost converter implementation. Full article

Improving the efficiency of spark-ignited (SI) engines while simultaneously reducing emissions remains a critical challenge in meeting global energy demands and increasingly stringent environmental regulations. Lean burn combustion is a proven strategy for increasing efficiency in SI engines. However, the air dilution level is limited by the mixture’s ignition ability and poor combustion efficiency and stability. A promising method to extend the dilution limit and ensure stable combustion is the implementation of an active pre-chamber combustion system. The pre-chamber spark-ignited (PCSI) engine facilitates stable and rapid combustion of very lean mixtures in the main chamber by utilizing high ignition energy from multiple flame jets penetrating from the pre-chamber (PC) to the main chamber (MC). Together with the increase in efficiency by dilution of the mixture, nitrogen oxide (NO_X) emissions are lowered. However, at peak efficiencies, the NO_X emissions are still too high and require aftertreatment. The use of exhaust gas recirculation (EGR) as a dilutant might enable simple aftertreatment by using a three-way catalyst. This study experimentally investigates the use of EGR as a dilution method in a PCSI engine fueled by methane and analyzes the benefits and drawbacks compared to the use of air as a dilution method. The experimental results are categorized into three sets: measurements at wide open throttle (WOT) conditions, at a constant engine load of indicated mean effective pressure (IMEP) of 5 bar, and at IMEP = 7 bar, all at a fixed engine speed of 1600 rpm. The experimental results were further enhanced with numerical 1D/0D simulations to obtain parameters such as the residual combustion products and excess air ratio in the pre-chamber, which could not be directly measured during the experimental testing. The findings indicate that air dilution achieves higher indicated efficiency than EGR, at all operating conditions. However, EGR shows an increasing trend in indicated efficiency with the increase in EGR rates but is limited due to misfires. In both dilution approaches, at peak efficiencies, aftertreatment is required for exhaust gases because they are above the legal limit, but a significant decrease in NO_X emissions can be observed. Full article

This review presents a comprehensive overview of metallic abradable coatings and the advanced testing methodologies used to evaluate their performance in gas turbine engines. Abradable materials are engineered to act as sacrificial coatings, enabling minimal blade tip wear while maintaining tight clearances between rotating blades and stationary components. Such functionality is critical in aerospace applications, where engines operate at high rotational speeds and across wide temperature ranges. The review examines the principal factors governing the design and selection of metallic-based abradable coatings, including material composition, thermal stability, and microstructural tailoring through the addition of phase modifiers, porosity formers, and solid lubricants. The performance of various metallic matrix materials is also discussed concerning their operational temperature ranges and wear characteristics. Particular attention is given to abradability evaluation methods, emphasizing the need to replicate engine-representative conditions to capture blade–coating interactions, frictional behavior, and wear mechanisms. This review consolidates advances in material compositions, microstructural engineering, and experimental testing, integrating perspectives from materials science, tribology, and methodology to guide the development of next-generation turbine coatings. It specifically addresses the lack of a unified review linking material design, thermal spray processes, and performance evaluation by summarizing key compositions, microstructures, and testing methods. Full article

A Ti-6Al-4V titanium alloy exhibits low hardness and poor wear resistance under sliding contact. This study evaluates the effect of low-pressure gas oxynitriding (LPON) followed by low-temperature oxidation on its microstructure and tribological performance. Specimens were nitrided at 1000 °C for 100 min, then oxidized at 450–600 °C for 120 min. Microstructural and phase changes were characterized by SEM and XRD; surface roughness, hardness, and wear were assessed using 3D laser scanning microscopy, microhardness profiling, and pin-on-disk tests under 2 N and 4 N loads. XRD revealed TiN, Ti₂N, Ti₂AlN, and TiO₂ phases, with oxidation temperature governing TiN grain growth and nitride-to-oxide transformation. Oxidation at 500–550 °C formed a dense TiO₂-rich layer over a stable TiN/Ti₂N substrate, achieving hardness up to ~670 HV_0.025 and the lowest wear volume. At low load (2 N), nitriding alone provided the highest wear resistance, while at higher load (4 N), oxidation yielded only slight improvement due to oxide embrittlement. Excessive oxidation at 600 °C increased roughness, induced spallation, and reduced wear resistance. The optimal condition (550 °C) offered synergistic protection from nitrides and stable oxides, enhancing load-bearing capacity. Overall, duplex nitriding–oxidation is most effective for low-to-moderate load applications, with potential use in biomedical implants, aerospace fasteners, and precision components. Full article

In this study, the effects of switching on the two-dimensional electron gas (2-DEG) channel in an E-mode GaN-on-Si HEMT are investigated using a GS-065-004-1-L device that is commercially available for educational practice. A practical prototype with a reduced number of components is proposed, with empirical concepts used to explain its predictive performance when a coreless transformer is series-connected to the E-mode GaN-on-Si HEMT for switching-mode conduction. Conduction modes arising at the p-GaN/n-AlGaN/i-GaN heterojunction in accordance with specifications from the manufacturer’s datasheet were validated using a didactic physical-based model dependent on semiconductor parameters of gallium nitride (GaN). Test circuit-examined waveforms were analyzed, which confirmed that the switching conduction mode of the 2-DEG channel is dependent on physical parameters such as switching operating frequency, temperature, low-field electron mobility, and space charge capacitance. Full article

Nanothermites are widely applied as specific power sources for microscale initiators and pyrotechnics. Increasing the charge density enhances energy storage within a confined combustion chamber, but it also alters the reaction kinetics. To systemically explore this phenomenon, the combustion and pressurization characteristics of electrosprayed nanothermite-based hybrid energetic materials (THEMs) with different metallic oxides (Fe₂O₃, CuO, and Bi₂O₃) and various energetic additives (nitrocellulose (NC), octogen (HMX), ammonium perchlorate (AP), and hexanitrohexaazaisowurtzitane (CL-20)) across various loading densities were tested. The results showed that increasing the loading density decreased the porosity of the loaded nanothermites and then rapidly decreased the convective heat transfer efficiency during the combustion propagation process. When the loading density exceeded a critical value, a dramatic decrease in the peak pressure, several orders-of-magnitude decrease in the pressurization rate, and an order-of-magnitude increase in the combustion duration occurred. Due to the dual effects of the porous microstructure on heat and mass transfer, the critical density of both the electrosprayed Al/CuO/NC/CL-20 composites and their physically mixed counterparts is between 37.9 and 43.9% theoretical maximum density (TMD). Because of the different synergistic catalytic effects, the fast reactivity at the high-loading-density maintaining capacity of the applied additives was AP > HMX ≈ CL-20 > NC. Owing to their intrinsic properties of low ignition temperature and high gas yield, the Bi₂O₃-THEMs could maintain high-speed reactivity even at 59.7% TMD. These results provide valuable insights into the rational design and tailoring of the reactivity of nanothermites for specific applications. Full article

Productive and healthy soils are essential in agriculture and other economic uses of land which depend on plant growth, and are under increasing pressure globally. The physical properties of soil, its porosity and pore structure, also have a significant impact on a wide range of environmental factors, such as surface water runoff and greenhouse gas exchange. Methods exist for evaluating soil porosity that are applied in a laboratory environment or by inserting sensors into soil in the field. However, such methods do not readily sample adequately in space or time and are labour-intensive. The purpose of the current study is to investigate the potential for estimation of soil porosity and pore size using the strength of reflection of audio pulses from natural soil surfaces. Estimation of porous material properties using acoustic reflections is well established. But because of the complex, viscous interactions between sound waves and pore structures, these methods are generally restricted to transmissions at low audio frequencies or at ultrasonic frequencies. In contrast, this study presents a novel design for an integrated broad band sensing system, which is compact, inexpensive, and which is capable of rapid, non-contact, and in situ sampling of a soil structure from a small, moving, farm vehicle. The new system is shown to have the capability of obtaining soil parameter estimates at sampling distances of less than 1 m and with accuracies of around 1%. In describing this novel design, special care is taken to consider the challenges presented by real agriculture soils. These challenges include the pasture, through which the sound must penetrate without significant losses, and soil roughness, which can potentially scatter sound away from the specular reflection path. The key to this new integrated acoustic design is an extension of an existing theory for acoustic interactions with porous materials and rigorous testing of assumptions via simulations. A configuration is suggested and tested, comprising seven audio frequencies and three angles of incidence. It is concluded that a practical, new operational tool of similar design should be readily manufactured. This tool would be inexpensive, compact, low-power, and non-intrusive to either the soil or the surrounding environment. Audio processing can be conducted within the scope of, say, mobile phones. The practical application is to be able to easily map regions of an agricultural space in some detail and to use that to guide land treatment and mitigation. Full article