Flow Pattern Monitoring Research Articles

High-accuracy recognition of gas–liquid two-phase flow patterns: A Flow–Hilbert–CNN hybrid model

In multiphase flow pipelines, the timely and precise identification of flow pattern characteristics is crucial to guide industrial production, assess pipeline safety, and facilitate early risk warnings. Current research highlights that the combined application of flow imagery and convolutional neural networks (CNNs) can yield high accuracy flow pattern recognition. However, in the industrial sector, conveniently measurable signals such as pressure and flow rate present as one-dimensional signals rather than two-dimensional signals such as images and matrices, which CNNs are equipped to process. Therefore, the selection of a suitable data encapsulation type to convert one-dimensional signals into matrix or image form is vital to the successful application of CNNs in this field and to the precise identification of flow patterns in industrial production. In this research, we propose a Flow–Hilbert–CNN hybrid model, which integrates the one-dimensional liquid holdup signal, the Hilbert curve technique, and CNNs. The experimental data from multiphase flow in an undulating pipeline with low liquid holdup demonstrates that this hybrid model provides excellent classification accuracy, the model effectively identified four flow patterns—slug flow, pseudo-slug flow, wavy-stratified flow, and smooth-stratified flow—with recognition accuracies of 100.0%, 90.38%, 93.07%, and 100.0%, respectively, from a 16,384-length data source. Compared to models that employ standard data encapsulation methods (Space Folding (SF) and Fast Fourier Transform (FFT)) combination with CNNs, this hybrid model exhibits remarkable recognition accuracy and superior adaptability to data source scales. Notably, in the case of small-scale datasets, the accuracy improvement was 43.24% and 35.89% in comparison with SF- and FFT-based models, respectively. Moreover, the recognition accuracy of the hybrid model shows significant improvement when compared to results from traditional machine learning models and deep neural networks combined with the liquid holdup signal. The hybrid model, when used with different CNNs—MobileNetV2, Resnet18, and VGG16—yields recognition accuracies of 95.87%, 97.76%, and 96.64%, respectively, while the accuracies of machine learning models—deep neural network, k-nearest neighbors algorithm, and support vector machine—stand at 91.35%, 74.12%, and 90.57%, respectively. These results convincingly demonstrate the universality of combining the Hilbert curve with CNNs in the hybrid model, as well as the model's reliability and superiority in industrial application. This hybrid model can facilitate online, high-precision monitoring of flow patterns in the industrial sector and aid in preventing risk accidents within the production process.

Material Transport and Flow Pattern Characteristics of Gas–Liquid–Solid Mixed Flows

Flow pattern monitoring of gas–liquid–solid mixed flow has great significance to enhance the quality and efficiency of material mixing, and the material transport mechanism and dynamic control strategy are faced with significant challenges. To solve these problems, a computational fluid mechanics and discrete element method (CFD-DEM) coupling modeling and solving approach based on soft sphere and porous models is presented to explore material transport mechanisms. The user-defined function (UDF) is adopted to perform data communication, and the porosity of the porous model is calculated to achieve the bidirectional calculation of Eulerian fluid and Lagrange particle phases. Material transport processes of gas–liquid–solid mixed flows are discussed to explore material transport mechanisms of particle flow and the flow pattern evolution laws under the inflation control are obtained. The results show that the particles are not evenly distributed under the synergistic action of impeller rotation and inflation. The particles in the upper and lower impeller have similar characteristics along the radial direction, and there is an aggregation phenomenon in the impeller center. A certain degree of inflation enhances the macroscopic mixing process of turbulent vortices, promotes the particle suspension effect inside the container, and improves the material transport efficiency inside the mixing space. Relevant research results can provide theoretical references for the material transport mechanism, flow pattern tracking models, and energy transfer and can also provide technical support for chemical process separation, food processing, battery homogenate mixing, and other production processes.

Simultaneous monitoring of flow patterns, and bubble, and plastics micro-particle characteristics in Dissolved Air Flotation (DAF)

Dissolved air flotation (DAF) is an important water treatment process for removing suspended solids. Understanding the characteristic size and spatial distribution of both bubbles and suspended solids, including their hydrodynamics, within flotation tanks is key to optimising flotation performance. In this study, a continuously operating lab scale DAF tank was constructed to simultaneously monitor size distributions and motions of microbubbles (Ø = 67–86 ± 13–19 µm) and spherical polyethylene microplastic particles (Ø = 69.8 ± 5.3 µm) in unbuffered water at circumneutral pH without salt addition and with 0.5 g L−1 sodium chloride (NaCl) added. Microplastic particles were used for the first time in such a setup. Particle tracking showed different flow regimes in the separation zone, with short-circuiting flow paths varying at different flow rates. Within the range of flowrates employed, the bubble bed that formed was not prominent enough to promote stratified flow. The addition of NaCl greatly reduced microplastic particle counts within the separation zone during flotation. This new multi-factorial approach provides critical insights into the variations in DAF performance.

Establishment of super sonic inlet flow pattern monitoring system: A workflow

To reduce the negative effects of the buzz flow state of supersonic air inlets, this paper proposes a process in which an online monitoring system for the supersonic inlet flow patterns is established with pressure sensors. The first step in this workflow is to build an experimental system based on the objective air inlet and a large number of pressure sensors. After that, for each pressure sensor, based on Sliding Window Short-Time Fourier Transform (SW-STFT) pre-processing and Deep residual network (ResNet) structure, a method to train a sub classifier (Classifier trained by only one sensor signal) with each pressure sensor is proposed to achieve efficient extraction of highly perturbed signal information and high classification accuracy of different flow patterns. However, the main limitation of this workflow is that too many pressure sensors can increase the complexity of aero engine control systems and make the installation difficult. Therefore, in the third step, a multi-objective optimization model is built to solve this problem and find the best subset of sensors in the experimental system. Compared with previous studies, the flow modal monitoring system established in this paper has the advantages of real-time monitoring, higher classification accuracy, and better performance robustness. At the same time, this workflow allows the construction of individualized flow monitoring systems for different inlets.

Beyond Vessel Diameters: Non-invasive Monitoring of Flow Patterns and Immune Cell Recruitment in Murine Abdominal Aortic Disorders by Multiparametric MRI.

The pathophysiology of the initiation and progression of abdominal aortic aneurysms (AAAs) and aortic dissections (AADs) is still unclear. However, there is strong evidence that monocytes and macrophages are of crucial importance in these processes. Here, we utilized a molecular imaging approach based on background-free 19F MRI and employed perfluorocarbon nanoemulsions (PFCs) for in situ 19F labeling of monocytes/macrophages to monitor vascular inflammation and AAA/AAD formation in angiotensin II (angII)-treated apolipoproteinE-deficient (apoE−/−) mice. In parallel, we used conventional 1H MRI for the characterization of aortic flow patterns and morphology. AngII (1 μg/kg/min) was infused into apoE−/− mice via osmotic minipumps for 10 days and mice were monitored by multiparametric 1H/19F MRI. PFCs were intravenously injected directly after pump implantation followed by additional applications on day 2 and 4 to allow an efficient 19F loading of circulating monocytes. The combination of angiographic, hemodynamic, and anatomical measurements allowed an unequivocal classification of mice in groups with developing AAAs, AADs or without any obvious aortic vessel alterations despite the exposure to angII. Maximal luminal and external diameters of the aorta were enlarged in AAAs, whereas AADs showed either a slight decrease of the luminal diameter or no alteration. 1H/19F MRI after intravenous PFC application demonstrated significantly higher 19F signals in aortae of mice that developed AAAs or AADs as compared to mice in which no aortic disorders were detected. High resolution 1H/19F MRI of excised aortae revealed a patchy pattern of the 19F signals predominantly in the adventitia of the aorta. Histological analysis confirmed the presence of macrophages in this area and flow cytometry revealed higher numbers of immune cells in aortae of mice that have developed AAA/AAD. Importantly, there was a linear correlation of the 19F signal with the total number of infiltrated macrophages. In conclusion, our approach enables a precise differentiation between AAA and AAD as well as visualization and quantitative assessment of inflammatory active vascular lesions, and therefore may help to unravel the complex interplay between macrophage accumulation, vascular inflammation, and the development and progression of AAAs and AADs.

Newly explored machine learning model for river flow time series forecasting at Mary River, Australia.

Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel data-intelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011-2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons.

Assessment of inhalation flow patterns of soft mist inhaler co-prescribed with dry powder inhaler using inspiratory flow meter for multi inhalation devices.

The patients’ inhalation flow pattern is one of the significant determinants for clinical performance of inhalation therapy. However, the development of inhalation flow meters for various inhalation devices has been unable to keep up with the increasing number of newly launched inhalation devices. In the present study, we developed simple attachment orifices for the inhalation flow pattern monitoring system, which are suitable for all commercial inhalers, and investigated the efficacy of the system on the clinical inhalation instruction for patients co-prescribed dry powder inhaler (DPI) and soft mist inhaler (SMI). First, we constructed simple attachment orifices that were adjusted for 13 commercial inhalers, and examined the correlation between orifice and inhalation device. Second, the inhalation flow patterns (peak inspiratory flow rate, PIFR; inhalation duration time, DT) of patients prescribed a combination of DPI and SMI were monitored before and after inhalation instruction. The inhalation resistance of commercial inhalers are listed in the following order; Twincaps® > Handihaler® > Swinghaler® = Clickhaler® > Twisthaler® > Turbuhaler® > Jenuair® > Diskus® = Ellipta® > Diskhaler® > Breezhaler® > Respimat® = pMDI. The pressure drop via orifice was significantly correlated with that via the commercial inhaler. For the confirmation, all participants achieved the DPI criterion of PIFR. On the other hand, 4 participants (6 clinical visits) of 10 experimented participants could not achieve the essential criterion of DT (> 1.5 sec) for SMI, but all participants improved their duration time after inhalation instruction by pharmacists (P<0.05). In the present study, we successfully developed simple attachment orifice suitable for 13 commercial inhalation devices. These data suggested that our simple attachment orifices for the inhalation flow pattern monitoring system can detect patients with inadequate inhalation patterns via SMI.

Cerebral Flow Pattern Monitoring by Transcranial Doppler during Cardiopulmonary Resuscitation

We describe the transcranial Doppler pattern during a period of cardiopulmonary resuscitation in a patient undergoing a transcatheter off-pump aortic valve implantation. Transcranial Doppler of the middle cerebral artery flow was measured as a part of a separate ongoing study. The patient developed a cardiac arrest during deployment of the valve prosthesis. The incidental presence of the transcranial Doppler allowed us to assess middle cerebral artery flow during cardiopulmonary resuscitation in real time. An initial lack of a diastolic flow pattern seen with transcranial Doppler at the beginning of cardiopulmonary resuscitation led to volume loading and optimisation of the resuscitation technique. After increasing the depth of external cardiac massage, the cerebral flow pattern improved to produce sufficient diastolic flow. The transcranial Doppler provided additional information during cardiopulmonary resuscitation, which was helpful in clinical management. The use of transcranial Doppler may be helpful for other cardiac procedures where cerebral malperfusion may occur.

Fluid flow in accretionary prisms: Evidence for focused, time‐variable discharge

Accretionary prisms are wedges of saturated sediment that are subject to intense deformation as a result of lithosphere convergence. Compressive stress and rapid burial of the accreted deposits result in sediment compaction and mineral dehydration. These latter processes, in conjunction with fermentation or thermal maturation of entrained organic matter, yield hydrocarbon‐bearing pore fluids that are expelled from the prism. Regional fluxes of heat and a number of dissolved chemical species, most notably carbon, are controlled by the advective expulsion of the pore waters. Numerical modeling, observation and monitoring of flow patterns and rates, and recent in situ hydrogeological tests quantify the conditions that control rates of fluid flow. Dispersed, intergranular flow (10−8 to 10−11 m/s), controlled by the vertical permeability of the prism (10−14 to 10−20 m²), is limited by low‐permeability lithologies and seems not to vary much from margin to margin. Focused flow (10−1 to 10−8 m/s) above the décollement is controlled by fault zones or sedimentary intrusions (diapiric structures). At low‐fluid pressures, fault zone permeability may be similar to that of adjacent wall rock, but as fluid pressure increases from hydrostatic (λ* = 0) to near lithostatic levels (λ* ≈ 1.0), fault zones dilate, and (fracture) permeability increases by 2–4 orders of magnitude (10−10 to 10−16 m²). Similarly, mud volcanoes and diapirs provide high‐permeability fluid conduits to the sediment‐water interface. As a result, faults and intrusions become primary flow paths and support surface vents at which syntectonic deposits (carbonate and gas hydrates) accumulate and chemosynthetic organisms cluster. Models of thermal and chemical anomalies and epigenetic deposits indicate that flow is temporally variable. That conclusion has been quantified by extended (1–10 months) seafloor and borehole experiments that measured temperature anomalies associated with flow events. On the Cascadia prism, flow (estimated velocity ∼3 × 10−5 m/s; 950 m/yr), confined to a thrust fault that cuts upward through the prism, has brought thermogenic hydrocarbons from depths &gt;1.5 km to the surface within the last 400 years.