Search Results (20)

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

Fire outbreaks, whether caused naturally or unintentionally, pose serious threats to safety, especially in household environments such as kitchens. Common triggers include overheated personal devices, electrical malfunctions, and unattended cooking appliances. This study aims to develop and enhance an automated fire suppression system designed specifically for residential kitchen settings. The system integrates multiple sensors, photoelectric, ionization, and flame detectors, paired with an Arduino microcontroller to ensure accurate detection and timely activation of a servo mechanism that triggers either a Class A or Class K fire extinguisher. Through controlled testing using both solid and liquid combustible materials, we examined key variables, including sensor placement, height, and nozzle angle. The results from 15 trials per session revealed a correlation coefficient exceeding 0.90 between detection time and distance and the significance level of an analysis of variance of less than 0.05, indicating that increased distance significantly affects response time. The percent error remained below 6.7% across all tests, with strong correlations above 0.8 between combustible material type and the corresponding extinguisher class. This research contributes to the advancement of intelligent fire suppression systems by enhancing detection accuracy, reducing false triggers, and optimizing efficient sensor configurations for residential safety. Full article

With the growth of global maritime transportation volume and fuel shortages caused by excessive oil consumption, energy conservation and emission reduction technologies for marine diesel engines have become a core research focus. A three-dimensional (3D) CFD model of a methanol–diesel dual-fuel marine diesel engine was developed in AVL-FIRE and coupled with a CHEMKIN reaction mechanism. The model was validated against experimental data, with errors in cylinder pressure, heat release rate, and major emissions below 5%. Based on the validated model, the effects of the methanol blending ratio (0–30%), injection advance angle, intake temperature, intake pressure, and EGR rate on combustion and emissions were investigated. The results show that increasing the methanol blending ratio reduced cylinder pressure, in-cylinder temperature, and NO and soot emissions, while increasing the peak heat release rate. Advancing injection timing improved combustion and reduced CO and soot emissions but increased NO formation. Higher intake temperature worsened combustion performance and increased NO, CO, and soot emissions. Orthogonal analysis and regression-based optimization identified an optimal condition with a methanol blending ratio of 27%, an EGR of 12.5%, an injection advance angle of 21.2 °CA, an intake temperature of 319.05 K, and an intake pressure of 0.223 MPa. Under this condition, the NOx mass fraction was 1.65 × 10⁻⁵. Full article

The aim of this research is to investigate and reduce the torque ripple in Switched Reluctance Motor (SRM) drives, which is one of the major barriers to their acceptance for electric vehicle propulsion applications despite the advantages of robustness, efficiency, and wide operating range. High torque ripple not only deteriorates drive smoothness but also contributes to noise and vibration, demanding an advanced control strategy beyond traditional current-shaping and switching-based approaches. In this context, this work proposes a DDPG (Deep Deterministic Policy Gradient) Actor–Critic Neural Network-based reinforcement learning control framework that learns the optimal firing angle offsets dynamically to ensure less ripple electromagnetic torque under varying speeds and load conditions. The developed strategy has been designed and trained in MATLAB Simulink R2024b and then deployed in real time using an FPGA-based digital controller for validation on hardware. Comparative analysis with TSF (Torque Sharing Function) and DITC (Direct Instantaneous Torque Control) demonstrates that the reinforcement learning approach gives a much smoother torque response with better dynamic behavior over the operating range analyzed. Full article

Frequent crude oil spills during offshore oil and gas production and transportation have inflicted irreversible detrimental effects on both human activities and marine ecosystems; with particular risks of secondary disasters such as combustion and explosions. To address these challenges; advanced oil sorption technologies have been developed to overcome the inherent limitations of conventional remediation methods. In this study, a flame-retardant protective coating was fabricated on melamine sponge (MS) through precipitation polymerization of octa-aminopropyl polyhedral oligomeric silsesquioxane (POSS) and hexachlorocyclotriphosphazene (HCCP), endowing the MS@PPOS-PDMS-Si composite with exceptional char-forming capability. Secondary functional layer: By coupling the complementary physicochemical properties of polydimethylsiloxane (PDMS) and SiO₂ nanofibers, we enabled them to function jointly, achieving superior performance in the material systems; this conferred enhanced hydrophobicity and structural stability to the MS matrix. Characterization results demonstrated a progressive reduction in peak heat release rate (PHRR) from 137.66 kW/m² to118.35 kW/m², 91.92 kW/m², and ultimately 46.23 kW/m², accompanied by a decrease in total smoke production (TSP) from 1.62 m² to 0.76 m², indicating significant smoke suppression. Furthermore, the water contact angle (WCA) exhibited substantial improvement from 0° (superhydrophilic) to 140.7° (highly hydrophobic). Cyclic sorption–desorption testing revealed maintained oil–water separation efficiency exceeding 95% after 10 operational cycles. These findings position the MS@PPOS-PDMS-Si composite as a promising candidate for emergency oil spill response and marine pollution remediation applications, demonstrating superior performance in fire safety, environmental durability, and operational reusability. Full article

This paper presents an integrated approach for optimizing the performance of a 36-pulses converter system by using artificial intelligence (AI) techniques to be included in a Supervisory Control and Data Acquisition (SCADA) environment. The focus of the proposal is on enhancing harmonic reduction through intelligent adjustment of switching angles and coordinated control of the reinjection transformer included in the power converter topology. A key component of the proposed methodology involves a simulation-based process to determine optimal firing angles (${\mathit{\alpha }}_1$, ${\mathit{\alpha }}_2$, and ${\mathit{\alpha }}_3$), based on Selective Harmonic Elimination (SHE) theory, that minimize Total Harmonic Distortion (THD). Using MATLAB with Simulink and PLECS models, a parametric sweep of the firing angles, generating a comprehensive dataset of THD outcomes. This dataset, consisting of THD evaluations across fine-grained angle variations, serves as the training foundation for supervised machine learning models—specifically, neural network regressors—that approximate the nonlinear mapping between firing angles and harmonic distortion. These predictive models are then employed as surrogates to estimate THD rapidly and guide the selection of optimal switching angles in real time without requiring iterative numerical solvers. Optimization heuristics and predictive models are then deployed to dynamically adapt system parameters in real time under varying load conditions. The proposed method demonstrates significant improvements in power quality and operational reliability, highlighting the potential of AI-assisted SCADA systems in advanced power electronics applications. Implementation results performed on a 36-pulses voltage source converter prototype are included to illustrate the appropriateness of the proposal. Full article

UAVs are essential for forest fire detection due to vast forest areas and inaccessibility of high-risk zones, enabling rapid long-range inspection and detailed close-range surveillance. However, aerial photography faces challenges like multi-scale target recognition and complex scenario adaptation (e.g., deformation, occlusion, lighting variations). RGB-Thermal fusion methods integrate visible-light texture and thermal infrared temperature features effectively, but current approaches are constrained by limited datasets and insufficient exploitation of cross-modal complementary information, ignoring cross-level feature interaction. A time-synchronized multi-scene, multi-angle aerial RGB-Thermal dataset (RGBT-3M) with “Smoke–Fire–Person” annotations and modal alignment via the M-RIFT method was constructed as a way to address the problem of data scarcity in wildfire scenarios. Finally, we propose a CP-YOLOv11-MF fusion detection model based on the advanced YOLOv11 framework, which can learn heterogeneous features complementary to each modality in a progressive manner. Experimental validation proves the superiority of our method, with a precision of 92.5%, a recall of 93.5%, a mAP50 of 96.3%, and a mAP50-95 of 62.9%. The model’s RGB-Thermal fusion capability enhances early fire detection, offering a benchmark dataset and methodological advancement for intelligent forest conservation, with implications for AI-driven ecological protection. Full article

Wildfires threaten ecosystems, biodiversity, and human livelihood while exacerbating climate change. Accurate identification and monitoring of burned areas (BA) are critical for effective post-fire recovery and management. Although satellite multi-spectral imagery offers a practical solution for BA monitoring, existing methods often prioritize specific spectral bands while neglecting full spectral shape information, which encapsulates overall spectral characteristics. This limitation compromises adaptability to diverse vegetation types and environmental conditions, particularly across varying spatial scales. To address these challenges, we propose the time-series spectral-angle-normalized burn index (TSSA-NBR). This unsupervised BA extraction method integrates normalized spectral angle and normalized burn ratio (NBR) to leverage full spectral shape and temporal features derived from Sentinel-2 time-series data. Seven globally distributed study areas with diverse climatic conditions and vegetation types were selected to evaluate the method’s adaptability and scalability. Evaluations compared Sentinel-2-derived BA with moderate-resolution products and high-resolution PlanetScope-derived BA, focusing on spatial scale and methodological performance. TSSA-NBR achieved a Dice Coefficient (DC) of 87.81%, with commission (CE) and omission errors (OE) of 8.52% and 15.58%, respectively, demonstrating robust performance across all regions. Across diverse land cover types, including forests, grasslands, and shrublands, TSSA-NBR exhibited high adaptability, with DC values ranging from 0.53 to 0.97, CE from 0.03 to 0.27, and OE from 0.02 to 0.61. The method effectively captured fire scars and outperformed band-specific and threshold-dependent approaches by integrating spectral shape features with fire indices, establishing a data-driven framework for BA detection. These results underscore its potential for fire monitoring and broader applications in detecting surface anomalies and environmental disturbances, advancing global ecological monitoring and management strategies. Full article

We describe GridMet as open-source software that automatically measures the spatial tuning parameters of grid cells, such as firing field size, spacing, and orientation angles. Applying these metrics to experimental data can help quantify changes in the geometric characteristics of grid cell firing across experimental conditions. GridMet uses clustering and other advanced methods to detect and characterize fields, increasing accuracy compared to alternative methods such as those based on peak firing. Novel contributions of this work include an effective approach for automated field size estimation and an original method for estimating field spacing that can overcome challenges encountered in other software. The user-friendly yet flexible design of GridMet aims to facilitate widespread community adoption. Specifically, GridMet allows basic usage with default parameter settings while also enabling the expert configuration of many parameter values for more advanced applications. Free release of the MATLAB source code will encourage the development of custom variations or integration with other software packages. At the same time, we also provide a runtime version of GridMet, thus avoiding the requirement to purchase any separate licenses. We have optimized GridMet for batch scripting workflows to aid investigations of multi-trial data on multiple grid cells. Full article

The integration of nanomaterials into the textile industry has significantly advanced the development of high-performance fabrics, offering enhanced properties such as UV blocking, fire resistance, breathability, hydrophobicity, antimicrobial activity, and dust rejection. In this context, our research explores the development and characterization of electrospun membranes composed of polyether ether ketone (PEEK) and various polyimides (PIs (1–6)), focusing on their application in protective clothing. The combination of phosphorus-containing polyimides and PEEK, along with the electrospinning process, enhances the distinctive properties of both PEEK and polyimides, leading to composite membranes that stand out according to key parameters essential for maintaining physiological balance. The structural and morphological characteristics of these membranes have been evaluated using Fourier transform infrared spectroscopy (FTIR) to identify the functional groups and scanning electron microscopy (SEM) to examine their morphology. These analyses provide critical insights into these materials’ properties, which influence key performance parameters such as moisture management, breathability, and barrier functions. The membranes’ breathability and impermeability were assessed through the water vapor transmission rate (WVTR), contact angle measurements, water and air permeability, and flame resistance tests. The results obtained indicate that PEEK/polyimide composite membranes meet the complex requirements of modern protective textiles, ensuring both safety and comfort for users through their optimized structural properties and enhanced functional capabilities. Full article

Interior permanent magnet synchronous machines (IPMSMs) driven with a square-wave control (i.e., six-step, block, or 120° control), known commonly as brushless direct current (BLDC) drives, are used widely due to their high power density and control simplicity. The advance firing (AF) angle is employed to achieve improved operation characteristics of the drive. The AF angle is, in general, applied to compensate for the commutation effects. In the case of an IPMSM, the AF angle can also be adjusted to exploit reluctance torque. In this paper, a detailed study was performed to understand its effect on the drive’s performance in regard to reluctance torque. Furthermore, a multi-objective optimization of the machine’s cross-section using neural network models was conducted to enhance performance at a constant AF angle. The reference and improved machine designs were evaluated in a system-level simulation, where the impact was considered of the commutation of currents. A significant improvement in the machine performance was achieved after optimizing the geometry and implementing a fixed AF angle of 10°. Full article

Multifunctional biofilms with early fire-warning capabilities are highly necessary for various indoor and outdoor applications, but a rational design of intelligent fire alarm films with strong weather resistance remains a major challenge. Herein, a multiscale hierarchical biofilm based on lignocellulose nanofibrils (LCNFs), carbon nanotubes (CNTs) and TiO₂ was developed through a vacuum-assisted alternate self-assembly and dipping method. Then, an early fire-warning system that changes from an insulating state to a conductive one was designed, relying on the rapid carbonization of LCNFs together with the unique electronic excitation characteristics of TiO₂. Typically, the L-CNT-TiO₂ film exhibited an ultrasensitive fire-response signal of ~0.30 s and a long-term warning time of ~1238 s when a fire disaster was about to occur, demonstrating a reliable fire-alarm performance and promising flame-resistance ability. More importantly, the L-CNT-TiO₂ biofilm also possessed a water contact angle (WCA) of 166 ± 1° and an ultraviolet protection factor (UPF) as high as 2000, resulting in excellent superhydrophobicity, antifouling, self-cleaning as well as incredible anti-ultraviolet (UV) capabilities. This work offers an innovative strategy for developing advanced intelligent films for fire safety and prevention applications, which holds great promise for the field of building materials. Full article

The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) onboard the geostationary meteorological satellite Himawari-8. In order to not miss early stage forest fire pixels with low temperature, we omit the potential fire pixel detection from the MOD14 algorithm and parameterize four contextual conditions included in the MOD14 algorithm as features. The proposed method detects fire pixels from forest areas using a random forest classifier taking these contextual parameters, nine AHI band values, solar zenith angle, and five meteorological values as inputs. To evaluate the proposed method, we trained the random forest classifier using an early stage forest fire data set generated by a time-reversal approach with MOD14 products and time-series AHI images in Australia. The results demonstrate that the proposed method with all parameters can detect fire pixels with about 90% precision and recall, and that the contribution of contextual parameters is particularly significant in the random forest classifier. The proposed method is applicable to other geostationary and polar-orbiting satellite sensors, and it is expected to be used as an effective method for forest fire detection. Full article

To study the influence of injection time and injection volume on the working process of a two-stroke kerosene direct injection engine, an experimental study was carried out on an improved two-stroke inline three-cylinder gasoline engine, combined with calculations and analysis with GT-POWER and AVL FIRE software. The results showed that when the injection end angle increased from 50° to 70° before the top dead center (BTDC), the average pressure and temperature in the cylinder increased rapidly, the peak value of pressure and temperature and the cumulative heat release increased, and the combustion process in the cylinder was more sufficient. The fuel injection volume was set to 7.5 mg, 8 mg, and 8.5 mg. With increasing fuel injection volume, the average pressure and average temperature first increased and then decreased, the peak value gradually increased, the heat release rate and cumulative heat release increased sharply, the corresponding time gradually advanced, and the peak value gradually increased. With increasing fuel injection volume, CO, NO, and soot gradually increased, while CO₂ slightly decreased. Full article

In this work, the fire reaction properties of flax-polypropylene (PP) composites were investigated at multiple sample angles both experimentally and numerically under two different heat flux conditions (35 and 50 kW/m²) in the cone calorimeter environment. An innovative testing setup which can accommodate a wide range of angles between 0° and 90° for the sample angle frame was developed to perform cone calorimeter tests at different sample angles. An advanced numerical predictive model based on the finite volume method was developed using the fire dynamics simulator (FDS) to quantify the dependency of ignition and combustion properties with sample angles. The numerical model was validated against experimental data from the cone calorimeter tests. The experimental and numerical analyses were conducted to quantify the effects of sample orientation on the different fire reaction properties i.e., ignition time, ignition temperature, burn time, heat release rate (HRR), critical heat flux, etc. The numerical method was utilised to analyse the mechanisms controlling the effect of heat convection and radiation blockage on the heating process. The study establishes that the sample orientation (with respect to the heat flux normal) has a significant influence on the fire reaction properties of natural fibre composites. Full article