Detailed Flow Research Articles

Numerical assessment of ice formation processes and its impact on a variable-pitch unmanned aerial vehicles propeller in forward flight

Unmanned aerial vehicles (UAVs) encounter significant challenges in freezing climates, as atmospheric ice accretion adversely impacts both flight safety and aerodynamic performance. This study provides an in-depth numerical investigation into the ice accretion process and its implications on the aerodynamic performance of UAV propeller. The analysis explores at various propeller blade pitching angles and rotational speeds. Detailed flow field analysis around propeller blade surfaces is conducted to address the performance degradations associated with ice accretion. The investigation reveals a noteworthy shift in ice shapes and extents with varying pitching angles and rotational speeds. The iced propeller demonstrates increased aerodynamic losses, marked by large size separation bubbles aft the ice shapes at outer radial locations. Remarkably, at higher pitching angles, the iced propeller outperforms the baseline propeller, followed by a propeller with increased rotating speed. For both baseline and higher pitching angles, the most significant losses in thrust coefficient 57.60% and 25.39%, respectively, occur at −2 °C, accompanied by maximum spikes in power coefficient of 140.08% and 93.92% at −4 °C. Meanwhile, an increase in rotating speed results in a decrease in thrust coefficient by 48.60% and an increase in power coefficient by 150.66% at an icing temperature of −4 °C.

Residual congestion in acute decompensated heart failure: A novel approach through microvascular imaging of renal circulation

Abstract Background In patients admitted for acute decompensated heart failure (ADHF), residual congestion at discharge is associated with adverse clinical outcomes. Traditionally, the extent of congestion has been inferred from central venous pressure, but it has also been highlighted that this is insufficient for a comprehensive evaluation of residual congestion. On the other hand, renal congestion is an important finding of organ congestion, and Superb Microvascular Imaging (SMI), a technique that reveals minute flow details, is promising for assessing renal circulation. We hypothesized that in patients with ADHF, assessment of renal circulation using SMI may be superior to conventional methods in assessing residual congestion. Methods In this prospective study, we enrolled 45 consecutive patients admitted with ADHF. We performed comprehensive evaluations including blood tests, echocardiography, and renal ultrasonography using SMI. The vascular index (VI), as measured by SMI, was calculated as the percentage of blood flow signal area in the region of interest. Subsequently, we calculated the intra-renal perfusion index (IRPI), representing cyclic variation in VI, as (maximum VI - minimum VI) within one cardiac cycle, divided by the maximum VI (Figure). Brain Natriuretic Peptide (BNP) levels were utilized to evaluate the state of decongestion. Results At discharge, the mean IRPI was 0.61±0.04, while the median BNP levels were 204.9 pg/ml (interquartile range: 102.7, 428.2). Patients exhibiting elevated log BNP levels at discharge (>2.31, based on median value) displayed significantly higher IRPI values [0.61(0.49, 0.86) vs 0.53(0.35, 0.59), p=0.02] and were older (80.8±8.5 vs 70.3±14.9 years, p=0.010). However, no significant differences were observed regarding laboratory parameters including hematocrit and creatinine levels, as well as echocardiographic parameters such as ejection fraction, E/e' ratio, and estimated right atrial pressure (RAP). Moreover, at discharge, creatinine levels (p=0.56) and estimated RAP (p=0.06) exhibited no significant correlation with IRPI. Conclusion Renal circulation evaluated by SMI may serve as a useful indicator of the extent of residual congestion at discharge compared to blood tests and echocardiographic estimates of right atrial pressure in patients admitted for ADHF.

Numerical Simulation and Experimental Verification of Rotor Airflow Field Based on Finite Volume Method and Lattice Boltzmann Method

The primary focus of research in agricultural unmanned aerial vehicle (UAV) pesticide application technology is the investigation of droplet drift and deposition. The influence of the rotor airflow on droplets is particularly significant, making numerical simulations a crucial tool for airflow field analysis. Among existing numerical simulation methods, the Finite Volume Method (FVM) and the Lattice Boltzmann Method (LBM) are commonly used, but there is limited research that compares the two approaches. Therefore, this paper conducts numerical simulations of the rotor airflow of an agricultural UAV using Fluent, representing the FVM, and XFlow, representing the LBM. This research aims to reveal the distribution patterns of airflow field numerical simulations under different theoretical methods, validate them through practical experiments, and select the optimal method for simulating rotor airflow. The ultimate goal is to establish an effective airflow field model to enhance the precision of pesticide application by an agricultural UAV. The results indicate that the lift error calculated by XFlow in this paper is 2.57% smaller than that by Fluent. The wind field of Fluent entered the “stable state” earlier than XFlow, and the speed value of Fluent was smaller than that of XFlow. The difference between the two speed values became larger and larger as the distance from the rotor was longer. Compared with XFlow, Fluent changes more obviously in the core region, and the center region gradually disappears with the distance from the rotor. However, in the velocity field calculated by XFlow, there are still more turbulent flows outside the core region, indicating that the transient calculation method based on the LBM can better show the details of fluid flow than the steady-state calculation method based on the FVM. Through comparison with the actual test data, it is found that the relative error of the velocity value of XFlow at 0.2 m and 0.4 m is small, while that of Fluent at 0 and 0.2 m is small. It shows that XFlow simulation has higher accuracy for external turbulent flow, while Fluent simulation has higher accuracy for steady laminar flow. The research results provide data comparison and a basis for further analysis of the wind field model of the rotor wing of the plant protection UAV, and they lay a foundation for further research on precision application technology.

Exploration on flutter mechanism of a damaged transonic rotor blade using high-fidelity fluid–solid coupling method

Structural damage in turbomachinery is a primary origin of aeronautic accidents, which is receiving increased attention. This study is thus focused on the aeroelastic analysis of damaged blades, including the onset of flutter and underlying mechanisms. First, a high-fidelity fluid–solid coupling system is established with computational fluid dynamics (CFD) and computational structural dynamics (CSD) technologies, via which the dynamic aeroelastic analysis is conducted based on static aeroelastic deformation. Second, a damaged rotor blade is parametrically modelled with variable damage levels, extents, and positions. Finally, the modal identification method of spectral proper orthogonal decomposition (SPOD) is applied to observe flow details and provide physical insight into the flutter mechanism for damaged blades. Numerical analysis finds that there is a critical damage level below which the aeroelastic stability is positively improved with increasing damage level; otherwise, a significant loss of stability is induced. The damage location and extent further affect this critical damage level and the change rate crossing the threshold. The simulation with CFD/CSD finds that the high pressure near the trailing edge induced from boundary layer separation suppresses vibrations in stable conditions, but motivates vibrations during flutter, which is because of the high-pressure spread to nearing blades. SPOD modes reveal that high-frequency disturbances with large scale are primary factors inducing flutter, which is further stimulated by the high-order disturbances with small scale. This study provides a crucial foundation for the fatigue prediction for rotor blades in service and the optimisation design for high-performance turbomachinery in the near future.

Numerical simulation on the influence of artificial island on reef hydrodynamics

The construction of artificial island can greatly change the reef hydrodynamics, leading to increased water level and wave height as well as changes in flow field distribution. These alterations can affect sediment transport on the reef, and increase the risk of overtopping and structure instability. A numerical model based on the Reynolds Averaged Navier-Stokes (RANS) equations and k-ε turbulence closure model was developed to investigate the influence of artificial island on reef hydrodynamics. The numerical model was validated against the experimental results of wave height, mean water level, and wave breaking morphology. Detailed flow field, wave height, and wave set-up in front of artificial island were further analyzed based on the validated model. After building the reef-top structure, the wave breaking and offshore currents at reef edge were amplified. The flow stratification and increase of the wave set-up were also found on the reef flat. Furthermore, we found the relationship between maximum flow velocities on the reef flat and incoming wave conditions could be characterized by two non-dimensional parameters: |u¯|max/g(η¯+hr), (η¯+hr)/Hi.

Bipolar to unipolar conversion in superjunction MOSFETs during resonant switching

Superjunction MOSFETs have been one of the promising switching components for realizing resonant converters. In spite of the numerous studies about the resonant converters in power electronics, the detailed current flow in the device has been still unclear, especially for the inner-current circulation between the body diode and the MOSFET. By employing five-terminals on the superjunction MOSFET, mixed-mode half-bridge resonant converter is simulated. During the turn-on of the MOSFET, the gate charging current flows into the drain-to-source capacitance to form a built-in depletion of the pillars. In the case of the turn-off, the source current reversely flows across the channel with a slightly negative drain-to-source bias and diverts to the gate discharging current.

Detection of Two-Phase Slug Flow Film Thickness by Ultrasonic Reflection

Slug flow is a common phenomenon in closed pipes, occurring in two-phase flow where liquid and gas phases mix, impacting the custody transfer or the efficiency of chemical reactions. This study aims to detect and analyze slug flow film thickness in two-phase flow, providing detailed structural flow information. The ultrasonic or Doppler reflection method is employed to identify slug flow and detect detailed thickness. Additionally, electrical resistance tomography (ERT) is used to image and confirm the presence of slug flow. A high-speed camera records the slug flow's shape in real time, validating its existence. The ultrasonic reflection method offers high accuracy, with a measurement error rate of less than 1% based on experimental results. The study uses a homogeneous block calibration method to measure slug flow thickness. Graphical results reveal clear differences between the slug flow regime, inner pipe wall, and outer pipe wall, with the first echo of slug flow being easily observable. The accuracy of results is attributed to the combination of FPGA (Field-Programmable-Gate-Array) instruments and measurement methods, showcasing the study's novel approach. This research introduces a new perspective or novelty on slug flow in multiphase flow studies, highlighting an innovative method for detecting film thickness.

Effect of radial inlet distortion on aerodynamic stability in a high load axial flow compressor

Radial inlet distortion induced by boundary layer separation can significantly affect the aerodynamic performance of the compressor. The effect of radial inlet distortion on the flow structure is numerically investigated in a high load axial flow compressor. The inlet boundary is determined by the experimental results at the downstream of the distortion generator. The results reveal that tip distortion leads to a reduction in the stall margin, whereas hub distortion extends it. Radial distortion redistributes the main flow toward undistorted region due to the obstructive effect of lattice ring. The deficit in axial velocity with tip radial distortion causes the rotor to operate at a higher incidence angle near the casing, while hub radial distortion alleviates the tip blade loading. The detailed three-dimensional flow field analysis indicates that increased blade loading with tip distortion shifts the trajectories of both primary and secondary tip leakage flow toward the leading edge, thereby expanding the blockage region. Conversely, hub radial distortion unloads the rotor tip region, thereby reducing the blockage region induced by tip leakage flow. Additionally, with hub distortion, the location of separation line on the blade suction surface moves closer to the leading edge, and the flow separation around the trailing edge is intensified.

Exploiting the determinant factors on the available flexibility area of ADNs at TSO‐DSO interface

AbstractActive distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

Internal flow analysis and design optimization of a membrane oxygenator

Abstract The membrane oxygenator is an essential component in the Extracorporeal Membrane Oxygenation (ECMO) system to offer temporary support to the respiratory system. This study aims to optimize the hemodynamic performance and reduce the thrombosis risk of a membrane oxygenator prototype using computational fluid dynamics. Numerical simulations of steady laminar flow in a full-scale oxygenator prototype (model 1) and two optimized structure Models 2 and 3 at flow rates of 5∼7L/min are carried out using the porous media model. Flow-field-based hydraulic performance indicators and the thrombus risk indicator of the three models are compared extensively. Detailed internal flow analysis revealed that adverse flow states such as insufficient flow circulation in the inlet shunt cone zone, large flow separations at the top corners of heat exchangers, and intensive flow impingement at the exit elbow tube are notably improved in the optimized model 2 and 3. The improvement is more significant at high flow rates. Performance parameters quantitatively validate the effect of optimized configurations. Specifically, at a flow rate of 7L/min, the flow uniformity indexes for the original model at the shunt cone exit increase from 0.884 to 0.923 and 0.890 in the two modified models. The total pressure loss is reduced by over 14%, and maximum wall shear stress is notably reduced from 241.46Pa to 135.9Pa. Additionally, the optimized models exhibited lower thrombus risk. The optimized designs emphasize the importance of smooth transitions between cross-sections and minimizing abrupt changes in flow direction. The employment of flow-field-based parameters allows for the establishment of the relationship between geometric and performance parameters to guide effective design optimization and ensure the safe clinical operation of oxygenator prototypes.

Understanding Operation of Automated Inland Vessels using Process Diagrams

Abstract The functional design of a vessel requires a comprehensive understanding of vessel operations. This understanding can be enhanced through the development of process diagrams. The research presented here emphasizes the importance of developing diagrams that represent detailed process flows and allow the identification of appropriate interfaces between them. On remotely controlled and automated vessels, a number of functions performed by human operators are taken over by systems, requiring a clear understanding of the processes and the instances at which the handover between human and system takes place. This paper presents process diagrams for conventional, remotely controlled, and autonomous inland vessels. Such process diagrams can be used as blueprints for integration of automated systems on inland vessels, because they reveal the process interruptions which occur when the human operators are removed from the vessel.

Data-driven multi-objective optimization of aerodynamics and aeroacoustics in dual Savonius wind turbines using large eddy simulation and machine learning

Savonius rotor is a popular form of vertical-axis wind turbine (VAWT) for small-scale and urban applications because of its straightforward design and self-starting ability. Dual VAWTs present challenges in terms of wake interactions and noise, particularly in urban areas. Optimizing these parameters is essential for future wind energy adoption. This research is the first to analyze how the interaction of wakes from adjacent rotors, combined with a deflector, affects both the aerodynamic performance and noise levels of dual Savonius rotors. Large Eddy Simulation is applied, as it effectively captures detailed turbulent wind flows and their interactions with wind turbines. A multi-objective optimization method combining Machine Learning and Computational Fluid Dynamics (CFD) is developed to optimize rotors for maximum power efficiency and minimum noise, considering their wake interactions with a unique deflector system. First, the influence of geometric parameters on aerodynamics and aeroacoustics characteristics of rotors is analyzed, and the database is generated using Design of Experiment approach. Next, the CFD model is replaced by Artificial Neural Network (ANN) model established for predicting rotor performances. A Multi-Objective Genetic Algorithm method is used to optimize aerodynamics and aeroacoustics characteristics of rotors. Finally, optimal design parameters are identified from the Pareto front using the technique for order of preference by similarity to ideal solution decision-making method. The ANN model demonstrated high accuracy with an RANN2 of 0.995 and 0.971 for the average power coefficient (CP) and overall sound pressure level (OSPL) predictions, respectively. Multi-objective optimization revealed the best configuration of the deflector with bleed jets, improving the average CP up to 57.5% and reducing OSPL to an almost 5.2% compared to the dual rotor case at TSR = 0.8.

Integration of deep learning and computational fluid dynamics for rapid aerodynamic force prediction of compressor blades

The distribution of flow fields around compressor blades is crucial for the performance and reliability of aircraft engines. To effectively obtain aerodynamic loads, this study combines deep learning with computational fluid dynamics (CFD) to develop an efficient aerodynamic prediction model. Initially, CFD is used to acquire detailed flow field data for the blade surface and its surrounding environment. Subsequently, a distance field parameterization method is applied to process the blade geometry, and deep learning models are used to capture the complex relationship between blade geometry and aerodynamic parameters with high precision. The results indicate that the proposed model can predict aerodynamic loads within seconds with a mean squared error of less than 2%. Compared to traditional parameterization methods and other deep learning approaches, this model exhibits higher accuracy. The findings highlight the effectiveness of integrating deep learning with CFD to enhance aerodynamic predictions and provide a promising approach for future aerodynamic modeling research.

Application of a novel stall margin improvement indicator in optimizing casing treatment for a transonic compressor

This paper presents a multi-objective design optimization of axial slot casing treatments in a 1.5 stage transonic compressor. To perform the optimization, an in-house optimization design platform is constructed using genetic algorithm and Kriging surrogate model. The optimization objectives are the peak efficiency at the design rotating speed and the stall margin improvements at both design and off-design rotating speeds. Instead of massive unsteady simulations that are required to accurately predict the stall margin, a stall margin improvement indicator is introduced based on the time-averaged axial momentum budget analysis at the rotor tip region. With this indicator, only one steady simulation is needed to evaluate the stall margin enhancement ability of a new casing treatment design at a given rotating speed. The axial slots are parameterized with a total of eight design parameters. The meridional profiles are described with two B-spline curves to define parameters such as the size, axial position, and shape of the slots. Circumferential parameters include the open area ratio and inclination angle. When the optimization finally converges, different optimal variants are selected from the database and their effects are investigated with both experiments and numerical simulations. The detailed flow fields are analyzed in depth with results from unsteady simulations. The application of the optimal casing treatments exerts a suction and re-injection effect, pushes the interface between tip leakage flow and incoming main flow downstream and reduced flow blockage in the blade tip region. Consequently, the stability of the compressor is significantly improved.

Impact of peripheral venoarterial extracorporeal membrane oxygenation support for heart failure on systemic hemodynamics and aortic blood flow

Peripheral venoarterial extracorporeal membrane oxygenation (VA-ECMO) is an advanced temporary life support system for patients with refractory cardiogenic shock or severe cardiopulmonary failure. However, the reperfusion of oxygenated blood into the arterial system via a peripheral artery will induce substantial hemodynamic changes that might contribute to the development of complications. In this study, we developed two types of computational models to quantify the hemodynamic changes induced by the peripheral VA-ECMO support for systolic heart failure (HF) of various severities. One was a lumped-parameter model used for exploring the optimal workload of extracorporeal membrane oxygenation (ECMO) for a specific severity of HF, whereas the other one was a geometrical multiscale model capable of simulating the detailed flow field in the aorta while accounting for the hemodynamic coupling of VA-ECMO with the cardiovascular system. Numerical results revealed that the retrograde transmission of ECMO-supplied blood flow toward the heart not only considerably inhibited cardiac output but also induced marked flow disturbance and regionally high or oscillatory wall shear stress (WSS) in the aorta that may increase the risk of thrombosis and vascular dysfunction. The major characteristics of flow disturbance and spatial distribution of abnormal WSS were codetermined by the cardiac function and workload of ECMO while less influenced by the morphology of aorta. These findings emphasized the importance of tuning the workload of ECMO based on patient-specific cardiac function to balance the amount of blood oxygenation support by ECMO against the risk of complications associated with hemodynamic abnormalities.

Numerical study on cavitation generation induced by the high-speed jet impact on the water surface

The complex interaction between shock waves and two-phase interfaces can generate cavitation. In this study, the cavitation induced by the high-speed jet impact on the water surface was investigated. The mixture fluid is modeled using the barotropic equation of state under the framework of the two-phase flow model, which can describe the mixture of air, water, and vapor with any proportion. Through constructing a 1D Riemann problem for the impact-induced cavitation phase transition, it indicates that the coupling effect of multiple rarefaction waves emitted from the two-phase interface is responsible for the cavitation phase transition inside the liquid. Then, a 3D (three-dimensional) simulation regarding the impact of a high-speed jet on the water surface was conducted and validated against previous experiments that captured the cavitation phase transition phenomenon in the central region after the jet impact. The 3D simulation results revealed the spatial structure and development process of shock waves in detail. The coupling effects of shock waves and two-phase interfaces generate a ring-shaped rarefaction wave, which develops radially inward and superimposes, resulting in the formation of acorn-shaped cavitation bubble nuclei inside the water. The 3D simulation can provide spatial shock/rarefaction wave structures and internal flow details that have never been obtained in experiments, such as shock generation and propagation, rarefaction wave generation and center convergence, and the internal structure of acorn-shaped cavitation nucleation. Furthermore, the influence of the jet velocity on the cavitation intensity was analyzed, and a quantitative relationship was provided.

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain. This approach provides significant advantages in addressing diverse difficulties in the field of complex fluid dynamics. We thoroughly investigated the design of the model architecture, the optimization of the convergence rate, and the development of computational modules for PINNs. However, efficiently and accurately utilizing PINNs to resolve complex fluid dynamics problems remain an enormous barrier. For instance, rapidly deriving surrogate models for turbulence from known data and accurately characterizing flow details in multiphase flow fields present substantial difficulties. Additionally, the prediction of parameters in multi-physics coupled models, achieving balance across all scales in multiscale modeling, and developing standardized test sets encompassing complex fluid dynamic problems are urgent technical breakthroughs needed. This paper discusses the latest advancements in PINNs and their potential applications in complex fluid dynamics, including turbulence, multiphase flows, multi-field coupled flows, and multiscale flows. Furthermore, we analyze the challenges that PINNs face in addressing these fluid dynamics problems and outline future trends in their growth. Our objective is to enhance the integration of deep learning and complex fluid dynamics, facilitating the resolution of more realistic and complex flow problems.

Deep learning-based reduced order model for three-dimensional unsteady flow using mesh transformation and stitching

Artificial intelligence-based three-dimensional fluid modeling has gained significant attention recently. However, the accuracy of such models is often limited by the preprocessing of irregular flow data. In order to bolster the credibility of near-wall flow prediction, we present a deep learning-based reduced order model for three-dimensional unsteady flow using the transformation and stitching techniques for multi-block structured meshes. To begin with, full-order flow data is provided by numerical simulations that rely on multi-block structured meshes. The mesh transformation technique is applied to convert each structured grid with data into a corresponding uniform and orthogonal grid, which is subsequently stitched and filled. The resulting snapshots in the transformed domain contain accurate flow information for multiple meshes and can be directly fed into a structured neural network without requiring any interpolation operation. Subsequently, a network model based on a fully convolutional neural network is constructed to predict flow dynamics accurately. To validate the strategy's feasibility, the flow around a sphere with Re=300 was investigated, and the results obtained using traditional Cartesian interpolation were used as the baseline for comparison. All the results demonstrate the preservation and accurate prediction of flow details near the wall, with the pressure correlation coefficient on the wall achieving a remarkable value of 0.9997. The stitching scheme that follows the proposed standard can effectively reduce the accumulation of inferring errors. Moreover, the periodic behavior of flow fields can be faithfully predicted during long-term inference. This work demonstrates that the proposed strategy has the advantage of high efficiency while providing accuracy assurance for downstream tasks.

Fuzzy credibility chance-constrained multi-objective optimization for multiple transactions of electricity–gas–carbon under uncertainty

This paper proposes a fuzzy credibility chance-constrained multi-objective optimization model to optimize market transactions in the electricity–gas–carbon sectors under uncertainty. The model aims to maximize the profit of an integrated energy service provider by incorporating a ladder-type carbon trading mechanism, which adjusts carbon prices based on emission levels, and detailed multi-energy flow constraints. To effectively manage uncertainties in electricity, gas, and carbon markets, we derive credibility distributions for uncertain variables and introduce fuzzy credibility chance constraints—tools that assess the likelihood of meeting multiple transactions under uncertainty. The proposed model hedges the risks associated with multiple uncertainties while maximizing the credibility of expected costs, effectively balancing risk and cost. Through simulation analysis on an IEEE 33-node power network and a 32-node heat network, the proposed model achieved a 9.8% reduction in total system cost and an 8.5% reduction in total carbon emissions. Additionally, the model effectively determined credibility levels under different risk preferences, demonstrating its robustness in enhancing electricity–gas–carbon trading and promoting a low-carbon economy. This research provides a novel planning method for formulating trading strategies in multi-energy markets, with significant real-world implications for energy management and environmental sustainability.

Application of Seagull Optimization Algorithm-BP Neural Network in Oil-Water Two-Phase Flow Pattern Forecasting

With the ongoing increase in global energy demand, the significance of innovations in oil exploration and development technologies is rising, especially in relation to the development of unconventional reservoirs. The application of horizontal wells is becoming increasingly important in this particular situation. However, accurately monitoring and analyzing fluids in horizontal wells remains challenging due to the complex and fluctuating flow patterns of oil-water two-phase flow within the wellbore. Several elements, including well slope angle, flow rate, and water content, are involved. This study aimed to explore and develop an effective method for forecasting flow patterns, improving the precision of the dynamic monitoring of oil-water two-phase flow in horizontal wells. By analyzing the flow patterns in different experimental conditions, a predictive model using the SOA-BP neural network was developed, providing a scientific basis for dynamic monitoring in actual production scenarios. Initially, the simulated experiment for oil-water two-phase flow was carried out at room temperature and pressure utilizing a multiphase flow simulator. An optically transparent wellbore, with a diameter comparable to that of a real downhole well, was utilized, and No. 10 industrial white oil and tap water were employed as the experimental fluids. The experiment considered multiple contributing factors, including different well deviation, total flow, and water cut. The flow characteristics of oil and water were observed via visual monitoring and high-definition video, followed by detailed analysis. After collecting the experimental data, flow regimes for various scenarios were classified based on the established theory of oil-water two-phase flow in horizontal wells; then, detailed flow distribution diagrams were drawn. These data and diagrams presented offer a visual representation of the behavioral patterns exhibited by oil-water two-phase flow under varying situations and form the basis for subsequent model training and testing. Subsequently, based on the experimental data, this study combined the Seagull Optimization Algorithm (SOA) with a BP neural network to effectively learn and predict the experimental data. The SOA optimized the weights and biases of the BP neural network, improving the model’s convergence speed and prediction accuracy. Through rigorous training and testing, an oil-water two-phase flow pattern forecasting model was established, effectively predicting flow patterns under different well deviation, total flow, and water cut conditions. Finally, to validate the efficiency of the established model, a total of 15 data points were chosen from a sample well for validation. By comparing the flow patterns predicted by the model with actual logging data, the results indicate that the model’s accuracy in identifying flow pattern was 86.67%. This demonstrates that the flow pattern prediction model based on the SOA-BP neural network achieved a high level of accuracy under different complicated working conditions. This model effectively fulfills the requirements for dynamic monitoring in actual production. This indicates that the SOA-BP neural network-based flow pattern forecasting method is highly valuable due to its practical application value and provides an efficient technical approach for the development of unconventional reservoirs and the dynamic monitoring of horizontal wells in the future.