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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.

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.

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.

This study aims on a broad review of Concrete's Rheological Properties. The Concrete is a commonly used engineering material because of its exquisite mechanical interpretation, but the addition of constituent amounts has significant effects on the concrete’s fresh properties. The workability of the concrete mixture is a short-term property, but it is anticipated to affect the concrete’s long-term property.

In this review, the concrete and workability definition; concrete’s rheology models like Bingham model, thixotropy model, H-B model and modified Bingham model; obtained rheological parameters of concrete; the effect of constituent’s rheological properties, which includes cement and aggregates; and the concrete’s rheological properties such as consistency, mobility, compatibility, workability and stability were studied in detail.

Also, this review study has detailed the constituents and concrete’s rheological properties effects. Moreover, it exhibits the relationship between yield stress and plastic viscosity in concrete’s rheological behavior. Hence, several methods have been reviewed, and performance has been noted. In that, the abrasion resistance concrete has attained the maximum compressive strength of 73.6 Mpa; the thixotropy approach has gained the lowest plastic viscosity at 22 Pa.s; and the model coaxial cylinder has recorded the lowest stress rate at 8 Pa.

This paper especially describes the possible strategies to constrain improper prediction of concrete’s rheological properties that make the workability and rheological behavior prediction simpler and more accurate. From this, future guidelines can afford for prediction of concrete rheological behavior by implementing novel enhancing numerical techniques and exploring the finest process to evaluate the workability.

This paper aims to study the fluid flow and heat transfer within the Casson nanofluid confined between disk and cone both rotating with distinct velocities. For a comprehensive investigation, two distinct nano-size particles, namely, silicon dioxide and silicon carbide, are submerged in ethanol taken as the base fluid.

This paper explores the disk and cone contraption mostly encountered for viscosity measurement in various industrial applications such as lubrication industry, hydraulic brakes, pharmaceutical industry, petroleum and gas industry and chemical industry.

It is worth mentioning here that the radially varying temperature profile at the disk surface is taken into the account. The effect of prominent emerging parameters on velocity fields and temperature distribution are studied graphically, while bar graphs are drawn to examine the physical quantities of industrial interest such as surface drag force and heat transfer rate at disk and cone.

To the best of the authors’ knowledge, no study in literature exists that discusses the thermal enhancement of nano-fluidic transport confined between disk and cone both rotating with distinct angular velocities with heat transfer.

Social media has emerged as a prominent platform for marketers and brands to disseminate brand-related information. This study aims to investigate the impact of color congruence between themes and background on marketing effectiveness, focusing specifically on Instagram.

A laboratory experiment was conducted to investigate how color congruence between themes and background in brand posts influences flow and aesthetic experience, subsequently affecting marketing communication parameters such as brand attitude, visit intention, and eWOM on Instagram. Moreover, Adidas Originals was selected as the focal brand, and blue and white color was chosen as the primary color palette for the experimental material.

This study demonstrates that color congruence, regardless of brand layout or post, significantly influences flow and aesthetic experience, subsequently affecting marketing effectiveness.

This study contributes to the theoretical understanding of congruence theory and social media marketing, providing valuable insights for brands to enhance their communication through photographs and effectively manage their official Instagram accounts.

This study aims to integrate the social cognitive theory and flow theory to examine how e-skills self-efficacy (ESSE) and trust-building self-efficacy (TBSE) impact work-related flow in remote work. It also explores how work-related flow (WOLF) mediates the connection between these factors and employee resilience (ER) in the context of the growing shift toward remote work.

The study tested an empirical mediation model with 535 remote workers from Indian organizations that adopted remote work either before or during the pandemic. Hypotheses were tested using Hayes’ PROCESS macro.

The study confirmed the direct influence of ESSE and TBSE on WOLF. Moreover, a mediation analysis demonstrated that WOLF significantly mediates the association linking ESSE, TBSE and ER.

This study, evaluating experienced remote employees in the quaternary sector, offers data-driven insights for informed organizational decisions. These findings aims to guide HR and training departments in formulating recruitment and training strategies, emphasizing digital skill development to enhance knowledge and skills effectively.

This study highlights the need for remote workers and organizations to be aware of the challenges of remote work. It is among the first to explore the link between ESSE, TBSE and ER, addressing psychological factor, i.e. WOLF, often overlooked in organizational contexts.

This study aims to explore the direct and indirect effects of sales opportunism and sales conflict as well as of non-economic and economic satisfaction in business-to-business (B2B) sales relationships. This understanding offers B2B buyers enhanced knowledge of sales business expectations towards sustainable business relationships in the future.

Through self-administered questionnaires, data were obtained from 237 sales or marketing managers/directors of small- and medium-sized companies across industries in Spain, who were randomly contacted via LinkedIn. The multivariate analysis of measurement and structural models was based on IBM SPSS Amos 27.

The study confirms that sales opportunism positively affects sales conflict. Moreover, sales opportunism is negatively associated with non-economic sales satisfaction, whereas non-economic sales satisfaction is positively associated with economic sales satisfaction. Consequently, if all associates are pleased with the relationship and the gains it can provide, a long-standing orientation can be achieved.

The study expands existing theory on seller–buyer relationships in a B2B context. It contextualises direct and indirect relationships between two antecedents (sales opportunism and sales conflict) and two postcedents (economic sales satisfaction and non-economic sales satisfaction) in sales business–buyer settings.

The study guides buyers in B2B relationships towards an improved understanding of how sales businesses perceive opportunism and conflict (as negative precursors) to impact non-economic satisfaction and how it can influence economic satisfaction.

Most studies explore B2B relationship building from the perspective of the buyer, thereby creating a shortfall in developing an understanding of all partner expectations in B2B relational intent. Moreover, the measurement of satisfaction as a multidimensional construct secured the integration of non-economic satisfaction and economic satisfaction within a single model allowing the constructs measured in this study to be holistically assessed.

Drawing upon social exchange theory and self-determination theory (SDT), this study investigates the influence of psychological empowerment (PE) on innovative work behavior (IWB) among faculty members in Iraqi institutions of higher education. Additionally, this study aims to explore the role of inclusive leadership (IL) as a mediator between PE and IWB. Academic institutions are vital contributors to economic expansion through their research and the development of highly qualified graduate employees.

To test the developed hypotheses, a quantitative approach was used with structural equation modeling via partial least squares (PLS-SEM) and bootstrapping estimation. The analysis was conducted using 497 responses.

The results of this study demonstrate that PE has a positive impact on IWB among faculty members. Furthermore, IL mediates the relationship between PE and IWB.

This study contributes to the existing literature by highlighting the significance of IL in encouraging employee participation in organizational change. The findings also suggest that IL plays a crucial mediating role in the relationship between PE and IWB among faculty members in Iraqi institutions of higher education.