Mixing Region Research Articles

Impact of Relational Coordination on Job Satisfaction and Willingness to Stay: A Cross-Sectional Survey of Healthcare Professionals in South Tyrol, Italy.

Job satisfaction and willingness to stay are critical for workforce stability in a challenging healthcare environment. This study examined how relational coordination, a key factor in teamwork and communication, influences outcomes among healthcare professionals in a bilingual, culturally mixed region of Italy. This cross-sectional survey included general practitioners, hospital physicians, nurses, and administrators from the South Tyrol Health Service, using the 'Relational Coordination Survey' and additional measures of job satisfaction and willingness to stay. The analytical methods used included descriptive statistics, correlations, and regression analyses. This study applied path analysis, including mediation and moderation techniques, to investigate the roles of relational coordination and job satisfaction in influencing the willingness to stay. It employs Conditional Process Analysis with the PROCESS macro in SPSS, focusing on models for moderated mediation analysis. The results indicated a critical influence of relational coordination on both job satisfaction and willingness to stay among the 525 healthcare professionals. Job satisfaction varied by health district and years of service, with midcareer professionals being the least satisfied. The findings highlight the central role of relational coordination in job satisfaction and willingness to stay and confirm that low job satisfaction increases turnover intentions. Relational coordination directly enhanced job satisfaction and willingness to stay, while also serving as a mediating factor that amplifies the impact of job satisfaction on retention intentions. This study reinforces the need for strong teamwork and communication to stabilize the healthcare workforce. Targeted interventions aimed at improving relational coordination could significantly enhance job satisfaction and retention among healthcare professionals, particularly in culturally diverse settings such as South Tyrol.

Temperature scaling unmixing framework based on convolutional autoencoder

Hyperspectral unmixing is a key technology in the development of remote sensing applications. However, since both endmembers and abundances are unknown, unmixing is a non-convex problem with a large solution space. To solve this, existing methods usually impose the same strength of sparsity constraint. However, this often does not hold in practice. Because the abundances of purer regions are generally sparse, while the abundances distribution of more mixed regions should be smoother. Temperature scaling is a technique of introducing a temperature parameter T into softmax activation function to adjust the sparsity of the output. Inspired by this, we propose a temperature scaling unmixing (TSU) framework based on convolutional autoencoder (CAE). In this framework, sparse constraints of different intensities are applied to diverse regions by considering spatial similarity of ground objects distribution while preserving the ability of CAE to extract spatial features. What is more, equal-frequency binning is adopted to guide the division of regions by similarity matrix to realize the automatic temperature parameter setting. In addition, a CAE network is designed under the TSU framework in this paper, called TSUCAE. The TSUCAE method exhibits superior accuracy compared to state-of-the-art approaches, as demonstrated through extensive comparative experiments. Furthermore, the TSU framework can be transferred to other CAE-based unmixing methods directly while keeping the network structure of these methods unchanged. Sufficient ablation experiments also prove that the transfer of framework can improve the performance of unmixing. The code is publicly available at https://github.com/UPCGIT/TSUCAE.

Microscopic investigation and local standard deviation analysis of liquid propane jets in supercritical environment

To improve the thermal efficiency of engines, the temperature and pressure in the combustion chamber are continually increased, potentially reaching the critical points of the fuels. Additionally, light fuels such as liquid ammonia and propane are utilized to reduce carbon emissions. The relatively low critical temperature of light fuels facilitates their transition into a supercritical state, which greatly change the jet characteristics. To gain a deeper understanding of the supercritical transition of fuels, jets of liquid propane are investigated through high-speed microscopic imaging technology, covering subcritical to supercritical environmental conditions. Liquid propane is injected through a transparent flat nozzle to observe the internal flow states within the nozzle. For image and data processing, local standard deviation (LSD) analysis and a Residual Network (ResNet-50) model are employed to identify the structures within the mixing region. Droplets counting and frequency domain analysis are also performed to reveal the features of supercritical transition. The findings highlight the importance of mask size in the jet feature extraction. Large mask highlights large scale structures including jet surface and mixing region structures, while small mask brings about full-scale feature including droplets and all other features. A criterion for mixing regime classification based on reduced temperature (Tr) and reduced pressure (Pr) is established directly from image data using the ResNet-50 model, where a condition of Tr1.6Pr0.4 ≥ 1.067 indicates a transcritical mixing regime. Both droplet counting and frequency domain analysis corroborate this mixing regime criterion. These insights enhance our understanding of supercritical transitions and are useful for simulation verifications.

Graph Neural Network for domain segmentation to predict regions of non-ideal mixing in two-dimensional baffle flow systems

This paper presents a novel approach to address computational challenges in predicting flow features by employing a Graph Neural Network (GNN), which is proficient in predicting flow domain values. Traditional Computational Fluid Dynamics (CFD) simulations, although effective, often require substantial computational resources and time, limiting their applicability in time-sensitive scenarios and optimization studies necessitating extensive case studies. The main objective was to evaluate the feasibility of employing node classification on a graph generated from a 2D baffle flow system to segment the domain based on relative fluid age. A second objective was to compare the computational time required for CFD simulations with the inference time of the network to quantify the efficiency gains achieved by utilizing the network. Results demonstrate the potential of utilizing graph convolutional networks for domain segmentation to predict regions of holdup and bypass. The GNN achieved 97% and 92% accuracy in predicting recirculation regions in single and double baffle cases, particularly excelling in high Reynolds number scenarios. Importantly, the proposed GNN-based approach reduces computation time by over 2100%, showcasing significant efficiency gains. Results here highlight the promise of employing graph convolutional networks for flow feature prediction, offering substantial computational efficiency improvements over traditional CFD simulations.

Simple retrieval of the real part of the refractive index for ambient aerosols using extinction coefficient analysis via camera and volume size distribution

This study explored a novel approach for estimating the variations in the refractive index (RI) of aerosols, which is a crucial optical parameter that contains information about particle scattering and absorption. RI is often subject to constraints related to observation time, wavelength selection, and analysis methods. To overcome these limitations, we employed real-time continuous measurements using a camera and optical particle counter (OPC) equipment and applied optimization techniques based on the Mie theory to reverse-calculate changes in the refractive index. Our methodology uses a camera to measure the aerosol extinction coefficients at three different wavelengths within the RGB spectrum (597 nm, 534 nm, and 459 nm), whereas OPC observations provide information on particle size distribution (0.127–17.58 μm as radius). Because the extinction efficiency at known wavelengths varies with the refractive index according to the Mie theory, we retrieved the refractive index, which is an unknown variable, from the extinction coefficient equation. We focused on the real part of the RI because it is challenging to discern the imaginary part of the RI from the visible wavelengths. Our observations were conducted in areas near Busan Harbor and in mixed residential regions. The real part of the RI was determined to be 1.53 ± 0.12. During periods of observed dust events, the refractive index's real part was 1.62 ± 0.05, while during periods anticipated to have emissions of fine aerosols, it was 1.51 ± 0.10. The retrieved RI fell within a range similar to that in other studies, and the changes in the refractive index showed meaningful patterns when analyzed for variations in the particle size distribution and changes in the scattering coefficient. This approach has the potential to advance our understanding of aerosol optical properties and can be applied to real-time analyses with a relatively simple equipment setup, thereby opening new possibilities for its use as a critical variable in optical measurements and environmental models.

Validation of Satellite‐Based Cloud Phase Distributions Using Global‐Scale In Situ Airborne Observations

AbstractUnderstanding distributions of cloud thermodynamic phases is important for accurately representing cloud radiative effects and cloud feedback in a changing climate. Satellite‐based cloud phase data have been frequently used to compare with climate models, yet few studies validated them against in situ observations at a near‐global scale. This study aims to validate three satellite‐based cloud phase products using a compositive in situ airborne data set developed from 11 flight campaigns. Latitudinal‐altitudinal cross sections of cloud phase occurrence frequencies are examined. The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) show the most similar vertical profiles of ice phase frequencies compared with in situ observations. The CloudSat data overestimate mixed‐phase frequencies up to 15 km but provide better sampling through cloud layers than lidar data. The DARDAR (raDAR/liDAR) data show a sharp transition between ice and liquid phase and overestimate ice phase frequency at most altitudes and latitudes. The satellite data are further evaluated for various latitudes, longitudes, and seasons, which show higher ice phase frequency in the extratropics in their respective wintertime and smaller impacts from longitudinal variations. The Southern Ocean shows a thicker mixing region where liquid and ice phases have similar frequencies compared with tropics and Northern Hemisphere (NH) extratropics. Two comparison methods with different spatiotemporal windows show similar results, which demonstrates the statistical robustness of these comparisons. Overall, this study develops a near global‐scale in situ observational data set to assess the accuracy of satellite‐based cloud phase products and investigates the key factors affecting the distributions of cloud phases.

Experimental Research on an Afterburner System Fueled with Hydrogen–Methane Mixtures

A new afterburner installation is proposed, fueled with pure hydrogen (100%H2) or hydrogen–methane mixtures (60% H2 + 40% CH4, 80% H2 + 20% CH4) for use in cogeneration applications. Two prototypes (P1 and P2) with the same expansion angle (45 degrees) were developed and tested. P1 was manufactured by the classic method and P2 by additive manufacturing. Both prototypes were manufactured from Inconel 625. During the tests, analysis of flue gas (CO2, CO, and NO concentration), PIV measurements, and noise measurements were conducted. The flue gas analysis emphasizes that the behavior of the two tested prototypes was very similar. For all three fuels used, the CO2 concentration levels were slightly lower in the case of the additive-manufactured prototype P2. The CO concentration levels were significantly higher in the case of the additive-manufactured prototype P2 when 60% H2/40% CH4 and 80% H2/20% CH4 mixtures were used as fuel. When pure H2 was used as fuel, the measured data suggest that no additional CO was produced during the combustion process, and the level of CO was similar to that from the Garrett micro gas turbine in all five measuring points. The NO emissions gradually decreased as the percentage of H2 in the fuel mixture increased. The NO concentration was significantly lower in the case of the additive-manufactured prototype (P2) in comparison with the classic manufactured prototype (P1). Examining the data obtained from the PIV measurements of the flow within the mixing region shows that the highest axial velocity component value on the centerline was measured for the P1 prototype. The acoustic measurements showed that a higher H2 concentration led to a reduction in noise of approximately 1.5 dB for both afterburner prototypes. The outcomes reveal that the examined V-gutter flame holder prototype flow was smooth, without any perpendicular oscillations, without chaotic motions or turbulent oscillations to the flow direction, across all tested conditions, keeping constant thermal power.

Acoustical Holography and Coherence-Based Noise Source Characterization of an Installed F404 Engine

Understanding the acoustic source characteristics of supersonic jets is vital to accurate noise field modeling and jet noise reduction strategies. This paper uses advanced, coherence-based partial field decomposition methods to characterize the acoustic sources in an installed, supersonic GE F404 engine. Partial field decomposition is accomplished using an equivalent source reconstruction via acoustical holography. Bandwidth is extended through the application of an array phase-unwrapping and interpolation scheme. The optimized-location virtual reference method is used. Apparent source distributions and source-related partial fields are shown as a function of frequency. Local maxima are observed in holography reconstructions at the nozzle lipline, distinct in frequency and space. The lowest-frequency local maximum may relate to noise generated by large-scale turbulence structures in the convectively subsonic region of the flow. Other local maxima are correlated primarily with Mach wave radiation originating from throughout the shear layer and into the fully mixed region downstream of the potential core tip. Source-elucidating decompositions show that the order and behavior of the decomposition lend to the local maxima being related to distinct subsources. Between the local maxima, however, there may be a combination of sources active, which is likely the cause of the spatiospectral lobes observed in other full-scale, supersonic jets.

CFD investigation of flow hydrodynamics and optimization in an industrial‐scale annular lance with swirl flow

AbstractSwirling flow has been applied in various fields due to its ability to enhance mass, heat, and momentum transfer performance. However, the generation of swirling flow occurs at the price of augmenting the pressure drop, and enhancing the friction and shear intensity of the jet with respect to the reactor wall. In the present work, the impact of geometrical configurations of the swirler on the hydrodynamics of the fluid in an industrial‐scale annular lance is investigated via the computational fluid dynamics method, with the discussion of the friction coefficient of the lance walls. It shows that the axial flow injected from the central lance is transformed into a weak swirl flow upon the introduction of swirl flow generated in the casing pipe. Within the mixing region, the interaction between axial and swirl flows results in elevated turbulent kinetic energy. Notably, under varying geometrical configuration conditions, the pressure drop between the inlet of the central pipe and the outlet is maximized. Additionally, the highest friction factor appears at a height of 1.35 m along the middle shell, with a value of 96.67.

Boundary mixing. Part 2. The impact of ventilation

Through a combination of laboratory experiments and theoretical models, we investigate the interaction of a mean upwelling through a closed basin with a vertical buoyancy flux. The fluid is mixed by a horizontally oscillating rake, which either traverses the whole basin or which oscillates just near one vertical boundary. We first review the steady state and demonstrate that, in both mixing regimes, the vertical density profile across the basin is controlled by the steady-state balance between the upward advective and diffusive fluxes of salinity as described by the classical model introduced by Munk (Deep-Sea Res., vol. 13, issue 4, 1966, pp. 707–730). However, with boundary mixing, we show that both the upwelling and the buoyancy transport are localised to the mixing zone near the boundary, and the interior fluid is stagnant. We then develop a model to describe the transient evolution of the system if there is either a discrete increase or gradual decrease to the buoyancy flux. In the boundary mixing case, the change in the buoyancy flux at the lower boundary leads to a change in the buoyancy of the fluid in the boundary mixing region, and this induces a transient, buoyancy-driven flow in the boundary region in addition to the steady upwelling. In turn, an equal and opposite vertical flow develops in the interior, and this leads to a change in the density stratification of the interior fluid as the system adjusts to a new equilibrium. However, in our experiments, there is no vertical mixing in the interior and interior fluid may upwell or downwell dependent on the change to the buoyancy forcing. We discuss the implications of our results for the transport and mixing in the deep ocean, and the associated interpretation of field experiments.

Characteristics of slurry-water mixing region in fractured rock mass grouting process: Experimental study

Grouting is one of the most commonly used methods to control water inrush disasters in fractured rock mass. At present, most of the research on the diffusion process of fracture grouting is carried out on the hypothesize that the groundwater was completely displaced by the slurry, which is lack of sufficient theoretical basis. Therefore, a one-dimensional visual fracture grouting experimental system was developed and a new characterization method of slurry concentration based on the image gray level was proposed in this paper in order to characterize the variation law of slurry-water mixing region during the grouting process. Results show that there exists slurry-water mixing region at the frontal surface of the diffusion slurry in the grouting process in fractured rock mass. The slurry-water mixing region can be characterized by the gray level of the images captured by cameras. The relationship of water to cement ratio of slurry (w/c) and image gray level of slurry (G) conforms to linear function, which can be expressed by G = 19.17 w/c when w/c is between 0.8 and 10. In addition, slurry concentration decreased linearly along the diffusion flow path in the slurry-water mixing region. The size of slurry-water mixing region is positively related to grouting flow rate, the mass ratio of water to cement (w/c) and fracture aperture when the grouting flow rate is between 0.6 L/min to 2.4 L/min, w/c is between 0.8 and 1.6 and fracture aperture is between 1 mm to 5 mm. Furthermore, the ratio of slurry-water mixing region size to hypothetical diffusion radius (RSR) was proposed to characterize development degree of slurry-water mixing region. The stable value of RSR was between 0.04 and 0.24 under the experimental conditions in this paper. The index of RSR can be used to provide theoretical basis for the simplification of the interface between slurry and water. The distribution rules of the slurry-water mixing region can be applied to predict the diffusion range more accurately and perfect the grouting theory.

Mapping large-scale pine wilt disease trees with a lightweight deep-learning model and very high-resolution UAV images

ABSTRACT Pine wilt disease (PWD), caused by pine wood nematodes, has brought a great loss in ecology and economy all over the world. In China, the forest health status is also significantly affected by PWD since 1980, especially in coniferous forests and mixed forest regions. The PWD spreads very fast and can cause a healthy pine wood tree to die within a very short time. An effective way to protect other healthy pine wood trees is to discover PWD trees early. Using unmanned aerial vehicle (UAV) images can help people discover the PWD tree quickly and accurately, and a few automatical methods have been developed to monitor PWD trees including the deep learning methods. Because of the robust spatial-temporal transferability, deep learning methods have become the mainstream algorithms to monitor PWD trees. As we know, the training dataset is the most important material to train a deep learning model. However, there is still a lack of a PWD segmentation dataset so far. To fill this gap, in this paper, we have generated the first open-sourced PWD segmentation dataset based on very high-resolution UAV images to help the community conduct PWD monitor research conveniently. This dataset has 994 training samples, and each sample has visible bands with 512 × 512 pixels, and the spatial resolution of this dataset is 0.05 m. In order to train an advanced segmentation model, we have designed a lightweight deep-learning model for mobile devices or edge devices in this manuscript, named MobileSeg. The main feature of MobileSeg is its decoupling, which uses the re-parameterization technology to improve the model performance. Finally, a large-scale real-world scenario experiment with very high-resolution UAV images was utilized to validate the performance of MobileSeg, the experiment result indicated that MobileSeg has achieved the best performance compared with the recent lightweight segmentation models, and the experiment also proved the effectiveness of the proposed PWD dataset.

Improving flight performance of UAVs by ice shape modulation

Aircraft icing poses a great threat to flight safety. In response to the characteristics of high-power consumption, large volume, and heavy weight of traditional anti-/de-icing technologies, the concept of ice shape modulation is proposed, which is called ice tolerant flight. Firstly, the flight performance of Unmanned Aerial Vehicle (UAV) was compared in three states: no ice, full ice, and modulated ice through flight tests. It was found that ice shape modulation has a significant improvement effect on the aerodynamic performance of aircraft under icing conditions. Under the three modulated ice shape conditions in this experiment, the lift coefficient of the UAV under different ice shape modulation conditions increased by 18%–33%, and the stalling angle was delayed by 3°-5°. Subsequently, the pressure distribution, streamlines in the flow field, and detached vortex distribution of the UAV model in these three states were obtained through numerical simulation, to study the mechanism of ice shape modulation on the aerodynamic performance of aircraft. The simulation found that the reason for the improvement of the wings effect after ice shape modulation is that the modulated area forms a leading-edge protrusion structure similar to a vortex generator. This structure prolongs the mixed flow region on the wings surface and reduces the trend of flow separation, which plays a role in increasing lift and reducing drag for UAVs under icing conditions. Finally, a reverse reachable set that can be used for unexpected state recovery is used as the definition of flight safety boundaries, and an aircraft dynamics model is established to obtain flight safety boundaries for different states. Research has found that the flight safety boundary of the UAV in a no ice state is greater than that in a modulated ice state, and the safety boundary in a modulated ice state is greater than that in a full ice state. Compared with the full ice state, the flight safety boundary after modulation has expanded by 27.0%. The scheme of ice shape modulation can provide a basis for the flight safety of aircraft under icing conditions.

Numerical Study on Prediction of Icing Phenomena in Fresh Air and Blow-by Gas Mixing Region of Diesel Engine under High Velocity of Intake Air Condition

The icing of an intake pipe that might happen in an actual vehicle was numerically predicted in this study. For various operating conditions, the amount of icing was estimated, and the variables influencing the amount of icing were identified. We compared the factors that affected icing: relative humidity, air temperature, and inlet velocity. Seven RPM and load conditions, an intake temperature range of 253–268 K, and a relative humidity range of 65–85% were used for the case studies. To verify the model accuracy, wind tunnel test results from chassis dynometer tests were compared to the data from simulations. The flow analysis was performed using the numerical analytical tool ANSYS Fluent (2019 R1), while the amount of condensed water and icing was predicted using FENSAP-ICE, a program that analyzes and predicts icing phenomena under mechanical systems. The ambient temperature, relative humidity, and inlet air velocity had the biggest effects on the icing rate. The total amount of icing increased for similar BB and input air velocities. When the input air and BB velocities are the same, the variables influencing icing are the ambient temperature and relative humidity. The amount of ice was less affected by outside temperature and relative humidity when the rpm was high, and the inlet air velocity also had an impact.

Intertwined crystal structure, magnetic, and charge transport properties in mixed valent A-site ordered manganite NdBaMn2O6

In the present work, we report a correlation between crystal structure, magnetic, and electrical properties in an exotic magnetic compound, NdBaMn2O6 having ∼92% ordering between Nd and Ba, investigated using temperature-dependent synchrotron x-ray diffraction (XRD), dc magnetization and transport measurements. Temperature-dependent XRD data reveals that the compound undergoes various complex crystallographic phase transitions from high-temperature (T > 320 K) P4/mmm phase to intermediate (320 K – 280 K) Cmmm phase to a mixed (Cmmm and P21am) phase (280 K – 220 K) and to low-temperature P21am phase (T < 220 K). It is found that these crystallographic phase compositions play a crucial role in controlling its magnetic and transport properties. Temperature-dependent dc magnetization data show a sharp drop at the onset of mixed phase (Cmmm + P21am) at 280 K followed by a broad hump at ∼ 220 K where mixed phase to P21am transition occurs, thus indicating a correlation between the structural and magnetic properties. The dc magnetization in the mixed phase region is calculated by considering a superposition of the magnetic moments of Cmmm and P21am phases weighted by the fraction of each phase, which exactly follows the experimental magnetization data. Temperature variation of resistivity data shows a jump at ∼260 K, a temperature corresponding to 50–50% phase composition of Cmmm and P21am phases. The compound shows insulating behavior over a whole temperature range as confirmed from the resistivity data. Further, application of magnetic field causes a shift of magnetic and transport transition temperatures which may be due to the magnetic field induced structural transition in the system.

FE-Net: Feature enhancement segmentation network

Semantic segmentation is one of the directions in image research. It aims to obtain the contours of objects of interest, facilitating subsequent engineering tasks such as measurement and feature selection. However, existing segmentation methods still lack precision in class edge, particularly in multi-class mixed region. To this end, we present the Feature Enhancement Network (FE-Net), a novel approach that leverages edge label and pixel-wise weights to enhance segmentation performance in complex backgrounds. Firstly, we propose a Smart Edge Head (SE-Head) to process shallow-level information from the backbone network. It is combined with the FCN-Head and SepASPP-Head, located at deeper layers, to form a transitional structure where the loss weights gradually transition from edge labels to semantic labels and a mixed loss is also designed to support this structure. Additionally, we propose a pixel-wise weight evaluation method, a pixel-wise weight block, and a feature enhancement loss to improve training effectiveness in multi-class regions. FE-Net achieves significant performance improvements over baselines on publicly datasets Pascal VOC2012, SBD, and ATR, with best mIoU enhancements of 15.19%, 1.42% and 3.51%, respectively. Furthermore, experiments conducted on Pole&Hole match dataset from our laboratory environment demonstrate the superior effectiveness of FE-Net in segmenting defined key pixels.

도시브랜딩 믹스 요소를 통한 광주 ‘오월길(5·18 Road)’ 브랜딩 현황 분석

This study started from the perspective that '5·18 Road' can play an important role in city branding in Gwangju. Based on the theoretical consideration of city branding strategy elements, the purpose of this study is to derive city branding mix elements and indicators, and apply them to systematically and comprehensively analyze the branding status of Gwangju 5·18 Road. The research contents include deriving the 'OIPTCR City Branding Mix Indicators' through the existing OIPTCR mix elements and city branding strategy elements research, arranging the process of promoting 5·18 Road, analyzing the current status of 5·18 Road branding, and deriving implications. The research method focused on literature research and content analysis, and case study applying empirical marketing and normative marketing methodology. As a result of the study, the 'OIPTCR City Branding Mix Indicators' (a total of 24 indicators, that is, 4 organization, 4 image, 5 product, 4 target, 4 channel, and 3 region), which are mix indicators for each element according to six elements of city branding mix (organization, image, product, target, channel, and region), was derived. As a result of applying and analyzing this to 5·18 Road, the project was promoted around a total of 18 indicators, and 6 indicators (1 organization, 2 image, 3 target) were found to have no project. The implications of this study are that it can contribute to the discussion of branding mix theory by deriving new city branding mix indicators, and that it can practically help revitalize the 5·18 Road business by theoretically, systematically, and comprehensively analyzing the current status of 5·18 Road branding.

Numerical simulation on the effect of non-condensable gas on direct contact condensation of steam jet in a narrow pipe

A numerical model was developed to investigate the phenomenon of direct contact condensation involving steam-air jet in subcooled waterflow. This study encompassed a comprehensive exploration of the influence exerted by non-condensable gas from a multi-faceted perspective. These aspects included an examination of gas plume shape, distribution of air mass fraction, variation in axial thermodynamic parameters, the local heat transfer coefficient, and the local condensation rate. It was observed that the presence of non-condensable gas had the effect of extending the penetration length of the steam plume. Notably, the non-condensable gas was predominantly concentrated in proximity to the gas-liquid interface, a phenomenon that introduced hindrances to both heat and mass transfer processes. A detailed analysis of axial thermodynamic parameters further unveiled an intriguing trend. As the air content increased, the expansion of gas plume weakened, subsequently leading to a reduction in the condensation rate. This resulted in diminished pressure fluctuations in the two-phase mixing region. Moreover, the presence of non-condensable gas was shown to curtail the local heat transfer coefficient and the local condensation rate at the interface. This observation offered valuable insights into the underlying mechanisms responsible for the protraction of the gas plume.

Using field-based, photogrammetric point cloud, orthophoto and LiDAR-derived metrics to assess forest structure–snowpack relationships in the Great Lakes–St. Lawrence Forest region

The efficacy of field-based, photogrammetric point cloud, orthophoto and light detection and ranging datasets to describe forest structure and resolve forest–snowpack relationships in a mixed forest region was evaluated over two years at the point and transect scales. Hemispheric photo-derived canopy metrics correlated well with remotely sensed metrics, but tree bole metrics were not effectively derived from remotely sensed data. Significant differences in melt rate and snow-free date were found across forest type at the transect scale. Field and remotely sensed estimates of canopy cover were highly correlated with melt rate and snow-free date at the point scale, which aligns with previous literature and understanding of snowmelt processes. However, significant correlations were only present during the 2016 study year, which was attributed to canopy-controlled solar radiation-driven melt in 2016 versus more spatially uniform turbulent flux-driven melt in 2017. Peak snow water equivalent metrics were not correlated well with canopy or tree height metrics, contrary to previous research. This was likely due to mid-winter melt events throughout both study years, where a mix of accumulation and melt processes confounded forest–snowpack relationships. This study demonstrates that widely available remotely sensed data with a broad coverage can be used to: (i) describe forest–snowpack relationships in mixed hardwood, coniferous forests and (ii) elucidate the variability of forest–snowpack relationships under different climate conditions in this environment.

Quantification of Mixing Quality Effects on Flashback Limits for H2-Rich Fuel Gases in Lean Premixed Combustion Using Laser-Induced Fluorescence of Acetone

Abstract One of the main challenges arising from hydrogen-rich fuel mixtures is to prevent flame flashback. In typical gas turbines, the fuel is commonly injected through holes in the axial swirler vanes to achieve a high mixing quality. However, this injection method is not perfect and can cause nonhomogeneous mixing regions, so that locally rich fuel clusters can significantly increase flashback propensity. This work aims to establish a link between the local mixing quality of fuel and air and flashback limits obtained experimentally under elevated pressure conditions. The nonreacting experiments have been conducted at an atmospheric mockup test rig and acetone-planar laser-induced fluorescence (PLIF) has quantified the local fuel concentration. Zones of high equivalence ratio are evident close to the center body wall. The near-wall equivalence ratio fields reveal that the critical probability of the local equivalence ratio being greater than the one for perfect premixing is between 20% and 35% for all hydrogen concentrations at the flashback limits observed. A probability of 35% is selected as a critical threshold to derive a correlation between the local and the global equivalence ratio in technical premixing. Even though the correlation is specific to the investigated burner configuration, the presented methodology offers valuable insights into the impact of the local mixing quality on flashback propensity, which can improve flashback prediction models formulated for perfect premixing conditions.