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The asymmetry of the velocity profile caused by geometric deformation, complex turbulent motion and other factors must be considered to effectively use the flowmeter on any section. This study aims to better capture the flow field information and establish a model to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics.

The deep learning method is used to establish the model of multipath linear velocity fitting profile average velocity. A total of 480 groups of data are taken for training and validation, with ten integer velocity flow fields from 1 m/s to 10 m/s. Finally, accuracy research with relative error as standard is carried out.

The numerical experiment yielded the following promising results: the maximum relative error is approximately 1%, and in the majority of cases, the relative error is significantly lower than 1%. These results demonstrate that it surpasses the classical optimization algorithm Equal Tab (5%) and the traditional artificial neural network (3%) in the same scenario. In contrast with the previous research on a fixed profile, we focus on all the velocity profiles of a certain length for the first time, which can expand the application scope of a multipath ultrasonic flowmeter and promote the research on flow measurement in any section.

This work proposes to divide the flow field of double elbow into three categories with certain common characteristics to better capture the flow field information and establish a model to predict the profile velocity.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

In a coaxial ultrasonic flow sensor (UFS), wall thickness is a vital parameter of the measurement tube, especially those with small inner diameters. The paper aims to investigate the influence of wall thickness on the transient signal characteristics in an UFS.

First, the problem was researched experimentally using a series of measurement tubes with different wall thicknesses. Second, a finite element method–based model in the time domain was established to validate the experimental results and further discussion. Finally, the plane wave assumption and oblique incident theory were used to analyze the wave propagation in the tube, and an idea of wave packet superposition was proposed to reveal the mechanism of the influence of wall thickness.

Both experimental and simulated results showed that the signal amplitude decreased periodically as the wall thickness increased, and the corresponding waveform varied dramatically. Based on the analysis of wave propagation in the measurement tube, a formula concerning the phase difference between wave packets was derived to characterize the signal variation.

This paper provides a new and explicit explanation of the influence of wall thickness on the transient signal in a co-axial UFS. Both experimental and simulated results were presented, and the mechanism was clearly described.

Powder bed fusion processes are common due to their ability to build complex components without the need for complex tooling. While additive manufacturing has gained increased interest in industry, academia and government, flaws are often still generated during the deposition process. Many flaws can be avoided through careful processing parameter selections including laser power, hatch spacing, spot size and shielding gas flow rate. The purpose of this paper is to study the effect of shielding gas flow on vapor plume behavior and on final deposition quality. The goal is to understand more fully how each parameter affects the plume and deposition process.

A filtered-photodiode based sensor was mounted onto a commercial EOS M280 machine to observed plume emissions. Three sets of single tracks were printed, each with one of three gas flow rates (nominal, 75% nominal and 50% nominal). Each set contained single-track beads deposited atop printed pedestals to ensure a steady-state, representative build environment. Each track had a set power and speed combination which covered the typical range of processing parameters. After deposition, coupons were cross-sectioned and bead width and depth were measured. Finally, bead geometry was compared to optical emissions originating in the plume.

The results show that decreasing gas flow rate, increasing laser power or increasing scan speed led to increased optical emissions. Furthermore, decreasing the gas cross-flow speed led to wider and shallower melt pools.

To the best of the authors’ knowledge, this paper is among the first to present a relationship among laser parameters (laser power, scan speed), gas flow speed, plume emissions and bead geometry using high-speed in situ data in a commercial machine. This study proposes that scattering and attenuation from the plume are responsible for deviations in physical geometry.

Sources highlight that insufficient skills of site supervisors considerably influence the progress of many construction projects in numerous countries. This study intends to identify the crucial supervisory competencies that influence the effectiveness of building project operations in the context of developing countries.

The crucial construction site supervisory competencies were qualitatively identified through a comprehensive literature survey and a series of expert interviews with the use of thematic analysis approaches. A questionnaire survey was then carried out among 154 building project firms to quantify the impacts of the competencies on the effectiveness of project tasks with the use of the relative importance index approach. Additionally, industry-consultative meetings were held using problem-focused communication strategies to scrutinise the necessary actions.

Overall, 22 cognitive elements and 24 skills/abilities of supervisors were determined as being critical according to their impact values, where the site supervisors cognitive domains in construction planning and construction materials were determined as the top-ranking competencies in the list, with their manual skills/abilities in labour management and labour performance evaluation. Accordingly, a group of key competency outcomes were produced for the considerations in developing new site supervisory training components. Relevant statistical analysis results and the industry consultative outcomes substantiated the validity and dependability of the overall results.

Although the study's focus was to site supervision procedures used in Sri Lankan building construction projects, the overall findings/outcomes might be put to the test in related situations in other emerging industries in other countries.

The study has constructed a base that shows how the significant site supervisory competencies influence the effectiveness of building construction operations, contributing to making a big difference in the methods of reskilling/upskilling in the industry associated with construction labour, supervision, efficiency management and productivity enhancement.

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.

An improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.

Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.

The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This paper aims to investigate the influence of nonuniform gas speed across the build area on the melt pool depth during laser powder bed fusion. This study focuses on whether a nonuniform gas speed is a source of process variation within an individual build.

Parts with many single-track laser scans were printed and characterized in different locations across the build area coupled with corresponding gas speed profile measurements. Cross-sectional melt pool depth, width and area are compared against build location/gas speed profiles, scan direction and laser scan speed.

This study shows that the melt pool depth of single-track laser scans produced on parts are highly variable. Despite this, trends were found showing a reduction in melt pool depth for slow laser scan speeds on the build platform near the inlet nozzle and when the laser scans are parallel to the gas flow direction.

A unique data set of single-track laser scan cross-sectional melt pool measurements and gas speed measurements was generated to assess process variation associated with nonuniform gas speed. Additionally, a novel sample design was used to increase the number of single-track tests per part, which is widely applicable to studying process variation across the build area.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

The purpose of this paper is to investigate the impact of near-wall treatment approaches, which are crucial parameters in predicting the flow characteristics of open channels, and the influence of different vegetation covers in different layers.

Ansys Fluent, a computational fluid dynamics software, was used to calculate the flow and turbulence characteristics using a three-dimensional, turbulent (k-e realizable), incompressible and steady-flow assumption, along with various near-wall treatment approaches (standard, scalable, non-equilibrium and enhanced) in the vegetated channel. The numerical study was validated concerning an experimental study conducted in the existing literature.

The numerical model successfully predicted experimental results with relative error rates below 10%. It was determined that nonequilibrium wall functions exhibited the highest predictive success in experiment Run 1, standard wall functions in experiment Run 2 and enhanced wall treatments in experiment Run 3. This study has found that plant growth significantly alters open channel flow. In the contact zones, the velocities and the eddy viscosity are low, while in the free zones they are high. On the other hand, the turbulence kinetic energy and turbulence eddy dissipation are maximum at the solid–liquid interface, while they are minimum at free zones.

This is the first study, to the best of the author’s knowledge, concerning the performance of different near-wall treatment approaches on the prediction of vegetation-covered open channel flow characteristics. And this study provides valuable insights to improve the hydraulic performance of open-channel systems.