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X80 pipeline steel is widely used in oil and gas pipelines because of its excellent strength, toughness, and corrosion resistance. It is welded via gas metal arc welding (GMAW), risking high cold crack sensitivities. There is a certain relationship between the joint hardness and cold crack sensitivity of welded joints; thus, predicting the joint hardness is necessary. Considering the inefficiency of welding experiments and the complexity of welding parameters, we designed a set of processes from temperature field analysis to microstructure prediction and finally hardness prediction. Firstly, we calculated the thermal cycle curve during welding through multi-layer welding numerical simulation using the finite element method (FEM). Afterwards, BP neural networks were used to predict the cooling rates in the temperature interval that ferrite nuclears and grows. Introducing the cooling rates to the Leblond function, the ferrite fraction of the joint was given. Based on the predicted ferrite fraction, mapping relationships between joint hardness and the joint ferrite fraction were built using BP neural networks. The results shows that the error during phase fraction prediction is less than 8%, and during joint hardness prediction, it is less than 5%.

During drilling in ultra-deep wells of petroleum engineering, the challenges associated with the dynamics of gas and drilling fluid in large annular spaces with diameters exceeding 190 mm, which significantly impacts pressure variations, flow stability, and operational safety. To explore these complex flow behaviors, numerical simulations were conducted based on Computational Fluid Dynamics. A total of 105 simulations were performed to analyze flow behavior under various combinations of air and water velocities. Superficial liquid velocities ranged from 0.001-1 m·s −1 , while superficial gas velocities varied from 0.01-30 m·s −1 . The simulations identified four distinct flow regimes, bubble flow, cap-slug flow, churn flow, and annular flow. Notably, slug flow was absent in the large annulus. The findings highlighted the critical influence of annular diameter on flow regime transitions. The larger diameter resulted in a reduced cross-sectional void fraction, diminished surface tension effects, and an increased gravitational impact, which inhibited the formation of a stable gas-liquid interface. A flow pattern transition chart was developed based on the drift flux model, identifying critical void fractions for flow pattern transitions, a void fraction of 0.3 indicated the transition from bubble flow to cap-slug flow, while a value of 0.51 marked the transition from cap-slug flow to churn flow. The insights gained from this research enhance the understanding of gas-liquid flow dynamics in large annuli, contributing to the development of more accurate predictive models for flow behavior. This knowledge is essential for optimizing wellbore design and management, ultimately improving well control strategies during ultra-deep drilling operations.

Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

The frictional pressure drop of air-water two-phase flow is a crucial parameter in hydrodynamic calculations. Solving for precisely calculating frictional pressure drop in it remains a significant challenge due to the various factors affecting two-phase flow characteristic. Currently, various scholars have proposed multiple frictional pressure drop prediction models of two-phase flow. Typically, these models exhibit great errors under low void fraction. To more accurately predict the frictional pressure drop of two-phase flow under low void fraction, this paper experimentally investigated the flow characteristics of air-water two-phase flow in a horizontal tube. The flow rates for water and air are 3-6 kg/s and 0.0004-0.003 kg/s, respectively. The void fraction ranged from 0 to 0.07%. The experimental data and flow patterns of two-phase flow were analyzed, and these data was compared with commonly used frictional pressure drop prediction model of two-phase flow. The results indicated that: (1) Within the scope of this paper, two-phase flow pattern observed include bubbly flow and slug flow. With changes in the void fraction, the number of bubbles and the length of slugs have undergo alterations; (2) All three commonly used prediction models underestimated the frictional pressure drop under low void fraction. Therefore, Considering more influencing factors to improve the accuracy of prediction model is the next research direction. This research has showed the limitations of existing prediction models and emphasized the importance of research on frictional pressure drop of two-phase flow.

Bubble columns are used in many different industrial applications, and their design and characterisation have always been very complex. In recent years, the use of Computational Fluid Dynamics (CFD) has become very popular in the field of multiphase flows, with the final goal of developing a predictive tool that can track the complex dynamic phenomena occurring in these types of reactors. For this reason, we present a detailed literature review on the numerical simulation of two-phase bubble columns. First, after a brief introduction to bubble column technology and flow regimes, we discuss the state-of-the-art modelling approaches, presenting the models describing the momentum exchange between the phases (i.e., drag, lift, turbulent dispersion, wall lubrication, and virtual mass forces), Bubble-Induced Turbulence (BIT), and bubble coalescence and breakup, along with an overview of the Population Balance Model (PBM). Second, we present different numerical studies from the literature highlighting different model settings, performance levels, and limitations. In addition, we provide the errors between numerical predictions and experimental results concerning global (gas holdup) and local (void fraction and liquid velocity) flow properties. Finally, we outline the major issues to be solved in future studies.

The output of the latent heat storage devices (LHSDs), based on some phase change materials (PCMs), depends upon the thermophysical properties of the phase change material used. In this study, a paraffin-based nanofluid, blended with aluminum oxide (Al2O3) nanoparticles, is used as PCM for performance evaluation. A three-dimensional (3D) numerical model of regenerative type shell-and-tube LHSD is prepared using COMSOL Multiphysics® 4.3a software to estimate the percentage of melt and the average temperature of the analyzed nanofluids. The results of this study are in close agreement with those reported in the literature, thereby ensuring the validation of the numerically predicted results. The effects of adding the nanoparticles on the rate of melting, as well as solidification and rate of stored/liberated energy, are studied. The results revealed that, by adding 10% nanoparticles of Al2O3, the melting rate of pure-paraffin-based LHSD improved by about 2.25 times. In addition, the rate of solidification was enhanced by 1.8 times. On the other hand, the heat of fusion and specific heat capacities were reduced, which, in turn, reduced the latent and sensible heat-storing capabilities. From the outcomes of the present research, it can be inferred that combining LHSD with a solar water heater may be used in technologies such as biogas generation.

In this work, we investigate six helicity amplitudes of the four-body B ( s ) → ( π π ) ( K K ¯ ) decays via an angular analysis in the perturbative QCD (PQCD) approach. The π π invariant mass spectrum is dominated by the vector resonance ρ ( 770 ) together with scalar resonance f 0 ( 980 ) , while the vector resonance ϕ ( 1020 ) and scalar resonance f 0 ( 980 ) are expected to contribute in the K K ¯ invariant mass range. We extract the two-body branching ratios B ( B ( s ) → ρ ϕ ) from the corresponding four-body decays B ( s ) → ρ ϕ → ( π π ) ( K K ¯ ) based on the narrow width approximation. The predicted B ( B s 0 → ρ ϕ ) agrees well with the current experimental data within errors. The longitudinal polarization fractions of the B ( s ) → ρ ϕ decays are found to be as large as 90 % , basically consistent with the previous two-body predictions within uncertainties. In addition to the direct CP asymmetries, the triple-product asymmetries (TPAs) originating from the interference among various helicity amplitudes are also presented for the first time. Since the B s 0 → ρ 0 ϕ → ( π + π - ) ( K + K - ) decay is induced by both tree and penguin operators, the values of the A dir CP and A T-true 1 are calculated to be ( 21 . 8 - 3.3 + 2.7 ) % and ( - 10 . 23 - 1.56 + 1.73 ) % respectively. While for pure penguin decays B 0 → ρ 0 ϕ → ( π + π - ) ( K + K - ) and B + → ρ + ϕ → ( π + π 0 ) ( K + K - ) , both the direct CP asymmetries and “true” TPAs are naturally expected to be zero in the standard model (SM) due to the absence of the weak phase difference. The “fake” TPAs requiring no weak phase difference are usually none zero for all considered decay channels. The sizable “fake” A T-fake 1 = ( - 20 . 92 - 2.80 + 6.26 ) % of the B 0 → ρ 0 ϕ → ( π + π - ) ( K + K - ) decay is predicted in the PQCD approach, which provides valuable information on the final-state interactions. The above predictions can be tested by the future LHCb and Belle-II experiments.

The relationship between microstructure features and mechanical properties plays an important role in the design of materials and improvement of properties. Hole expansion capacity plays a fundamental role in defining the formability of metal sheets. Due to the complexity of the experimental procedure of testing hole expansion capacity, where many influencing factors contribute to the resulting values, the relationship between microstructure features and hole expansion capacity and the complexity of this relation is not yet fully understood. In the present study, an experimental dataset containing the phase constituents of 55 microstructures as well as corresponding properties, such as hole expansion capacity and yield strength, is collected from the literature. Statistical analysis of these data is conducted with the focus on hole expansion capacity in relation to individual phases, combinations of phases and number of phases. In addition, different machine learning methods contribute to the prediction of hole expansion capacity based on both phase fractions and chemical content. Deep learning gives the best prediction accuracy of hole expansion capacity based on phase fractions and chemical composition. Meanwhile, the influence of different microstructure features on hole expansion capacity is revealed. Graphical abstract

Purpose Understanding the interaction of turbulence and cavitation is an essential step towards better controlling the cavitation phenomenon. The purpose of this paper is to bring out the efficacy of different modelling approaches to predict turbulence and cavitation-induced phase changes. Design/methodology/approach This paper compares the dynamic cavitation (DCM) and Schnerr–Sauer models. Also, the effects of different modelling methods for turbulence, unsteady Reynolds-averaged Navier–Stokes (URANS) and detached eddy simulations (DES) are also brought out. Numerical predictions of internal flow through a venturi are compared with experimental results from the literature. Findings The improved predictive capability of cavitating structures by DCM is brought out clearly. The temporal variation of the cavity size and velocity illustrates the involvement of re-entrant jet in cavity shedding. From the vapour fraction contours and the attached cavity length, it is found that the formation of the re-entrant jet is stronger in DES results compared with that by URANS. Variation of pressure, velocity, void fraction and the mass transfer rate at cavity shedding and collapse regions are presented. Wavelet analysis is used to capture the shedding frequency and also the corresponding occurrence of features of cavity collapse. Originality/value Based on the performance, computational time and resource requirements, this paper shows that the combination of DES and DCM is the most suitable option for predicting turbulent-cavitating flows.

Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown. This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP). Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches. The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82. The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.