Flow Distribution Research Articles

An Optimization Strategy for Truncating Ultralong Bus Lines Integrated with Metro Networks

For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs.

Ship Flow of the Ryuko-maru Calculated by the Reynolds Stress Model Using the Roughness Function at the Full Scale

The k-omega SST turbulence model is extensively employed in Reynolds-averaged Navier–Stokes (RANS)-based Computational Fluid Dynamics (CFD) calculations. However, the accuracy of the estimation of viscous resistance and companion flow distribution for full-sized vessels is not sufficient. This study conducted a computational analysis of the flow around the Ryuko-maru at model-scale and full-scale Reynolds numbers utilizing the Reynolds stress turbulence model (RSM). The obtained Reynolds stress distribution from the model-scale computation was compared against experimental measurements to assess the capability of the RSM. Furthermore, full-scale computations were performed, incorporating the influence of hull surface roughness, with the resulting wake distributions juxtaposed with the actual ship measurements. The full-scale calculation employed the sand-grain roughness function, and an optimal roughness length scale was determined by aligning the computed wake distribution with Ryuko-maru’s measured data. The results of this study will allow for the direct performance estimation of full-scale ships and contribute to the design technology of performance.

Design and Study of a Sediment Erosion Test Device for a Single-Flow Channel in the Guide Apparatus of a Reaction Hydraulic Turbine

Sediment erosion damage is one of the main causes of structural failure in reaction turbine units. To study the mechanism through which sediment erosion affects the water-guiding mechanism of a reaction turbine unit, this study obtained the average concentration and particle size of sediment during the flood season based on the statistics of the measured sediment data from the power station. Additionally, the characteristics of the solid–liquid two-phase flow of the diversion components of the reaction hydraulic turbine were numerically calculated. Based on the velocity triangle change in the guide apparatus and the flow similarity principle, a flow-around wear test device for the guide apparatus of the reaction turbine was designed. Furthermore, the similarity of the sand–water flow field between the guide apparatus of the prototype unit and the test device was compared and analyzed. The results demonstrated that the sand–water flow field of the diversion components of the prototype unit was axisymmetric and exhibited a potential flow distribution. Additionally, uniform sand–water flow occurred within the guide apparatus, with a small sand–water velocity gradient near the wall of the stay vanes (SV) and the guide vanes (GV). The maximum volume fraction of sediment particles was observed in the tailing area of the spiral casing, indicating an enrichment phenomenon of sediment particles. The velocity of the sediment particles on the surface of the guide vane in the single-channel sediment wear test device and prototype unit ranged from 6.2 to 7.8 m/s, and the velocity of the sediment particles on the surface of the stay vane ranged from 5.1 to 14.6 m/s, and the difference of the sediment particles’ velocity near the wall was 1 to 3 m/s. The trailing vorticity of the guide vane reached a maximum of 120 s−1. Consequently, the single-channel sediment erosion test device can unveil the sediment erosion mechanism of the guide apparatus of a reaction turbine.

Anti-fouling rotating polymer-based heat exchanger for zero liquid discharge humidification-dehumidification desalination

The objective of this investigation is to assess the performance of various heat exchangers for application in a novel solar-powered zero liquid discharge humidification-dehumidification desalination system. In this study four heat exchangers (HX) having two different flow configurations namely counter flow (cf), cross flow (cr) made up of three different materials namely high-density polyethylene (HDPE), aluminum (Al), and Polypropelyne (PP) were compared in terms of their effectiveness and overall heat transfer coefficient under varying salinity levels (up to 10%) and mixing ratios (0.22–0.45). At a mixing ratio of 0.22 and 0% salinity, PP-HXcr and Al-HXcf exhibited similar effectiveness (∼85%), surpassing that of HDPE-HXcf (∼65%). Despite PP-HXcr's lower thermal conductivity in comparison to Al-HXcf, comparable effectiveness was achieved due to the superior flow distribution in PP-HXcr. Further investigations focused on the impact of salinity on heat exchanger performance. At 3.5% salinity, all heat exchangers experienced a decline in effectiveness and heat transfer coefficient (HTC), with Al-HXcf experiencing a more pronounced decrease compared to PP-HXcr. The higher thermal conductivity of Al-HXcf led to greater salt accumulation, while PP-HXcr demonstrated minimal fouling. As the experiment progressed, fouling increased for all heat exchangers, with the Al-HXcf being practically ineffective at 10% salinity with an effectiveness below 10%. To address the issue of fouling, a rotating cross-flow heat exchanger (RPP-HXcr) was introduced. While the effectiveness of the PP-HXcr drops from 85% to approximately 60% with increasing salinity from 0% to 10%, the RPP-HXcr demonstrates only a marginal decline in effectiveness with increasing salinity. For instance, at mixing ratio of 0.22 when the salinity is increased from 0% to 10%, the effectiveness of RPP-HXcr only drops from 83% to 77%. This exceptional performance was attributed to the continuous contact between the rotating tubes and the incoming feed, effectively preventing fouling and ensuring sustained efficiency. Rotating cross-flow heat exchanger (RPP-HXcr) is introduced and validated as a potentially reliable solution for mitigating fouling, as it demonstrates sustained efficiency and minimal performance degradation across different salinity conditions.

Кількісне оцінювання функціональної ефективності мережевих структур за аналізом чутливості оптимізаційної моделі до зовнішніх впливів

The real network organizational structure is constantly under the influence of external influences, which can cause unexpected consequences due to the cascading spread of relevant disturbances directed at critically important parameters. Parametric sensitivity analysis is the latest computational procedure, the results of which provide the researcher with an idea and quantitative assessment of the appropriate response of the structure in the form of an analytical platform as a composition and interaction of scenario analysis, cascade process development and optimization modeling technology. The main task is timely adaptation to expected changes in business analytics, oriented to optimal resources distribution. The generalized minimum cost problem is a typical example of widespread problems of optimal distribution and use of limited, in particular, energy resources, of any nature and purpose, using the scientific management and analysis of decisions, in its network model attention is paid to the potentials of nodes. The maximum flow problem is an example of the active and effective application of methods and models of flows optimization, where the processes and objects used a network organization, the energy systems and complexes investigated, where arc parameters are critical in its model. In the article, for these classic optimization problems, mathematical and spreadsheet models were built on concrete examples. Direct and dual mathematical programming problems were solved, the following stages were carried out - the organization of a computer experiment and the construction of an analytical platform for evaluating the behavior of the network structure under the influence of external influences and disturbances as their consequences. To take into account the specific conditions of real objects with a network structure at the stage of modification, the model supplemented with appropriate restrictions and correction of input data sets. The obtained results should be useful for planning and management personnel and top-management for discussion and decision-making regarding, for example, the optimal placement of energy equipment, the weighted distribution of energy flows in the "source-sink" system or the appointment of executors for work, automated project management using project networks, etc. Keywords: optimization modeling, maximum network flow, minimum cost flows, network organizational structures, decision making, spreadsheet modeling and analytics.

DEM-CFD Simulation Analysis of Heat Transfer Characteristics for Hydrogen Flow in Randomly Packed Beds

In this study, three randomly packed beds with varying tube-to-particle diameter ratios (D/d) are constructed using the discrete element method (DEM) and simulated via CFD under low pore Reynolds numbers (Rep < 100). An innovation of this research lies in the application of hydrogen in randomly packed beds, coupled with the consideration of its temperature-dependent thermal properties. The axial analysis of the heat transfer characteristics shows that PB−5 and PB−6 achieve thermal equilibrium 44% and 58% faster than PB−4, respectively, demonstrating enhanced heat transfer efficiency. However, at higher flow rates (0.8 m/s), the large-sized fluid channels in PB−6 severely impact the heat transfer efficiency, slightly reducing it compared to PB−5. Additionally, this study introduces a localized segmentation method for calculating the axial local Nusselt number, showing that the axial local Nusselt number (Nu) not only exhibits an inverse relationship with the axial porosity distribution, but also matches its amplitude fluctuations. The wall effect significantly impacts the flow and temperature distribution in the packed bed, causing notable velocity and temperature oscillations in the near-wall region. In the near-wall region, the average temperature is lower than in the core region, and the axial temperature profile exhibits more intense oscillations. These findings may provide insights into the use of hydrogen in randomly packed beds, which are vital for enhancing industrial applications such as hydrogen storage and utilization.

Optimization of a microchannel heat sink with surrogate model and genetic algorithm

Flow maldistribution significantly impacts the performance and operation of microchannel heat sinks, which are extensively utilized across various industries. This study develops a surrogate model to address the flow maldistribution in microchannel heat sinks, employing a combination of genetic algorithm backpropagation neural networks and numerical simulations. Genetic algorithms optimize the microchannel heat sink manifold, involving five variables. The optimized manifold reduces the flow maldistribution factor by 38 % at a coolant inlet velocity of 0.1 m/s compared to that of the conventional structure. The mechanisms of this improvement are explored through comparative analyses of the manifold shape and pressure distribution. However, addressing the flow maldistribution alone proves insufficient in eliminating local hot zones within the microchannel heat sink. Consequently, this study introduces the concept of a corrected maldistribution factor and establishes a new structure guided by this factor as the optimization index. The results indicate a 47 % reduction in the maximum-to-minimum channel flow velocity ratio compared to that of the conventional structure at a coolant inlet velocity of 2 m/s. Furthermore, the maximum temperature drops by 20 K, significantly enhancing the flow and temperature distribution within the microchannel heat sink.

A novel hypothesis on cystic changes in pituitary adenomas and their association with a distinct local blood supply pattern

Pituitary adenoma, a common neuroendocrine tumor, commonly exhibits cystic changes as a distinct feature. Here we introduce and investigate a novel hypothesis suggesting a potential correlation between cystic pituitary adenomas and the unique local blood supply patterns of pituitary adenomas. As the tumor expands, compression of the superior vessels results in reduced blood supply from the superior hypophyseal artery, resulting in uneven distribution of the blood flow. Detailed analysis of clinical and surgical features of pituitary macroadenomas showed that cystic changes predominantly manifest in the upper part of the tumor and are driven by metabolic activity and relatively insufficient blood supply in this region. Supporting evidence from dynamic contrast-enhanced magnetic resonance imaging studies further substantiated this hypothesis. Overall, our findings offer a fresh perspective for early diagnosis and surgical decision-making for pituitary adenomas, contributing valuable information for individualized treatment and an improved prognosis. Despite some potential limitations, this hypothesis provides crucial insights into the understanding and management of pituitary adenomas.

One-dimensional model of manifold microchannels for embedded cooling: Prediction of thermal performance and flow non-uniformity

A one-dimensional model has been developed to accurately predict the thermal performance and flow non-uniformity of the manifold microchannels (MMC) for embedded liquid cooling. The model consists of one-dimensional governing equations derived from the integral relations of momentum and energy over appropriately-defined two separate control volumes. To validate the model, a series of 3-D numerical simulation is conducted over the wide ranges of the Reynolds number (Rem,in) at the manifold inlet from 560 to 3190, the dimensionless hydraulic flow length (x+) from 0.012 to 0.123, and the dimensionless thermal flow length (x∗) from 0.002 to 0.023. It is shown that the model provides accurate predictions of the thermal performance and flow non-uniformity (CV) of MMC heat sinks within the root mean square percentage error (RMSPE) of 6% and 26% for 50 data points, respectively. The significant improvement of the prediction accuracy is made over the earlier models with an error reduction of 82%. Finally, a design guideline for the uniform flow distribution is suggested for the first time based on a newly proposed explicit correlation for predicting the flow non-uniformity: the dynamic pressure at the manifold inlet should be kept smaller than the pressure drop across the microchannels.

Optimization of the first wall cooling circuits to achieve an efficient coolant flow distribution in the DCLL breeding blanket

In the current design proposal of the Dual Coolant Lithium Lead (DCLL) breeding blanket for the EU DEMO reactor, the first wall is cooled down by a succession of He channels arranged along the poloidal length. In this scheme, since He extracts around 30% of the blanket thermal power, a conservative oversizing of the coolant mass flow rate compelled by highly heterogeneous heat loads, can severely jeopardize the blanket thermal efficiency and the consumption of the auxiliary systems. A meticulous design of the hydraulic network can enhance the cooling system efficiency by ensuring that each channel is fed with a mass flow rate according to its specific needs.In this study, a novel approach employing genetic algorithms has been developed to optimize the geometry of the first wall, encompassing the feeding/collecting manifolds and channels. This optimization aims to replicate the mass flow rate distribution obtained from TOMFLOW (Tool for Optimization of Helium Mass Flow Rate Distribution), a code dedicated to determine the minimum mass flow rate required in each channel to maintain the EUROFER steel structure below 550 °C.

A passenger flow spatial–temporal distribution model for a passenger transit hub considering node queuing

Passenger transit hub is a quintessential complex system, characterized by intricate interactions among humans, facilities, and the surrounding environment. External disturbances often precipitate crowd congestion and safety risks. Modeling the spatial–temporal distribution of passenger flow within the hub is an important element in operational management. Prevailing research predominantly focuses on static models or monitoring data to assess the spatial–temporal characteristics of passenger flow throughout the hub. Nevertheless, few studies have been found in modeling passenger flow distribution for passenger transit hub from the vantage point of traveler behavior in the intricate ‘human-facility-environment’ complex network. Thus, this paper proposes a computational model for the passenger flow spatial–temporal distribution based on traveler behavior. First, combining consideration of critical spatial facilities and passenger flow streamlines, a passenger flow network is established. Second, the instantaneous travel times of link and node are defined while considering queuing and congestion at crucial facilities in the hub. Then, a passenger flow spatial–temporal distribution model for the passenger transit hub is constructed, which consists of dynamic route choice model and dynamic passenger flow loading model. A solution algorithm is simultaneously designed. Finally, the effectiveness of the model and algorithm are verified by a numerical example. The results show that the proposed model can effectively capture real-time congestion and the dynamic distribution of passenger flow in the hub. Therefore, this study contributes to the safety management and layout optimization of the hub, holding significant importance for improving hub operational efficiency and service levels.

Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees

Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges – incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary anatomies combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. We propose an optimization-based method to personalize multiscale coronary flow simulations by assimilating clinical CT myocardial perfusion imaging and cardiac function measurements to yield patient-specific flow distributions and model parameters. Using this proof-of-concept study on a cohort of six patients, we reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based purely on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.

Flat plate solar collector networks: Design and retrofit considering fouling effects

This study presents the thermohydraulic principles for retrofitting existing flat plate solar collector networks with the aim of increasing energy capture using the installed capacity. The arrangement of a solar collector field influences its thermohydraulic performance and pumping costs. In this study, factors such as scaling-induced fouling, solar radiation, and flow distribution are considered. The case of an existing plant consisting of a total of 40 collectors is examined. It is concluded that the optimal arrangement should include 8 collectors per line connected in series as a limit to enhance thermohydraulic performance and reduce overall operating costs. The annual savings due to reduced natural gas consumption in the original plant structure (4Lx5px2s) amount to $17,522.72 USD per year; however, modifying the structure to 4Lx10s, the savings increase to $19,818.64 USD per year. Under fouling conditions, the new structure reaches savings of $19,512.41 USD per year. This suggests that, in design, the network configuration should prioritise the number of collectors in series per line, only limited by the increase in pressure drop.

Enhancing fluid distribution in additively manufactured heat exchangers using autogenerated geometries

The heat exchanger distributor, which connects the tube carrying the medium to the heat transfer geometry, has received less attention in research studies compared to heat transfer geometries. However, the flow distribution is critical to efficiency. Therefore, a model-in-the-loop approach is presented to optimize additively manufactured distributors. A quasi-fractal distributor and all connected geometries, including pipe connections, heat transfer geometry and collector, are meshed and simulated in OpenFOAM using steady-state computational fluid dynamics. The volumetric flow rates determined from the simulation are assigned to the respective flow cross-sections during the distribution process. The flow cross-sections of the geometric model are modified within an optimization process, considering the prevailing volumetric flows. The results indicate a 64 % decrease in the standard deviation of volume flow across all 85 channels with a simple optimizer and few simulation loops. The printable geometry can be extracted directly out of Python using common file formats. The presented workflow has been proven feasible. Future improvements could involve using the automated model-in-the-loop simulation and creating optimizing algorithms considering fluid flow behavior.

Predicting red blood cell traffcking and capillary hemodynamics in angiogenic and tumor microcirculation in silico

Objective: Angiogenic and tumor microvasculatures are known to have abnormal topology due to the presence of frequent vessel junctions, irregular and deflated blood vessels, multi-furcations, and tessellated vessel organization. Although recent advances in imaging techniques in vivo have enabled mapping such vasculatures at high spatial resolution, simultaneous measurements of hemodynamic parameters, such as the wall shear stress (WSS) with full 3D details, remain a challenge. Theoretical network flow models, often used for hemodynamic predictions in such experimentally acquired images, cannot provide the full 3D hemodynamic details either, as these models treat each blood vessel as 1D segment and do not explicitly model red blood cells (RBCs). To overcome this limitation, we have developed a high-fidelity, 3D Computational Fluid Dynamics modeling to predict the flow of a large number of deformable RBCs through physiologically realistic tumor/angiogenic microvascular networks in silico. Methods: We use in vivo images to create such vascular networks in silico and then predict RBC traffcking and capillary hemodynamics. Deformation of every flowing RBC is considered with high accuracy, and 3D geometry of each vessel is accurately modeled. Flow is driven by specifying physiological pressure boundary conditions. Model predictions have been validated against in vivo data. This in-house predictive tool is versatile, can be applied to any microvascular network image obtained in vivo in any organ, and can predict trajectories of diverse cell types including leukocytes, platelets and circulating tumor cells, drug and molecular transport in capillary blood, and cell-vessel adhesion. Results: We provide quantitative differences between healthy microvascular networks and tumor/angiogenic networks in terms of RBC distribution, perfusion, and wall shear stress. Our model shows increased heterogeneity in RBC and flow distribution in both tumor and angiogenic vasculatures than the healthy one. Also, we predict reduced flow and hematocrit in several vessels in both tumor and angiogenic vasculatures. Interestingly, several vessels in the angiogenic vasculature are predicted to have higher flow than the healthy one, while most vessels in the tumor vasculature show flow reduction. This in silico prediction is consistent with a recent in vivo study which showed higher flow in peri-tumor region and reduced flow in tumor. We further predict a significant heterogeneity in WSS and WSS gradient, blood velocity profiles, and near-wall RBC-depleted region. Conclusion: In conclusion, we have developed a versatile, in silico model that allows high-fidelity prediction of capillary hemodynamics in tumor microcirculation and provide information on hemodynamic variables that are not readily measurable in vivo but have physiological significance in tumor progression and treatment. NIH (R01EY033003) and NSF (CBET1804591). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Numerical simulation of droplet impact onto heated surfaces below the boiling point

Droplet impact onto heated surfaces is a widespread process in industrial applications, particularly in the context of spray cooling techniques. Therefore, it is essential to study the complex phenomenon of droplet spreading, heat removal and flow distributions during this process. The primary focus of this research centers on the wetting dynamics at a surface temperature below the saturation temperature of the liquid. This study employs a Computational fluid dynamics (CFD) framework to simulate the impact dynamics of a water droplet onto a heated surface. The impact dynamics, spreading, air entrapment and heat transfer in the process are discussed using simulation results. The Direct Numerical Simulation (DNS) tool Free Surface 3D (FS3D), an in-house code at the Institute of Aerospace Thermodynamics, University of Stuttgart, is utilized. The Finite-Volume method is used to solve the incompressible Navier-Stokes equations, and the interface is defined by the Volume of fluid (VOF) method. In this study, the temperature change above the entrapped air bubble formed due to droplet inertia is shown, which can influence the heat removal rate in the process. Therefore, conducting a comprehensive study on the temperature shift caused by the air bubble is crucial.

Impact of distance on heat transfer at the leading edge of blades in a rotating state

To enhance the high-temperature capability of turbine blades and meet the requirements of advanced design, this research examines the intricate flow characteristics and the heat transfer occurring on the internal walls effects of leading-edge impingement cooling structures under different impingement distances. In this study, copper block method is employed for heat transfer testing, extending the testing range of rotating impingement cooling channels from Redj = 1 × 104, Ro = 0.2 to Redj = 5 × 104, Ro = 2.0. The findings reveal that the Nusselt number along the channel is significantly influenced by flow distribution and turbulence intensity. At lower Re (Re ≤ 2 × 104), the flow velocity increases along the channel due to the decrease in pressure drop, resulting in enhanced heat transfer. However, at higher Re (Re ＞ 2 × 104), increased flow rate leads to intensified turbulence and thickening of the boundary layer, consuming fluid energy and reducing the temperature difference, thereby adversely affecting heat exchange around the impingement holes. Moreover, Nu decreases with increasing rotation speed, with the highest average decrease of 15.3 % when Ro reaches 0.25. For cases where Redj ＜ 4.3 × 104, an impingement distance-to-diameter ratio (Z/D) = 2 exhibits optimal heat transfer performance. Conversely, when Redj＞4.3 × 104, the influence of impingement distance will diminish. Larger impingement distances increase the HTC but are accompanied by increased non-uniformity. Therefore, selecting an appropriate impingement distance can reduce boundary layer formation and flow separation, thereby forming an optimal flow structure, which contributes to promoting heat exchange and improving heat transfer uniformity. A correlation based on geometrical parameters has been proposed to predict the average Nusselt number for jet impingement arrays with varying geometrical configurations.

Machine learning techniques for predicting microvascular network hemodynamics

Existing techniques to image microvascular networks in vivo do not allow a direct and accurate measurement of several hemodynamic variables such as the wall shear stress (WSS). They cannot also provide a full 3D characterization of blood velocity and red blood cell (RBC) concentration profiles in every vessel in a microvascular network. Such physiological parameters however play critical roles in blood flow regulation, disease progression, angiogenesis, and hemostasis. Theoretical models of network blood flow, often used for hemodynamic predictions in experimentally acquired images of the microvascular network, cannot provide the full 3D blood velocity, RBC concentration and WSS profiles as these models assume one-dimensional flow governed by Poiseuille’s law. In contrast, high-fidelity computational models that simulate flow of blood as a suspension of deformable RBCs and model the 3D vessel geometry can readily predict such physiological variables with high accuracy. Such high-fidelity computational models have recently been applied to predict hemodynamic variables in microvascular networks in silico composed of many vessels and vascular junctions. Such models, however, tend to become computationally expensive with increasing size of the network; therefore, they are not feasible for use in large networks at organ scale. To overcome this limitation, we developed a machine learning (ML) approach that can predict hemodynamic variables in 3D and with high accuracy. The ML models are based on artificial neural network and convolution-based U-net models. Using experimentally acquired images of the microvascular networks, inputs at a few inlets, and a “bank” of database that we have developed, these ML models can predict flow and RBC distribution in all vessels in the network, 3D blood velocity and RBC concentration profiles over the cross-section of every vessel, 3D WSS profiles, and time-dependent variations in blood flow in any vessel. The prediction from the ML models compare very well against the true data. Also, the prediction time using ML is significantly less than that of the high-fidelity models. The current ML models, which to our knowledge are the first of their kind, are highly promising for image-based predictions of sub-cellular resolved capillary hemodynamics in organ-scale networks. This study paves the way for AI to make hemodynamic predictions in organ-scale capillary vessel networks while retaining the sub-cellular scale details and overcoming the limitations of the in vivo imaging techniques, with potential applications in hematological and microvascular disorders, angiogenesis, and vascular-mediated drug delivery. Funded by NIH (R01-EY033003) and NSF (CBET 2302212). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Spatiotemporal modulation of ultrafast plasmonic vortices with spin–orbit coupling

The vortex field of surface plasmon polariton with orbital angular momentum (OAM), called plasmonic vortex, has played an important role in various research fields. However, the spatiotemporal properties of plasmonic vortex pulses excited by ultrafast laser, especially the dynamics of spin‒orbit coupling in the ultrafast plasmonic vortex field, have yet to be investigated deeply. Here, we study the spatiotemporal modulation of ultrafast plasmonic vortices with spin‒orbit coupling, using both analytical and simulation methods. The ultrafast plasmonic vortices are excited by a ring-shaped plasmonic lens, with an incident light composed of two time-delayed femtosecond sub-pulses carrying the same OAM but orthogonal circular polarizations. The dynamics of time-varying electric field, energy flow and angular momentum distributions of the plasmonic vortices are demonstrated, revealing details of the spin‒orbit coupling in spatiotemporal domain, such as the merging of multiple phase singularities with energy flow loops, and the variation of spin/orbital angular momentum per photon over time. This work could deepen the understanding of spin‒orbit coupling in plasmonic field and provide new ideas for ultrafast on-chip optical information processing.

Research on pinch analysis of union station a subsystem

To realize the cascade utilization of energy from the subsystem of Union Station A and improve the extraction of residual heat from sewage in the oilfield, this paper presents a process flow chart based on the Union Station A subsystem and analyses the energy flow of the entire Union Station A subsystem through the method of pinch analysis. In this study, we examine the energy flow distribution across the temperature spectrum within the system, identify the system’s energy consumption “bottleneck”, and optimize the system. The optimization findings reveal that the appropriate range for the pinch point temperature difference in the distributed energy system of the station is 8 to 14°C, with an associated energy-saving potential of 2.73%.