Real Flow Research Articles

Distinct detection and discrimination sensitivities in visual processing of real versus unreal optic flow.

We examined the intricate mechanisms underlying visual processing of complex motion stimuli by measuring the detection sensitivity to contraction and expansion patterns and the discrimination sensitivityto the location of the center of motion (CoM) in various real and unreal optic flow stimuli. We conducted two experiments (N = 20 each) and compared responses to both "real" optic flow stimuli containing information about self-movement in a three-dimensional scene and "unreal" optic flow stimuli lacking such information. We found that detection sensitivity to contraction surpassed that to expansion patterns for unreal optic flow stimuli, whereas this trend was reversed for real optic flow stimuli. Furthermore, while discrimination sensitivity to the CoM location was not affected by stimulus duration for unreal optic flow stimuli, it showed a significant improvement when stimulus duration increased from 100 to 400 ms for real optic flow stimuli. These findings provide compelling evidence that the visual system employs distinct processing approaches for real versus unreal optic flow even when they are perfectly matched for two-dimensional global features and local motion signals. These differences reveal influences of self-movement in natural environments, enabling the visual system to uniquely process stimuli with significant survival implications.

Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation.

Optical flow has made great progress in clean scenes, while suffers degradation under adverse weather due to the violation of the brightness constancy and gradient continuity assumptions of optical flow. Typically, existing methods mainly adopt domain adaptation to transfer motion knowledge from clean to degraded domain through one-stage adaptation. However, this direct adaptation is ineffective, since there exists a large gap due to adverse weather and scene style between clean and real degraded domains. Moreover, even within the degraded domain itself, static weather (e.g., fog) and dynamic weather (e.g., rain) have different impacts on optical flow. To address above issues, we explore synthetic degraded domain as an intermediate bridge between clean and real degraded domains, and propose a cumulative homogeneous-heterogeneous adaptation framework for real adverse weather optical flow. Specifically, for clean-degraded transfer, our key insight is that static weather possesses the depth-association homogeneous feature which does not change the intrinsic motion of the scene, while dynamic weather additionally introduces the heterogeneous feature which results in a significant boundary discrepancy in warp errors between clean and degraded domains. For synthetic-real transfer, we figureout that cost volume correlation shares a similar statistical histogram between synthetic and real degraded domains, benefiting to holistically aligning the homogeneous correlation distribution for synthetic-real knowledge distillation. Under this unified framework, the proposed method can progressively and explicitly transfer knowledge from clean scenes to real adverse weather. In addition, we further collect a real adverse weather dataset with manually annotated optical flow labels and perform extensive experiments to verify the superiority of the proposed method. Both the code and the dataset will be available at https://github.com/hyzhouboy/CH2DA-Flow.

Novel Framework for Artificial Bubble Image Generation and Boundary Detection Using Superformula Regression and Computer Vision Techniques

Bubble multiphase systems are crucial in industries such as biotechnology, medicine, oil and gas, and water treatment. Optical data analysis provides critical insights into bubble characteristics, such as the shape and size, complementing physical sensor data. Existing detection techniques rely on classical computer vision algorithms and neural network models. While neural networks achieve a higher accuracy, they require extensive annotated datasets, and classical methods often struggle with complex systems due to their lower accuracy. This study proposes a novel framework to address these limitations. Using Superformula parameter regression, we introduce an advanced border detection method for accurately identifying gas inclusions and complex-shaped objects in multiphase environments. The framework also includes a new approach for generating realistic artificial bubble images based on physical flow conditions, leveraging the Superformula to create extensive, labeled datasets without manual annotation. Tested on real bubble flows in mass transfer equipment, the algorithms enable bubble classification by shape and size, enhance detection accuracy, and reduce development time for neural network solutions. This work provides a robust method for object detection and dataset generation in multiphase systems, paving the way for more precise modeling and analysis.

Phantom Cooling Effects on Rotor Blade Surface Heat Flux in a Transonic Full Scale 1 + 1/2 Stage Rotating Turbine

Abstract An experimental and numerical investigation of phantom cooling effects on cooled and uncooled rotating high pressure turbine blades in a full scale 1 + 1/2 stage turbine test is presented. Objectives set to capture, separate, and quantify the effects of upstream vane film-cooling and leakage flows on downstream rotor blade surface heat flux. Multiple series of 1 + 1/2 stage rotating high pressure turbine tests were carried out in the Air Force Research Laboratory, Turbine Research Facility, at Wright-Patterson Air Force Base, Ohio. A non-proprietary research turbine was instrumented with high frequency double-sided thin film heat flux gauges custom made at AFRL. High bandwidth, time-resolved surface heat flux is measured on multiple film-cooled and non-film-cooled HPT rotor blades downstream of both film-cooled and non-film-cooled vane sectors. Upstream wake passing and heat flux is characterized on both rotor pressure and suction side surfaces, along with quantifying rotor phantom cooling effects from non-uniform first-stage vane film cooling and leakage flows. Fast response heat flux measurements quantify how rotor phantom cooling impacts the blade pressure side greatest. Measurements show upstream vane film-cooling alone can account for 50% of the rotor blade cooling effect in some instances, and even outweigh the rotor blade film cooling effect far from the blade showerhead holes. Added unsteady aero numerical simulation demonstrates how variations in inlet total temperature and incidence angle also contribute to the circumferentially non-uniform rotor heat flux. Results from this investigation aid modeling and design efforts in optimizing film cooling performance in real high-pressure turbine flow fields. Understanding the behavior of such non-uniform circumferential rotor phantom cooling effects can be critical to optimizing the efficiency, fuel consumption, range, and durability of advanced turbomachines.

Blending deep-learning and observers for improved state of charge estimation in vanadium flow batteries.

This paper presents the design and implementation of a deep-learning-based observer for accurately estimating the State of Charge (SoC) of a vanadium flow battery. The novelty of the proposal lies in its direct use of terminal voltage and the application of a machine learning algorithm to model the battery's overpotentials, leading to greater accuracy and reduced complexity compared to classical models. The overpotentials model consists of a neural network trained using data generated by a classical observer that estimates species concentration using a physical electrochemical model and the open-circuit voltage measurement. The trained model is then integrated with the observer to improve SoC estimation accuracy. The proposed method is validated through comprehensive numerical simulations and experimental studies using a real vanadium flow battery setup, demonstrating its effectiveness in providing reliable SoC estimation with a relative error of less than 5%.

Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction

With the development of information technology, massive traffic data-driven short-term traffic situation analysis of urban road networks has become a research hotspot in urban traffic management. Accurate vehicle trajectory and traffic flow prediction can provide technical support for vehicle path planning and road congestion warning. Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. Firstly, transforming the checkpoint data into daily vehicle trajectories with time series characteristics, realizing the vehicle trajectory travel chain division. Secondly, the adjacency matrix is established by using the spatial relationship of each checkpoint, and the feature matrix of the vehicle’s driving trajectory over time is established, which is used as the input of GCN to learn the spatial characteristics of the vehicle while driving on the road network, and then GRU is added to further process the data after GCN training, constructing a GCN-GRU vehicle trajectory prediction model for vehicle trajectory prediction. Finally, the traffic flow of each checkpoint is calculated based on the prediction result of vehicle trajectory and compared with the real checkpoint flow. This paper conducts many experiments on the Qingdao City Shinan district checkpoint dataset. The results show that compared with the single models GCN, GRU, BiGRU, and BiLSTM, the GCN-GRU model has reduced the MAE by 0.75, 0.46, 0.52, and 0.57, and the RMSE by 0.76, 0.52, 0.58, and 0.68, respectively, demonstrating stronger spatial and temporal correlation characteristics and higher prediction accuracy. The MAPE between the forecasted flow and the real flow is 0.18, which verifies the reliability of the proposed method.

Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop

AbstractMeasurement of certain variables within the industrial sector remains a challenge due to the prohibitive costs of sensors, the intricate installation processes, or the continuous nature of production demands. Moreover, if a backup sensor is required in case the main sensor fails, the installation and maintenance difficulties are further increased. A possibility to address this issue is the indirect estimation of the desired variable by leveraging other correlated measures within the operational process. Data-driven techniques are well-suited for this aim, given their capacity to model potentially complex industrial processes. This paper proposes the implementation of a virtual flow sensor for its integration in the control loop of an industrial process. More specifically, four different data-driven methods have been tested to obtain the virtual sensor: multiple linear regression (MLR), multilayer perceptron (MLP), long-short term memory (LSTM) and deep long-short term memory (DeepLSTM). MAE, RMSE and $$R^2$$ R 2 have been chosen as evaluation metrics for model selection and testing. Furthermore, the robustness of the virtual flow sensor is not only evaluated under ideal operating conditions, but it is also tested under adverse conditions with various noise levels added to the measured signals. Additionally, the performance of the flow control loop using the real and virtual sensors is also evaluated in both ideal and adverse conditions. IAE, ITAE, and IAVU indices are used to assess the control performance. The results prove the robustness of the LSTM-based virtual flow sensor and the effectiveness of the control loop using it, avoiding the modification of the controller and interrupting the process when the real flow sensor fails.

Permeability estimation from rock digital images segmented through deep learning

An accurate estimation of permeability is very important for the development and management of petroleum reservoirs. Knowledge of petrophysical properties is essential to achieve profitability in production in the oil and gas (O&G) industry. Modeling of flow properties at pore scales has gained great prominence in oil industry projects with the advances achieved with digital transformation. From digital images of reservoir rocks, it is possible to extract information from the porous system, in order to obtain petrophysical properties such as porosity, permeability, and, make suitable the comprehension of the connectivity of this system. The feasibility of acquiring three-dimensional images of these rocks, the industry began to use machine learning tools to assist the rock characterizations, thus, the entire modeling process is more efficient. And with high-performance computers, both the optimization and expansion of porous network have led to increasingly real flow calculations, which contributes to the knowledge of these properties. The objective of this research is to estimate the absolute permeability of rocks using micro tomography (microCT) images segmented using deep learning. It was used 12 samples of coquinas from the Morro do Chaves formation (Sergipe-Alagoas Basin), these samples are similar to those from the Itapema formation (Santos Basin). The images, with 42-micron resolution and in gray scale. Data were separated into training, validation and testing. Each batch of training images has 32 sub-images with 64x64 pixels (‘patch’) and the stride ratio equal to 1. It was used 100 learning epochs with an early stopping criterion. The model used for segmentation will be U-net convolutional neural network with cross entropy as cost function and Adadelta ([1]) optimization algorithm. Additionally, sensitivities were made regarding the use or not of the data augmentation technique and also regarding the use of input images with and without denoising filters. Deep learning models were generated in the Dragonfly program ([2]), and the modeling of the pore network, using the PNM algorithm (Pore Network Modeling).[1] Zeiler, Matthew D.. “Adadelta: an adaptive learning rate method” [Online] Available: https://doi.org/10.48550/arXiv.1212.5701[2] Dragonfly: commercial software [online] Available: https://dragonfly.comet.tech/

Digital traffic state analysis for urban regions considering complex multi-directional flow changes

In this research, a digital analytical model based on three-flow full-cycle state macroscopic fundamental diagram (MFD) with observable different perimeter control management and flow loading modes has been proposed on the basis of real network and peak hour traffic flow loading scenarios in Rostov-on-Don. It is known that the aim of digital management of urban intelligent traffic is to ultimately optimize the overall state of multi-area traffic operation in the city. The goal of this research was to provide a strong basis to establish digital management strategies for intelligent transport, determine the change trends of steady-state equilibrium of regional traffic flow and their effects on full-cycle states of traffic flow under various peripheral control management strategies and traffic loading patterns. Simulation results confirmed that with the evolution of traffic state, stricter peripheral control management always results in a wider attraction area ultimately giving rise to steady-state equilibrium of traffic flow.

A tensor decomposition method based on embedded geographic meta-knowledge for urban traffic flow imputation

Accurate and reliable traffic flow data are essential for intelligent transportation systems; however, limitations arising from hardware and communication costs often lead to missing data. Tensor decomposition is widely used to address these issues. However, existing imputation methods employ a fixed geographic feature similarity matrix to constrain the tensor decomposition process, which fails to accurately capture the spatial heterogeneity of traffic flows, thus limiting the imputation accuracy and robustness. This study proposes a tensor decomposition method embedded with geographic meta-knowledge (Meta-TD) to accurately determine the spatial heterogeneity of traffic flows. The key innovation is establishing a dynamic relationship between the geographic meta-knowledge and spatial heterogeneity of traffic flows, and then using the spatial heterogeneity of the traffic flows to constrain the tensor decomposition process. Experimental results based on real urban traffic flows demonstrated the superiority of Meta-TD over fifteen baseline models under random, block, and long time-series missing patterns, achieving reductions in MAE, RMSE, and MAPE of 6.97–97.05%, 3.33–94.68%, and 0.72–90.89%, respectively. Notably, Meta-TD maintained high accuracy for sudden changes in traffic flow states, evidencing its robustness to varying missing data rates and distribution patterns. This adaptability makes it highly suitable for complex and dynamic urban traffic environments.

In-situ blade strain measurements and fatigue analysis of a cross-flow turbine operating in a tidal flow

Cross-flow turbines (CFTs) are inherently unsteady devices with regards to operating principle and loading. By improving our understanding of the dynamic loading on these turbines, we hope to better inform CFT design, improve survivability, and reduce overall costs. The University of New Hampshire (UNH) and the National Renewable Energy Laboratory (NREL) collaborated on a project to instrument and test a four-bladed New Energy Corp. vertical axis cross-flow turbine in a real tidal flow. One blade from the 3.2 m diameter x 1.7 m height turbine was instrumented with eight full-bridge strain gauges along the span of the blade. The turbine was then deployed at the UNH-Atlantic Marine Energy Center (AMEC) Tidal Energy Test Site in Portsmouth, NH. Time-synchronized measurements of blade strain, inflow, thrust, rotational speed, and electrical output were obtained to characterize blade loading under various conditions. The blade strain was examined to assess the dynamic loading and conduct a fatigue analysis on the device.

S‐2DV: A New Reduced Model Generating Submesoscale‐Like Flows

AbstractOceanic mesoscale flows are characterized by an inverse kinetic energy cascade and the subsequent formation of large coherent vortices, and these flow features are captured well by the quasi‐geostrophic (QG) model. Oceanic submesoscale flow dynamics are however significantly different from those of mesoscales. The increase in unbalanced energy levels and the Rossby number at submesoscales results in cyclone‐anticyclone asymmetry in vorticity structures, forward kinetic energy cascades, and enhanced small‐scale dissipation. In this paper, we develop a reduced single‐equation model that can generate submesoscale‐like flows in two dimensions. We start from the two‐dimensional barotropic QG equation and add an external random vorticity field, to mimic the effect of unbalanced flow components. Thereafter, we add a vorticity‐squared term, to generate asymmetry in the vorticity structures. By varying the strength of these two terms, we observe that the model can generate submesoscale‐like flows that compare qualitatively well with realistic flows generated by complex ocean models. The reduced model is seen to be capable of generating flows that are intermittent in nature, are characterized by a forward energy flux, and are composed of small‐scale flow structures along with enhanced energy dissipation. We further demonstrate the practical utility of the model by applying it to a passive tracer dispersion and a plankton patchiness problem, these being applications that require submesoscale‐like flows. Our investigation points out that the new model could serve as a convenient platform for various applications that require submesoscale‐like flows, such as testing and developing different kinds of parameterizations.

Car following trajectory planning of CAVs: An improved APF model with considering the stochasticity of HDVs

This paper considered the stochasticity of Human-Driven Vehicles (HDVs) and proposed an improved Artificial Potential Field (APF) method for car-following trajectory planning of Connected and automated vehicles (CAVs) based on real mixed traffic flow experiment. Firstly, the heterogeneity between HDVs and CAVs was considered to determine the type of attractive field. Then to adapt the APF model to dynamic traffic environments, the field functions were improved by incorporating the speed and position differences. In addition, taking into account both the vehicle itself and its impact on traffic flow, Grey Wolf Optimization-Chaos (GWO-C) was proposed to calibrate parameters, which helps avoid local optima. Furthermore, the proposed model was compared with experimental data and original APF method. The results show that the proposed APF improves the speed, jerk, and speed standard deviation of the platoon. Finally, the impact of different CAVs’ market penetration rates (MPR) on the stability and fundamental diagram were explored. It was found that traffic stability and capacity can be enhanced by CAVs, with a more significant impact at higher MPR.

Data-Driven Based Path Planning of Underwater Vehicles Under Local Flow Field

Navigating through complex flow fields, underwater vehicles often face insufficient thrust to traverse particularly strong current areas, necessitating consideration of the physical feasibility of paths during route planning. By constructing a flow field database through Computational Fluid Dynamics (CFD) simulations of the operational environment, we were able to analyze local uncertainties within the flow field. Our investigation into path planning using these flow field data has led to the proposal of a hierarchical planning strategy that integrates global sampling with local optimization, ensuring both completeness and optimality of the planner. Initially, we developed an improved global sampling algorithm derived from RRT to attain nearly optimal theoretical feasible solutions on a global scale. Subsequently, we implemented corrective measures using directed expansion to address locally infeasible sections. The algorithm’s efficacy was theoretically validated, and simulated results based on real flow field environments were provided.

Blockchain and IoT Integration for Secure Supply Chain Operations: Unlocking Real-Time Transparency and Business Continuity

This paper presents an analysis of the ways in which Blockchain and Internet of Things (IoT) systems can be combined for the betterment of supply chain management as well as a discussion of the increased security, transparency and/or continuity of business that may result from such an integration. In the current world where the global supply chains are continuing to expand and integrate, it has been noted that there is need to protect real time information flows and enhance the integrity and traceability of information. Using Blockchain’s decentralized and immutable structure together with IoT’s data-collection aspect, this research explores ways in which supply chain processes could benefit from these technologies while minimizing the risks posed by data theft, forgery, and disruptions. The research employs published data analysis and a case study, encompassing cross-industry benchmarks, research, and empirical evaluation of Blockchain-IoT applications in industries. Survey data was obtained from recent implementations in industries including pharmaceutical, F&B and logistics. Quantitative tools and techniques were applied to evaluate the integrity and the reliability of Blockchain-IoT systems operations. Some insights discovered reveals that the integration between Blockchain and IoT fosters better real-time monitoring, minimizes counterfeiting and promotes better traceability of an average of 40% based on observed instances. Literature is advanced in this paper by presenting grounded data on Blockchain and IoT’s ability to maintain enduring and reliable supply chain security. The research implies that when the technologies are well deployed, they act as a key driver in facilitating secure and efficient supply chain. The presented research provides guidance on the adaptation of Blockchain and IoT to enhance the stability of the supply chain; it maps a tactical plan that industry participants can follow to achieve sustainable, secure supply chain operations.

Zeolite intervention in soil nitrate dynamics: insights from column experiments and modelling

ABSTRACT This study aims to address the harmful effects of excessive nitrogen fertilizer by investigating the mobility retardance effect of zeolite on nitrate movement in loam soil columns. Disturbed and undisturbed soil columns, treated with zeolite, as well as control soil columns, were analysed over a span of three years. Breakthrough curves generated from the experiments were evaluated using the HYDRUS1D code with mobile–immobile water (MIM) and dual-permeability (DP) models. Our findings highlight that zeolite application significantly reduced nitrate mobility, with recovery rates decreasing by 8% in disturbed and 11% in undisturbed columns. The DP model, which accurately reflects real soil water flow conditions, outperformed the MIM model in predictive accuracy. This research offers a novel long-term perspective on the benefits of zeolite in enhancing soil properties and mitigating nitrate leaching, recommending the DP model for superior prediction of contaminant transport in structured soils within the vadose zone.

Ignition study of hypergolic solid fuels through counterflow experiment using hydrogen peroxide spray

Hypergolic solid fuels provide rapid and reliable ignition for hybrid rockets, offering significant advantages over conventional methods. However, a practical experimental approach is necessary to study ignition and combustion behavior under realistic oxidizer flow conditions. This study introduced a novel counterflow spray experiment using 90 % rocket-grade hydrogen peroxide sprays applied to low-density-polyethylene-based fuel pellets embedded with sodium borohydride and manganese acetate tetrahydrate. The results showed that sodium borohydride caused vigorous reactions, damaging the pellets, while the addition of manganese acetate tetrahydrate moderated this effect. Hypergolic ignition and sustained combustion were achieved at oxidizer mass flow rates of 0.38–0.57 g/s, with ignition delay times of 15 to 30 ms, consistent with droplet test results. In addition, reignition was successful under the same flow conditions, although the char layer formed on pellet surface after the first ignition limited the surface additive reactivity, leading to longer ignition delay times. 3D surface analysis provided quantitative data on surface roughness across various fuel compositions and flow rates. This novel counterflow spray experiment is expected to be both effective and simple, with the ability to offer valuable insights into hypergolic ignition and combustion process.

Reconstruction of blood flow velocity with deep learning information fusion from spectral ct projections and vessel geometry

In this work, we investigate a new deep learning reconstruction method of blood flow velocity within deformed vessels from contrast enhanced X-ray projections and vessel geometry. The principle of the method is to perform linear or nonlinear dimension reductions on the Radon projections and on the mesh of the vessel. These low dimensional projections are then fused to obtain the velocity field in the vessel. The accuracy of the reconstruction method is proved using various neural network architectures with realistic unsteady blood flows. The approach leverages the vessel geometry information and outperforms the simple PCA-net.

A Thread-Safe Lattice Boltzmann Model for Multicomponent Turbulent Jet Simulations

In this work an optimized multicomponent lattice Boltzmann (LB) model is deployed to simulate axisymmetric turbulent jets of a fluid evolving in a quiescent, immiscible environment over a wide range of dynamic regimes. The implementation of the multicomponent LB code achieves peak performance on graphic processing units (GPUs) with a significant reduction of the memory footprint, retains the algorithmic simplicity inherent to standard LB computing, and, being based on a high-order extension of the thread-safe LB algorithm, allows us to perform stable simulations at vanishingly low viscosities. The proposed approach opens attractive prospects for high-performance computing simulations of realistic turbulent flows with interfaces on GPU-based architectures.

Stress–strain curve estimation from load–depth curve of spherical indentation test based on finite element analysis and optimization

A stress–strain curve is one of the most important indicators of material properties for characterizing constitutive behavior. It can be directly obtained from a conventional uniaxial tensile test or load–displacement curve using an instrumented indentation test (IIT). An IIT uses formulations of the effective indentation strain and stress or the iterative finite element analysis (FEA) of the indentation process using a constitutive model, such as a power-law constitutive model. In this study, an iterative FEA is used to obtain the engineering stress–strain curve. However, it focused on considering the realistic plastic flow of specific materials without idealizing the power-law constitutive model. A concept for stress–strain estimation using a master stress–strain curve for specific materials was proposed, and its usefulness was validated via FEA and optimization techniques. In the future, this methodology can be extended to deep learning technology to address the issue of time consumption in iterative optimization methods.