Search Results (6,925)

The increasing global demand for marine energy resources, coupled with the deployment of offshore oil and gas pipelines and submarine power cables, highlights the requirement for reliable subsea infrastructure. To protect these assets from environmental hazards and anthropogenic disturbances, seabed burial via trenching is widely adopted, with submarine trenchers serving as the main installation equipment. Trenching involves excavating a trench on the seabed to place pipelines, cables, or other subsea infrastructure. These operations involve complex soil–tool interactions that fundamentally govern cutting resistance, trench-wall stability, and overall equipment performance. Specifically, distinct engineering challenges arise across different trencher configurations: plough trenchers often encounter complex seabed structures, jet-type trenchers are prone to trench sidewall collapse, and mechanical trenchers face cutting difficulties in hard clay. A thorough understanding of these interactions is therefore critical for resolving operational challenges and optimizing trencher efficiency in engineering practice. To deeply understand these type-specific issues, this review summarizes the geomechanical problems associated with various trenching technologies, synthesizes recent research advances from analytical frameworks, physical experiments, and numerical simulations, and identifies existing knowledge gaps. By consolidating these findings, the paper provides a reference for addressing trencher-related engineering challenges, supporting equipment optimization, and facilitating the deployment of offshore energy transmission networks. Full article

Conventional fire monitoring systems frequently exhibit high false alarm rates, delayed response times, and a lack of closed-loop control capabilities, which severely constrain their deployment in complex real-world environments. To address these issues, this paper proposes an embedded fire detection, tracking, and extinguishing system based on multimodal information fusion and a lightweight neural model. The system follows a “Perception–Decision–Execution–Feedback” closed-loop paradigm and is implemented on a heterogeneous cooperative computing architecture comprising OpenMV4 H7 Plus and STM32F103C8T6 microcontrollers. The perception layer implements a decision-level RGB-infrared fusion mechanism that incorporates a pruned, INT8-quantized lightweight FOMO model, enabling real-time fire detection with an inference latency of 210 ms and a model size of merely 1.8 MB under resource-constrained embedded conditions. The decision layer employs a Bayesian inference-based multimodal fusion framework that effectively suppresses spurious fire interference. The vision-only false detection rate is 15.3%. After infrared fusion verification, the system-level false alarm rate is reduced to 2.0% on the interference test set. In the execution layer, a sixth-degree polynomial jet trajectory model was established and combined with an improved PID–PI dual-loop controller to enable dynamic optimization of spray angle and flow rate in real time. Experimental results demonstrate that the proposed system achieves an average fire recognition accuracy of 95.6% with a false alarm rate as low as 1.4%. Furthermore, it realizes an extinguishing accuracy better than ±5 cm within an effective operating range of 10–60 cm and completes the entire perception-to-extinguishing cycle within 8.5 s under illumination conditions ranging from 50 to 100,000 lux. These results demonstrate the excellent real-time capability, robustness, and energy efficiency of the proposed system, providing a practical and scalable solution for autonomous embedded fire-fighting applications in household, industrial, and warehouse environments. Full article

This study investigates the application of the Co-Flow Jet (CFJ) active flow-control methodology to an automotive rear wing through a combined CFD and experimental campaign conducted on a modified McLaren 765LT. The work evaluates the aerodynamic response, energy performance, and practical integration of embedded Co-Flow systems under representative on-track conditions. An extensive CFD design campaign assessed multiple Co-Flow architectures, from which three representative configurations incorporating embedded ducted axial fans were selected for experimental testing. The results indicate that aerodynamic performance is strongly influenced by the interaction between momentum injection, vehicle conditions, and duct architecture. The most effective configuration achieved drag reductions of up to 9% together with downforce increases of approximately 15% under highly loaded conditions, significantly exceeding the repeatability levels of the measurements. The efficiency analysis further showed that, under selected operating conditions, the aerodynamic benefits obtained from the Co-Flow system can exceed the electrical power required by the actuation system. However, increased mass-flow capability alone was not found to guarantee improved aerodynamic performance or efficiency. The results demonstrate the successful integration and operation of a fan-driven Co-Flow system on a production-based vehicle and highlight the importance of momentum injection level and duct design. The findings should be interpreted within the scope of the investigated vehicle and operating envelope. Due to confidentiality constraints, part of the absolute aerodynamic data could not be disclosed, and the results are therefore presented primarily as relative variations. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

In this paper, we describe a method for computing calendar time forecasts in a local area for large earthquakes of a target magnitude M_T using a count of small earthquakes in the magnitude range M_S to M_T in the area. Using the idea that the Gutenberg–Richter (GR) relation is valid throughout the surrounding region, we define an ensemble of earthquakes in larger surrounding regions to be used in computing the forecast. What follows is simple data mining. “Local” is defined by the probability of a large earthquake occurring within a defined circle of arbitrary radius surrounding a point of interest. The main (and for that matter, the only) assumption for all these works is that the GR magnitude–frequency relation holds. The method has significant skill, as defined by the Receiver Operating Characteristic (ROC) test, which improves as the time since the last major earthquake increases. The probability is conditioned on the number of small earthquakes n(t), with M ≥ M_S = 3.49, that have occurred since the last large earthquake. The probability is computed directly as the Positive Predictive Value (PPV) associated with the ROC curve. The method is compared with the UCERF3 forecasts for the UCERF3-defined geographic boxes centered on Los Angeles and San Francisco and serves as an indicative benchmark. The method is then applied to a 125 km radius circular area around Los Angeles, California, following the 17 January 1994 magnitude M6.7 Northridge earthquake, and short-term forecasts (1-year and 5-year) are computed. We further apply the method to six additional geographic regions with validation by comparison with an estimate of the time-independent conditional Poisson probability. These regions are Athens, Greece; Chengdu, China; Jakarta, Indonesia; Lima, Peru; Santiago, Chile; and Tangshan, China. Full article

Porous ceramics have garnered widespread attention in high-temperature insulation, aerospace, and other fields due to their excellent thermal stability, low density, and superior thermal insulation performance. However, traditional preparation technologies suffer from limitations such as poor pore structure controllability, unstable mechanical properties, and long production cycles. In recent years, 3D printing (additive manufacturing) technology has emerged as a disruptive approach to address these challenges, enabling precise fabrication of porous ceramics with complex structures and tailored properties. This review comprehensively summarizes the research progress on 3D-printed porous ceramics, focusing on preparation technologies, structure optimization, and performance regulation. First, the principles and drawbacks of traditional preparation methods are analyzed. Then, four mainstream 3D printing technologies (Binder Jetting, Material Extrusion, Vat Photopolymerization, and Material Jetting) for porous ceramics are elaborated on in terms of forming mechanisms, process characteristics, typical cases, and performance advantages/disadvantages. Additionally, the structure–property optimization strategies, including the design of Triply Periodic Minimal Surface structures and the application of computational modeling and simulation, are discussed to achieve the balance between thermal insulation and mechanical properties. Finally, current challenges and future development trends of 3D-printed porous ceramics are prospected. This review provides a systematic reference for the rational selection of preparation technologies, structural design, and performance optimization of porous ceramics, promoting their engineering applications in high-value fields. Full article

Porous ceramics have shown great application potential in aerospace, electronics, and lithium-ion battery thermal management due to their low density, high specific strength, and excellent thermal insulation. Material Jetting (MJ), a high-precision 3D printing technology, enables the fabrication of porous ceramics with tailored pore structures, but the synergistic effects of pore structure parameters (configuration, porosity, and number of periods) on their thermal insulation performance remain insufficiently explored. This study systematically investigates the thermal insulation behavior of zirconia porous ceramics fabricated by MJ through experimental tests and numerical simulations. Three typical lattice configurations (Octet, Schwarz, and Gyroid) were selected, and samples with varying porosities (40%, 50%, 60%) and numbers of periods (1, 2, 3) were prepared. The results indicate that the Octet configuration (60% porosity, 3 periods) exhibits the optimal thermal insulation performance, with a minimum cold-end temperature of 58.5 °C (experiment) and 59.21 °C (simulation), attributed to its strut-based structure that forms a more tortuous heat conduction path. For the Gyroid configuration, thermal insulation performance improves with increasing porosity (reducing solid conduction dominance under non-forced convection) and decreases with decreasing number of periods (due to inhomogeneous pore distribution extending heat transfer paths). Notably, the trend of porosity affecting thermal insulation is opposite to that of compressive performance. Numerical simulation results are consistent with experimental data in both values and trends, verifying the reliability of the model. This work clarifies the key factors regulating the thermal insulation of MJ-fabricated porous ceramics and provides practical structural design guidelines for applications such as lithium-ion battery thermal runaway management. Full article

This work examines a complex-valued data partition within the improved Loewner Framework to enhance the efficiency of modal parameter identification for aerospace structures. The method is applied to the General Dynamics F-16 Ground Vibration Test dataset, assessing accuracy and computational performance against the standard real-valued formulation. The complex-valued approach reduces execution time by an order of magnitude while preserving the quality of the identified poles. The extracted modal parameters align well with established benchmark results, confirming the suitability of the proposed formulation for reliable and scalable modal analysis of aircraft structures. Full article

Characterizing the human health risk posed by constituents in drinking water is often challenging due to a lack of published toxicity values. The PIANO (Paraffin, Isoparaffin, Aromatic, Naphthene, and Olefin) analytical method measures nearly 300 compounds in JP-5 jet fuel, 43 of which have published oral reference doses (RfDs). The remaining compounds are typically assigned surrogate toxicity values. We predict RfDs for 290 PIANO compounds using Quantitative Structure–Activity Relationship (QSAR) models based on stepwise linear regression of 2-dimensional molecular descriptors (MDs) and published toxicity values. Five training groups, created by randomly selecting 80% of the non-PIANO compounds and 50% of the 43 PIANO compounds that have RfDs within a master dataset of 1113 compounds, were used to develop five QSAR models. We used the geometric means of four QSAR model results of sufficient quality to predict RfDs for compounds lacking toxicological information. For compounds with known RfDs, 884 (79%) were within 8-fold of published RfDs, well within the acknowledged uncertainty inherent in published RfDs. Our approach has applicability beyond PIANO compounds and represents a new alternative methodology (NAM) that may be used to reduce uncertainty in human health risk assessment and guide regulatory decisions. Full article

In this work, Direct Numerical Simulation (DNS) investigates the combustion behaviour of a reactive transverse lean premixed jet of an ammonia blend (10% NH₃, 11% H₂, 16% O₂ and 63% N₂ by volume) injected through a rectangular nozzle in a pre-heated non-vitiated air crossflow at a pressure of 5 bar. The configuration has been chosen from a Reynolds-Averaged Navier–Stokes (RANS) test campaign to ensure low NO and low unburned fuel, while maintaining a high temperature profile at the turbine inlet. The DNS shows that the flame stabilises on the leeward side of the rectangular jet, within and downstream of the recirculation region, while high scalar dissipation and short residence times prevent persistent anchoring on the windward side. Joint statistics reveal that the reaction does not follow a constant equivalence ratio path, since intermediate progress states are shifted towards leaner mixtures by entrainment, dilution and differential diffusion. The strongest heat-release and displacement-speed events occur in localised regions where mixture state, stretch and flame-front geometry act jointly. The displacement-speed budget is mainly controlled by the chemical source term, with diffusion reducing the net propagation speed and stratification-induced cross terms remaining small. Under intense stretch, positively curved flame elements exhibit larger displacement speeds, indicating a coupled effect of curvature, preferential diffusion and local radical transport. NO formation is dominated by fuel-nitrogen chemistry: HNO and NH₂ are the main NO-producing routes, whereas N₂ and N₂O provide the dominant NO-sink channels. The DNS predicts an outlet-averaged NO level of 400 dppm, while extended-domain RANS calculations indicate that longer residence times could reduce it below 100 dppm. Full article

The transition toward carbon-neutral energy systems has accelerated interest in hydrogen-fueled combustion technologies, where efficient fuel–air mixing is essential for stable and clean combustion. In the present study, the mixing characteristics of under-expanded supersonic jets injected into a pressurized environment are numerically investigated using validated computational fluid dynamics simulations. Two nozzle configurations are examined: a straight nozzle and sudden-expansion nozzles with different expansion ratios and expansion locations. The governing compressible flow equations are solved using the rhoCentralFoam solver with the SST k–ω turbulence model. The numerical framework is validated against Sod’s shock tube solution and experimental data for under-expanded supersonic free jets. The results show that sudden-expansion nozzles significantly modify the shock-wave structure, jet penetration, and lateral spreading compared with the straight nozzle. Among the investigated configurations, nozzles with intermediate expansion-section lengths exhibited pronounced Mach-disk oscillations with a dominant frequency of approximately 10 kHz. The normalized supersonic core length decreased from 17.79 for the straight nozzle to 5.50 for the best-performing sudden-expansion configuration, while the normalized jet half-width increased from 0.82 to 1.70, indicating substantially enhanced mixing performance. The findings demonstrate that nozzle geometry strongly governs the trade-off between flow stability and mixing enhancement in high-pressure supersonic jets. Full article

Non-uniform fiber deposition remains a critical limitation in electrospun poly(lactic acid) (PLA) coating systems. In the present study, experimental characterization was combined with numerical simulations to evaluate the influence of electrospinning parameters on fiber morphology, coating uniformity, and thickness distribution. A 3% PLA solution was electrospun under different processing conditions by varying the applied voltage, needle-to-collector distance, flow rate, and deposition time. The resulting coatings were further analyzed using numerical simulations performed with ANSYS Fluent 2020 R2 software. The results demonstrated that both solution-related and operational parameters strongly influence fiber morphology and spatial deposition behavior. Increasing the applied voltage promoted the formation of thinner fibers; however, excessively high voltage values generated jet instability associated with fiber fragmentation and spray formation. Furthermore, the deposited fibrous layers showed preferential accumulation in the central region of the collector, together with a gradual decrease in coating thickness toward the peripheral areas. A strong correlation was observed between the numerical simulations and the experimental results, confirming the reliability of the proposed modeling approach. Among the investigated conditions, the optimal electrospinning parameters were identified as an applied voltage of 16 kV, a needle-to-collector distance of 17 cm, and a flow rate of 2.5 mL/h. These conditions enabled the formation of homogeneous PLA nanofibers with minimal structural defects and improved substrate adhesion. The combined experimental and numerical approach provides valuable insight into the optimization of electrospinning parameters governing fiber formation and deposition behavior. Full article

Aero-engine predictive maintenance is challenged by heterogeneous operating conditions, complex degradation patterns, and the need for interpretable maintenance alerts rather than solely numerical life estimates. This study investigates a condition-aware data-driven framework for jet engine health assessment using the NASA C-MAPSS dataset, which contains four benchmark subsets (FD001–FD004) with different operating conditions and fault modes. Instead of formulating the task as conventional remaining useful life regression, this study reformulates degradation assessment as a three-class health state classification problem, including Normal, Warning, and Fault. A unified preprocessing pipeline is developed, incorporating condition-wise normalization, first-order differential feature construction, and per-unit sliding window segmentation to reduce operating-condition bias, capture degradation dynamics, and prevent data leakage. Five representative models are evaluated under the same framework, including XGBoost, LightGBM, Random Forest, a context-aware multi-scale temporal attention convolutional neural network, and a bidirectional long short-term memory network. The results show that the proposed framework achieves consistently high classification accuracy across all four subsets, with the best results of 0.9841 on FD001, 0.9764 on FD002, 0.9891 on FD003, and 0.9832 on FD004. In addition, Bi-LSTM outperforms MSTA-CNN on all subsets, for example improving accuracy from 0.9614 to 0.9747 on FD002 and from 0.9773 to 0.9806 on FD004, which is consistent with the importance of long-term temporal dependency modeling for this task. These findings suggest that the proposed framework provides an effective and maintenance-decision-aligned solution for C-MAPSS-based health monitoring, where the three-class alert output offers clearer operational meaning than a single numerical life estimate. Full article

Nanoemulsion (NEM) is an effective adjuvant and delivery system for vaccines and nucleic acids, capable of inducing immune responses against diverse pathogens. Background/Objectives: Conventional NEM manufacture uses multi-step operations, typically high-shear homogenization and then microfluidization (HSHM), thereby increasing process complexity and contamination risk. As water-rich colloidal dispersions, NEM is prone to microbial proliferation and droplet coalescence; freezing further disrupts microstructure, causing phase fusion and separation, so NEM adjuvants are often stored separately from antigens in multi-vial formats. Lyophilization could reduce cold-chain dependence and enable single-vial products, but there is no systematic study on lyoprotectants comparation and process optimization of lyophilized NEM. Methods: An impingement jet mixing (IJM) process was evaluated as a simplified, scalable route for NEM production. Key IJM parameters, including flow ratio, total flow rate, preparation temperature, microchannel type, and shear mode—were examined to match attributes of conventional HSHM. Lyophilized and reconstituted NEM were characterized by dynamic light scattering, scanning electron microscopy, transmission electron microscopy, differential scanning calorimetry and/or in vitro potency to inform lyoprotectant selection, and Taguchi Design of Experiment (DOE) methodology guided lyophilization processes. Results: IJM yielded NEM with droplet size, polydispersity index (PDI) and morphology comparable to HSHM, with higher throughput and fewer unit operations. Optimized lyophilization technique with designed lyoprotectant and process formed closed structures to prevent the easy-to-flow monolayer of the emulsion from fusing, producing robust and stable NEM. Conclusions: Coupling IJM with targeted lyophilization establishes a scalable, lower-risk manufacturing paradigm for NEM that preserves critical quality attributes, reduces cold-chain reliance and enables single-vial adjuvanted vaccine formats with tangible industrial and clinical benefits. Full article

Cavitating jet impingement is a key phenomenon in marine and ocean engineering that is responsible for cavitation-induced material erosion while also being harnessed for surface treatment applications. However, decoupling these concurrent effects is challenging since hydrodynamic jet pressure, microjet impacts, and shockwave emissions often coincide in space and time, making it difficult to isolate their individual contributions. To address this challenge, this review surveys recent advances in measurement techniques designed to decouple these overlapping effects. It highlights multi-source synchronous measurement methods, such as high-speed optical imaging and broadband piezoelectric pressure sensing combined with advanced signal and image processing, to capture mechanism-specific signatures. The review treats mechanism decoupling as a linked task of mechanism identification, mechanism attribution, and contribution quantification and synthesizes the literature under distinct criteria, such as energy, peak pressure, and damage dominance. It shows that synchronized multi-source diagnostics improve attribution reliability but that true quantitative decoupling remains limited by configuration dependence, inconsistent normalization, and a lack of benchmark evaluation criteria. Full article