Representation Of Flow Research Articles

Harmonic and Heat Flow Representations of the Gaussian White Noise

Harmonic and Heat Flow Representations of the Gaussian White Noise

Exploring the efficiency of employing Fe3O4-MWCNT Nanofluids in a heat sink equipped with circular micro pin-fins

This work investigates the effects of adding different turbulence-inducing elements and varying the concentration of Fe3O4-MWCNT hybrid nanofluid to evaluate the efficiency of a micro pin-fin heat sink. To achieve this objective, a multiphase Lagrangian-Eulerian method that includes gravity, thermophoresis, drag force, Saffman lift, virtual mass, and pressure gradient-induced force to model will employ hybrid nanofluids. The evaluation of heat sink efficiency includes the analysis of quantitative variables like surface Nusselt number, average Nusselt number, and the logarithmic mean temperature difference. Furthermore, a qualitative representation of flow and thermal distributions is illustrated by visualizing flow streamlines, vortex contours, and thermal contours. This topic is addressed in the current work by using the finite volume approach with Ansys Fluent 18.1. Using the multiphase Lagrangian-Eulerian approach, the hydrothermal performance of the Fe3O4-MWCNT hybrid nanofluid in a pin-fin heat sink is investigated. This article's novelty lies in its utilization of four distinct turbulator models, each with unique geometries and widths. These models are positioned within a heat sink with micro fins and are tested in the presence of Fe3O4-MWCNT nanofluids. According to the findings, using the hybrid nanofluid at a concentration of 5 % rather than 1 % causes an average Nusselt number rise of 7.3 %, 9.4 %, and 12.5 % in various heat sink segments. This study's most significant thermal enhancement is associated with case D, which demonstrated a 7 % improvement over the baseline scenario by employing Fe3O4-MWCNT at a concentration of 5 % and a Reynolds number of 2400. It has been demonstrated that hybrid nanofluids have thermal efficiency between 600 and 2400 Reynolds numbers. The thermal efficiency criteria represent the base model, denoted by η = 1. A higher thermal efficiency denotes a more cost-effective design that offers superior heat transmission at a reduced pressure drop. It has been discovered that raising the concentration of hybrid nanofluids improves the micro pin fin heat sink's thermal performance by increasing the heat transfer coefficient.

Network analysis of aggregated money flows in stock markets

We introduce the formation of a network of money flows between assets in stock markets, which captures directed relations between assets in terms of how investors have re-allocated money in the stock exchange. Our approach is based on identifying a directed link, or money flow, that occurs when an investor funds a purchase of an asset by selling another asset(s). We extract investor-level money flow networks on daily basis from shareholder registration data, which are then aggregated for both financial institutional and retail investors. Overall, we have a time series of 877 daily networks from 2006 to 2009, which is exceptionally long data on temporal networks. Through our analysis of non-reciprocated triadic patterns in the aggregated money-flow networks, we find that these patterns are both recurrent and significant. However, they are not related to the 2008 financial crisis. Additionally, we observe that the counts on different triadic motifs exhibit not only an autoregressive process but are also interconnected contemporaneously and dynamically. These findings suggest the need for further research using sophisticated network models to provide a comprehensive representation of money flows in stock markets.

Hybrid Extreme Learning for Reliable Short-Term Traffic Flow Forecasting

Reliable forecasting of short-term traffic flow is an essential component of modern intelligent transport systems. However, existing methods fail to deal with the non-linear nature of short-term traffic flow, often making the forecasting unreliable. Herein, we propose a reliable short-term traffic flow forecasting method, termed hybrid extreme learning, that effectively learns the non-linear representation of traffic flow, boosting forecasting reliability. This new algorithm probes the non-linear nature of short-term traffic data by exploiting the artificial bee colony that selects the best-implied layer deviation and input weight matrix to enhance the multi-structural information perception capability. It speeds up the forecasting time by calculating the output weight matrix, which guarantees the real usage of the forecasting method, boosting the time reliability. We extensively evaluate the proposed hybrid extreme learning method on well-known short-term traffic flow forecasting datasets. The experimental results show that our method outperforms existing methods by a large margin in both forecasting accuracy and time, effectively demonstrating the reliability improvement of the proposed method. This reliable method may open the avenue of deep learning techniques in short-term traffic flow forecasting in real scenarios.

Data-based feature representation of traffic flow for predicting bridge displacement responses with ensemble learning model

Data-based feature representation of traffic flow for predicting bridge displacement responses with ensemble learning model

Advancing human mobility modeling: a novel path flow approach to mining traffic congestion dynamics

Mining traffic congestion dynamics presents difficulties in data structure and spatiotemporal analysis. Existing studies mainly provide insights from a supply perspective, with a restricted examination of why congestion occurs and how travel demands affect congestion. This study introduces an innovative framework to mine congestion dynamics from the perspective of human mobility. In human mobility modeling, we refine the conventional origin-destination representation of activity flow by introducing "path flow" (PF) which considers space-time paths and movement patterns. In congestion scenarios, congestion-related path flow (CPF) and congested path sub-flow (CPSF) are extended to track individuals’ congestion exposure and explore the correlations between congestion and human mobility. To finely classify congestion-related travel demands, a Bayesian inference approach, incorporating destination and spatiotemporal heterogeneities, is developed to deduce trip purposes. The experiments conducted in Wuhan demonstrate the availability and importance of PF in spatiotemporal dynamics analysis of human mobility. Interestingly, we find that 1) the job-housing relationship is imbalanced, with massive residents opting for cross-district living and working; 2) individuals tend to visit tertiary hospitals on weekends and secondary medical facilities with less congestion on weekdays. Notably, path flow can promote the fine-grained modeling of human mobility and provide theoretical support for many urban issues.

Enhanced Heat Transfer in Ternary Tangent Hyperbolic Nanofluids through Non-Darcy Porous Media

The effectiveness of heat transfer fluids (HTFs) is pivotal in maximizing the efficiency and longevity of various devices, from small-scale applications to large industrial systems. A comprehensive understanding of the properties of innovative ternary heat transfer nanofluids (TNFs) is essential, particularly when utilized over a Darcy-Forchheimer porous medium. This study explores tangent hyperbolic thermal nanofluids (TNFs) made up of nanoparticles such as graphene, zirconium oxide and magnesium oxide and, suspended in an ethylene glycol base fluid, within a non-Darcy porous medium. Similarity variables are used to streamline the mathematical representation of fluid flow and heat transmission in TNFs. Then, semi-analytical solutions to the reduced governing equations are obtained using the homotopy analysis method. The influence of tri-nanoparticles, porosity, and the Forchheimer parameter on skin friction, fluid flow dynamics, heat transfer rates, and the Nusselt number is investigated. The Forchheimer parameter lowers the Nusselt number by 27.80% for TNFs, 21.27% for the hybrid nanofluid, and 21.08% for the nanofluid. As a result, the temperature within TNFs is more evenly distributed. TNFs can transfer more heat by raising the medium’s porosity and the tri-nanoparticle volume fraction. These results unveil groundbreaking insights into enhancing the efficiency of heat transfer fluids. The introduction of a porous medium emerges as an alternate strategy to boost the performance of TNFs.

Objective representative flow field selection for tidal array layout design

The representation of flow across influential spatiotemporal scales introduces a challenge when micro-siting tidal stream turbine arrays. Robust representative approximations could accelerate design optimisation, yet there is no consensus on what defines the most appropriate flow conditions. We summarise existing approaches to representative flow field selection for array optimisation and propose an objective-driven process. The method curates a subset of flow fields that best captures relevant dynamics, enabling the streamlined representation of tidal cycles. To demonstrate the method, we consider flow modelling data in the Inner Sound of the Pentland Firth, Scotland, UK. We examine the impact of flow field inputs to array design through comparative analyses using a heuristic array optimisation process. Results indicate notable sensitivity of the turbine layout to the flow conditions selected. For the case study presented, our method led to 4%–5% energy yield prediction improvements relative to use of simple time-interval based approaches and up to 2% improvement against using peak flow fields; these can be pivotal margins to secure feasibility by developers. We also find that using the data associated with a single monitored point across the array for flow field selection can lead to sub-optimal results, emphasising the need for accurate spatiotemporal representation.

Representation of bulk water flow in the goldfish (Carassius auratus) midbrain.

With the mechanosensory lateral line system, fish and semi-aquatic amphibians detect water movements and pressure gradients. Hydrodynamic information picked up by the lateral line receptors is relayed via peripheral nerves to the lateral line brainstem and from there to the midbrain torus semicircularis. Most prior electrophysiological studies of the lateral line were done under still-water conditions, even though natural environments encountered by fish include bulk-flow. Flow velocity and direction sensing are likely important to fish as they navigate variable, turbulent environments, but to date, only few studies have gathered information on the processing of bulk water flow by midbrain units. Here, we recorded from lateral line units in the torus semicircularis while presenting various bulk flow velocities in anterior-to-posterior and posterior-to-anterior flow directions. We studied (1) the temporal spike patterns of mechanosensory midbrain units, (2) the processing of bulk water flow velocity by these units, and (3) the processing of bulk water flow direction. We found that midbrain mechanosensory units alter their discharge rate during bulk water flow - some units responded to flow by increasing their discharge rate but did not vary this rate significantly with flow velocity, while others exhibited increasing discharge rates with increasing flow velocity. Units directly coding for flow direction were not found.

Facet separation for disjunctive constraints with network flow representation

We present a novel algorithm for separating facet-inducing inequalities for the convex-hull of the union of polytopes representing a disjunctive constraint of special structure. It is required that the union of polytopes admit a certain network flow representation. The algorithm is based on a new, graph theoretic characterization of the facets of the convex-hull. Moreover, we characterize the family of polytopes that are representable by the networks under consideration. We demonstrate the effectiveness of our approach by computational results on a set of benchmark problems.

Microwave Technique for Linear Skull Fracture Detection-Simulation and Experimental Study Using Realistic Human Head Models.

Microwave (MW) sensing is regarded as a promising technique for various medical monitoring and diagnostic applications due to its numerous advantages and the potential to be developed into a portable device for use outside hospital settings. The detection of skull fractures and the monitoring of their healing process would greatly benefit from a rapidly and frequently usable application that can be employed outside the hospital. This paper presents a simulation- and experiment-based study on skull fracture detection with the MW technique using realistic models for the first time. It also presents assessments on the most promising frequency ranges for skull fracture detection within the Industrial, Scientific and Medical (ISM) and ultrawideband (UWB) ranges. Evaluations are carried out with electromagnetic simulations using different head tissue layer models corresponding to different locations in the human head, as well as an anatomically realistic human head simulation model. The measurements are conducted with a real human skull combined with tissue phantoms developed in our laboratory. The comprehensive evaluations show that fractures cause clear differences in antenna and channel parameters (S11 and S21). The difference in S11 is 0.1-20 dB and in S21 is 0.1-30 dB, depending on the fracture width and location. Skull fractures with a less than 1 mm width can be detected with microwaves at different fracture locations. The detectability is frequency dependent. Power flow representations illustrate how fractures impact on the signal propagation at different frequencies. MW-based detection of skull fractures provides the possibility to (1) detect fractures using a safe and low-cost portable device, (2) monitor the healing-process of fractures, and (3) bring essential information for emerging portable MW-based diagnostic applications that can detect, e.g., strokes.

Hi‐Z fault location identification on low voltage distribution systems using IoT‐based digital twin model computation

AbstractDistribution feeders carry and supply power to industrial, commercial and residential loads from the point where sub‐transmission (33 kV level) ends after stepping down to suitable voltages, such as 11 kV and further down to 400/230 V. In recent times, high impedance (Hi‐Z) faults on distribution systems are creating unique challenges to utilities both from operational and safety perspectives. Most of these Hi‐Z faults occur at distribution voltages of 15 kV or below, with the problem being worse at lower voltages. Hi‐Z fault detection technologies emerged and were developed and incorporated on embedded platforms, such as relays, reclosers and sensors, which protect and monitor distribution systems. Although these technologies can detect Hi‐Z fault on feeders, most of them cannot identify the exact location of the fault. Specifically, there is no solution available in literature for detecting Hi‐Z fault location on low voltage (LV) circuits like 3‐phase 4‐wire 400 V distribution network. In this paper, we introduce a novel and a unique algorithm to identify the location of Hi‐Z faults using proposed smart metres IoT data‐based distribution system load flow and digital twin model representation of the network. Furthermore, a case study on the standard 33 bus LV system clearly depicts the functionality of the proposed algorithm.

EvHandPose: Event-Based 3D Hand Pose Estimation With Sparse Supervision.

Event camera shows great potential in 3D hand pose estimation, especially addressing the challenges of fast motion and high dynamic range in a low-power way. However, due to the asynchronous differential imaging mechanism, it is challenging to design event representation to encode hand motion information especially when the hands are not moving (causing motion ambiguity), and it is infeasible to fully annotate the temporally dense event stream. In this paper, we propose EvHandPose with novel hand flow representations in Event-to-Pose module for accurate hand pose estimation and alleviating the motion ambiguity issue. To solve the problem under sparse annotation, we design contrast maximization and hand-edge constraints in Pose-to-IWE (Image with Warped Events) module and formulate EvHandPose in a weakly-supervision framework. We further build EvRealHands, the first large-scale real-world event-based hand pose dataset on several challenging scenes to bridge the real-synthetic domain gap. Experiments on EvRealHands demonstrate that EvHandPose outperforms previous event-based methods under all evaluation scenes, achieves accurate and stable hand pose estimation with high temporal resolution in fast motion and strong light scenes compared with RGB-based methods, generalizes well to outdoor scenes and another type of event camera, and shows the potential for the hand gesture recognition task.

A Machine Learning Bias Correction on Large‐Scale Environment of High‐Impact Weather Systems in E3SM Atmosphere Model

AbstractLarge‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models.

Identifying fluid pathways in hydrothermal deposits using hidden Markov models: Representation of fluid flow as exploration criteria

Hydrothermal mineral systems are formed by the transport of metals from large source areas through convective fluid flow, subsequent leading to deposition of these metals at specific sites. The fluid pathways are crucial for connecting mineral sources with favorable zones of mineral deposition. However, due to the complexity of fluid flow and limitations in sampling cost, assay cost, and expert experience, inferring fluid pathways poses a significant challenge. In this paper, we leverage the continuous and extensive characteristics of exploration data to identify fluid pathways in hydrothermal deposits, uncovering the hidden patterns from their mineralization footprints and favorable structural features within the data. By modeling the fluid flow as a Markov process, we tailor a hidden Markov model (HMM) to identify fluid pathways using observations of mineralization and structural features. Specifically, we identify the latent geometry of fluid pathways by maximizing their posterior probability as represented by the HMM. We then represent the identified fluid pathways as two quantitative and mappable exploration criteria—trajectory length and pathway flux—which serve as predictor variables in 3D mineral prospectivity mapping. Our method is applied to the Xiadian orogenic gold deposit in the Jiaodong Peninsula, China. The results suggest that the formation of Xiadian deposit is attributed to a series of fluid trajectories originating from two injection points. By using the exploration criteria derived from the identified fluid pathways, we significantly enhance the accuracy and efficacy of mineral prospectivity mapping, demonstrating the proposed HMM as an effective artificial intelligence tool for mineral exploration targeting.

A numerical analysis on magnetized dissipative non-Darcian Maxwell fluid with heat radiation through stretching horizontal surface

This work examines the numerical representation of boundary layer flow across a horizontal plate using the non-Darcian Maxwell model. The inspiration of mass transfer, heat generation, the Soret effect, the non-Darcy effect, Arrhenius activation energy and thermal radiation are all covered in this study. The relevant similarity transformations are applied in primary equations and a built-in bvp4c program is employed for solutions. The effectiveness of the numerical approach is demonstrated by a thorough agreement with results that have been published in the past. The results are shown in tables and graphical representations that highlight multiple parameters including the non-Darcy parameter, Deborah number, magnetic parameter, Soret parameter, chemical reactions, activation energy, etc. Figures show that fluid velocity is enhanced by Deborah number whereas it slips due to non-Darcy parameter and magnetic parameter. Eckert number, Radiation enhances the temperature profile and slips due to the Prandtl number. Activation energy enhances the Concentration field but it declines due to chemical reaction.

Space–time isogeometric analysis of tire aerodynamics with complex tread pattern, road contact, and tire deformation

AbstractThe space–time (ST) computational method “ST-SI-TC-IGA” and recently-introduced complex-geometry isogeometric analysis (IGA) mesh generation methods have enabled high-fidelity computational analysis of tire aerodynamics with near-actual tire geometry, road contact, tire deformation, and aerodynamic influence of the car body. The tire geometries used in the computations so far included the longitudinal and transverse grooves. Here, we bring the tire geometry much closer to an actual tire geometry by using a complex, asymmetric tread pattern. The complexity of the tread pattern required an updated version of the NURBS Surface-to-Volume Guided Mesh Generation (NSVGMG) method, which was introduced recently and is robust even in mesh generation for complex shapes with distorted boundaries. The core component of the ST-SI-TC-IGA is the ST Variational Multiscale (ST-VMS) method, and the other key components are the ST Slip Interface (ST-SI) and ST Topology Change (ST-TC) methods and the ST Isogeometric Analysis (ST-IGA). They all play a key role. The ST-TC, uniquely offered by the ST framework, enables moving-mesh computation even with the topology change created by the contact between the tire and the road. It deals with the contact while maintaining high-resolution flow representation near the tire.The computational analysis we present is the first of its kind and shows the effectiveness of the ST-SI-TC-IGA and NSVGMG in tire aerodynamic analysis with complex tread pattern, road contact, and tire deformation.

Real-time representation and rendering of high-resolution 3D light field based on texture-enhanced optical flow prediction.

Three-dimensional (3D) light field displays can provide an immersive visual perception and have attracted widespread attention, especially in 3D light field communications, where 3D light field displays can provide face-to-face communication experiences. However, due to limitations in 3D reconstruction and dense views rendering efficiency, generating high-quality 3D light field content in real-time remains a challenge. Traditional 3D light field capturing and reconstruction methods suffer from high reconstruction complexity and low rendering efficiency. Here, a Real-time optical flow representation for the high-resolution light field is proposed. Based on the principle of 3D light field display, we use optical flow to ray trace and multiplex sparse view pixels. We simultaneously synthesize 3D light field images during the real-time interpolation process of views. In addition, we built a complete capturing-display system to verify the effectiveness of our method. The experiments' results show that the proposed method can synthesize 8 K 3D light field videos containing 100 views in real-time. The PSNR of the virtual views is around 32 dB and SSIM is over 0.99, and the rendered frame rate is 32 fps. Qualitative experimental results show that this method can be used for high-resolution 3D light field communication.

Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of a spatially-resolved Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST. This allows us to extend the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Amanzi–ATS, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5000 annual synthetic storm events in a ∼2000km2 Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.

Assessing the impact of surface water and groundwater interactions for regional-scale simulations of water table elevation

Groundwater flow models are increasingly considered for the regional scale simulation of hydraulic heads and water table elevation. In the most complete configuration, models explicitly simulate two-way interactions between surface water (SW) and groundwater (GW) to reproduce and forecast both SW and GW water levels. In most regional scale groundwater models, however, SW-GW interactions are represented by simplified boundary conditions that only allow one-way interaction from SW to GW, neglecting most of the dynamic exchange fluxes between SW and GW. To evaluate the potential consequences of such simplifications on the simulation of regional GW levels, we compare two models on a 36,900 km2 regional aquifer system in Southern Quebec. One model explicitly simulates both SW and GW flow with two-way SW-GW feedback and the other model only simulates GW flow with a surface boundary flux to represent a one-way interaction with the land surface. Both models are developed with the same numerical code to ensure that the only differences are the representation of SW water flow and SW-GW feedback. The one-way model simulates overall deeper water tables because it removes all exfiltrated groundwater from the system once it exits the subsurface, therefore not allowing exfiltrating groundwater to re-infiltrate. This effect is most pronounced in areas where the water table is close to the surface and for low-flow periods. The inclusion of two-way feedback also reduces the sensitivity of simulated GW levels to the magnitude of the hydraulic conductivity. This result highlights the need for additional data on other system states to improve the calibration of regional scale models that explicitly simulate two-way SW-GW interactions.