Flow Patterns Research Articles

Ex Ante Construction of Flow Pattern Maps for Pulsating Heat Pipes

A novel methodology is proposed for the development of empirical flow pattern maps for pulsating heat pipes (PHPs), which relies on the concept of virtual superficial velocity of the liquid and vapour phases. The virtual superficial velocity of each phase is defined using solely the design and operational parameters of the pulsating heat pipe, allowing the resulting flow pattern map to serve as a predictive instrument. This contrasts with existing flow pattern maps that necessitate direct measurements of temperatures and/or velocities within one or more channels of the pulsating heat pipe. Specifically, the virtual superficial velocities are derived from the relative significance of the driving forces and the resistances encountered by each phase during flow. The proposed methodology is validated using flow visualisation datasets obtained from two separate experimental campaigns conducted on flat-plate polypropylene pulsating heat pipe prototypes featuring transparent walls and meandering channels with three turns, five turns, seven turns, and eleven turns, respectively. The PHP prototypes were tested for gravity levels ranging between 0 g and 1 g and heat inputs ranging from 5 W to 35 W. The proposed approach enables the identification of empirical boundaries for flow pattern transitions as well as the establishment of an empirical criterion for start-up.

Hydrodynamics of cruise swimming and turning maneuvers in Euchaeta antarctica.

The hydrodynamic disturbance generated by the adult copepod Euchaeta antarctica during cruise swimming is quantified. Kinematic results are compared to previous results reported for different Euchaeta species. The results reveal a linear relationship between cruise speed and prosome length across Euchaeta species, indicating a size-proportional trend that is indicative of a complicated interaction of species size and environmental factors such as fluid temperature and viscosity. The detailed fluid flow measurements using the tomographic Particle Image Velocimetry (tomo-PIV) technique provide insight into copepod cruise propulsion during turning events in comparison to straight motions. During straight swimming, E. antarctica demonstrates streamlined flow patterns and reduced vorticity in the near-body fluid shear layer, which is beneficial for sustained motion and energy conservation. In contrast, turning maneuvers are characterized by maximum flow velocities reaching 1.5 times greater values than during straight cruising with increased flow field complexity and enhanced vorticity. The viscous dissipation rate generated in the flow disturbance is also greater during turning events, with the total dissipation rate reaching W compared to W during straight cruising. The flow disturbance also generates a hydrodynamic cue that prey may sense in order to avoid the predator E. antarctica. For the adult E. antarctica, the hydrodynamic cue extends to a volume that is 11-13 times larger than the copepod exoskeleton volume during the straight swimming motion and 22-25 times larger during the turn events.

Regional range extension of two xanthid crab species (Decapoda, Brachyura) to the mid-Sanriku Coast, the coldest waters off Honshu, Japan

Abstract This paper reports the first occurrences of two brachyuran crab species of the family Xanthidae MacLeay, 1838, viz., Actaea semblatae Guinot, 1976 and Gaillardiellus orientalis (Odhner, 1925), from the mid-Sanriku Coast, along the Pacific coast of northeastern Japan. While these records do not represent the northern limit of distribution of these species, they do show a range extension into the coldest waters of Honshu, the main island of Japan, probably due to global warming and changes in flow patterns of the local sea currents.

Complex interplay between the microfluidic and optical properties of Hoplia sp. beetles.

All living organisms exist in a world affected by many external influences, especially water and light. Photonic nanostructures present in certain insects, have evolved over time in response to diverse environmental conditions, facilitating communication within and between species, camouflage, thermoregulation, hydration, and more. Up to now, only a few insect species have been discovered whose elytron changes its color due to permeation of water (or its vapor) through cuticle. Here we report on a scarabaeid beetle Hoplia argentea remarkable in its ability to shift from green to brownish-red when exposed to water, demonstrating reversible changes. Here we show that elytron and scales form a complex and efficient micro/nano-optofluidic system. Water is channeled into the elytral lacunae, then transported internally to the petals of the scales, where it is wicked inside each scale, pushing the entrapped air out. Wicking is a very fast process, occurring during a few seconds. The advantage of this principle is in extremely high pressure (approximately 15bar) produced by capillary forces, which expediates permeation of air. We present optical models that explain the physical mechanisms behind the coloration, detailing how superhydrophilic properties influence optical behavior. Species within the genus Hoplia exhibit diverse coloration strategies, likely linked to their specific ecological niches. These organisms have evolved intricate optical and microfluidic systems that facilitate rapid alterations in body coloration, potentially serving purposes such as environmental camouflage and thermoregulation. Studying microfluidic and optical properties of the elytra will not only enhance our understanding of the biological purposes behind color change but also inspires design of artificial biomimetic devices. Dynamic fluid flow patterns, described in this paper, are fairly constant and unique and can be used in security applications as a, so called, physically unclonable functions (PUF). More broadly, this kind of microfluidic system can be used for controlled drug release, sensing, hydraulic and pneumatic pumping.

Evidence of brines interconnections and different flow patterns within the boulder clay glacier and its moraine (Victoria Land, East Antarctica).

Multi-technique integrated surveys were carried out to investigate brine characteristics, connectivity and flow patterns in the Boulder Clay Glacier area, Victoria Land, East Antarctica. Specifically, electromagnetic geophysical surveys focused mainly on Ground Penetrating Radar (GPR) and integrated by Frequency Domain induction, not only demonstrated the presence of brines in the subsurface, but also allowed to image several structures and glaciological elements. Chemical analyses suggested the origin and differentiation of the brines, providing evidence for interconnected pathways. To the best of our knowledge, this is the first time that brines interconnection and their chemical common footprint is demonstrated below a glacier and its moraine at shallow depth, combining boreholes, geophysical, geomorphological, and chemical data. This demonstrates that liquid flows can occur at very shallow depths, even in continental Antarctica, and that pressurised brines can flow beneath and through glaciers and moraines.

Investigation of burnout effect in turbine blade tip leakage at transonic conditions – a numerical study

Abstract In the aviation industry, turbine blade tip leakage significantly impacts the economy due to aerodynamic losses in the turbine. The tip leakage flow increases when the tip surface is exposed to high heat loads from the burnout effect, contributing to nearly 30% of the total loss in the turbine stage. This study numerically investigates two-dimensional flat tip and burnt-out tip models under different flow accelerations at transonic conditions. Variations in the discharge coefficient are examined for different pressure ratios across the tip gap. Flow and shockwave patterns for various blade tip geometries are obtained and analyzed. The burnt-out tip notably increases tip leakage, and a significant decrease in turbine efficiency is observed beyond a critical burnout limit. The quantified losses at different stages of blade tip burnout are used to predict the effective operational life of the blade. A correlation is developed to relate the non-dimensional tip-leakage flow parameters to the normalized blade-tip geometry.

An Experimental Investigation of the Effect of Two-Phase Flow in a Manifold on Water Jet Lengths

The outlet flow rates and changes in behaviors of five outlet ports where water and air–water (two-phase) mixtures pass horizontally in a manifold pipe system were investigated experimentally. The effects of different air-flow rates, vacuumed from the atmosphere with a Venturi device in the system, on the outlet flow rates and diameters of the manifold port outlets were compared by measuring the outlet jet lengths. The system performance provided homogeneity of manifold port outlet flows and was tested. As a result, it was observed that homogeneous jet lengths were obtained in both single and two-phase low main manifold flows and equal outlet port diameters. When the main manifold flow rate V is 1.5–2 m/s, the system is stable and produces high jet lengths. The manifold pipe systems used in the experimental setup provide suitable working conditions for d/D = 0.433. The system does not show a smooth flow pattern with Venturi devices for d/D < 0.433. The low flow rates in this study’s tests are key. They are vital for designing micro irrigation systems. This depends on the critical d/D ratio of the system.

Application of machine learning for reconstruction of multiphase fluid structure measured by a capacitance multi-electrode sensor

Non-invasive real-time measurements of phase content in the reservoir fluid are highly advantageous in the oil and gas industry and remain a current research topic. The paper presents an innovative, self-designed multi-electrode capacitance meter intended for detecting multiphase flow patterns in a low-permittivity medium, such as the reservoir fluid. The ca-pacitance sensor is built with delta-sigma charge modulators capacitance-to-digital converters. Machine learning is applied to convert the capacitance measurements into a tomographic image of the flow pattern. At present, the meter is built with eight electrodes. It is shown that the measurements are repeatable and have a good signal-to-noise ratio. The implemented neural network is capable of correctly reconstructing the tomographic images for a test tube filled with reservoir fluid and placed in various locations inside the test section.

Spontaneous symmetry breaking in two dimensions under non-equilibrium laminar flows

Abstract We study the long-range order in two dimensions, where an order parameter is advected by laminar flows such as rotational, shear and elongational flows. Under these flows, we analyze the ordered state of the O(N) scalar model in the large-N limit. We show that the stability of the ordered state depends on the flow pattern; shear and elongational flows stabilize the long-range order but rotational flow does not. We discuss the physical mechanism underlying our results by connecting static correlations between fluctuations and their dynamics based on the interaction representation used in quantum mechanics. We find that advective transport induces superdiffusion under shear and elongational flows, thereby stabilizing the long-range order.

Abstract 4141445: Long-Term Predictive Value of 4D Flow MRI in Bicuspid Aortic Valve Patients: A 10-Year Assessment for Aortic Surgery Risk

Introduction: Bicuspid aortic valve (BAV) is associated with progressive ascending aorta (AAo) dilation, often leading to aneurysms, dissections, and ruptures. Thus, current guidelines recommend preventive surgery for AAo dilation. Recent 4D flow MRI studies show that BAV morphology causes abnormal transvalvular flow patterns, increasing wall shear stress (WSS), a trigger of aortic growth. Further studies have delineated areas of abnormally high WSS by comparing to estimates of matched controls, and show promise in detecting risk for aortic growth. However, since the long-term prognostic significance of this marker is unclear, we aimed to quantify WSS in BAV patients to assess its value in predicting the need for aortic surgery up to 10 years post-4D flow MRI acquisition. Methods: BAV patients without prior surgical intervention scanned before April 1, 2014 were identified. Using medical records, patients were categorized as ‘operated’ if they underwent aortic surgery post-scan and ‘non-operated’ if they were surgery-free for at least 10 years post-scan. 4D flow MRIs were processed with an AI pipeline, including 3D segmentation of the aorta, followed by peak velocity (PV) and WSS quantification in the AAo (Fig. 1A-C). Patient-specific WSS heatmaps were computed relative to a map based on the WSS of 10 or more sex and age-matched controls. Relative areas of elevated WSS in the AAo were then calculated (Fig. 1D-F). Results: 115 patients were included, with 73 non-operated (age: 42.5±11.5y, 49M) and 42 operated patients (age: 53.5±12.1y, 34M). The mean baseline mid-AAo diameters for non-operated and operated patients were 3.8±0.6 cm and 4.1±0.5 cm, respectively. Among operated patients, the mean scan to surgery time was 5.7±3.3y. All three 4D flow metrics were significantly higher in operated compared to non-operated patients: PV: 2.6±0.6 vs. 1.7±0.4 m/s (p<0.01); WSS: 2.2±0.5 vs. 1.6±0.4 Pa (p<0.01); relative area of high WSS on heatmaps: 31±12 vs. 15±13% (p<0.01). Conclusions: 4D flow MRI parameters provide long-term predictive value in BAV patients, as elevated peak velocity, wall shear stress, and relative area of high wall shear stress on heatmaps are associated with subsequent aortic surgery within 10 years post-scan.

Numerical investigation of noise reduction of a pump-jet propulsor using porous metal

A novel strategy for noise reduction in pump-jet propulsor (PJP) is proposed through the design of porous metal trailing edges on stator blades. The interference between stator wake vortices and rotor blades is one of the primary mechanisms for noise generation in PJP. The goal is to use a porous medium to diminish the intensity of turbulent vortices behind the stator blades, control the interaction between these shedding vortices and the rotor blades downstream of the stator, and ultimately suppress the rotor noise generation source. Large eddy simulation and acoustic analogy analysis of the entire propulsion system based on the Ffowcs-Williams and Hawkings equation are performed. It is found that with the use of porous stator trailing edges, the wake flow pattern of the stator is notably altered, the strong impact of the turbulent vortices on the rotor blades is weakened, and the wall pressure fluctuations on the rotor blades and duct are significantly reduced. As a result, the far-field radiation noise and tonal noise are both effectively reduced, with the maximum reduction in total and tonal sound pressure levels reaching 3.8 dB and 12.53 dB, respectively, corresponding to percentage reductions of 6.0% and 21.69%. These findings demonstrate that porous media have great potential for practical engineering applications in PJP noise control.

Abstract 4131796: Non-Contact Biometric System for Early Detection of Hypertension and Diabetes Using AI and RGB Imaging

Introduction: In recent years, the development of wearable devices and bioinformation analysis applications utilizing smartphone sensors has seen significant advancements. Despite these technological strides, their adoption and sustained use remain limited to health-conscious individuals. The general populace, particularly those indifferent to health management, stands to miss out on the potential benefits of wearable technologies due to a lack of engagement. This disconnect underscores the necessity for more accessible health monitoring solutions. Research Questions and Goals: This study aims to bridge the gap in health monitoring accessibility by introducing a system capable of non-invasive, non-contact acquisition of biometric information. Such a system seeks to democratize the early detection and prevention of diseases across various demographics. Methods and Results: A prospective cohort clinical study was conducted in a hospital setting with approximately 200 consenting patients and healthy controls. Images of the subjects' faces and hands were captured with a high-speed camera, and blood pressure measurements were taken simultaneously for correlation. A machine learning algorithm was developed to analyze skin blood flow and spectral features from these images to identify potential diseases. The study specifically focused on early detection of hypertension and diabetes. Hypertension was categorized for individuals with systolic blood pressure ≥115 mmHg or diastolic blood pressure ≥75 mmHg. Diabetes was identified in subjects with HbA1c levels ≥6.5 or those previously diagnosed. The algorithm's performance in detecting hypertension showed a 94.2% in alignment with the AHA guidelines for stage1 hypertension, based on pulse wave features. For a subset including 40% early hypertension cases, accuracy rates were 86.2% for 30-second data segments and 80.9% for 5-second segments. For diabetes detection, the application of the algorithm to video data achieved a 75.3%, utilizing blood flow patterns as markers. Hypertension and diabetes were analyzed using different features. Conclusions: The integration of AI algorithms with video data analysis of skin and blood vessels demonstrates a promising avenue for the non-contact, early detection of hypertension and diabetes. This approach not only offers a viable non-contact monitoring technology, but also represents a significant leap towards inclusive, accessible health care prevention and management strategies.

The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

Abstract Modern radial compressors are designed to operate with high performance and a wide range of applications while minimizing their environmental impact. It is necessary to study the machine near the stability limit and gain a comprehensive understanding of the fluid dynamic mechanisms that trigger the instability in the system to extend the operating range at high efficiency. Machine learning aids in developing pattern identification models for detecting compressor instability. In a prior study, a two-stage radial compressor for refrigerant gas underwent extensive simulation, capturing unsteady RANS conditions and generating a substantial dataset of pressure signals from multiple probes. Selected signals, coupled with detailed computational fluid dynamics (CFD) post-processing, revealed fluid dynamic structures near surge conditions. This study utilizes all pressure signals to assess the stage's operational state (stable, transient, or unstable/surge). An additional goal is to develop an algorithm predicting flow patterns in different stage sections with minimal pressure signals at the rotor inlet. This is a preliminary step toward the development of a smart monitoring and diagnostic model for the compressor installed in a plant. The developed model consists of three submodels, trained with CFD results obtained from unsteady Reynolds averaged Navier–Stokes (URANS) simulations. The three submodels are a regression submodule, a classification submodule, and a forecasting algorithm. The regression submodule predicts diffuser static pressure fields, the classification submodule categorizes whether the compressor is in a critical condition or not, and the forecasting algorithm predicts the inducer pressure signals in the future impeller rotations according to the actual operation history. These sub-modules work together to forecast whether the compressor, according to the operation strategy, is going into instability conditions.

Temporal Patterns in Migration Flows Evidence from South Sudan

ABSTRACTWhat explains the variation in migration flows over time and space? Existing work has contributed to a rich understanding of the factors that affect why and when people leave. What is less understood are the dynamics of migration flows over time. Existing work typically focuses on static variables at the country‐year level and ignores the temporal dynamics. Are there recurring temporal patterns in migration flows? And can we use these patterns to improve our forecasts of the number of migrants? Here, we introduce new methods to uncover temporal sequences—motifs—in the number of migrants over time and use these motifs for forecasting. By developing a multivariable shape similarity‐based model, we show that temporal patterns do exist. Moreover, using these patterns results in better out‐of‐sample forecasts than a benchmark of statistical and neural networks models. We apply the new method to the case of South Sudan.

Groundwater dynamics simulation in Hodna (Algeria): aquifer modeling approach

Groundwater modeling using MODFLOW and GIS tools is crucial for understanding the complexities of groundwater dynamics in Algeria's semi-arid and arid climates. This study focuses on the quantitative assessment of the phreatic aquifer within the Hodna aquifer system over the period of 1973-2005. The study employs MODFLOW and GIS tools to model groundwater flow. Calibration and validation processes, using a trial and error technique, refine the models to understand water table behaviors and drawdown within the Hodna basin. Calibration efforts yield insights into groundwater dynamics, particularly significant drawdowns observed in cities like M’sila and Boussaada. Parameters such as hydraulic heads, piezometric levels, drainage, and hydraulic conductivity values are extracted from MODFLOW simulations. Analysis reveals a decline in groundwater volume and consistent drops in borehole water levels over the past 32 years, highlighting the challenges of comprehending recharge resources and the impact of groundwater depletion in the region. The study underscores the importance of calibrated groundwater models in managing and mitigating groundwater depletion in Algeria's Hodna basin. It elucidates predominant groundwater flow patterns towards Chott and emphasizes the need for comprehensive understanding and management strategies to sustainably manage groundwater resources in semi-arid and arid regions.

Crowd Flow Prediction: An Integrated Approach Using Dynamic Spatial–Temporal Adaptive Modeling for Pattern Flow Relationships

ABSTRACTPredicting crowd flows in smart cities poses a significant challenge for the intelligent transportation system (ITS). Traffic management and behavioral analysis are crucial and have garnered considerable attention from researchers. However, accurately and timely predicting crowd flow is difficult due to various complex factors, including dependencies on recent crowd flow and neighboring regions. Existing studies often focus on spatial–temporal dependencies but neglect to model the relationship between crowd flow in distant areas. In our study, we observe that the daily flow of each region remains relatively consistent, and certain regions, despite being far apart, exhibit similar flow patterns, indicating a strong correlation between them. In this paper, we proposed a novel Multiscale Adaptive Graph‐Gated Network (MSAGGN) model. The main components of MSAGGN can be divided into three major parts: (1) To capture the parallel periodic learning architecture through a layer‐wise gated mechanism, a layer‐wise functional approach is employed to modify gated mechanism, establishing parallel skip periodic connections to effectively manage temporal and external factor information at each time interval; (2) a graph convolutional‐based adaptive mechanism that effectively captures crowd flow traffic data by considering dynamic spatial–temporal correlations; and (3) we proposed a novel intelligent channel encoder (ICE). The task of this block is to capture citywide spatial–temporal correlation along external factors to preserve correlation for distant regions with external elements. To integrate spatio‐temporal flexibility, we introduce the adaptive transformation module. We assessed our model's performance by comparing it with previous state‐of‐the‐art models and conducting experiments using two real‐world datasets for evaluation.

Two distinct current systems in the ionosphere of Mars

When the solar wind interacts with the ionosphere of an unmagnetized planet, it induces currents that form an induced magnetosphere. These currents and their associated magnetic fields play a pivotal role in controlling the movement of charged particles, which is essential for understanding the escape of planetary ions. Unlike the well-documented magnetospheric current systems, the ionospheric current systems driven by solar wind and atmospheric neutral winds have not been quantitatively observed, which constrains the quantification of energy transfer from stars to these planets. Here, utilizing eight years of data from the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission, we investigate the global distribution of ionospheric currents on Mars. We identify two distinct current systems in the ionosphere: one aligns with the solar wind electric field but exhibits hemispheric asymmetry perpendicular to the solar wind electric field direction; the other corresponds to the flow pattern of annually averaged neutral winds. We propose that these two current systems are driven by the solar wind and atmospheric neutral winds, respectively. Our findings reveal that Martian ionospheric dynamics are influenced by the neutral winds from below and the solar wind from above, highlighting the complex and intriguing nature of current systems on unmagnetized planets.

Generalization Limits of Data-Driven Turbulence Models

AbstractMany industrial applications require turbulent closure models that yield accurate predictions across a wide spectrum of flow regimes. In this study, we investigate how data-driven augmentations of popular eddy viscosity models affect their generalization properties. We perform a systematic generalization study with a particular closure model that was trained for a single flow regime. We systematically increase the complexity of the test cases up to an industrial application governed by a multitude of flow patterns and thereby demonstrate that tailoring a model to a specific flow phenomenon decreases its generalization capability. In fact, the accuracy gain in regions that the model was explicitly calibrated for is smaller than the loss elsewhere. We furthermore show that extrapolation or, generally, a lack of training samples with a similar feature vector is not the main reason for generalization errors. There is actually only a weak correlation. Accordingly, generalization errors are probably due to a data-mismatch, i.e., a systematic difference in the mappings from the model inputs to the required responses. More diverse training sets unlikely provide a remedy due to the strict stability requirements emerging from the ill-conditioned RANS equations. The universality of data-driven eddy viscosity models with variable coefficients is, therefore, inherently limited.

Investigating the role of thrombosis and false lumen orbital orientation in the hemodynamics of Type B aortic dissection

While much about the fundamental mechanisms behind the initiation and progression of Type B aortic dissection (TBAD) is still unknown, predictive models based on patient-specific fluid-structure interaction (FSI) simulations can help in risk stratification and optimal clinical decision-making. Aiming at the development of personalized treatment, FSI models can be leveraged to investigate the interplay between complex aortic flow patterns and anatomical features, while considering the deformation of the arterial wall and the dissection flap. In this study, the hemodynamics of false lumen thrombosis, a large fenestration, and the orbital orientation of the false lumen is studied through image-based FSI simulations on three TBAD patient-specific geometries. A new pipeline is developed leveraging the open-source software SimVascular and ParaView to analyze multiple patients simultaneously and to achieve large-scale parallelization in FSI results based on patients’ computed tomography (CT) images. The results of this study suggest that the internal orbital orientation of the false lumen contributes to maintaining a positive luminal pressure difference ΔPTL-FL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta P_{TL-FL}$$\\end{document} = PTL-PFL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{TL}-P_{FL}$$\\end{document} between the true lumen (TL) and the false lumen (FL), despite an impingement area in the false lumen near the entry tear. A positive and high luminal pressure difference is thought to promote TL expansion and FL compression. Moreover, it was also found that FL thrombosis at the entry tear region reduce the magnitude of the negative luminal pressure difference, which in turn may reduce FL expansion and the risk of unstable aortic growth. Finally, this FSI study suggests that the aortic wall and dissection flap stiffness determines the effects of a large fenestration in the descending thoracic aorta on the luminal pressure difference.

IMPROVING METHODS FOR PREDICTING THE STRUCTURE OF GAS-LIQUID FLOW IN HORIZONTAL PIPES

Analysis of existing research on the hydrodynamics of two-phase mixtures allows us to trace the main trends in the development of engineering calculation methods, as well as identify the most promising approaches for developing algorithms for predicting the patterns of gas-liquid flows in horizontal pipes. Predicting the patterns of two-phase flows is necessary to solve a number of applied problems related to the design of field pipeline systems, monitoring of operational parameters during the transportation of well products, calculation of hydraulic pressure drop in pipes, etc. Conventionally, in creating methods for predicting flow regimes in horizontal pipes, there are stages based on empirical approaches, mechanistic modeling and unification of hydrodynamic criteria for assessing the processes of patterns transformations of two-phase flows. Approaches based on the unification of hydrodynamic models of gas-liquid flows are noted as promising directions in the development of hydrodynamic forecasting criteria. The principles of unification consist in bringing to uniformity a mathematical apparatus capable of predicting all possible patterns of gas-liquid flow in vertical, inclined and horizontal pipes based on known volumetric flow rates of liquid and gas. The article proposes the improvement of known unification approaches in the development of criteria for predicting the annular and dispersed-bubble patterns of gas-liquid flow in field pipes. The verification of the obtained algorithms for pattern prediction of annular and dispersed-bubble modes of horizontal gas-liquid flow was carried out.