Separate Edge Research Articles

Growth of organized flow coherent motions within a single-stream shear layer: 4D-PTV measurements

This study investigates the evolution of a single-stream shear layer (SSSL) originating from a wall boundary layer past a backward-facing step. Utilizing a time-resolved 3D-Particle Tracking Velocimetry (4D-PTV) technique, we track the trajectories of fluorescent particles to gain insight into the flow characteristics of the SSSL. A compact water tunnel facility (Reτ=1240\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{Re}_\ au =1\\,240$$\\end{document}) is fabricated to obtain an SSSL with a perpendicular slow entrainment stream past the separation edge. A hybrid interpolation approach that combines ensemble binning and Gaussian weighting is implemented to derive minimally filtered mean and instantaneous lower- and higher-order flow field parameters. Spanwise-dominant coherent motion accompanied by finer flow scales is observed to grow due to flow entrainment through “nibbling” actions of small-scale vortices, “engulfing” by large-scale vortices, and vortex pairing events. Furthermore, the non-zero-speed stream edge grows relatively faster than the zero-speed stream edge, showing a strong asymmetry in mixing composition across a mixing layer. The SSSL reaches self-similarity at a streamwise distance of ≈55θ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 55\\,\ heta _{0}$$\\end{document}, where θ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta _0$$\\end{document} is the initial momentum thickness from the separation edge, i.e., considerably shorter than reported in previous studies. A literature comparison of growth rate parameters raises intriguing questions regarding a potential inclusive growth scaling unifying the free shear layers. A turbulent kinetic energy (TKE) budget analysis reveals a negative production region immediately downstream of the separation edge attributed to a large positive streamwise gradient of streamwise velocity. In the self-similar region, the phase-averaged flow mapping demonstrates a larger concentration of turbulence production rate around the outer edges of spanwise vortices, specifically at the intersection of braids and vortices. Furthermore, a spatial separation exists in the regions of peak production and dissipation rates within the vortex core region favoring dissipation. The braids exhibit a larger concentration of turbulence diffusion rates, indicating their function as a conduit for exchanging turbulence between neighboring coherent motions.

Towards a robust scale‐free network in internet of health things against multiple attacks using an inter‐core based reconnection strategy

SummaryWireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs), with a special emphasis on the Internet of Health Things (IoHT) network. SFNs' vulnerabilities to malicious assaults while remaining resilient to random attacks. The proposed ICRS overcomes this issue by offering a novel reconnection approach that employs separate edges between network centers. Destructive assaults that have a significant impact on network connectivity, emphasizing the importance of a robust network that can resist a variety of attacks. ICRS is positioned as a solution that optimizes the network via reconnection techniques, changing it into an onion‐like structure with increased robustness. The simulation results depict that ICRS outperforms the existing algorithms in terms of robustness enhancement. The results show that ICRS performs 48%, 29%, 22%, and 16% better than Barabasi Albert (BA), Hill Climbing (HC), Simulated Annealing (SA), Random Edge Swap Mechanism (RESM), and Robustness Strategy (ROSE), respectively.

Reconstruction techniques for quantum trees

The inverse problem of recovery of a potential on a quantum tree graph from the Weyl matrix given at a number of points is considered. A method for its numerical solution is proposed. The overall approach is based on the leaf peeling method combined with Neumann series of Bessel functions (NSBF) representations for solutions of Sturm–Liouville equations. In each step, the solution of the arising inverse problems reduces to dealing with the NSBF coefficients. The leaf peeling method allows one to localize the general inverse problem to local problems on sheaves, while the approach based on the NSBF representations leads to splitting the local problems into two‐spectrum inverse problems on separate edges and reduces them to systems of linear algebraic equations for the NSBF coefficients. Moreover, the potential on each edge is recovered from the very first NSBF coefficient. The proposed method leads to an efficient numerical algorithm that is illustrated by numerical tests.

Modulation of dual-spin filtering by edge-hybridized pairing of β-SiC7 nanoribbons

Atomic capping is a common approach in edge modification to tune band engineering and can be achieved experimentally through hydrogen plasma etching. Based on first-principles calculations, nine symmetric and asymmetric structures are constructed with different numbers of hydrogen atoms passivated at the edge of β-SiC7 nanoribbons. An in-depth study reveals that different edge hydrogenations realize the transition from semiconductors to half-metals to metals, firstly, because different numbers of hydrogen atoms lead to different edge atomic orbital hybridizations, and, more importantly and specifically, because the bands around the Fermi level of β-SiC7 nanoribbons are all derived from the spatially separated edge states, so that arbitrary pairings of upper and lower edge hybridizations are able to diversify the bands near the Fermi level and bring about a richer electronic properties. We also design the metallic H-zSiC7NR-H and semiconducting 2H-zSiC7NR-H into a heterojunction, which possesses good dual spin filtering properties at low bias, can achieve a spin polarization transition from -100 % to 100 %, and exhibits the NDR phenomenon. These findings suggest that β-SiC7 nanoribbons have promising applications in the field of spintronics.

A balanced and reliable data replica placement scheme based on reinforcement learning in edge–cloud environments

With the rapid development of edge–cloud computing, distributing resources to edge nodes and terminal devices to provide high-quality services for latency-sensitive applications and reduce network communication costs has become increasingly important. However, the complexity and heterogeneity of the edge–cloud environment pose significant challenges to the reliability of data storage and device load balancing. To address these issues, in this paper, we propose a Deep Reinforcement Learning (DRL)-based data replica placement scheme, BRPS. This scheme considers the different geographic locations of hardware devices, the heterogeneous storage capacity and reliability, and the different data requirements of varying user services in edge–cloud environments. Firstly, we constructed a model for data replica placement in the edge–cloud environment, addressing key factors such as latency, reliability, and load, focusing on the heterogeneity of device resources and reliability, and the diverse data needs of user services. Furthermore, we propose the BRPS scheme using the Double Deep Q-Network (DDQN) method of DRL, transforming the data replica placement issue into a multi-objective optimization problem. By placing the DRL-based decision process in a separate management edge node, the separation of decision and execution is achieved, which enhances the efficiency of data replica placement, ensures data reliability, reduces latency, and achieves system load balance. Experimental results demonstrate that BRPS significantly outperforms the existing comparison schemes while ensuring data reliability. The BRPS scheme reduces latency by 8.12% compared to the Random scheme and outperforms the best heuristic in system load balancing and memory utilization by 13.15% and 11.91%, respectively. Moreover, BRPS shows superior adaptability in extreme network congestion scenarios and effectively adapts to the dynamic changes of nodes in edge–cloud environments.

Experimental and numerical investigation on the influence of different detailed and deformed tires on the aerodynamics of electric vehicles

To increase the driving range of electric vehicles, the reduction of aerodynamic drag plays an extremely important role. The wheels contribute more than a quarter of the overall aerodynamic drag for electric cars with relatively smooth underbody. As an essential component of the wheel, the aerodynamic performance of tires has also received extensive attention in recent years. This study investigated the influence of four different production tires combined with two rim designs on the vehicle’s aerodynamic performance based on an electric SUV. The wind tunnel tests were conducted to evaluate the aerodynamic forces, and the flow field changes around the tires were measured using a 14-hole pressure probe. Then, the dynamic deformation of the tires was captured to reproduce the actual tire geometry for simulation. The reasons for the drag differences caused by varying tires were thoroughly analyzed by comparing simulation and experimental results. The results show that the difference in drag coefficient between tires reaches up to 0.015, and the enlarged upstream separation induced by the front tires also alters the rear wake structure, significantly reducing base pressure. The effect of tire sidewall labeling and separation edge on drag was isolated by simulation, where the labeling protruding from the sidewall raises the drag coefficient by 0.004, accompanied by more vortices, while the separation edge increases the drag by up to 0.005 depending on its strength and number. It was also found that the dynamic deformation expands the size difference between tires, further increasing the drag differences.

Harnessing Artificial Intelligence for Risk Stratification in Acute Myeloid Leukemia (AML): Evaluating the Utility of Longitudinal Electronic Health Record (EHR) Data Via Graph Neural Networks

Background: AML is a life-threatening disease, and to determine which patients need allogeneic stem cell transplantation, hematologists risk-stratify each case. However, standard risk stratification using the European LeukemiaNet (ELN) criteria is focused on baseline mutations and chromosomal aberrations, and the risk estimate is not updated during a patient's course. In other blood cancers, recalculating the risk with treatment response data can help guide the need for more intensive therapy (Kurtz, et al, Cell, 2019). Furthermore, deep learning graph neural networks (GNN) applied to EHR data have strong predictive power in a hematology context (Fouladvand, et al, J Biomed Inform, 2023). Thus, we evaluated the power of a GNN to predict survival in AML using longitudinal EHR data, specifically with labs and histological features that are not included in the ELN but may capture the treatment response. Methods: Patients who were seen at the Stanford Cancer Institute, had EHR data available within six months of diagnosis, and were diagnosed with AML between June 1998 and January 2021 were included in this retrospective analysis. The GNN was trained to predict survival at two years from diagnosis using the first six months of clinical data. Patients were excluded if they were lost to follow-up before two years or died before six months. Data were collected from structured databases associated with Stanford's EHR, except that diagnosis dates were from Stanford's Cancer Registry, and survival data was supplemented with other databases including the Social Security Death Index. Dysplasia, bone marrow cellularity, and bone marrow blast percentages from pathology reports (“pathology report data”) were extracted using text processing algorithms and weakly supervised machine learning (Ratner, et al, ArXiv, 2017). To represent time series information, we framed each patient's timeline as a network (or “graph”) of events. The primary GNN model was a heterogenous graph transformer classifier with two node types: complete blood count (CBC) data and pathology report data (Hu, et al, ArXiv, 2020). Data from the same week were assumed to be from the same timeframe and connected with bidirectional edges. Data separated by longer time periods were connected with unidirectional edges of a separate edge type. The independent test dataset consisted of patients whose ELN 2022 classification was available, and to train the model, the remaining data were divided into train/validation splits of 0.9/0.1. Results: Of the 2,535 patients with survival data, 1,029 met inclusion criteria. Table 1 summarizes the data available in the EHR for each variable, and nearly all patients had CBC and pathology report data. The area under the receiver operating characteristic (AUROC) using the ELN 2022 criteria for predicting survival in the test dataset was 0.79. The AUROC curve for the GNN model was comparable at 0.76, despite not using any variables from the ELN criteria, and the model effectively stratified patients' disease into high- and low-risk in the independent test dataset (hazard ratio [HR] 3.0, log-rank p = 0.0009). Interestingly, despite not having access to mutation or cytogenetic data, the high-risk cases were enriched in known high-risk mutations, like TP53 and RUNX1, and in high-risk chromosomal aberrations, like 5q deletion (Table 1). Although the model predictions correlated with the ELN criteria in some ways, they also stratified the ELN intermediate-risk AML cases into high and low risk (HR 6.1 for model-predicted high risk among ELN intermediate cases, p = 0.07). Conclusions: Risk stratification using artificial intelligence and longitudinal data from the EHR performed comparably to the ELN 2022 criteria and has the potential to further stratify the ELN categories. The model performed well despite only using histological features and lab values, which are more readily available and more frequently updated than next-generation sequencing results. In the future, this approach may further improve with a larger sample size and additional variables, such as measurable residual disease and treatment information. Given the heterogeneity and increasing complexity of AML classification, leveraging artificial intelligence to assist with classification will be crucial, and these results are a step towards a future where data are automatically extracted from the EHR and used for continuously updated risk stratification.

Load Emulation with Independent Metering for a Pump Test Bench

For the investigation of new types of internal gear pumps under realistic conditions, a test bench is presented that enables dynamic load emulation via the pressure with simultaneous dynamic speed control of the pump. For pressure control, a hydraulic half-bridge with separate control edges is used on both sides of the pump and a pressure control is presented. An error-based adaptive controller is used for pressure control and tested experimentally. It is shown that the error-based adaptive controller has a better performance compared to a simple PID control.

Influence of trailing edge flap length and deflection angle on the performance of the multiphase pump

At medium to high gas content rates, the gas-liquid separation phenomenon and the stagnation behaviors of the gas masses occurring in the impeller channels are essential factors affecting the hydraulic performance of multiphase pumps. This paper designs a split trailing edge flap suitable for the multiphase pump to improve the hydraulic performance in the impeller by varying the flap deflection angle and length. The model’s reliability was assessed by comparing the experimental outcome with the external characteristics of the experimental data. Based on the Eulerian-Eulerian multiphase model and the SST k‐ω turbulence model, the effects of different flap lengths on external characteristics, the turbulent kinetic energy and pressure distribution are analyzed. The study results show that when the length of the separated trailing edge flap is 0.25 l0 and the deflection angle is 5°, the transporting efficiency of the multiphase pump is the highest at IGVF=50% with an efficiency increase of 3.40% compared to the original model. Its optimal flap length scheme effectively reduces the radial pressure gradient and the inverse pressure gradient in the impeller channel, which suppresses the gas blockage phenomenon to a certain extent.

Flow Control in Multiphase Pumps Based on Separated Trailing Edge Flap

In developing and transporting significant oil fields in deep-sea environments, multiphase pumps are considered crucial energy conversion equipment. Ensuring their safe, efficient, and stable operation is currently a primary focus of research. The intermittent aggregation of the gas phase at the trailing edge flap of the impeller blades in multiphase pumps can lead to periodic and significant fluctuations in flow rate and outlet pressure, posing a threat to the overall operational safety of the pump system. Based on aerodynamic principles, this paper presents the design of a separate trailing edge flap for the impeller blades. The inner nodal method is applied to determine the design scheme of the trailing edge flap for the multiphase pump. A numerical approach is employed to analyze the impact of the flap deflection angle on the internal flow characteristics to provide theoretical guidance for the structural optimization of multiphase pumps. The results indicate that the influence of the deflection angle on the pump efficiency is complex and affected by a critical angle value. When the deflection angle is below the critical value, the trailing edge flap can effectively reduce the formation of energy dissipation vortices and radial pressure gradients within the impeller channel, enabling a significant improvement in the gas-phase aggregation phenomenon caused by gas–liquid separation. However, additional energy losses occur at the connection between the trailing edge flap and the main blade body for deflection angles exceeding the critical value. When the trailing edge flap length is 0.25 l and the deflection angle is 5°, the efficiency is improved by 3.4% compared to the original model. Consequently, the pressurization capacity of the pump is compromised. In the design and application of trailing edge flaps, a careful balance between various factors is required to ensure both the stability and high efficiency of the pump system.

From Local Hydration Motifs in Aqueous Acetic Acid Solutions to Macroscopic Mixing Thermodynamics: A Quantitative Connection from THz-Calorimetry.

We report the results of THz measurements (30-440 cm-1) of aqueous acetic acid solutions over the full mixing range (XAcAc = 0-1). We recorded spectroscopic observables as a function of the acetic acid concentration in the frequency range of the intermolecular stretch at 150 cm-1 and of the librational modes at 350-440 cm-1. This allowed us to unravel changes in hydrophobic and hydrophilic hydration motifs, respectively. By means of a novel THz-calorimetry approach, we quantitatively correlated these changes in local hydration motifs to excess mixing entropy and enthalpy. We find that ΔHmix is determined by both hydrophobic and hydrophilic solvation contributions. In contrast, ΔSmix is governed by hydrophobic cavity formation. Our results further suggest that acetic acid-water mixtures are systems at the edge of phase separation due to endothermic contributions from both hydrophilic and hydrophobic solvation in a large portion of the miscibility range. Our work establishes a quantitative relationship between the balance of local hydrophobic and hydrophilic solvation motifs and the macroscopic mixing thermodynamic properties.

Cooperative Task Offloading for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Driven by the prevalence of the computation-intensive and delay-intensive mobile applications, Mobile Edge Computing (MEC) is emerging as a promising solution. Traditional task offloading methods usually rely on centralized decision making, which inevitably involves a high computational complexity and a large state space. However, the MEC is a typical distributed system, where the edge servers are geographically separated, and independently perform the computing tasks. This fact inspires us to conceive a distributed cooperative task offloading system, where each edge server makes its own decision on how to allocate local computing resources and how to migrate tasks among the edge servers. To characterize diverse task requirements, we divide the arrival tasks into different priorities according to the tolerance time, which enables to dynamically schedule the local computing resources for reducing the task timeout. In order to coordinate the independent decision makings of geographically separate edge servers, we propose a priority driven cooperative task offloading algorithm based on multi-agent deep reinforcement learning, where the decision making of each edge server not only depends on its own state but also on the shared global information. We further develop a Variational Recurrent Neural Network (VRNN) based global state sharing model which significantly reduces the communication overhead among edge servers. The performance evaluation conducted on a movement trajectories dataset of mobile devices verifies that the proposed algorithm can reduce the task consumption time and improve the edge computing resources utilization.

Modulation of edge defects on dual-spin filtering in zigzag β-SiC7 nanoribbons.

The unique edge states of the zigzag β-SiC7 nanoribbons aroused our attention, and therefore, based on first-principles calculations, we investigated their spin-dependent electronic transport properties by constructing controllable defects to modulate these special edge states. Interestingly, by introducing rectangular edge defects in the SiSi and SiC edge-terminated systems, not only the spin-unpolarized is successfully converted to completely spin-polarized, but also the direction of polarization can be switched, thus enabling a dual spin filter. The analyses further reveal that the two transmission channels with opposite spins are spatially separated and that the transmission eigenstates are highly concentrated at the relative edges. The specific edge defect introduced only suppresses the transmission channel at the same edge but reserves the transmission channel at the other edge. In addition, for the CSi and CC edge-terminated systems, an additional spin-down band exists due to spin splitting in the spin-up band at EF, so that besides the original spatially separated two spin-opposite channels, an extra spin channel is distributed at the upper edge, resulting in unidirectional fully spin-polarized transport. The peculiar spatially separated edge states and excellent spin filtering properties could open up further possibilities for β-SiC7-based electronic devices in spintronics applications.

V-035 TRANSVAGINAL EVISCERATION, LAPAROSCOPIC APPROACH

Abstract Aim We present the laparoscopic approach of a case of transvaginal evisceration after hysterectomy. Material & Methods 50 yo Female, with a history of bilateral ovaric and endometrial endometroid carcinoma, for which bilateral laparoscopic anexectomy and subsequent hysterectomy and cytoreduction surgery were performed two months earlier. She consulted to emergency department for metrorrhagia and abdominal discomfort of one week's evolution. Physical examination revealed a dehisced vaginal vault with evisceration of small bowel loops. Abdominal CT scan was performed in which other intra-abdominal complications were ruled out. Urgent surgical intervention was decided and a minimally invasive approach was proposed. During the operation, dehiscence of the vaginal vault was observed without other findings. The separated edges are refreshed and a running suture is performed with a barbed suture. As reinforcement, a titanized polypropylene mesh was placed and fixed with cyanoacrylate. Results The patient was discharged on the 2nd postoperative day without incident. Conclusions Transvaginal evisceration is a rare complication that usually occurs after hysterectomies. If it is not possible to reduce the contents, it can cause suffering of the herniated structures. The treatment consists of closing the defect and acting on the complications it may cause. The minimally invasive approach provides the already known benefits for the patient and a better vision and surgical field for the surgeon.

Heterogeneous graph convolutional neural network for protein-ligand scoring

Aim: Drug discovery is a long process, often taking decades of research endeavors. It is still an active area of research in both academic and industrial sectors with efforts on reducing time and cost. Computational simulations like molecular docking enable fast exploration of large databases of compounds and extract the most promising molecule candidates for further in vitro and in vivo tests. Structure-based molecular docking is a complex process mixing both surface exploration and energy estimation to find the minimal free energy of binding corresponding to the best interaction location. Methods: Hereafter, heterogeneous graph score (HGScore), a new scoring function is proposed and is developed in the context of a protein-small compound-complex. Each complex is represented by a heterogeneous graph allowing to separate edges according to their class (inter- or intra-molecular). Then a heterogeneous graph convolutional network (HGCN) is used allowing the discrimination of the information according to the edge crossed. In the end, the model produces the affinity score of the complex. Results: HGScore has been tested on the comparative assessment of scoring functions (CASF) 2013 and 2016 benchmarks for scoring, ranking, and docking powers. It has achieved good performances by outperforming classical methods and being among the best artificial intelligence (AI) methods. Conclusions: Thus, HGScore brings a new way to represent protein-ligand interactions. Using a representation that involves classical graph neural networks (GNNs) and splitting the learning process regarding the edge type makes the proposed model to be the best adapted for future transfer learning on other (protein-DNA, protein-sugar, protein-protein, etc.) biological complexes.

Automated Exploration and Implementation of Distributed CNN Inference at the Edge

For model inference of convolutional neural networks (CNNs), we nowadays witness a shift from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute- and memory-intensive CNNs on Internet of Things devices at the Edge is challenging as they typically have limited resources. One approach to address this challenge is to leverage all available resources across multiple edge devices to execute a large CNN by properly partitioning it and running each CNN partition on a separate edge device. However, there currently does not exist a design and programming framework that takes a trained CNN model as input and subsequently allows for efficiently exploring and automatically implementing a range of different CNN partitions on multiple edge devices to facilitate distributed CNN inference. Therefore, in this article, we propose a novel framework that automates the splitting of a CNN model into a set of submodels as well as the code generation needed for the distributed and collaborative execution of these submodels on multiple, possibly heterogeneous, edge devices, while supporting the exploitation of parallelism among and within the edge devices. In addition, since the number of different CNN mapping possibilities on multiple edge devices is vast, our framework also features a multistage and hierarchical design space exploration methodology to efficiently search for (near-)optimal distributed CNN inference implementations. Our experimental results demonstrate that our work allows for rapidly finding and realizing distributed CNN inference implementations with reduced energy consumption and memory usage per edge device, and under certain conditions, with improved system throughput as well.

Anomalous quantized plateaus in two-dimensional electron gas with gate confinement

Quantum information can be coded by the topologically protected edges of fractional quantum Hall (FQH) states. Investigation on FQH edges in the hope of searching and utilizing non-Abelian statistics has been a focused challenge for years. Manipulating the edges, e.g. to bring edges close to each other or to separate edges spatially, is a common and essential step for such studies. The FQH edge structures in a confined region are typically presupposed to be the same as that in the open region in analysis of experimental results, but whether they remain unchanged with extra confinement is obscure. In this work, we present a series of unexpected plateaus in a confined single-layer two-dimensional electron gas (2DEG), which are quantized at anomalous fractions such as 9/4, 17/11, 16/13 and the reported 3/2. We explain all the plateaus by assuming surprisingly larger filling factors in the confined region. Our findings enrich the understanding of edge states in the confined region and in the applications of gate manipulation, which is crucial for the experiments with quantum point contact and interferometer.

Current-driven collective dynamics of non-Hermitian edge vibrations in armchair graphene nanoribbons

We study non-Hermitian collective dynamics of spatially separated edge carbon dimer vibrations in armchair graphene nanoribbons, mediated by coherent and dissipative coupling to nonequilibrium electron transport. We show that the indirect coupling between two dimers depends crucially on gating and source-drain bias. In particular, we analyze the competition between two distinctly different energy transfer mechanism from nonequilibrium electrons to vibrations. One is the deterministic work done by nonconservative current-induced force, and the other is stochastic Joule heating. We find that the effect of the former can be effectively tuned electrically through gating and bias. Our work suggests that armchair graphene nanoribbons could serve as promising candidates in the experimental search for signatures of nonconservative current-induced forces in nanoconductors, which remains elusive despite intense theoretical study.

Transient nature of secondary vortices in an axial compressor stage with a tandem rotor

An efficient and compact design of a compressor continues to be a challenging area of research. The unavoidable streamwise adverse pressure gradient together with passage transverse pressure gradient restricts the maximum allowable blade turning. Tandem blading is an interesting concept for increasing the pressure rise by permitting a higher blade turning angle. The energized flow through the tandem blade nozzle gap helps to minimize the possibility of flow separation over the suction surface of the aft blade. However, a coherent transient analysis of a tandem rotor stage in an axial compressor is yet to be well explored. In the present paper, the complex flow field over a tandem rotor and the succeeding stator passage is analyzed in detail. Although the tandem rotor increases the flow turning and diffusion effect, the presence of separate trailing edge wakes and hub corner vortex causes early onset of flow separation over the stator suction. The flow structures developed within the rotor and the stator passages at different time instances are highlighted with the help of limiting streamlines and iso-surface Q-criterion superimposed with entropy contours. The results indicate that within the rotor passage, transient flow features are observed near the hub corner region close to the aft blade trailing edge rather than the tip region. When the rotor passes the stator leading edge, the rotor trailing edge leakage flow at the hub is entrained into the stator leading edge reverse flow region. This is then further circumferentially dragged into the mid-passage region. The interference of multiple rotor wakes with the stator leading edge leads to the formation of longitudinal and arch like separation vortices at the stator-hub and the stator-casing regions, respectively. These separation vortices grow in size while being convected downstream. Eventually, as time progresses, the vortices split and shed periodically from the stator surface. The present investigation highlights the requirement of a new stator design in a tandem rotor–conventional stator configuration. Such designs could further magnify the significant aerodynamic performance obtained using a tandem configuration.

Toward Lightweight, Privacy-Preserving Cooperative Object Classification for Connected Autonomous Vehicles

Collaborative perception enables autonomous vehicles to exchange sensor data among each other to achieve cooperative object classification, which is considered an effective means to improve the perception accuracy of connected autonomous vehicles (CAVs). To protect information privacy in cooperative perception, we propose a lightweight, privacy-preserving cooperative object classification framework that allows CAVs to exchange raw sensor data (e.g., images captured by HD camera), without leaking private information. Leveraging chaotic encryption and additive secret sharing technique, image data are first encrypted into two ciphertexts and processed, in the encrypted format, by two separate edge servers. The use of chaotic mapping can avoid information leakage during data uploading. The encrypted images are then processed by the proposed privacy-preserving convolutional neural network (P-CNN) model embedded in the designed secure computing protocols. Finally, the processed results are combined/decrypted on the receiving vehicles to realize cooperative object classification. We formally prove the correctness and security of the proposed framework and carry out intensive experiments to evaluate its performance. The experimental results indicate that P-CNN offers exactly almost the same object classification results as the original CNN model, while offering great privacy protection of shared data and lightweight execution efficiency.