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A new measurement device, consisting of swirling blades and capsule-shaped throttling elements, is proposed in this study to eliminate typical measurement errors caused by complex flow patterns in gas-liquid flow. The swirling blades are used to transform the complex flow pattern into a forced annular flow. Drawing on the research of existing blockage flow meters and also exploiting the single-phase flow measurement theory, a formula is introduced to measure the phase-separated flow of gas and liquid. The formula requires the pressure ratio, Lockhart-Martinelli number (L-M number), and the gas phase Froude number. The unknown parameters appearing in the formula are fitted through numerical simulation using computational fluid dynamics (CFD), which involves a comprehensive analysis of the flow field inside the device from multiple perspectives, and takes into account the influence of pressure fluctuations. Finally, the measurement model is validated through an experimental error analysis. The results demonstrate that the measurement error can be maintained within ±8% for various flow patterns, including stratified flow, bubble flow, and wave flow. [ABSTRACT FROM AUTHOR]

In this article, considering the factors such as well trajectory and tubing string structures, the transient shut‐in tubing string pressure fluctuation model of ultra‐deep high‐temperature and high‐pressure (hereinafter referred to as HTHP) gas well is established, the propagation of pressure wave along the well depth is obtained, and the effects of different production and shut‐in time on the fluctuating pressure and its variation range are analyzed. Through calculation and analysis, it can be seen that there are differences in pressure waveforms at different well depths. The closer to the bottom of the well, the more delayed the occurrence of water hammer, and the smaller the water hammer pressure value. The larger the production, the greater the pressure wave velocity, and the closer to the wellhead, the more obvious the change. For well X, the pressure wave velocity in the well section below 6500 m varies greatly with the increase of production. When the production is 140 × 104 m3 D−1, the difference of pressure wave velocity between the bottom hole and the wellhead is 105.19 m s−1. After instantaneous shut‐in, the wellhead pressure increases by 3.2% compared with the initial pressure and the bottom hole pressure increases by 0.6%. [ABSTRACT FROM AUTHOR]

From an engineering point of view pulsatile pipe flows, which include the hydrodynamic and acoustic fluctuations, can cause many problems such as vibration, noise and structural fatigue etc. Knowledge of the characteristics of hydrodynamic fluctuations (pseudo sound) and acoustic waves inside pipes particularly those with bends make is crucial to pipeline vibration and noise reduction. Numerical simulation of the pulsatile pipe flows shows challenges especially in curved pipes with different flow modes compounded, and predicting the acoustic fluctuations is even tougher. In this work, the unsteady flows of a straight and curved pipe with a 90° elbow induced by a pulsatile pressure inlet were calculated by Detached Eddy Simulations (DES). The pipe acoustic fields are solved through an acoustic perturbation equation, which takes the time derivative of hydrodynamic pressure as the aeroacoustic source. The characteristics of the flow and sound fields are discussed detailly, and special attention is paid to the influence of the 90° elbow on the curved pipe flow. The results show that the amplitude of hydrodynamic pressure fluctuations decrease almost linearly along the straight pipe, and the 90° elbow has obvious local influence on the flow pressure and velocity especially in the downstream side. The acoustic field is of 1-D standing wave pattern because of the fundamental pulsation frequency lower than the pipe cutoff frequency, and the 90° elbow has only negligible effect on the acoustic field in the flow frequency range. [ABSTRACT FROM AUTHOR]

Pressure fluctuations in a mixing tank can provide valuable information about the existing flow regime within the tank, which in turn influences the degree of mixing that can be achieved. In the present work, we propose a prototype for identifying the flow regime in mechanically stirred tanks equipped with four vertical baffles through the characterization of pressure fluctuations. Our innovative proposal is based on force sensors strategically placed in the baffles of the mixing tank. The signals coming from the sensors are transmitted to an electronic module based on an Arduino UNO development board. In the electronic module, the pressure signals are conditioned, amplified and sent via Bluetooth to a computer. In the computer, the signals can be plotted or stored in an Excel file. In addition, the proposed system includes a moving average filtering and a hierarchical bottom-up clustering analysis that can determine the real-time flow regime (i.e., the Reynolds number, Re) in which the tank was operated during the mixing process. Finally, to demonstrate the versatility of the proposed prototype, experiments were conducted to identify the Reynolds number for different flow regimes (static, laminar, transition and turbulent), i.e., 0 ≤ R e ≤ 42,955. Obtained results were in agreement with the prevailing consensus on the onset and developed from different flow regimes in mechanically stirred tanks. [ABSTRACT FROM AUTHOR]

While repurposing the existing pipelines for hydrogen transport contribute to accelerated development of the full-scale hydrogen economy, the suitability of the aged pipelines should be assessed on their hydrogen embrittlement (HE) susceptibility in high-pressure gaseous hydrogen environments. In this work, a three-dimensional mechanics-hydrogen diffusion coupling finite element model was developed to determine the distribution of hydrogen (H) atoms at corrosion defect on pipelines under pressure fluctuations. Parametric effects, including corrosion defect dimensions (i.e., width, length, and depth) and pressure fluctuating parameters (i.e., cyclic load ratio and loading frequency), were determined. A high stress concentration exists in the longitudinal edge of the defect, while the stress level in the circumferential edge is low. The defect center is associated with the greatest stress and stress variation amplitude. H atoms tend to concentrate at the corrosion defect, especially the defect center, representing the most vulnerable site to initiate hydrogen-induced cracks. Most H atoms reside at the lattice sites, rather than the traps, indicating a limited capacity of the traps to host H atoms as compared to the crystalline lattice sites. With the increase in defect depth and length, both the stress level and stress variation amplitude at the corrosion defect are apparently elevated. More H atoms accumulate at the defect center, increasing the susceptibility to HE. As a comparison, the HE susceptibility of the corroded pipelines decreases with increased corrosion width. As the cyclic load ratio decreases, less H atoms accumulate at the corrosion defect. A decreasing cyclic loading frequency results in decreased stress variations but an increased H atom concentration at the corrosion center. The results provide a base to control pipeline HE by properly adjusting the operating pressure. • Develop a mechanics-hydrogen diffusion coupling model. • Model the hydrogen atom distribution at corrosion defect on aged pipelines repurposed for hydrogen transport. • Determine the effect of pressure fluctuations on hydrogen atom distribution. • Define the effect of corrosion defect dimension on hydrogen atom distribution. [ABSTRACT FROM AUTHOR]

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This paper presents a methodology for estimation of hydrodynamic loads acting on the bottom and at the walls of a stilling basin of a stepped chute with converging walls, based on the pressure measurements at the selected points of a scale model. This is the first study of hydrodynamic loads for this type of structure, and the first one of the loads on the stilling basin walls in general. For selected flow discharges, step heights and hydraulic jump submergence ratio, the hydrodynamic pressures were measured at a significant number of points, providing the spatio-temporal distribution of relevant hydrodynamic loads. The most influential effect proved to be a convergence angle of the chute walls. Based on these measurements, appropriate regression expressions were proposed for predicting hydrodynamic loads. These expressions show good agreement with measurements, offering a reliable tool for the structural design of stepped spillway stilling basins. [ABSTRACT FROM AUTHOR]

Pressure fluctuations in a mixing tank can provide valuable information about the existing flow regime within the tank, which in turn influences the degree of mixing that can be achieved. In the present work, we propose a prototype for identifying the flow regime in mechanically stirred tanks equipped with four vertical baffles through the characterization of pressure fluctuations. Our innovative proposal is based on force sensors strategically placed in the baffles of the mixing tank. The signals coming from the sensors are transmitted to an electronic module based on an Arduino UNO development board. In the electronic module, the pressure signals are conditioned, amplified and sent via Bluetooth to a computer. In the computer, the signals can be plotted or stored in an Excel file. In addition, the proposed system includes a moving average filtering and a hierarchical bottom-up clustering analysis that can determine the real-time flow regime (i.e., the Reynolds number, Re) in which the tank was operated during the mixing process. Finally, to demonstrate the versatility of the proposed prototype, experiments were conducted to identify the Reynolds number for different flow regimes (static, laminar, transition and turbulent), i.e., 0≤Re≤ 42,955. Obtained results were in agreement with the prevailing consensus on the onset and developed from different flow regimes in mechanically stirred tanks.

Unsteady cavitating flows around the INSEAN E779A propeller were studied numerically with the propeller operating downstream in a non-uniform wake. The objective of this work is to predict the cavitating flow along the rotating blades and to establish the relationship between the cavitation evolution and the pressure fluctuations. The analysis used the k - ω shear stress transport (k - ω SST) turbulence model and the ZGB cavitation model. Results using various grids show the solve methods capabilities for a wide range of conditions. The numerical results compare well with experimental data for the transient cavitating flow predictions in a non-uniform wake. Although the predictions slightly underestimate the cloud cavity formation, the evolution of the sheet cavity, tip vortex cavity and side-entrant jets are well reproduced as the blade goes through the wake. Further analyses demonstrate that the side-entrant jet is the primary cause driving the cavity to detach from the blade surface. Finally, cavity volume evolution and the excited pressure data are analyzed to show that the pressure fluctuations are mainly driven by the accelerating cavity volume growth. • Turbulent cavitating flow around a propeller in a non-uniform wake is simulated. • Rotating cavitation dynamics around a propeller is demonstrated. • The relationship between the cavitation evolution and the pressure fluctuations is discussed. [ABSTRACT FROM AUTHOR]

This study presents a new method for predicting the Critical Heat Flux (CHF) approaching condition in subcooled flow boiling based on pressure fluctuation analysis. Through experimental investigations, water flow in an upward direction was conducted within a mini-channel with one side heated to analyze the sequential boiling phenomena, including ONB (Onset of Nucleate Boiling), OFI (Onset of Flow Instability), and CHF. While ONB was determined using the slope of the wall temperature–heat flux, OFI was determined by considering the pressure drop through the channel and outlet pressure fluctuation. By analyzing the inlet pressure fluctuations, a new condition called "the CHF approaching condition" was introduced, which serves as a CHF precursor. The CHF approaching condition was found to be approximately 90% of the CHF value across all experimental scenarios. The proposed method provides an effective tool for predicting and preventing CHF, thereby reducing the potential risk of damage to the heating surface. [ABSTRACT FROM AUTHOR]

Pressure fluctuations in a gas-solid fluidized bed involve much information of the dynamic system. To uncover the value and significance of the pressure fluctuations time series, two meaningful tasks, i.e., the fluidization regime classification and the future state prediction, are investigated using Machine Learning (ML) algorithms, including Back Propagation neural network (BP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), Radial Basis Function network (RBF), Random Forest (RF) and Support Vector Machine (SVM). Findings indicate that the six ML methods rank their performance for the classification task from high to low as: BP ∼ RF > SVM > CNN ∼ LSTM ∼ RBF. For the one-step-ahead future state prediction, the accuracy goes from high to low as: BP ∼ RBF > CNN ∼ LSTM > SVM ∼ RF, whilst for the multistep-ahead prediction the LSTM approach shows the best performance. Additionally, the sampling frequency of pressure time series has significant effects on both tasks. Overall, this study demonstrates the capability of machine learning methods for the value analysis of nonlinear time series and the better operation of gas-solid fluidization systems. [Display omitted] • Two meaningful tasks (classification and prediction) are studied by six ML methods. • pse , min , pp and rms are important features for flow regime classification by ML. • BP method shows best performance on one-step-ahead prediction of future state. • LSTM method shows best performance on multistep-ahead prediction of future state. [ABSTRACT FROM AUTHOR]

The study of the interaction between crossflow and jets has been ongoing for many years, however, the fluctuating pressure generated by jets in crossflow(JICF) is still rarely studied. In this work, the wall pressure fluctuation characteristics of JICF with three jet angles are analyzed in detail by large eddy simulation(LES). It is found that adjusting the injection angle effectively reduces the intensity and range of the fluctuating pressure. In order to reveal the mechanism of this process, the differences in the source terms of the Poisson equation under three cases are studied, and a visualization method for characterizing the source terms of partial Poisson equation by convective velocity is proposed. By analyzing the proper orthogonal decomposition (POD) modes of velocity and pressure, the reasons for the difference in the source terms of the Poisson equation are further revealed, and the noise reduction mechanism of adjusting the jet angle is explained from the perspective of eddy current transport. The results show that the main influence area of the fluctuating pressure is from 2 times the aperture upstream to 20 times the aperture downstream. The low jet angle condition has a lower mean shear-turbulence interaction(MTI) and turbulence-turbulence interaction(TTI) terms, and the MTI1 sub-term is the main contribution component of the wall fluctuating pressure. The POD spatial model shows that the vertical hanging wake vortex structure establishes the pressure relationship between the flow field and the wall surface in the process of providing vorticity for the main stage hairpin vortex. Reducing the jet angle effectively reduces the eddy current transport between the wall and the flow field, so the low jet angle condition has a lower fluctuating pressure level. • Simulation of a jet in cross-flow with different injection angles. • The relationship between vortex motion and pressure fluctuation. • The decomposition of hydrodynamic and acoustic pressures in the near field of the jet in cross-flow. • Poisson equation source term analysis of wall pressure and spatial POD modal analysis of velocity and pressure fields. [ABSTRACT FROM AUTHOR]