Downstream Layer Research Articles

A sustainable semiconductor supply chain under regulation

Modern life would not exist without semiconductors as all electronic components used in computers, telecommunications, health care, transportation, and energy systems are equipped with chips. To examine both backward and forward activities in semiconductor industry, this paper formulates the industry as a closed-loop supply chain. It articulates how old semiconductors are processed and recycled to manufacture new silicon and chips, and examines the impact of a commonly applied subsidy scheme on the performance of semiconductor firms which operate in upstream and downstream layers of the industry. Specifically, the proposed semiconductor supply chain involves (i) a return function sensitive to monetary incentives; (ii) a subsidy legislation rewarding end-users for recycling; (iii) upstream industry where silicon is produced using virgin and scrap materials; (iv) downstream industry in which semiconductor manufacturers (such as TSMC, Samsung, Intel) buy silicon and other materials, hire workers, and then produce and sell chips. We characterize Stackelberg equilibrium silicon and semiconductor prices and outputs and calibrate model parameters using actual data to quantify the effects of subsidy and collection channels on silicon and semiconductor firms’ performance. We find that the subsidy scheme neither distorts firms’ strategies nor causes any inefficiency for the semiconductor industry. It stimulates circular economy activities and provides economic and environmental benefits.

Experimental control of the flow separation behind a backward facing step using deep reinforcement learning

In this experimental study, a value-based reinforcement learning algorithm (deep Q-network, DQN) is used to control the flow separation behind a backward facing step at a Reynolds number of 2.9 × 104. The flow is forced by a dielectric barrier discharge (DBD) plasma actuator pasted at the upstream of the step edge, and the feedback information of the separation zone is provided by a hotwire sensor submerged in the downstream shear layer. The control law represented by a deep neural network is implemented on a field programable gate array (FPGA), able to execute in real-time at a frequency as high as 1000 Hz. Results show that both open-loop periodical control and DQN control can effectively reduce the reattachment length and the recirculation area. Compared with the former, which requires dozens of trail-and-error measurements lasting for hours, the latter is able to find an optimal control law in only two minutes, achieving a long-term reward 7% higher. Moreover, by introducing a weak penalty term for plasma actuation, the mean actuator power consumption in DQN can be cut down to only 60% of that in the optimal open-loop control, meanwhile sacrificing a negligible amount of control effectiveness. Physically, the open-loop periodical control destabilizes the shear layer earlier, increasing both the area and the peak amplitude of the high turbulent kinetic energy (TKE) zone, whereas under DQN control, only a slight increase in the TKE peak is observed, and the overall spatial distribution remains the same as baseline.

How can the microbial community in watershed sediment maintain its resistance in the presence of shifting antibiotic residuals?

The widespread presence of antibiotics in global watershed environments poses a serious threat to public health and ecosystems. It is essential to examine the resistance of microbial communities in watershed environments in response to shifting antibiotic residues. Sediment samples were collected from seven sites across a watershed, encompassing surface sediment (0–10 cm) and bottom sediment (30–40 cm) depths. The aim was to replicate exposure scenarios to different antibiotics (oxytetracycline (OTC) and sulfadiazine (SD)) at varying concentrations (0, 10, and 100 μg/L) in sediment overlying water, within controlled laboratory settings. The study findings revealed significant variations in the microbial community structure of sediments between different treatments, with distinct differences observed in the upper stream and top sediment layers compared to the sediments located downstream and in the bottom layers. After the introduction of antibiotics, a significant decrease in microbial nodes was observed in the genus-level co-occurrence network analysis of the bottom sediment layer, particularly in the OTC treatment groups. In contrast, the downstream region displayed more robust correlations among the top 20 genera than the upstream area. There was no significant variance observed in the expression of Antibiotic resistance genes (ARGs), consisting of tetracycline resistance genes (tetC, tetG, tetM, tetW, and tetX) and sulfonamide resistance genes (sul1, sul2, and sul3), between sediments in the top and bottom layers. Nevertheless, downstream samples exhibited significantly higher levels of ARGs when compared to upstream samples. Network correlation analysis indicated notably lower correlations between ARGs and bacterial genera in sediments from upstream or surface layers compared to those in downstream or deeper layers. Moreover, correlations in the sediments from surface layers and upstream regions showed a decreasing trend with increasing SD exposure concentrations, while those in deeper layers and downstream areas remained relatively stable. The presence of antibiotics notably enhanced the correlation between sediment properties and ARGs, particularly emphasizing associations with total carbon, nitrogen, and sulfur content. However, the introduction of SD and OTC resulted in a decrease in the influence of these sediment factors on microbial community functions related to sulfur and nitrogen metabolism, as indicated by KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation. The research provided empirical evidence on how microbial resistance responds to changes in antibiotics in sediment samples taken from various depths and locations within a watershed. It emphasized the urgent need for heightened awareness of the movement and alteration of antibiotic resistance patterns in watershed ecosystems.

At the onset of active whisking, the input layer of barrel cortex exhibits a 24 h window of increased excitability that depends on prior experience.

The development of motor control over sensory organs is a critical milestone in sensory processing, enabling active exploration and shaping of the sensory environment. However, whether the onset of sensory organ motor control directly influences the development of corresponding sensory cortices remains unknown. Here, we exploit the late onset of whisking behavior in mice to address this question in the somatosensory system. Using ex vivo electrophysiology, we discovered a transient increase in the intrinsic excitability of excitatory neurons in layer IV of the barrel cortex, which processes whisker input, precisely coinciding with the onset of active whisking at postnatal day 14 (P14). This increase in neuronal gain was specific to layer IV, independent of changes in synaptic strength, and required prior sensory experience. Strikingly, the effect was not observed in layer II/III of the barrel cortex or in the visual cortex upon eye opening, suggesting a unique interaction between the development of active sensing and the thalamocortical input layer in the somatosensory system. Predictive modeling indicated that changes in active membrane conductances alone could reliably distinguish P14 neurons in control but not whisker-deprived hemispheres. Our findings demonstrate an experience-dependent, lamina-specific refinement of neuronal excitability tightly linked to the emergence of active whisking. This transient increase in the gain of the thalamic input layer coincides with a critical period for synaptic plasticity in downstream layers, suggesting a role in facilitating cortical maturation and sensory processing. Together, our results provide evidence for a direct interaction between the development of motor control and sensory cortex, offering new insights into the experience-dependent development and refinement of sensory systems. These findings have broad implications for understanding the interplay between motor and sensory development, and how the mechanisms of perception cooperate with behavior.

Seepage–Deformation Coupling Analysis of a Core Wall Rockfill Dam Subject to Rapid Fluctuations in the Reservoir Water Level

Core wall rockfill dams are susceptible to cracking at the dam’s crest, as well as collapse and settlement of the rockfill during storage and operation periods, particularly due to rapid fluctuations in the water level in pumped storage power stations. Most studies on the impact of fluctuations in the reservoir’s water level on dam deformation have considered fluctuations of less than 5 m/d, while pumped storage power stations experience much larger fluctuations. Additionally, the seepage and stress fields within the dam’s rock and soil interact and influence each other. Few studies have used the coupling theory of seepage and stress to analyze seepage and deformation in core wall rockfill dams. To address these issues, a finite element model using seepage–stress coupling theory was utilized to investigate the variations in the phreatic line, earth pressure, and deformation of a core wall rockfill dam due to rapid fluctuations in the reservoir’s water level. Additionally, the results of the finite element simulation were compared with and analyzed alongside safety monitoring data. The results indicated that, upon a sudden decrease in the reservoir’s water level, there was a lag in the decline of the phreatic line in Rockfill I, which created a large hydraulic gradient, resulting in a reverse seepage field on the dam’s slope surface and generating a drag force directed upstream. Consequently, a significant concentration of stress occurred on one-third of the upstream slope surface of the dam and the seepage curtain, and the increase in horizontal displacement was substantially greater than the increase in settlement from one-third of the rockfill’s height to the dam’s foundation. The deformation was more sensitive to the lowest water level of the reservoir rather than to the fastest rate of decline. Sudden rises in the reservoir’s water level result in decreased horizontal displacements and settlement of the dam. Amid rapid fluctuations of the reservoir’s water level, changes in the vertical earth pressure were more pronounced at the bottom of the core wall than in its midsection. Compared with the core wall, variations in the vertical earth pressure in the upstream and downstream filter layers were minor at similar elevations. A peak horizontal displacement of 6.5 mm was noted at one-third the height of Rockfill I, with the greatest increase in settlement of 3.5 mm at the dam’s crest. To ensure a project’s safety, it is crucial to control the elevation of the lowest point during a sudden drop in the reservoir’s level and to carefully monitor for cracks or voids within approximately one-third of the dam’s height in Rockfill I and at the dam crest. This study’s results provide a scientific basis for assessing core wall rockfill dams’ health and securing long-term safety at pumped storage power facilities.

Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice

Objective. This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how the FBR affects the functional stability of neural circuits near implanted brain–computer interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders. Approach. This study utilized single-shank, 16-channel,100 µm site-spacing Michigan-style microelectrodes (3 mm length, 703 µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11–13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (LFP) and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation. Main results. The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations. Significance. This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study’s understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

Sharp corner singularity of the White–Metzner model

The stress singularity is determined using matched asymptotics for complete flow of a White–Metzner (WM) fluid around a re-entrant corner. The model is considered in the absence of a solvent viscosity, with power-law forms for the relaxation time and polymer viscosity. In this form, the model shares the same stress singularity as the upper convected Maxwell (UCM) model, but its wall boundary layers may be thinner or thicker than those for UCM depending upon the relative difference in the power-law exponents. If the exponent for the relaxation time is greater than that for the polymer viscosity, the boundary layer is narrower, whilst it is thicker if the polymer viscosity exponent exceeds that of the relaxation time. When the exponents are the same, the WM boundary layer thickness is the same size as that for UCM. A self-similar solution is derived for the stress and velocity fields and matched to both upstream and downstream boundary layers. Restrictions on the sizes of the power-law exponents are also given for validity of this solution.

Large eddy simulation of combustion characteristics during dual fuel switching process in gas turbine combustor

The present study aims to investigate the combustion characteristics, fuel switching strategy, and flame stability associated with fuel switching processes in a dual-fuel gas turbine combustor. Specifically, the Large Eddy Simulation method is employed in conjunction with a skeletal chemical reaction mechanism to simulate the fuel switching process. It is revealed that the presence of a local rich combustion zone, formed by an increased accumulation of fuel in the head region, leads to the displacement of the heat release position towards the downstream inner shear layer. Additionally, this condition causes a reduction in the recirculation zone area and a deterioration in the uniformity of fuel blending. Simultaneously, the combustor experiences low-frequency, high-amplitude pressure pulsations, as well as pulsations in the heat release rate. Furthermore, a comparative analysis is conducted to assess the discrepancies among three distinct fuel supply strategies employed during fuel switching. The fast-opening strategy is found to demonstrate superior performance indicators, as it accomplishes filling the combustor performance indicators within 80% of the allocated time due to its lower initial fuel changing rate.

Effects of Reynolds number and blockage ratio on the turbulence characteristics of open channel flow passing through trash rack

Trash racks are extensively used to prevent debris from being transported to down-stream reaches of waterways. However, debris accumulation at the screens can significantly affect their hydraulic performance, resulting in negative economic and environmental consequences. Basic research related to the mechanism of these physical processes is still rather limited. In this experimental study, the flow fields, including the turbulence intensities, Reynolds stresses, and turbulent kinetic energy near a trash rack in a fixed-bed open channel for subcritical flow conditions are investigated for two Reynolds numbers and two blockage ratios. The experimental study investigates the important physical mechanisms relating to turbulence generation and possible sediment deposition induced by the vertical trash rack. Reynolds number effect on time mean streamwise velocity is predominant in the trash rack’s downstream free surface layer as a higher Reynolds number flow has higher normalized streamwise velocity in the free surface layer and lower in the inner layer. In both the Reynolds number effect case and the blockage ratio effect case peak normalized turbulent kinetic energy was observed immediately downstream of the trash rack and with the increment of longitudinal distance downstream of the trash rack, normalized Turbulent kinetic energy is decreased.

Diverse Data Generation for Retinal Layer Segmentation With Potential Structure Modeling.

Accurate retinal layer segmentation on optical coherence tomography (OCT) images is hampered by the challenges of collecting OCT images with diverse pathological characterization and balanced distribution. Current generative models can produce high-realistic images and corresponding labels without quantitative limitations by fitting distributions of real collected data. Nevertheless, the diversity of their generated data is still limited due to the inherent imbalance of training data. To address these issues, we propose an image-label pair generation framework that generates diverse and balanced potential data from imbalanced real samples. Specifically, the framework first generates diverse layer masks, and then generates plausible OCT images corresponding to these layer masks using two customized diffusion probabilistic models respectively. To learn from imbalanced data and facilitate balanced generation, we introduce pathological-related conditions to guide the generation processes. To enhance the diversity of the generated image-label pairs, we propose a potential structure modeling technique that transfers the knowledge of diverse sub-structures from lowly- or non-pathological samples to highly pathological samples. We conducted extensive experiments on two public datasets for retinal layer segmentation. Firstly, our method generates OCT images with higher image quality and diversity compared to other generative methods. Furthermore, based on the extensive training with the generated OCT images, downstream retinal layer segmentation tasks demonstrate improved results. The code is publicly available at: https://github.com/nicetomeetu21/GenPSM.

Boundary layer transition due to distributed roughness: effect of roughness spacing

The influence of roughness spacing on boundary layer transition over distributed roughness elements is studied using direct numerical simulation and global stability analysis, and compared with isolated roughness elements at the same Reynolds number $Re_h=U_eh/\nu$ ( $U_e$ is the boundary layer edge velocity, h is roughness height and $\nu$ is the kinetic viscosity of the fluid). Small spanwise spacing ( $\lambda _z=2.5h$ ) inhibits the formation of counter-rotating vortex pairs (CVP) and, as a result, hairpin vortices are not generated and the downstream shear layer is steady. For $\lambda _z=5h$ , the CVP and hairpin vortices are induced by the first row of roughness, perturbing the downstream shear layer and causing transition. The temporal periodicity of the primary hairpin vortices appears to be independent of the streamwise spacing. Distributed roughness leads to a lower critical roughness Reynolds number for instability to occur and a more significant breakdown of the boundary layer compared with isolated roughness. When the streamwise spacing is $\lambda _x=5h$ , the high-momentum fluid barely moves downward into the cavities and the wake flow has little impact on the following roughness elements. The leading unstable varicose mode is associated with the central low-speed streaks along the aligned roughness elements, and its frequency is close to the shedding frequency of hairpin vortices in the isolated case. For larger streamwise spacing ( $\lambda _x=10h$ ), two distinct modes are obtained from global stability analysis. The first mode shows varicose symmetry, corresponding to the primary hairpin vortex shedding induced by the first-row roughness. The high-speed streaks formed in the longitudinal grooves are also found to be unstable and interact with the varicose mode. The second mode is a sinuous mode with lower frequency, induced as the wake flow of the first-row roughness runs into the second row. It extracts most energy from the spanwise shear between the high- and low-speed streaks.

How well do rudimentary plasticity rules predict adult visual object learning?

A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer. Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear-in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings. Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n = 2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n = 371,000 trials), which we then used to develop (and publicly release) empirical benchmarks for comparing learning models to humans. We evaluated each learning model on these benchmarks, and found those based on deep, high-level representations from neural networks were surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem.

Large-Eddy simulations on interaction flow structures of pulsed jets in a crossflow

Numerical investigations of pulsed jets in a crossflow are carried out using large-eddy simulation. The flow mechanism and coherent structure evolution of pulsed jets with different frequencies (f = 20 Hz, 50 Hz, and 100 Hz) are analyzed and compared with the steady-state jet. The proper orthogonal decomposition (POD) method is used to analyze the effect of different vortex structures in the flow field, which further elaborates the influence mechanism of frequency on pulse jets in the crossflow. The results show that under the same velocity ratio, the normal penetration depth of pulsed jets to the crossflow are greater than that of the steady-state jet. Due to the pulsating effect, the pulsed jets will form a large-scale shear layer vortex, which strengthens the turbulence pulsation and promotes the mixing between jets and the crossflow. As the pulse frequency increases, the scale of the vortex rings in the downstream shear layer will become smaller but the number will increase, and fine vortex clusters will be formed around them. At f = 50 Hz, the counter-rotating vortex pair (CVP) and the jet front shear layer vortex have a greater influence in the flow field, followed by the downstream shear layer vortex, and the wake vortex has the least influence in the flow field. At f = 20 Hz and 100 Hz, the wake vortex has a greater influence in the flow field than that at f = 50 Hz.

FMLGLN: Fast Multi-layer Graph Linear Network

Graph Convolutional Network (GCN) is an efficient way to deal with graph data. However, GCN aggregates node information from the downstream layers in the training and test process. In this way, GCN may expand nodes outside the current training batch, even outside the training set. In the optimization process, updating the learnable matrix involves all connected nodes in the graph convolutional operation. Therefore, learning in batched and inductive ways is unsuitable for GCN, and large-scale graph data also challenge GCN. To this end, this work proposes a Fast Multi-layer Graph Linear Network (FMLGLN) with a straightforward structure and a low amount of hyper-parameters to deal with large-scale graph data. In the implementation, FMLGLN raises the normalized adjacency matrix to the power of different values and multiplies these matrices with original features to get embedding features of nodes in pre-processing. Finally, FMLGLN concatenates these embedding features and uses a linear neural network to conduct training. The matrix multiplication is linear, and the network structure consists of linear layers, bringing a fast training speed. The features of FMLGLN are (1) batched training, (2) inductive learning, and (3) suiting large-scale graph data. Experimental results on large-scale graph data demonstrate the effectiveness of the proposed FMLGLN.

Graph gating-mixer for sequential recommendation

Recent Transformer-based architectures have achieved encouraging performance for sequential recommendation, whereas their computational complexity is quadratic to the sequence length. MLP4Rec is a promising solution to settle this issue, which captures item transition patterns by a MLP-Mixer layer. Despite effectiveness, we argue that it still faces two critical limitations. On the one hand, it employs the one-hot ID technique to generate each user/item embedding, which has no specific semantics apart from being an identifier. In this case, given these ID embeddings as the original input of a MLP-Mixer layer, it is non-trivial to distill the useful information for other layers. On the other hand, it fails to explicitly differentiate the significance of different factors of an item, which is unrealistic to capture the user’s true taste in a short context; meanwhile, it also does not discriminate the importance of each item instance given the recent actions of a user. To overcome such two limitations, we propose a new solution for sequential recommendation, namely a graph Gating-Mixer Recommender (GMRec). Our solution decomposes the sequential recommendation workflow into three steps. First, by means of graph neural networks, we embed a linear graph propagation module to produce high-quality user and item embeddings. Afterwards, we replace the MLP-Mixer layer in MLP4Rec with a devised dual gating block, which could dynamically control what features and which items can be passed to the downstream layers. Lastly, we devise a user-specific gating strategy to adaptively integrate two components in GMRec. Extensive experiments are performed on the Beauty, Cellphone, Gowalla, and ML-10M datasets, demonstrating the rationality and effectiveness of our solution. Specifically, when Precision@10, Recall@10, MAP@10, and NDCG@10 are adopted as evaluation metrics, the performance gains of GMRec over recent state-of-the-art methods on four datasets are 11.91%, 19.46%, 9.56%, and 13.01%, respectively. Our implemented codes and datasets are available via https://github.com/wubinzzu/GMRec.

H4K20me3 is important for Ash1-mediated H3K36me3 and transcriptional silencing in facultative heterochromatin in a fungal pathogen.

Facultative heterochromatin controls development and differentiation in many eukaryotes. In metazoans, plants, and many filamentous fungi, facultative heterochromatin is characterized by transcriptional repression and enrichment with nucleosomes that are trimethylated at histone H3 lysine 27 (H3K27me3). While loss of H3K27me3 results in derepression of transcriptional gene silencing in many species, additional up- and downstream layers of regulation are necessary to mediate control of transcription in chromosome regions enriched with H3K27me3. Here, we investigated the effects of one histone mark on histone H4, namely H4K20me3, in the fungus Zymoseptoria tritici, a globally important pathogen of wheat. Deletion of kmt5, the gene encoding the sole methyltransferase responsible for H4K20 methylation, resulted in global derepression of transcription, especially in regions of facultative heterochromatin. Derepression in the absence of H4K20me3 not only affected known genes but also a large number of novel, previously undetected transcripts generated from regions of facultative heterochromatin on accessory chromosomes. Transcriptional activation in kmt5 deletion strains was accompanied by a complete loss of Ash1-mediated H3K36me3 and chromatin reorganization affecting H3K27me3 and H3K4me2 distribution in regions of facultative heterochromatin. Strains with H4K20L, M or Q mutations in the single histone H4 gene of Z. tritici recapitulated these chromatin changes, suggesting that H4K20me3 is important for Ash1-mediated H3K36me3. The ∆kmt5 mutants we obtained were more sensitive to genotoxic stressors than wild type and both, ∆kmt5 and ∆ash1, showed greatly increased rates of accessory chromosome loss. Taken together, our results provide insights into an unsuspected mechanism involved in the assembly and maintenance of facultative heterochromatin.

Development of an asymmetric composite PPS-based bag-filter material through membrane laminating and superfine fiber blending: Lab test, field application and development of numerical models

Dedusting is crucial for air pollution control, and nonwoven needle felt (NWNF) bag-filters are widely applied for this purpose. Surface treatment of the filter materials can enhance NWNF’s performance, but the large discrepancy in pore size between the surface and NWNF layers causes interface effects, impairing reverse cleaning and shortening service life. In this study, a novel PTFE membrane-laminated asymmetrical composite bag-filter was developed, by blending superfine polyphenylene sulfide fiber (PPS) in the original NWNF structure. Image analysis shows a gradual increase in pore size from the surface to the downstream layer. In standard lab-scale tests, the novel M-PPSF-S filter showed moderately higher resistance, significantly longer service life, higher dedusting efficiencies and better cleaning performance, compared to filters without surface laminating and/or superfine fiber blending. Numerical modelling was performed, and the flow fields and pressure distribution in these filter materials were visualized, confirming that M-PPSF-S′ unique structure facilitated the alleviation of interface effect and non-steady flow. M-PPSF-S was further scaled up to treat real flue gas from a coal-fired power plant, where constant good performance was observed over 5 months. This study offers a novel and practical way to develop low-cost, high-performance filter materials for high temperature flue gas treatment.

Analysis of Heavy Metal Pollution in Soil along the Shuimo River by the Grey Relational Method and Factor Analysis

Soil samples were collected from the upstream, midstream and downstream areas of the Shuimo River in three layers of 0–20, 20–40 and 40–60 cm, and each group of sample points was located 5 m, 1 km and 2 km away from the river bank, respectively. The analysis was carried out. Based on the investigation and research, six indicators, including As, Pb, Zn, Cu, Ni and Cr, were designated as evaluation factors in combination with the results of the sample collection with low or no detectable values of Cd and Hg. The mean values of the samples measured in the upper, middle and downstream layers were taken, and the degree and source of pollution were evaluated and jointly analyzed using the gray correlation analysis and factor analysis methods. By using the gray correlation analysis, it was found that the evaluation results of the upstream and middle reaches of the soil along the Shuimo River were both level 3, with slight pollution, and the evaluation results of the downstream areas were level 2, with good soil quality. There are two main sources of pollution obtained through the factor analysis; source 1 is mainly heavy metals such as Zn, Cu, Cr, Pb and Ni, while source 2 is mainly heavy metals such as As, Pb and Ni. The amount of pollution sources is inferred from the heavy metal types of each source and the soil environment along the Shuimo River as industrial and human sources of pollution. From the analysis results, the combination of the gray correlation analysis model and factor analysis model is convenient and fast and can accurately quantify the source contribution of various pollution sources. Not only can it reflect the actual situation more objectively and realistically in the evaluation of soil heavy metal pollution and pollution sources, but also the calculation is simple and easily applied with low data requirements.

Experimental and computational investigation into hydrodynamic and heat transfer characteristics of subcooled flow boiling on the International Space Station

This study is part of a long-term initiative to investigate flow boiling of n-perfluorohexane (n-PFH) under microgravity on the International Space Station (ISS). Investigated are data received from the ISS during 2022 using the Flow Boiling and Condensation Experiment (FBCE) (actual name of experimental payload). FBCE is designed to accommodate one of two test modules, one for flow boiling and another for condensation. Data for the present study are acquired using FBCE's Flow Boiling Module (FBM), which features a rectangular channel of 5.0-mm height, 2.5-mm width, and 114.6-mm heated length. Examined are results for three different mass velocities of 199.9, 799.9, and 2399.9 kg/m2.s, and heat fluxes corresponding to approximately 20, 40, and 60% of critical heat flux (CHF). Detailed development of the interfacial behavior is captured by high-speed video through the channel's transparent sidewalls. In the Computational Fluid Dynamics (CFD) model, numerical solver is constructed in ANSYS-Fluent wherein the multiphase model is combined with appropriate models for turbulence, surface tension, and interfacial phase change. Also incorporated are momentum source terms governing bubble shear-lift, bubble drag, and bubble dispersion. Accuracy of the CFD predictions is assessed by comparison against both the heat transfer data and video-captured interfacial behavior. Very good agreement is achieved against measured axial profile of heated wall temperatures and further validated by accurately capturing such interfacial features as bubble formation, detachment, coalescence, and downstream wavy vapor layer development. Additionally, the CFD simulations enable prediction of other transport parameters vitally important to understanding evolution of the boiling flow but not possible from experiment, such as cross-sectional profiles of void fraction, fluid velocity, and mixture temperature.

Reacting and non-reacting, three-dimensional shear layers with spanwise stretching

A three-dimensional, steady, laminar shear-layer flow spatially developing under a boundary-layer approximation with mixing, chemical reaction, and imposed normal strain is analyzed. The purpose of this study is to determine conditions by which certain stretched vortex layers appearing in turbulent combustion are the asymptotic result of a spatially developing shear flow with imposed compressive strain. The imposed strain creates a counterflow that stretches the vorticity in the spanwise direction. Equations are reduced to a two-dimensional form for three velocity components. The non-reactive and reactive cases of the two-dimensional form of the governing equations are solved numerically, with consideration of several parameter inputs, such as the Damköhler number, the Prandtl number, chemical composition, and free-stream velocity ratios. The analysis of the non-reactive case focuses on the mixing between hotter gaseous oxygen and cooler gaseous propane. The free-stream strain rate κ* is predicted by ordinary differential equations based on the imposed spanwise pressure variation. One-step chemical kinetics are used to describe diffusion flames and multi-flame structures. The imposed normal strain rate has a significant effect on the width of downstream mixing layers as well as the burning rate. Asymptotically in the downstream direction, a constant width of the shear layer is obtained if the imposed normal strain rate is constant. The one-dimensional asymptotic result is an exact solution to the multicomponent Navier–Stokes equation for both reacting and non-reacting flows, although it was obtained using the boundary-layer approximation. A similar solution with the layer width growing with the square root of downstream distance is found when the imposed strain rate decreases as the reciprocal of downstream distance. The reduced-order asymptotic solutions can provide useful guidance in developing flamelet models for simulations of turbulent combustion.