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This chapter covers the principles of groundwater flow. It begins with defining hydraulic conductivity and Darcy's Law for one-dimensional flow, and then generalizes to three dimensional flow. The concepts of specific discharge, average linear velocity, and intrinsic permeability are introduced. Heterogeneity and anisotropy in hydraulic conductivity fields are discussed along with methods for assigning bulk average conductivities. A range of methods for measuring or estimating hydraulic conductivity are discussed, including grain size correlations, lab tests, field tests, tracer tests, and models. Sections near the end of the chapter deal with flow in individual fractures in fractured rock, flow in the unsaturated zone, and groundwater flow with variable density water. Coastal fresh–salt interface flow is discussed and the Ghyben-Herzberg method of estimating interface elevation is covered.

Temperature field control is crucial for the comprehensive performance of Ni-Co-Mn layered cathode material that is the most important part of lithium-ion batteries. Starting from the aspect of a class of distributed parameter systems described by highly dissipative partial differential equations (PDEs), an event-triggered optimal control (ETOC) method based on adaptive dynamic programming (ADP) for the roller kiln temperature field is proposed. First, we formulate the event-triggered control problem of the temperature field under the general framework of PDE systems. Then, an event-triggered condition is designed based on the stability of the closed-loop PDE system, which also guarantees the upper bound of the performance index. Subsequently, ADP technology is adopted to realize the ETOC, where the critic network is employed to approximate the optimal value function. Since the studied system can be regarded as an impulsive dynamic system with flow dynamics and jump dynamics simultaneously, the stability of the impulsive dynamic system combined with the ADP-based closed-loop PDE system is proved. Finally, simulation results on the temperature field verify the effectiveness of the proposed method.

This article proposes a hybrid systems approach to address the sampled-data leaderless and leader-following bipartite consensus problems of multiagent systems (MAS) with communication delays. First, distributed asynchronous sampled-data bipartite consensus protocols are proposed based on estimators. Then, by introducing appropriate intermediate variables and internal auxiliary variables, a unified hybrid model, consisting of flow dynamics and jump dynamics, is constructed to describe the closed-loop dynamics of both leaderless and leader-following MAS. Based on this model, the leaderless and leader-following bipartite consensus is equivalent to stability of a hybrid system, and Lyapunov-based stability results are then developed under hybrid systems framework. With the proposed method, explicit upper bounds of sampling periods and communication delays can be calculated. Finally, simulation examples are given to show the effectiveness.

Information about the effect of the facial flow concept on the smile esthetic perception of laypeople is lacking.The purpose of this study was to determine the effect of the facial flow concept on laypeople's perception of smile esthetics.Frontal full-face images of a female volunteer were digitally altered to create commissural line and transverse occlusal plane inclinations, a dental midline shift, and facial asymmetries. A questionnaire was developed, and a single researcher asked 400 evaluators to rate the attractiveness of the different smiles by using a visual analog scale. The subsequent data were analyzed by using 3-factor repeated measures ANOVA and post hoc Bonferroni tests.Images with commissural line and transverse occlusal plane inclinations pointing toward the green side of the facial flow curve were found to be more attractive than those with inclinations pointing to the red side (P.001). Asymmetric facial images with the dental midline coinciding with the facial flow curve had higher esthetic scores than those without (P.001). Images that showed parallelism between the transverse occlusal plane and commissural line inclinations were perceived as more esthetic (P.001).In an asymmetric face model, the degree and direction of commissural line and transverse occlusal plane inclinations and dental midline shift influenced the perceived attractiveness of a smile.

Some basic characteristics of a classic hydraulic jump flow over a pebbled rough bed, as well as on a smooth bed as a reference, are presented in this experimental study. For the experiments, an inflow Froude number Fr1 from 1.54 to 4.94 and inflow Reynolds number Re1 from 42 000 to 230 000 were considered. Visual observations and measurements suggested some differences between the formation of a hydraulic jump on rough and smooth bed configurations, including different air entrainment processes, larger vortical structures in the roller length and stronger backward flow in the upper layer. Furthermore, the jump roller and aerated flow lengths were shorter on a pebbled rough bed than on a smooth bed, while the dimensionless advection velocity of large vortices was the same for both bed types. The instantaneous jump toe perimeter showed the largest variation at the largest Fr1 and was generally larger on rough bed than on smooth bed. Larger oscillations of the free-surface profile were observed on smooth bed, highlighting that roughness resulted in smaller free-surface oscillations, suggesting the higher rate of energy dissipation.

Objective: A numerical and theoretical study is developed to analyze the combined effect of activation energy and chemical reaction in the flow of nanofluids due to the thin moving needle using the mathematical nanofluid model offered by Buongiorno. A passively controlled nanoparticle volume fraction boundary is assumed rather than actively controlled. Methods: A similarity transformation is utilized to convert the governing partial differential equations to a set of ordinary differential equations which are then solved numerically by Runge-Kutta Shooting Method (RKSM). The physical characteristics of flow, heat and mass transfer are illustrated via graphs and tables for some set of values of governing parameters. Results: In addition, the basic non-linear governing equations are solved analytically using semianalytical technique called Differential transform method (DTM) and the comparison has been made with the numerical and the published results. Conclusion: The present study reveals that the ratio between the needle velocity and the composite velocity brings out to increases the velocity distribution with λ

The focal role of flow in promoting desirable outcomes in companies, such as increased employees' well-being and performance, led scholars to study flow in the context of work. However, current measurement approaches which assess flow via self-report scales after task execution are limited due to obtrusiveness and a lack of real-time support. Hence, new measurement approaches must be created to overcome these limitations. In this paper, we use cardiac features (heart rate variability; HRV) and a Random Forest classifier to distinguish high and low flow. Our results from a large-scale lab experiment with 158 participants and a field study with nine participants reveal, that with HRV features alone, flow-classifiers can be built with an accuracy of 68.5% (lab) and 70.6% (field). Our research contributes to the challenge of developing a less obtrusive, real-time measurement method of flow based on physiological features and to investigate flow from a physiological perspective. Our findings may serve as foundation for future work aiming to build physio-adaptive systems which can improve employee's performance. For instance, these systems could ensure that no notifications are forwarded to employees while the system is 'sensing' flow.

In this paper, a simple but effective method is proposed to generate a bio-mimic wave propagating motion in a magnetic soft actuator. Two examples, a metachronal wave in artificial cilia and a crawling motion of an artificial caterpillar, are shown as demonstrations. These are kinds of wave propagating motions which are popular in natural systems and are also primitive systems of natural creatures. In the proposed system, an elastic material and a magnetic powder were used to fabricate a flexible magnetic structure. There are 2 essential features in the system; one is periodic arrangement of magnetic anisotropy, and the other is application of a rotating magnetic field. Using this method of wave generation, we realized drastic change in flow making behavior of artificial cilia and a crawling motion very similar to living caterpillars. The proposed system would be useful to enhance the field of the bio-mimic soft robotics.

Obtaining fine-grained crowd flow distribution with recognized human intention is extremely valuable for a series of applications for a metropolitan city. We address this problem by leveraging a key insight people's intention behind their movement is highly correlated with the point-of-interest (POI) distribution of the corresponding regions. We propose DeepFlowGen to model the complicated relationship between crowd flow, POI, and time to generate intention-aware fine-grained crowd flow. Specifically, we solve the conflict between dynamic crowd flow and static POI distribution by fusing the information in both time and POI domains. In addition, we employ a sequence of residual blocks in DeepFlowGen to address the challenges of modeling the diverse temporal rhythms and heterogeneous influence of POI. Extensive experiments demonstrate that our model outperforms the state-of-the-art solutions by more than 15% in terms of RMSE of total crowd flow. Moreover, the correlation between the generated intention-aware crowd flow and the check-in distribution across different categories of POIs is as high as 0.90 and 0.75 in Beijing and Shanghai. Combined with extensive case studies, we demonstrate the strong ability of our model in generating intention-aware fine-grained crowd flow.

Urban crowd flow prediction is very challenging for public management and planning in smart city applications. Existing work mostly focuses on spatial and temporal dependence based flow prediction that are not well suited for predictions of instantaneous flow change usually due to social emergency incidents and accidents. In this paper, we propose an Online to Offline Interaction based Dilated Casual Convolutional Neural Network framework (O2O-DCNN) for crowd flow prediction. Both online attention behavior and offline crowd shift factors are considered, in case to capture the dependence between them and make more accurate predictions especially for instantaneous flow variations. The online and offline features are processed by dilated casual convolutions and then put into CBOW model based full connected network to make interactions. The performance evaluations are based on realistic User Detail Record (UDR) dataset of a southern city in China provided by China Unicom. O2O-DCNN is compared with the other related baselines in terms of MASE and MAE. The results show that our framework is with much better accuracy, especially for instantaneous flow variation scenario.

Problem definition: Airports and airlines have been challenged to improve decision making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers’ connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas. Academic/practical relevance: To our knowledge, this work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system. Results: We show that, when compared with benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas. Managerial implications: Our predictive system can produce accurate forecasts frequently and in real time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late passengers, and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted. Funding: This work was funded by Eurocontrol (APOC Business Process Reengineering Big Data Study) [Grant 15-220643-AWP6.3.1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0975 .

In this paper, we present new homogenization results of a stochastic model for flow of a single-phase fluid through a partially fissured porous medium. The model is a double-porosity model with two flow fields, one associated with the system of fissures and the other associated with the porous system. This model is mathematically described by a system of nonlinear stochastic partial differential equations defined on perforated domain. The main tools to derive the homogenized stochastic model are the Nguetseng’s two-scale convergence, tightness of constructed probability measures, Prokhorov and Skorokhod compactness process and Minty’s monotonicity method.

The problem of two phase bio-convective nano fluid flow past a vertical stretching flat surface in presence of volume fraction of the dust particles has been investigated. Governing partial differential equations of the problem for both fluid and dust phases are transformed into ordinary differential equations using suitable similarity transformations. The resulting non-linear differential equations are solved numerically using MATLAB built-in bvp4c solver scheme. The velocity, temperature of fluid motion and concentration of micro-organisms are presented graphically for various flow parameters. Coefficient of local skin friction, local Nusselt number and local density number of the micro-organisms are calculated and presented in tables for various parameters.

Here, we developed novel extended piecewise bilinear power law (C-m) models to describe flow stresses under broad ranges of strain, strain rate, and temperature for mechanical and metallurgical calculations during metal forming at elevated temperatures. The traditional C-m model is improved upon by formulating the material parameters C and m, defined at sample strains and temperatures as functions of the strain rate. The coefficients are described as a linear combination of the basis functions defined in piecewise patches of the sample strain and temperature domain. A comparison with traditional closed-form function flow models revealed that our approach using the extended piecewise bilinear C-m model is superior in terms of accuracy, ease of use, and adaptability; additionally, the extended C-m model was applicable to numerical analysis of mechanical, metallurgical, and microstructural problems. Moreover, metallurgy-related values can be calculated directly from the flow stress information. Although the proposed model was developed for materials at elevated temperatures, it can be applied over a broad temperature range.

With the increasing demand for intelligent transportation systems, short-term traffic flow prediction has become an important research direction. The memory unit of a Long Short-Term Memory (LSTM) neural network can store data characteristics over a certain period of time, hence the suitability of this network for time series processing. This paper uses an improved Gate Recurrent Unit (GRU) neural network to study the time series of traffic parameter flows. The LSTM short-term traffic flow prediction based on the flow series is first investigated, and then the GRU model is introduced. The GRU can be regarded as a simplified LSTM. After extracting the spatial and temporal characteristics of the flow matrix, an improved GRU with a bidirectional positive and negative feedback called the Bi-GRU prediction model is used to complete the short-term traffic flow prediction and study its characteristics. The Rectified Adaptive (RAdam) model is adopted to improve the shortcomings of the common optimizer. The cosine learning rate attenuation is also used for the model to avoid converging to the local optimal solution and for the appropriate convergence speed to be controlled. Furthermore, the scientific and reliable model learning rate is set together with the adaptive learning rate in RAdam. In this manner, the accuracy of network prediction can be further improved. Finally, an experiment of the Bi-GRU model is conducted. The comprehensive Bi-GRU prediction results demonstrate the effectiveness of the proposed method.

Gabion weirs have been widely used in rivers restoration and diversion water projects because of their hydro-environmental aspects and eco-friendly features. In this study, a series of laboratory tests were performed to investigate the effects of side ramp slope, crest length, and porous media properties on the flow regimes, water-surface profiles, discharge coefficients, and energy dissipation in embankment gabion weirs with upstream and downstream slopes. 24 physical models of solid and gabion weirs with three different upstream/downstream slopes (90°, 45° and 26.5°) were created. For gabion weirs, three different filling materials were tested. To investigate the complexity of flow over the porous-fluid interface and through the porous material, three-dimensional (3D) numerical simulations were developed. The results show that decreasing upstream slopes, from 90º to 26.5º, leads to decreased discharge coefficients. However, in all cases, gabion weirs lead to greater discharge coefficients than those of similar solid weirs. For milder side slopes, discharge ratios passing through all faces of the gabion weirs decreased nonlinearly. Moreover, with increasing the inlet discharge, relative energy dissipation was reduced up to 45% in gabion weirs.

Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: How do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We therefore propose here a novel dimensionality reduction framework: Delayed Latents Across Groups (DLAG). DLAG disentangles signals relayed in each direction, identifies how these signals are represented by each population, and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signaling contributes to cortical computation.

Eco-driving incorporating multiple signalized intersections simultaneously has been proven to substantially benefit Connected Vehicles (CVs) in energy performance. However, ignoring the dynamic variation of waiting queues before downstream intersections may prevent CVs from following the obtained speed profile on security grounds. In this paper, the dynamic variation of waiting queue is modelled and predicted based on shockwave theory and data-driven based traffic flow prediction. To formulate the waiting queues as additional time-varying constraints for optimization problem, an extended traffic signal model is constructed based on the prediction. Furthermore, a hierarchical optimization framework is proposed, under which the hybrid optimization problem is decomposed into a discrete problem and a continuous one. Monte Carlo simulation demonstrates that if the proposed eco-driving approach is implemented, failure to follow the reference speed profile decreases by 79.4%. Also, the fuel consumption can be saved by over 4% compared with approaches ignoring the waiting queue.

In this study, the dual solutions for mixed convection flow of an Oldroyd-B fluid containing alumina nanoparticles over a shrinking sheet are investigated taking into account the effects of magnetic field and heat source/sink. By utilizing the similarity transformations, the governing equations are reduced to ordinary differential equations which have been solved by the well-known shooting method. The present investigation reveals that some published results are not realistic. It is remarkable that for higher Deborah numbers for relaxation and retardation times, suction parameter and magnetic field parameter, the domain of the occurrence of dual solutions becomes wider and the local skin friction coefficient and local Nusselt number are found to increase. On the other hand, dual solutions exist in a broader domain as long as the volume fraction of alumina nanoparticles is less than or equal to 0.05 and after that it decreases. The critical values of the shrinking parameter relating to the existence of dual solutions are rendered in approximate functions. These characteristics of an Oldroyd-B nanofluid are not reported yet and might be useful in the development of the existing technology.

The main limitation of Smoothed Particle Hydrodynamics (SPH) method that resists the method's potential is its lack of providing stability and accuracy to the numerical technique. We improve the accuracy of the standard SPH technique, by formulating a new inverse logarithmic kernel function. This new kernel function is derived based on the underlying properties of kernel functions. The approximation technique used here is based on the Moving Least Squares based technique. The adequacy of the proposed kernel function is tested by simulation of 2D shock wave propagation and 3D dam-break free surface flow against a cuboidal obstacle. The method was validated against experimental data by Kleefsman et al., [1] . The numerical results reveal that our new SPH approach using inverse logarithmic kernel function outperforms existing ones in particle restoration, zero error, better accuracy and enhanced efficiency in kernel approximation. This new kernel function showed some improvement over existing kernels by showing very less error approximation value of 0.035h2. The results showed some improvements over standard technique by being capable of handling problems with large deformations accurately and precisely.

This paper proposes a new accelerating technique for simulating low speed flows, termed as pSeudo High Speed method (SHS), which uses governing equations and numerical methods of compressible flows. SHS method has advantages of simple formula, easy manipulation, and only need to modify flux of Euler equations. It can directly employ the existing well-developed schemes of hyperbolic conservation laws. To verify the technique, several numerical experiments are performed, such as: flow past airfoils and flow past a cylinder. Analysis of SHS method and comparisons with some precondition methods are made numerically. All tests show that SHS method can greatly improve the efficiency of compressible method simulating low speed flow fields, which exhibits in accelerating the convergence rate and increasing the accuracy of the numerical results.

Flow field is a crucial factor to influence the stability and surface quality in the electrochemical machining (ECM) of blisks. A four-way flow mode was proposed to eliminate mixing regions of electrolyte at the leading and tailing edges. Two flow field models were described separately in this report: a W-shaped flow mode and a four-way flow mode. The flow field was analyzed through a finite element method. The results showed that, in comparison with the W-shaped flow mode, the distribution of electrolyte flow was more uniformed and the mixed region in the flow channel was improved. The pressure of the leading and tailing edges inlets was optimized, and optimal pressure of 0.6 MPa was determined. In addition, verification experiments were performed, and the results showed that the stability, efficiency, and quality of the profiles of the blisk blade manufactured by ECM were enhanced in the new flow mode.

PURPOSE: Pseudophakic cystoid macular edema (CME) occurs in up to 2% of uneventful cataract surgeries. This study evaluates changes in macular blood flow succeeding uneventful phacoemulsification cataract extraction among otherwise visually healthy subjects.METHODS: This prospective study included 18 eyes of 18 patients undergoing routine phacoemulsification. Optical coherence tomography angiography (OCT-A) was performed using the Angio-Retina 6x6mm protocol with the XR Avanti Angio-Vue system (Optovue Inc., Fremont, California) prior to the surgery and 4-8 weeks thereafter. Exclusion criteria included motion artifacts, segmentation errors and signal strength index (SSI) ˂40. The main outcome measure was change in flow index (FI) measured in all 4 retinal segmentation layers within an area of 1mm diameter around the foveal center.RESULTS: Following surgery, a significant increase in SSI (46.65±8.62 versus 53.12±8.07, p=0.01), superficial plexus FI (0.98±0.23 versus 1.16±0.16, p=0.02) and deep plexus FI (0.54±0.46 versus 0.93±0.39, p=0.01) was found. No significant changes were noted in the outer retina or the choriocapillaris. CONCLUSION: The study demonstrates a significant increase in FI in the superficial and deep retinal plexus following uneventful cataract surgery, with the greatest changes occurring in the latter. These findings corroborate evidence from structural imaging and support the vascular etiology of pseudophakic CME.

When dealing with traffic big data under the background of Internet of Things (IoT), traffic control under the single-machine computing environment is difficult to adapt to the massive and rapid analysis and decision-making. To tackle this problem, we propose a parallel computing approach of traffic network flow control based on the mechanism of model predictive control (MPC). A non-analytical rule-based traffic flow model is developed to forecast vehicle movements in the prediction horizon according to the real-time feedback information of traffic flow, and evaluate the performances of candidate control strategies. Furthermore, to accelerate the solution process of obtaining the optimal control schemes in the prediction horizon, a two-level hierarchical parallel genetic algorithm (HPGA) based on Spark cloud computing is designed. Through the parallel computing architecture, the computationally intensive optimization tasks are decomposed into multiple parallel sub-tasks with the aid of resilient distributed datasets (RDDs), which improves the computational efficiency. The simulation results demonstrate the validity of the proposed methodology for traffic network flow predictive control with respect to unsaturated and oversaturated traffic scenarios. The Spark-based parallel optimization approach has the potential to satisfy the computing requirements of online optimization when dealing with the big data of traffic network flow control while keeps favorable control performances.

Robustness is one of the most important performance criteria for any rail transit network (RTN), because it helps us enhance the efficiency of RTN. Several studies have addressed the issue of RTN robustness primarily from the perspectives of given rail network structures or static distributions of passenger flow. An open problem that remains in fully understanding RTN robustness is how to take the spatio-temporal characteristics of passenger travel into consideration, since the dynamic passenger flow in an RTN can readily trigger unexpected cascading failures. This paper addresses this problem as follows: (1) we propose a two-layer rail transit network (TL-RTN) model that captures the interactions between a rail network and its corresponding dynamic passenger flow network, and then (2) we conduct the cascading failure analysis of the TL-RTN model based on an extended coupled map lattice (CML). Specifically, our proposed model takes the strategy of passenger flow redistribution and the passenger flow capacity of each station into account to simulate the human mobility behaviors and to estimate the maximum passenger flow appeal in each station, respectively. Based on the smart card data of RTN passengers in Shanghai, our experiments show that the TL-RTN robustness is related to both external perturbations and failure modes. Moreover, during the peak hours on weekdays, due to the large passenger flow, a small perturbation will trigger a 20% cascading failure of a network. Having ranked the cascade size caused by the stations, we find that this phenomenon is determined by both the hub nodes and their neighbors.

Since the mid-1960s, it has been known that regular or deterministic flow lines (DFLs) with an arbitrary arrival process possess three essential behaviors: 1) a customer exit time recursion; 2) a server reordering principle; and 3) a delay equivalence with prototype queueing systems. Even though these three fundamental behaviors were discovered decades ago, analogous results for hybrid DFLs (HDFLs)--possessing multiple servers for each stage--have not been unearthed. In this article, we prove the existence of a recursion for customer exit times in HDFLs. This result leads naturally to a server reordering principle. Furthermore, we identify specific conditions under which the total delay faced by a customer in an HDFL is equivalent to that in a G/D/c queue. Exploiting these results, we numerically determine the proportion of HDFL systems with a delay equivalent, study the computational burdens required for HDFL simulation, and assess the performance of approximations for HDFL systems. We hope these newly identified behaviors will lead to improved approximation methods and optimization models for use in modern automated manufacturing systems.

We discuss the asymptotic stability of stationary solutions to the incompressible Navier–Stokes equations on the whole space in Besov spaces. A critical estimate for the semigroup generated by the Laplacian with a perturbation is the main ingredient of the argument.

The independent metering system decouples the meter-in and meter-out flows of the chamber, giving extra control flexibility and can reducing energy consumption while keeping motion control accuracy. The control performance of the independent metering system is significantly determined by the valve characteristics. Valve nonlinearities, such as deadband, nonlinear flow gain, and leakage, may restrict the control accuracy. To address the issue and improve system control precision, the nonlinearities of the valve need to be further modeled. In this study, a nonlinear valve flow model is proposed to accurately depict the relationships between the flow rate, pressure drop, and input voltage. The valve deadband is compensated by the static inverse function, while a certain polynomial projection function is used to mimic the nonlinear flow curve outside the deadband. To deal with uncertainties and nonlinearities in the independent metering system, an adaptive robust control approach is synthesized. The least squares estimation law is adopted to online adapt the valve parameter and other system parameters. Comparative experiments were carried out to illustrate the effectiveness of the proposed approach. The results show that the valve flow nonlinearities are further captured by the proposed model, and the independent metering system performed a higher-level control precision.

Modern metro systems in big cities have accumulated large amounts of passenger transit transaction data via the deployment of automatic fare collection (AFC) devices, which could facilitate a thorough analysis of passenger flow dynamics. To discover the underlying passenger flow patterns over all stations in an entire metro system, this paper proposes a multivariate functional principal component analysis (MFPCA) method. The functional integrity of a single daily passenger flow profile at each station is explicitly utilized, and the complex correlation among a multitude of daily passenger flow profiles from all stations is comprehensively modeled. Moreover, to simultaneously improve the interpretability of eigenvectors and mitigate the sensitivity to outliers, the MFPCA is formulated as a minimization problem with both a sparsity and a robustness penalty term. A computationally efficient algorithm is developed accordingly to obtain the eigenvectors. The superiority of our proposed sparse and robust MFPCA (SRMFPCA) is validated using a Hong Kong Mass Transit Railway (MTR) dataset. The derived sparse and smooth eigenvectors can be well interpreted as empirically meaningful passenger flow patterns. The results of our method can be further used as solid foundations for station clustering, correlation analysis and outlier identification.

Flow consolidation has been proposed for merging multiple flows from different services into an aggregate flow to remedy the state explosion problem in SDN. However, we observe that the QoS requirements are no longer sustained in aggregate flows since the bandwidth decided by TCP is usually different from the desired rate of each service. Therefore, this paper explores an idea to control the rates of only a few service flows so that the rates of all uncontrolled flows allocated by TCP will meet their QoS requirements. We design a new architecture, called Scalable Per-Flow Rate Allocation (SPFRA), and formulate a new optimization problem, termed Scalable Rate Allocation for Aggregate Flows (SRAF), to find a minimum number of controlled flows to increase the scalability of SDN with diverse service requirements. We prove the NP-hardness and inapproximability of SRAF. To solve the problem, we design an algorithm, named Aggregate Flow Selection and Flow Release (AFSFR), to achieve the tightest bound and extend it to support distributed computation and dynamic traffic for instant services. Simulations and implementation on an SDN testbed manifest that AFSFR performs nearly optimally in real networks, and the number of controlled flows can be effectively reduced by 50%.

Exchange processes between a turbulent free flow and a porous media flow are sensitive to the flow dynamics in both flow regimes, as well as to the interface that separates them. Resolving these complex exchange processes across irregular interfaces is key in understanding many natural and engineered systems. With soil-water evaporation as the natural application of interest, the coupled behavior and exchange between flow regimes are investigated numerically, considering a turbulent free flow as well as interfacial forms and obstacles. Interfacial forms and obstacles will alter the flow conditions at the interface, creating flow structures that either enhance or reduce exchange rates based on their velocity conditions and their mixing with the main flow. To evaluate how these interfacial forms change the exchange rates, interfacial conditions are isolated and investigated numerically. First, different flow speeds are compared for a flat surface. Second, a porous obstacle of varied height is introduced at the interface, and the effects the flow structures that develop have on the interface are analyzed. The flow parameters of this obstacle are then varied and the interfacial exchange rates investigated. Next, to evaluate the interaction of flow structures between obstacles, a second obstacle is introduced, separated by a varied distance. Finally, the shape of these obstacles is modified to create different wave forms. Each of these interfacial forms and obstacles is shown to create different flow structures adjacent to the surface which alter the mass, momentum, and energy conditions at the interface. These changes will enhance the exchange rate in locations where higher velocity gradients and more mixing with the main flow develop, but will reduce the exchange rate in locations where low velocity gradients and limited mixing with the main flow occur., Deutsche Forschungsgemeinschaft, Projekt DEAL

The effectiveness of the parabolic dish system (PDS) is greatly affected by the heat losses associated with high temperatures. The complexity of flow and temperature patterns in and around the cavity receiver makes it a challenging task to determine the convective heat loss from the cavity. Various studies have been carried out to determine the convection heat losses from isolated cavities of different shapes. In the presence of a dish structure, the free stream wind may affect the stability of structure and the heat losses from the PDS. In this study, the effect of focal length on the performance of the coupled cavity–dish system was analyzed using numerical simulations. The loading and the convective heat loss from the cavity were examined with three different cavity positions and different operating conditions in the presence of the dish. The results showed that the shallow dish experienced higher local air velocities near the cavity receiver than in the case of the deep dish. It was concluded that the heat loss is a stronger function of tilt angle rather than focal length, and in essence, the heat losses due to variations in this are negligible.

Heat and mass transfer analysis of viscous liquid flow generated by rotation of disk has generated prodigious interest due to promising utilizations in numerous processes such as thermal energy generation systems, turbine rotators, geothermal energy preservations, chemical processing, medicinal instrumentations, computing devices and so forth. In view of such extraordinary utilizations in numerous engineering procedures existent exertion is excogitated to disclose flowing phenomenon over rotating disk. To raise the importance of current analysis influential physical aspects like magnetic field, permeability, Dufour and Soret diffusion phenomenon are also incorporated. Subsequently, flow field distributions are analyzed for suction and injection cases. Modelling is structured via PDE’s by obliging constitutive conservation laws. Boundary layer approach is executed to reduce complexity of attained partial differential system. Transformations developed by Karman are implemented to convert developed differential framework into ODE’s. Implicitly finite differenced technique known as Keller Box is engaged to find solution of coupled intricate high order ordinary differential equations. Influence of flow controlling parameters on associated distributions are revealed through graphical and tabular representations. The related quantities of engineering interest like coefficients of wall drag force, along radial and tangential directions are also computed. Credibility of presently computed results is established by constructing comparison with previously published literature. It is inferred that magnetic strength parameter enhances tangential and radial components of velocity whereas contrary trend is depicted in axially directed velocity. In addition, temperature and momentum distributions show up surging attribute versus magnetic field parameter. All associated profiles have exhibited decrementing aspects against suction parameter. It is also revealed that increment in Soret tends to produce depreciation in temperature profile whereas concentration distribution is enhanced.

This research aims to develop a mathematical model based on the flow of Eyring-Powell fluid with an assessment of combined heat and mass transfer features confined by a horizontal channel. The lower wall of the horizontal channel has been assumed to be convectively heated. Following the fundamental laws and associated stress tensor of Eyring-Powell nanofluid, the governing equations are developed. The formulated flow expressions are altered into non-dimensional forms with appliances of normalized variables. The analytical procedure based on the perturbation technique is employed to access the solution. The physical exploration of interesting quantities govern to the flow is addressed. The numerical data is presented based on the assessment of the heat and mass transfer phenomenon. A comprehensive discussion regarding the physical insight of all flow variables has been presented.

There are various models which describe the hydro-dynamical aspects of the fluid flow and solute transport in small diameter permeable tubes without incorporating the slip effects, which play an important role to predict the flow behavior with higher accuracy. This study aims to investigate the slip effects on the slow, steady-state and axisymmetric flow of an incompressible second order Rivin-Ericksen fluid in a small diameter cylindrical tube with constant wall permeability. The momentum equations have been derived in cylindrical coordinates and are converted in the form of a single compatibility equation which governs the stream function of the flow. Navier slip condition has been used to take slip phenomenon into account near the wall of the tube. Compatibility equation has been solved analytically subject to the associated boundary conditions by using Langlois recursive approach method. The detailed expressions have been obtained for velocity components, pressure, volume flow rate, pressure drop along longitudinal axis, wall shear stress, fractional reabsorption and leakage flux. Behavior of all the flow variables against the slip parameter has been examined graphically in detail. Overall, the solutions obtained her e show a great agreement with the work in literature. One of the key findings in this study is that the volume flow rate Q is found to be independent of the slip parameter ϕ .

BACKGROUND A correlation between atypical recurrences and minimally-invasive surgery has been suggested in case of urothelial cancer; however, very few data are available on the role of pneumo peritoneum in terms of gas flow and intra-abdominal pressure. The objective of the study is to analyze the impact of CO2 pneumoperitoneum variation on an inert material as surrogate of neoplastic cells. MATERIAL AND METHODS We designed an experimental model mimicking pneumoperitoneum in three settings: sealed flow (no leakage), pulsatile flow (alternating efflux and influx) and continuous flow (AirSeal® insufflator). Each experiment was characterized by a pre-determined gas flow and pressure, trocar distance and position from the particles. Hounsfield density (HD) variation in the areas of interest was measured as index of graphite powder dispersion. A Linear regression model was employed to measure the correlation between modifiable variables and HD. RESULTS HD was lower in the pulsatile compared to both the sealed and continuous flows (p 0.05). CONCLUSIONS Flow and pressure setting, in-flow trocar distance and contiguity from the tumor, and pneumoperitoneum stability may be all crucial component in minimally invasive surgery. In vivo, these variables should be considered as potential risk factors for tumor cells spread within the abdominal cavity.

Koopman mode decomposition is a flow analysis technique developed by Igor Mezic in 2004, based upon the Koopman operator first proposed by Bernard Koopman in 1931. Via Koopman decomposition any non-chaotic well-sampled dynamic system - linear, non-linear, laminar or turbulent - is broken down into single-frequency repetitive components (modes). This paper presents a survey consolidating published information regarding data-driven Koopman analysis techniques. It is intended to aid researchers exploring the suitability of data-driven Koopman analysis in anticipation of developing their own modeling. A basic mathematical explanation of Koopman analysis is given with emphasis toward the data-driven Dynamic Mode Decomposition (DMD) solution, which converges to the Koopman operator given a highly-sampled dataset. The four primary uses of Koopman analysis: flow analysis, power grid analysis, building thermal analysis, and biomedical analysis are discussed, along with other publications. Finally, weaknesses and problems inherent within Koopman analysis/DMD will be enumerated, alongside potential solutions. Koopman analysis is a computationally complex, yet often suitable method for determining periodic motion in any highly-sampled dataset. When compared to a similar analysis method, Proper Orthogonal Decomposition, Koopman analysis often provides additional detail regarding the structure of less significant modes present, albeit at the cost of increased computational complexity.

In the current study, we focused on the shape effects of copper (Cu) nano-particles on heat transmission of three-dimensional magnetohydrodynamic (MHD) nano-fluid. A particular type of flow is considered, i.e., rotating flow over an exponentially stretching sheet. Significant actions of thermal radiation and Grashof number are also formulated to study. The modified Chebyshev wavelets method is introduced to examine the numerical solutions of the accomplished model. Error analysis and further comparison with existing results reflect the appropriateness of the proposed modification. Graphical behavior of dimensionless velocities, temperature, Nusselt number, and skin friction under the inspiration of several parameters is also presented. The attained solutions propose that the modification is beneficial and can be protracted to other highly nonlinear problems. The modified Chebyshev wavelets technique lowers computing time, according to the study. The research takes into account the form impacts of nano-particles. There are three kinds of nano-particles to examine (spherical, laminar, and cylinder). The behavior of various particles varies depending on the flow and temperature profile. For microparticles with a laminar structure, velocity and temperature have higher values.

Saturation flow is a fundamental performance measure parameter used for the design of intersections. However, in mixed traffic conditions, the evaluation of saturation flow is more sensitive due to heterogeneous traffic where vehicles are travelling in the same right of way without any lane separation. In such cases, the passenger car unit (PCU) plays a major role in analysing heterogeneity using an equivalent value for each vehicle. This paper presents a novel approach using the optimisation technique to determine PCU values and saturation flow for mixed traffic streams. Field data were collected at six signalised intersections in three Indian cities. The optimisation technique produced reasonable PCU values and an accurate estimation of saturation flow of 1906 PCUs/h. The saturation flow prediction model was developed using the multi-linear regression technique as per Pearson's correlation between saturation flow and vehicle compositional share. The developed saturation flow model was validated using field data from another site and the predicted value was in good agreement with the observed value. Finally, the predictive model was used to analyse the impact of traffic composition on saturation flow in mixed traffic streams.

Ride-hailing systems (RHSs) provide passengers with convenient and flexible mobility services, have played an important role in modern urban transportation. With the limited vehicles, RHSs wish to optimize the dispatch of vehicles to requests with the objective of serving as many requests as possible. To address such a city-scale vehicle dispatch problem with thousands of vehicles and requests in each epoch, existing algorithms always take a tradeoff between effectiveness (i.e., real-time) and efficiency (i.e., service rate), such as ignoring future demands to guarantee real-time or solving a complex combinatorial optimization to improve service rate. To guarantee the service rate in a real-time fashion, this paper proposes two novel network flow-based vehicle dispatch algorithms. A network flow-based algorithm (NFBA) is provided to deal with offline scenarios. By constructing the vehicle-shareability network, a min-cost flow is built to find the optimal dispatch of vehicles to requests. To improve the request service rate in real-time, an efficient multi-sample multi-network flow-based algorithm (MNFBA) is proposed for the online scenarios. Each min-cost flow is utilized for a sample of future requests, and online vehicle dispatch policy is averaged over these flows. Extensive simulations based on real-world trip datasets in New York City are conducted. The experimental results show that compared to the benchmarks, our proposed algorithm can generate the dispatch of vehicles to requests within seconds, but can greatly increase the daily request service rate.

In this paper we have computed squeezing channel flow of hybrid nanofluid ( Al 2 O 3 + SiO 2 + Water ). The lower surface of the channel is subjected to stretching property. Upper surface moves (squeezes) towards lower surface. Heat transport is explored through Cattaneo-Christov (C-C) heat flux and convective boundary condition. This model is constructed via modification in Fourier’s law by addition of thermal relaxation time factor. The governing expressions (PDEs) are non-dimensionalized through suitable variables. The obtained non-dimensional systems (PDEs) are solved using FDM (finite difference method). Velocities, temperature and skin friction are graphically evaluated with respect to influential variables.

We present a numerical approximation of Darcy's flow through a porous medium that incorporates networks of fractures with non empty intersection. Our scheme employs PolyDG methods, i.e. discontinuous Galerkin methods on general polygonal and polyhedral (polytopic, for short) grids, featuring elements with edges/faces that may be in arbitrary number (potentially unlimited) and whose measure may be arbitrarily small. Our approach is then very well suited to tame the geometrical complexity featured by most of applications in the computational geoscience field. From the modelling point of view, we adopt a reduction strategy that treats fractures as manifolds of codimension one and we employ the primal version of Darcy's law to describe the flow in both the bulk and in the fracture network. In addition, some physically consistent conditions couple the two problems, allowing for jump of pressure at their interface, and they as well prescribe the behaviour of the fluid along the intersections, imposing pressure continuity and flux conservation. Both the bulk and fracture discretizations are obtained employing the Symmetric Interior Penalty DG method extended to the polytopic setting. The key instrument to obtain a polyDG approximation of the problem in the fracture network is the generalization of the concepts of jump and average at the intersection, so that the contribution from all the fractures is taken into account. We prove the well-posedness of the discrete formulation and perform an error analysis obtaining a priori hp-error estimates. All our theoretical results are validated performing preliminary numerical tests with known analytical solution.

Because of the rapid advancement of microdevices technologies, electroosmotic flow (EOF) has gotten a lot of coverage in recent years. EOF has a range of benefits over other micropumps, including the ability to create continuous pulse-free flows and elimination of moving parts. Our aim is to investigate the unsteady electroosmotic flow (EOF) of an electrolyte solution of generalized fractional second grade hybrid nanofluid (Cu-TiO 2 / Water) confined between vertical two coaxial tubes. The energy equation will be altered by the fractional Cattaneo model. The finite Hankel technique and Laplace transformation will be employed to acquire the solution of the mathematical model. Since obtaining the inversion of Laplace of the transformed functions in the closed-form is a challenging task, the numerical inversion algorithm will be used. The results showed that the hybrid nanofluid became more viscous with increasing the volume concentration which reduces the electroosmotic (EO) velocity and the flow rate. Moreover, the electroosmotic flow is boosted by the free convection force.