Search results

Cushioning is a useful material property applicable for a range of applications from medical devices to personal protective equipment. The current ability to apply cushioning in a product context is limited by the appropriateness of available materials, with polyurethane foams being the current gold standard material. The purpose of this study is to investigate additively manufactured flexible printing of scaffold structures as an alternative.

In this study, this study investigates triply periodic minimal surface (TPMS) structures, including Gyroid, Diamond and Schwarz P formed in thermoplastic polyurethane (TPU), as a possible alternative. Each TPMS structure was fabricated using material extrusion additive manufacturing and evaluated to ASTM mechanical testing standard for polymers. This study focuses attention to TPMS structures fabricated for a fixed unit cell size of 10 mm and examine the compressive properties for changes in the scaffold porosity for samples fabricated in TPU with a shore hardness of 63A and 90A.

It was discovered that for increased porosity there was a measured reduction in the load required to deform the scaffold. Additionally, a complex relationship between the shore hardness and the stiffness of a structure. It was highlighted that through the adjustment of porosity, the compressive strength required to deform the scaffolds to a point of densification could be controlled and predicted with high repeatability.

The results indicate the ability to tailor the scaffold design parameters using both 63A and 90A TPU material, to mimic the loading properties of common polyurethane foams. The use of these structures indicates a next generation of tailored cushioning using additive manufacturing techniques by tailoring both geometry and porosity to loading and compressive strengths.

This study aims to study the formation mechanism of micro-arc oxidation (MAO) coating on AZ31 magnesium alloy and how the annealing process affects its corrosion resistance.

This study involved immersion experiments, electrochemical experiments and slow strain rate tensile experiments, along with scanning electron microscopy, optical microscopy observation and X-ray diffraction analysis.

The findings suggest that annealing treatment can refine the grain size of AZ31 magnesium alloy to an average of 6.9 µm at 300°C. The change in grain size leads to a change in conductivity, which affects the performance of MAO coatings. The MAO coating obtained by annealing the substrate at 300°C has smaller pores and porosity, resulting in better adhesion and wear resistance.

The coating acts as a barrier to prevent corrosive substances from entering the substrate. However, the smaller pores and porosity reduce the channels for the corrosive solution to pass through the coating. When the coating cracks or falls off, the corrosive medium and substrate come into direct contact. Smaller and uniform grains have better corrosion resistance.

This paper aims to obtain a calculation method by hand without iteration.

This paper adopts strains as known quantities to solve the internal forces and deformations of the section, simplifies the deflection curve of the column and obtains nomograms that can calculate the bearing capacity and reinforcement of circular reinforced concrete (RC) columns by hand.

Nomograms include five variables: mechanical reinforcement ratio, relative normal force, dimensionless bending moment, slenderness ratio and ultimate dimensionless curvature. Nomograms corresponding to all classes of concrete have been drawn, and their dimensionless form makes them widely applicable. The calculation results of nomograms are compared and analysed with numerical calculation results, and the difference is within 5%, meeting the engineering requirements.

Calculating the bearing capacity of compression bending components requires considering second-order effects. Therefore, the calculation of the bearing capacity of circular RC columns requires iterative calculation, as it includes dual nonlinearity of material and geometry, and the two are coupled with each other. To calculate the bearing capacity of the section adopting ordinary concrete, it is necessary to solve the transcendental equation iteratively. For high-strength concrete, it can only be solved by numerical integration. A fast calculation method by hand is proposed in this paper.

Ferrofluids are aqueous or non-aqueous solutions with colloidal particles of iron oxide nanoparticles with high magnetic characteristics. Their magnetic characteristics enable them to be controlled and manipulated when ferrofluids are exposed to magnetic fields. This study aims to inspect the features of unsteady stagnation point flow (SPF) and heat flux from the surface by incorporating ferromagnetic particles through a special kind of second-grade fluid (SGF) across a movable sheet with a nonlinear heat source/sink and magnetic field effect. The mass suction/injection and stretching/shrinking boundary conditions are also inspected to calculate the fine points of the features of multiple solutions.

The leading equations that govern the ferrofluid flow are reduced to a group of ordinary differential equations by applying similarity variables. The converted equations are numerically solved through the bvp4c solver. Afterward, study and discussion are carried out to examine the different physical parameters of the characteristics of nanofluid flow and thermal properties.

Multiple solutions are revealed to happen for situations of unsteadiness, shrinking as well as stretching sheets. Greater suction slows the separation of the boundary layers and causes the critical values to expand. The region where the multiple solutions appear is observed to expand with increasing values of the magnetic, non-Newtonian and suction parameters. Moreover, the fluid velocity significantly uplifts while the temperature declines due to the suction parameter.

The novelty of the work is to deliberate the impact of mass suction/injection on the unsteady SPF through the special second-grade ferrofluids across a movable sheet with an erratic heat source/sink. The confirmed results provide a very good consistency with the accepted papers. Previous studies have not yet fully explored the entire analysis of the proposed model.

Reynolds-averaged Navier–Stokes (RANS) models often perform poorly in shock/turbulence interaction regions, resulting in excessive wall heat load and incorrect representation of the separation length in shockwave/turbulent boundary layer interactions. The authors suggest that this can be traced back to inadequate numerical treatment of the inviscid fluxes. The purpose of this study is an extension to the well-known Harten, Lax, van Leer, Einfeldt (HLLE) Riemann solver to overcome this issue.

It explicitly takes into account the broadening of waves due to the averaging procedure, which adds numerical dissipation and reduces excessive turbulence production across shocks. The scheme is derived based on the HLLE equations, and it is tested against three numerical experiments.

Sod’s shock tube case shows that the scheme succeeds in reducing turbulence amplification across shocks. A shock-free turbulent flat plate boundary layer indicates that smooth flow at moderate turbulence intensity is largely unaffected by the scheme. A shock/turbulent boundary layer interaction case with higher turbulence intensity shows that the added numerical dissipation can, however, impair the wall heat flux distribution.

The proposed scheme is motivated by implicit large eddy simulations that use numerical dissipation as subgrid-scale model. Introducing physical aspects of turbulence into the numerical treatment for RANS simulations is a novel approach.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

The purpose of this paper is to showcase the utilization of the magnetohydrodynamics-microrotating Casson’s nanofluid flow model (MHD-MRCNFM) in examining the impact of an inclined magnetic field within a porous medium on a nonlinear stretching plate. This investigation is conducted by using neural networking techniques, specifically using neural networks-backpropagated with the Levenberg–Marquardt scheme (NN-BLMS).

The initial nonlinear coupled PDEs system that represented the MRCNFM is transformed into an analogous nonlinear ODEs system by the adoption of similarity variables. The reference data set is created by varying important MHD-MRCNFM parameters using the renowned Lobatto IIIA solver. The numerical reference data are used in validation, testing and training sets to locate and analyze the estimated outcome of the created NN-LMA and its comparison with the corresponding reference solution. With mean squared error curves, error histogram analysis and a regression index, better performance is consistently demonstrated. Mu is a controller that controls the complete training process, and the NN-BLMS mainly concentrates on the higher precision of nonlinear systems.

The peculiar behavior of the appropriate physical parameters on nondimensional shapes is demonstrated and explored via sketches and tables. For escalating amounts of inclination angle and Brinkman number, a viable entropy profile is accomplished. The angular velocity curve grows as the rotation viscosity and surface condition factors rise. The dominance of friction-induced irreversibility is observed in the vicinity of the sheet, whereas in the farthest region, the situation is reversed with heat transfer playing a more significant role in causing irreversibilities.

To improve the efficiency of any thermodynamic system, it is essential to identify and track the sources of irreversible heat losses. Therefore, the authors analyze both flow phenomena and heat transport, with a particular focus on evaluating the generation of entropy within the system.

This study aims to examine the relationships among fear of COVID-19, job stress, job commitment and intention to quit. It also investigates the boundary conditions of the impact of fear of COVID-19 on job stress and intention to quit, a research gap that has not been addressed yet.

This research focused on full-time frontline hotel employees who have been working in four- and five-star hotels in Ho Chi Minh City, Vietnam. A pilot test was performed before collecting formal data. The survey was conducted face-to-face on site so that any potential confusion could be clarified right away. 487 valid responses were analyzed using SPSS 28 and SmartPLS 4.

The majority of hypotheses were supported, with the results suggested that supervisor support contributes to reducing the tendency of hotel employees to quit their job and their job stress. Besides, government support is necessary to make staff feel secure during the pandemic.

This study contributes to pointing out central priorities in making decisions by hotel managers when facing crises. Managers need to focus more on measures to help their employees feel secure and should be available for guidance and feedback when nedeed. Additionally, supportive policies from the government, especially financial support, can provide hotel employees with resources they need to maintain a basic level of living in the face of a severe drop in their income. The study provides the hotel industry not only in Vietnam but also in countries with comparable settings with strategies to cope with unexpected events.

Research on intention to quit a job has mainly focused on a complex interplay of internal factors. However, the influence of fear of COVID-19 on intention to quit a job has not been studied in the context of Vietnamese hotel industry yet. During the COVID-19 pandemic, a number of hotels in Vietnam had to close due to a lack of visitors, which had a negative impact on human resources. Accordingly, fear, stress, commitment and intention to quit a job are the issues faced by staff.

This study aims to investigate the relationship between high-commitment human resource management (HCHRM) practices and emigration intentions in a developing country context. It further examines the mediating roles of perceived organizational support (POS) and job satisfaction in this relationship.

Using the survey method, data were collected online from 407 employees. Structural equation modeling (SEM) in Amos v. 23 was conducted to scrutinize the structural relationships among the variables.

The study revealed that HCHRM practices do not directly impact emigration intentions. However, they do significantly influence POS, which, in turn, has a positive effect on job satisfaction. Consequently, HCHRM practices indirectly affect emigration intentions through the serial mediation of POS and job satisfaction.

While this study provides valuable insights into the intricate dynamics of HCHRM practices, organizational support, job satisfaction and emigration intentions, it has certain limitations, such as its specific focus on Kosovo and its reliance on cross-sectional data. Future research could explore these relationships in diverse settings and use longitudinal designs for a more profound understanding.

To the best of the authors’ knowledge, this study represents the first empirical investigation into the connection between HCHRM practices and emigration intentions within a developing country context. It underscores the significance of considering not only specific HRM practices but also broader contextual factors and mediating mechanisms, shedding light on how HCHRM practices influence employee intentions to emigrate. The findings provide a unique perspective for organizations and policymakers dealing with emigration challenges in developing countries.