Range Of Operating Conditions Research Articles

Performance and emission characteristics of domestic LPG gas burners with hydrogen integration

Abstract Domestic gas burners are a significant source of indoor air pollution and contribute substantially to household energy consumption. While transitioning to carbon-free fuels, such as hydrogen, presents considerable safety challenges, blending hydrogen with conventional fuels can improve combustion efficiency and reduce emissions. However, factors like the differences in the Wobbe index, loading height, power input, and fuel blending complicate understanding the aerated burner performance. This is particularly relevant for large establishments like restaurants and canteens, where hydrogen blending can have a notable impact. This study evaluates four burner designs—commercial (A), straight holes (B), slots (C), and swirl (D)—across thermal inputs (0.8–1.6 kW), with a focus on the swirl burner using hydrogen blends (0%, 34%, and 52% by volume) and varying loading heights (5 mm, 10 mm, and 15 mm). Straight hole (B) and swirl (D) burners were 3D-printed using stainless steel metal powder, while the slot (C) burner was machined. Key performance metrics such as flame behavior, thermal efficiency, pressure drop, and CO emissions were analyzed. Improving primary aeration by reducing flow resistance and enhancing secondary aeration through swirl action resulted in better flame-load interaction and minimized heat loss. The slot burner (C) achieved a thermal efficiency of 63.5%, while the swirl burner (D) reached 65% efficiency at a 10 mm height. The swirl burner maintained high efficiency with up to 52% hydrogen by volume across a wide range of operational conditions, with some exceptions. Additionally, the use of hydrogen eliminated soot emissions. Optimal performance occurred at a 10 mm loading height, with stable efficiency and lower emissions across various power inputs and hydrogen blends. The complex interactions between primary aeration, secondary mixing, and flame-load dynamics during hydrogen blending require thorough evaluation before incorporating hydrogen into aerated LPG burners.

Integrating Particle Motion Tracking into Thermal Gel Electrophoresis for Label-Free Sugar Sensing.

Bioanalytical sensors are adept at quantifying target analytes from complex sample matrices with high sensitivity, but their multiplexing capacity is limited. Conversely, analytical separations afford great multiplexing capacity but typically require analyte labeling to increase sensitivity. Here, we report the development of a separation-based sensor to sensitively quantify unlabeled polysaccharides using particle motion tracking within a microfluidic electrophoresis platform. Carboxymethyl dextran (20 kDa) was spiked into Pluronic thermal gel along with fluorescent nanoparticles (200 nm diameter) and loaded into single-channel microfluidic devices. Upon voltage application, the soluble sugar enriched into a concentrated band that induced motion of the insoluble particles as it passed. Bead displacement was tracked over time to produce electropherograms where peak areas were proportional to analyte concentrations. Key studies herein established the range of acceptable operating conditions (e.g., gel concentration, temperature) to characterize how the temperature-dependent rigidity of thermal gel influenced the analysis. Data processing strategies were then evaluated to identify conditions (e.g., exposure intervals, particle averaging, motion directionality) to maximize sensitivity. The quantitative response of the method was evaluated over a broad concentration range (0.5-5000 nM) where detection limits were found to be 520 pM for the 20 kDa sugar, providing a 106-fold superior mass LOD than a gold standard UV-vis absorbance method. Studies into the detection mechanism found that sensitivity was dependent on the molecular weight of the sugar as larger sugars produced greater responses. Collectively, these studies established best practices for integrating particle sensing into thermal gel separations for label-free polysaccharide quantitation.

Physics-Assisted Machine Learning Models for Predicting Pool Boiling Critical Heat Flux for Cryogenic Fluids

Abstract Cryogenic fluid management is highly crucial for NASA?s space missions and one challenge identified by NASA is the tank fill process for pure cryogenic fluids. To date, no predictive tool can accurately estimate the pool boiling curve during this process. We aim to address this gap by first accurately predicting critical heat flux. We use both traditional machine learning and physics-assisted machine learning methods to predict critical heat flux on a large dataset of 1322 datapoints from 53 published studies with a wide range of operational conditions and surface properties. While the traditional machine learning approach used no prior knowledge in modeling, the physics-assisted machine learning approach assumes the correlation form of the Zuber instability model [1,2] and predicts the multiplier for the hydrodynamic instability term to estimate the critical heat flux. Two groups of input features are compared including one with fluid-only properties and another with both fluid and solid surface properties. Various feature selection methods are employed to reduce the redundant features, and multiple machine learning models are used for modeling the dataset. Physics-assisted machine learning methods predict much better than traditional machine learning methods. The inclusion of solid properties further improves the predictions of critical heat flux. The best predictions are obtained using the extreme gradient boosting model based on the physics-assisted machine learning framework resulting in a mean absolute percentage error of 8.88% which is far better than the semi-empirical correlations.

A diffusion-based wind turbine wake model

Describing the evolution of a wind turbine's wake from a top-hat profile near the turbine to a Gaussian profile in the far wake is a central feature of many engineering wake models. Existing approaches, such as super-Gaussian wake models, rely on a set of tuning parameters that are typically obtained from fitting high-fidelity data. In the current study, we present a new engineering wake model that leverages the similarity between the shape of a turbine's wake normal to the streamwise direction and the diffusion of a passive scalar from a disk source. This new wake model provides an analytical expression for a streamwise scaling function that ensures the conservation of linear momentum in the wake region downstream of a turbine. The model also considers the different rates of wake expansion that are known to occur in the near- and far-wake regions. Validation is presented against high-fidelity numerical data and experimental measurements from the literature, confirming a consistent good agreement across a wide range of turbine operating conditions. A comparison is also drawn with several existing engineering wake models, indicating that the diffusion-based model consistently provides more accurate wake predictions. This new unified framework allows for extensions to more complex wake profiles by making adjustments to the diffusion equation. The derivation of the proposed model included the evaluation of analytical solutions to several mathematical integrals that can be useful for other physical applications.

Screening Oxidoreductase Activities of Nanozymes: Toward Activity‐Tailored Guidelines

Nanozymes (NZs) are nanomaterials able to mimic natural enzymes, offering the advantages of better durability, wider range of operational conditions, and easier functionalization. In addition, some NZs exhibit multiple enzyme‐like activities and effective tandem/self‐cascade reactions. In this context, metallic and metal–oxide nanoparticles are among the most promising candidates for biomedical/biotechnological applications, thanks to their efficient oxidoreductase activities. However, characterizing NZs is challenging because the experimental set‐up of different assays strongly influence their performances. Consequently, currently available literature provides limited understanding of the characteristics of the various NZs, especially in terms of activity‐related performance, hindering their optimal selection and applicative potential. Here, leveraging on accurate characterization methodology, we report a direct comparison of several NZs (gold‐Au, platinum‐Pt, palladium‐Pd, ceria, iron oxide) possessing multi‐enzymatic activities. Our main findings indicate that 1) PtNZs outperform the other tested NZs in catalase‐like activity; 2) Pt and PdNZs present superior superoxide dismutase‐like activity; 3) for peroxidase‐ and oxidase‐like activity, the best‐performing NZ is substrate‐dependent; 4) competitive reactions in multifunctional NZs play a key role in their specific performance, and AuNZs can be the optimal choice in some peroxidase/oxidase configurations; 5) metallic NZs are more efficient than metal‐oxide ones, but 6) ceria presents unique phosphatase‐like activity.

Fluidized-bed gasification kinetics model development using genetic algorithm for biomass, coal, municipal plastic waste, and their blends

This study aims to solve the inverse problem of chemical kinetics with the genetic algorithm (GA) as an optimization tool to develop robust gasification kinetic models that target a variety of feedstocks, such as southern pine biomass, lignite coal, municipal plastic wastes, and their blends. A dataset (syngas compositions, tar compositions, and char yield) obtained by conducting oxy-steam fluidized-bed gasification experiments of various feedstocks, including southern pine biomass, lignite coal, municipal plastic waste, and their twelve different blends have been utilized to develop and validate gasification kinetic model. The results generated by the developed GA-based gasification model surpassed the performance of the conventional model (developed using a trial and error approach), with the least average absolute errors of 0.9 % for H2, 1 % for CO, 0.65 % for CO2, and 3 % for CH4 in the syngas prediction. The study contributed by developing individual gasification kinetics models for various feedstocks, including biomass, coal, plastics, and their blends. Furthermore, the model was found to be suitable for a range of gasification operating conditions, such as steam-to-carbon ratios of 1–3, equivalence ratios of 0–0.2, and gasifier temperatures of 750–950 °C.

Effects of Selected Fuel Cell Operating Conditions on the Distribution of Liquid Water in the Cathode Gas Diffusion Layer

Effective water management is required to enable polymer electrolyte fuel cells (PEFCs) to operate efficiently, particularly at the high current densities needed to make them relevant for transportation and stationary applications. The presence of liquid water can influence the performance of a PEFC when the rate of water production exceeds the rate of water removal [1]. More specifically, accumulation of water within the cell becomes problematic when the pores within the gas diffusion layer (GDL) become filled, resulting in restriction of the transport of reactants to the catalyst layer [2]. Identifying operational conditions that minimize mass transport losses associated with excess water accumulation is crucial for improving the performance of PEFCs, especially at high current densities. Additionally, GDL design plays an important role in reducing mass transport losses, with thermal conductivity being a key factor influencing the distribution of liquid water.In this work, miniaturized fuel cells were operated under various conditions and imaged in-operando using X-ray Computed Tomography (XCT) [3]. This imaging technique facilitates the evaluation of the distribution of liquid water at steady state over a range of operational conditions. The influence of current density on liquid water distribution was assessed at selected cell temperatures between 40°C and 70°C. The results of this study showed high saturations under both the land and the channel regions at 50 °C. However, the channels appeared to be dry at 70 °C, with liquid water present only under the lands. These data were compared to predictions generated using a one-dimensional (1-D) model of the through-plane GDL, which considers specific operating conditions and GDL characteristics, such as thermal conductivity [4]. These results demonstrate that the 1-D model can be used to predict the presence or absence of liquid water within the GDL (and, by extension, PEFC performance) under a broad range of operating conditions. Hence, the 1-D model may be useful for focusing future laboratory investigations. Keywords – operando, X-ray tomographic microscopy, GDL, water visualization Acknowledgement Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada, Ballard Power Systems, Simon Fraser University, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, and Canada Research Chairs.

An Actuator-Disk Model Augmentation for Low-Pressure Axial Fan Simulation

Abstract Actuator-disk rotor models are important simulation tools for cost-effective industrial axial fan system analysis. Actuator-disk fan model performance, however, is constrained by the conventional use of two-dimensional airfoil coefficient input data, which limits the accuracy of the models to a narrow operating range free from significant radial blade flow. Radial blade flow is characteristic of off-design fan operation, which is often unavoidable within typical industrial fan system environments, so the enhancement of actuator-disk model performance for these conditions is desired. This paper accordingly presents a new means of robustly determining actuator-disk model coefficient inputs that are suitable for a wide range of fan operating conditions. The proposed augmented actuator-disk method (AADM) capitalizes on new insights into the unique aerodynamic behavior of low-pressure axial fan rotors. The performance of the AADM is evaluated for two different industrial cooling fans and is shown to outperform existing actuator-disk coefficient formulations through computational fluid dynamics simulations. The AADM is shown to better predict key fan performance metrics, spanwise blade force distributions, and to produce flow fields that are more physically representative (an important feature for industrial heat exchanger studies where the AADM is anticipated to be commonly applied). The AADM has been developed to be easily adopted in generic industrial fan analyses and is expected to serve as a valuable springboard for future actuator-disk fan model developments.

Design of an Active Axis Wind Turbine (AAWT) That Can Balance Centrifugal and Aerodynamic Forces to Reduce Support Infrastructure While Maintaining a Stable Flight Path

This study introduces a novel approach to wind energy by investigating a novel Active Axis Wind Turbine design. The turbine is neither a horizontal nor vertical axis wind turbine but has an axis of operation that can actively change during operation. The design features a rotor with a single blade capable of dynamic pitch and tilt control during a single rotor rotation. This study examines the potential to balance the centrifugal and aerodynamic lift forces acting on the rotor blade assembly, significantly reducing blade, tower, foundation and infrastructure costs in larger-scale devices and decreasing the levelised cost of energy for wind energy. The design of a laboratory prototype rotor assembly is optimised by varying the masses and lengths in a lumped mass model to achieve equilibrium between centrifugal and lift forces acting on the turbine’s rotor assembly. The method involves an investigation of the variation of blade pitch angle to provide a balance between centrifugal and aerodynamic forces, thereby facilitating the cost advantages and opening the opportunity to improve the turbine efficiency across a range of operation conditions. The implication of this study extends to different applications of wind turbines, both onshore and offshore, introducing insight into innovation for sustainable energy and cost-effective solutions.

Experimental Investigation of Heat Pipe Flow Dynamics and Performance

Due to their safety, efficiency, and passive operation, heat pipes have found diverse applications that include nuclear microreactors. Heat pipes enable increased reliability in microreactors, as they eliminate the need for reactor coolant pumps and their associated auxiliary systems while resulting in a greatly reduced spatial footprint. Experimental work is needed to support and expedite the design and licensing of heat pipe microreactors, especially the validation of heat pipe performance, as key heat transfer components. The present work develops a comprehensive heat pipe experimental database covering a wide range of heat pipe operating conditions. In addition, two-phase thermosyphon experiments are conducted to serve as a benchmark for performance. The operating conditions are determined based on previously developed scaling laws for heat pipes and two-phase thermosyphons using low-temperature working fluids. The tested heat pipe is about 2 m long and equipped with in-house-developed annulus screen wicks. To allow for the investigation of heat pipe flow dynamics, various instruments are incorporated to acquire heat pipe pressures, pressure drops, and temperatures. In particular, a fiberoptic sensor is implemented to measure temperatures along the centerline of the entire heat pipe. The results can be directly applied to the advancement of numerical tools currently under development for heat pipe microreactor analysis.

Equivalence Ratio-Driven Flame Response of an Industrial Premixed Burner: Experiments and Modeling

Abstract This paper presents an analysis of the unsteady heat release rate response of premixed flames to equivalence ratio perturbations for an industrial premixed swirl-based burner. During this investigation, perfectly and technically premixed flames were acoustically forced via fuel/air mixture flow and air flow modulations, respectively, at the same operating conditions. From the resulting flame transfer functions (FTFs), measured using the multimicrophone method, the equivalence ratio driven FTF was isolated and extracted by removing the velocity driven component, i.e., the measured FTF from the perfectly premixed flame, from the technically premixed FTF with two novel extraction techniques. The results are compared with FTFs obtained directly in a previous experimental campaign where the fuel flow was acoustically forced, the resulting equivalence ratio fluctuations measured via an infrared absorption technique, and the heat release rate response to the forcing was quantified using chemiluminescence measurements. The results from both measurement approaches agreed well highlighting the validity of the techniques. Further, to understand the governing features of the equivalence ratio driven FTF, a physics-based analytical model following the G-equation approach was developed. The contributions from flame surface area, flame speed, and heat of reaction oscillations were modeled to describe the heat release rate dynamics. A limited number of physical parameters in the analytical model were anchored on one test condition, optimized and restricted to values, which were all physically reasonable, and were subsequently used for model predictions at other operating conditions. The FTF model predictions compared well with experimental data across a range of different operating conditions. Finally, the relative contributions from flame surface area, flame speed, and heat of reaction oscillations on the features of the FTFs were identified and explored.

Determination and analysis of compensation capacitor for a robust distance-variable wireless power transfer system

Wireless power transfer (WPT) systems have been widely adopted for full autonomy in various fields due to their convenience. However, changes in the air gap between the Tx and Rx coils significantly affect efficiency. To overcome this challenge, this paper introduces the determination of a compensation capacitor for a distance-variable WPT system that is robust in varying air gap conditions. The proposed method was verified using theoretical analysis, simulation, and experimental measurement. The electrical circuit was modeled using a T-equivalent model in a series–series (SS) topology to calculate power transfer efficiency (PTE). Specifically, compensation capacitors were analyzed at distances of 10, 30, and 50 mm, considering different self-inductance values. These results are compared against varying load resistances to demonstrate the effectiveness of the proposed approach. Additionally, the PTE drop ratio was defined to facilitate comparison. The results show that the PTE drop ratio for the compensation capacitor at the farthest distance was consistently smaller than that for the capacitor at the nearest distance under varying air gaps and load resistances. In this research, the difference in the PTE drop ratio between 10 and 50 mm was measured, demonstrating that determining the capacitor at the farthest distance reduces the PTE drop ratio across a range of operational conditions.

Sand Detection Using Acoustic Sand Monitors for Liquid-Dominated Multiphase Flow Production

Summary Solid particle erosion in the oil and natural gas industry can be damaging to pipe fittings and equipment, which can lead to maintenance or, in the worst case, a production shutdown. In both cases, this represents a huge economic loss for industry. Acoustic sand monitoring is one of the most widely used practices to provide an alarm and/or estimate the amount of sand entrained in the flow. In addition to being commercially available, they can be easily clamped to the outside of a pipe wall, measuring the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. In this work, a broad range of multiphase operating conditions has been experimentally investigated with acoustic sand monitors in large-scale flow loop facilities to determine the effectiveness of sand monitoring in liquid-dominated multiphase flow. The main objective is to use acoustic sand monitors to determine the threshold sand rate (TSR). This is the minimum sand rate necessary to achieve monitor output higher than the background noise (BN) level. Acoustic monitors were placed upstream and downstream of standard (r/D = 1.5) elbows while varying superficial gas and liquid velocities, sand sizes (25 μm, 75 μm, 150 μm, 300 μm, and 600 μm), and pipe diameters (2 in. and 3 in.) in the vertical orientation. The experimental conditions were selected to cover slug-churn, dispersed-bubble flow, and liquid-sand flow conditions. These results are compared with the previously obtained test results on 50.8-mm (2-in.) internal diameter (ID) test loops. In the 50.8-mm (2-in.) loop, threshold limits were observed for dispersed-bubble flow regimes and liquid-sand as compared with gas-dominated flow conditions. While TSR is the highest in dispersed-bubble flow regimes, the present data were compared with previous results obtained for annular, slug, and stratified flow patterns (FPs) in vertical flow. It was observed that the annular flow regime has the lowest TSR values, showing an increase in TSR when the liquid rate increased. The results from this work can help operators understand how acoustic monitors can detect and distinguish sand impact noise from the background flow noise in liquid-dominated multiphase flow in production facilities. In this way, greater assurance is provided to operators for optimizing oil and gas production rates, especially in wells that tend to produce solids.

Enhancing Industrial Valve Diagnostics: Comparison of Two Preprocessing Methods on the Performance of a Stiction Detection Method Using an Artificial Neural Network

The detection and mitigation of stiction are crucial for maintaining control system performance. This paper proposes the comparison of two preprocessing methods for detecting stiction in control valves via pattern recognition via an artificial neural network (ANN). This method utilizes process variables (PVs) and controller outputs (OPs) to accurately identify stiction within control loops. The ANN was comprehensively trained using data from a data-driven model after processing them. Validation and testing were conducted with real industrial data from the International Stiction Database (ISDB), ensuring a practical assessment framework. This study evaluated the impact of two preprocessing methods on fault detection accuracy, namely, the D-value and principal component analysis (PCA) methods, where the D-value method achieved a commendable overall accuracy of 76%, with 86% precision in stiction prediction and a 66% success rate in nonstiction scenarios. This signifies that feature reduction leads to a degraded stiction detection. The data-driven model was implemented in SIMULINK, and the ANN was trained in MATLAB with the Pattern Recognition Toolbox. These promising results highlight the method’s reliability in diagnosing stiction in industrial settings. Integrating this technique into existing control systems is expected to enhance maintenance protocols, reduce operational downtime, and improve efficiency. Future research should aim to expand this method’s applicability to a wider range of control systems and operational conditions, further solidifying its industrial value.

On the Numerical Investigation of Natural-Convection Heat Sinks Across a Wide Range of Flow and Operating Conditions

Many designs for natural-convection heat sinks and semi-empirical correlations have been proposed in the recent years, but they are only valid in a limited range of Elenbaas numbers El and were mostly tested for laminar flows. To alleviate those limits, parametric studies with 2D and quasi-3D models were carried out, in ranges of Grashof numbers up to 1.55×1011 and Elenbaas numbers up to 3.42×107. Ansys Fluent’s laminar, transition-SST, SST k-ω and k-ϵ models were applied. In addition, when used in this valid range, i.e., mean Elenbaas numbers, with the simplified quasi-3D model, the transition-SST model could predict better results, overestimating the heat flux by 10 to 15% compared to semi-empirical correlations. The 2D model was not deemed satisfying, regarding turbulence models. Consequently, a quasi-3D model was developed: it appeared to be an efficient trade-off between computational time and prediction accuracy, in particular for turbulence models. New grouping factors were also found, to ensure proper dimensioning of natural-convection heat sinks. They corresponded to non-dimensional parameters that dictated the physical behaviour of the heat sink with respect to the semi-empirical correlations. Typically, the ratio of the spacing to the optimal spacing predicted by Bar-Cohen’s correlation turned out to be an appropriate grouping factor with a threshold of 1, above which the fins could safely be considered as isolated, thus greatly simplifying all further calculations.

Rancang Bangun Sistem Monitoring Kebocoran ydrofluorocarbon (HFC) pada Cold Storage System Berbasis IoT di Kapal AHTS

This research aims to design and developing a hydrofluorocarbon (HFC) leak monitoring system in the cold storage cooling system on the Anchor Handling Tug Supply (AHTS) ship by utilizing Internet of Things (IoT) technology. HFC leaks in cooling systems can cause environmental hazards and operational inefficiencies. The proposed system integrates sensors and IoT devices to continuously monitor HFC levels, detect leaks in real-time, and provide instant alerts to crew and management. The system architecture includes sensors for HFC detection, a microcontroller for data processing, and a communication module for data transmission to a central monitoring platform. Real-time data can be accessed through a user-friendly interface, enabling timely intervention and maintenance. Initial testing shows that the system is effective in identifying and reporting HFC leaks, potentially reducing environmental impacts and improving the safety and efficiency of cold storage operations on AHTS vessels. Further development and extensive field testing are planned to refine the system and validate its performance across a wide range of operational conditions.

Multi-scale self-attention feature decoupling transfer network-based cross-domain capacity prediction of lithium-ion batteries

Lithium-ion battery capacity prediction is paramount for improving the safety and reliability of energy storage devices. However, accurately predicting capacity continues to pose a challenge, stemming from the diverse range of operational conditions and the lack of capacity labels under unpredictable scenarios. To tackle this problem, a multi-scale self-attention feature decoupling transfer network is proposed for predicting the capacity of lithium-ion batteries across diverse operational conditions. More specifically, multi-scale multi-head self-attentive encoders are designed to adaptively extract multi-scale domain-invariant features by using a series of loss function constraints. Then, transfer learning integrated with domain-invariant features is applied to migrate the knowledge of the source domain to the target domain for cross-domain capacity prediction. Finally, the performance of our methods is authenticated through utilization of two battery dataset, each including three different charging and discharging scenarios. Experimental results indicate that the proposed method effectively extracts domain-invariant features from charging data characterized by varying distributions, thereby mitigating the effects of distributional differences. Compared with the state-of-the-art methods, the proposed method shows excellent accuracy and bustling ability in two datasets.

Dynamic Models of Mechanical Seals for Turbomachinery Application

One of the primary causes of mechanical face seal failure is rotor vibration. Traditional dynamic seal models often cannot fully explain failure mechanisms. The dynamic models of seals proposed in this paper, including those developed by the authors, are valuable for predicting seal dynamics during operation in specific turbomachinery and for explaining the causes of seal failure. The single-mass dynamic model is suitable for analyzing the dynamics of contact mechanical face seals and simply designed dry gas seals. The two-mass dynamic model is used to investigate the operational dynamics processes of classical dry gas seals under complex loading conditions. The three-mass dynamic model is used to study various complex types of mechanical face seals. This model can determine the normal operating condition range and explain leakage mechanisms in the presence of excessive rotor vibrations.

Enhancing Ignition Reliability with Tail-Groove Strut Designs in Cavity-Based Combustors

As the flight envelopes of turbine-based combined cycle (TBCC) engines expand, ensuring reliable ignition and flame stability under varying conditions becomes increasingly critical. Previous work has shown a significantly changed ignition performance and flow pattern in cavity-based combustors when strut structure parameters were altered, indicating a strong correlation between the ignition process, flame structure, and the strut configuration. This suggests that further investigation is required to determine the optimal strut design. Therefore, this study examines the impact of various strut configurations through numerical simulations, validated by high-speed imaging. Findings show that the tail-groove strut designs improve the flame propagation performance compared to the normal struts, with a critical depth beyond which further increases do not enhance performance. Changes in strut length have a lesser impact than depth. Flow analysis indicates that tail-groove struts create additional recirculation zones that enhance fuel atomization and flame stability. These results suggest that optimizing strut configurations is vital for achieving reliable ignition and flame stability, advancing the development of efficient engines across a wide range of operational conditions.

Deep Learning-Driven Thermal Imaging Hotspot Detection in Solar Photovoltaic Arrays Using YOLOv10

The effective management of solar photovoltaic (PV) arrays is vital to maximising energy generation and ensuring long-term performance reliability. The presence of faults, or partial shading, can give rise to hotspots in PV arrays, making their detection critical for timely maintenance and avoiding further impacts on their performance. This paper describes a new approach to the hotspot detection issue in thermal images of solar PV arrays by leveraging deep learning. This study employs the YOLOv10 (You Only Look Once, version 10) model to deliver very high accuracy and speed in identifying and localising hotspots under defined regions of interest (ROIs). The proposed method begins with taking thermal images from multiple solar PV installations while capturing a range of operational conditions and fault types. These images are annotated with hotspot regions to create a robust training dataset. Given YOLO's capability for real-time object detection with high precision and mean average precision (mAP), the YOLOv10 model is then implemented to train on this dataset. Multiple changes were made to the YOLOv10 hyperparameters to optimise them for detecting hotspots in thermal images. The experimental scenario of this paper demonstrates that this approach indeed performs significantly better than standard image processing methods as well as prior deep learning models for detecting both hotspot accuracy as well as speed of processing the images. The YOLOv10 method demonstrated the highest available classification performance with a mAP at intersection over union (IoU) of 0.5 accuracy of 0.91 with inference time suitable for real-time applications. The sample results demonstrated the model's continuous ability to detect hotspots and provide location data under various conditions, such as crossing through different times of day and weather. The results of this study demonstrate that YOLOv10 can be used for improved detection of hotspots in the explicably thermal imagery of solar PV arrays.