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Most support surfaces in comfort applications and sporting equipment are made from pressure-relieving foam such as viscoelastic polyurethane. However, for some users, foam is not the best material as it acts as a thermal insulator and it may not offer adequate postural support. The additive manufacturing of such surfaces and equipment may alleviate these issues, but material and design investigation is needed to optimize the printing parameters for use in pressure relief applications. This study aims to assess the ability of an additive manufactured flexible polymer to perform similarly to a viscoelastic foam for use in comfort applications.

Three-dimensional (3D) printed samples of thermoplastic polyurethane (TPU) are tested in uniaxial compression with four different infill patterns and varying infill percentage. The behaviours of the samples are compared to a viscoelastic polyurethane foam used in various comfort applications.

Results indicate that TPU experiences an increase in strength with an increasing infill percentage. Findings from the study suggest that infill pattern impacts the compressive response of 3D printed material, with two-dimensional patterns inducing an elasto-plastic buckling of the cell walls in TPU depending on infill percentage. Such buckling may not be a beneficial property for comfort applications. Based on the results, the authors suggest printing from TPU with a low-density 3D infill, such as 5% gyroid.

Several common infill patterns are characterised in compression in this work, suggesting the importance of infill choices when 3D printing end-use products and design for manufacturing.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

Particles bed binding by selective cement activation (SCA) method is a computer-aided manufacturing (CAM) technique used to produce cementitious elements. A computer-aided design file is sliced to generate G-codes before printing. This paper aims to study the effect of key input parameters for slicer software on the final properties of printed products.

The one factor at a time (OFAT) methodology is used to investigate the impact of selected parameters on the final properties of printed specimens, and the causes for the variations in outcomes of each variable are discussed.

Finer aggregates can generate a more compact layer, resulting in a denser product with higher strength. Fluid pressure is directly determined by voxel rate (r_V); however, high pressures enable better fluid penetration control for fortified products; for extreme r_Vs, residual voids in the interfaces between successive layers and single-line primitives impair mechanical strength. It was understood that printhead movement along the orientation of the parts in the powder bed improved the mechanical properties.

The design of experiment (DOE) method assesses the influence of process parameters on various input printing variables at the same time. As the resources are limited, a fractional factorial plan is carried out on a subset of a full factorial design; hence, providing physical interpretation behind changes in each factor is difficult. OFAT aids in analyzing the effect of a change in one factor on output while all other parameters are kept constant. The results assist engineers in properly considering the influence of variable variations for future DOE designs.

The purpose of this paper is to show that the droplet impact phenomenon is important for the advancement of industrial technologies in many fields such as spray cooling and ink jet printing. Droplet bouncing on the nonwetting surfaces is a special phenomenon in the impact process which has attracted lots of attention.

In this work, the authors fabricated two kinds of representative nonwetting surfaces including superhydrophobic surfaces (SHS) and a slippery liquid-infused porous surface (SLIPS) with advanced UV laser processing.

The droplet bouncing behavior on the two kinds of nonwetting surfaces were compared in the experiments. The results indicate that the increasing Weber number enlarges the maximum droplet spreading diameter and raises the droplet bounce height but has no effect on contact time.

In addition, the authors find that the topological SHS and SLIPS with the laser-processed microwedge groove array produce asymmetric droplet bouncing with opposite offset direction. Microdroplets can be continuously transported without any additional driving force on such a topological SLIPS. The promising method for manipulating droplets has potential applications for the droplet-based microfluidic platforms.

This study aims to analyse the non-linear losses of a porous media (stack) composed by parallel plates and inserted in a resonator tube in oscillatory flows by proposing numerical correlations between pressure gradient and velocity.

The numerical correlations origin from computational fluid dynamics simulations, conducted at the microscopic scale, in which three fluid channels representing the porous media are taken into account. More specifically, for a specific frequency and stack porosity, the oscillating pressure input is varied, and the velocity and the pressure-drop are post-processed in the frequency domain (Fast Fourier Transform analysis).

It emerges that the viscous component of pressure drop follows a quadratic trend with respect to velocity inside the stack, while the inertial component is linear also at high-velocity regimes. Furthermore, the non-linear coefficient b of the correlation ax + bx² (related to the Forchheimer coefficient) is discovered to be dependent on frequency. The largest value of the b is found at low frequencies as the fluid particle displacement is comparable to the stack length. Furthermore, the lower the porosity the higher the Forchheimer term because the velocity gradients at the stack geometrical discontinuities are more pronounced.

The main novelty of this work is that, for the first time, non-linear losses of a parallel plate stack are investigated from a macroscopic point of view and summarised into a non-linear correlation, similar to the steady-state and well-known Darcy–Forchheimer law. The main difference is that it considers the frequency dependence of both Darcy and Forchheimer terms. The results can be used to enhance the analysis and design of thermoacoustic devices, which use the kind of stacks studied in the present work.

The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train, and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence, the study analyzes aerodynamic parameters with multi-objective optimization design.

The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics. Then the modified vehicle modeling function (VMF) parameterization method and surface discretization method are adopted for the parametric design of the nose. For the 12 key design parameters extracted, combined with computational fluid dynamics (CFD), support vector machine (SVR) model and multi-objective particle swarm optimization (MPSO) algorithm, the multi-objective aerodynamic optimization design of high-speed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint. The engineering improvement and wind tunnel test verification of the optimized shape are done.

Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train. The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.

Compared with the original shape, the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%, and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%, respectively, after adopting the optimized shape modified according to engineering design requirements.

Analyses of teaching and learning in higher education are increasingly being based on a distinction between surface and deep learning. This distinction is helpful for investigating approaches used by teachers as well as student preferences for teaching and learning. Surface learning places an emphasis on memorizing facts and information as well as the relatively passive reproduction of content. In contrast, deep learning involves an intention to understand, the critical assessment of content and relating new information to past knowledge in meaningful ways. There has been an assumption that in the U.A.E. there is an orientation to surface learning in schools and higher education. To examine this assumption, an adaptation of questionnaires used with Western students (the Approaches to Study Skills Inventory for Students) was used with a small sample of ZU students. There are limitations in the use of this procedure and difficulties in interpreting the results. However, the results suggest that ZU students show strong beliefs and preference for deep learning approaches in addition to surface learning approaches. This finding is consistent with evidence obtained from student responses to assessment tasks, where there was evidence of deep learning. It was concluded that learning outcomes for ZU students could be enhanced by employing deep learning approaches to teaching and learning.

This study aims to carry out an investigation of design approaches that should be used for the design of unconventional, innovative transmission system for construction yards to privilege a smooth behaviour efficiency, and the use of innovative production techniques. Results are quite surprising, as with a proper method it is possible to demonstrate that a cycloidal drive with Wolfrom topology should be an interesting solution for the proposed application.

With a functional approach, also considering materials and specifications related to the investigated application, it is possible to demonstrate that possible optimal solutions should be quite different respect to the ones that can be suggested with a conventional approach. In particular for proposed applications constraints related to encumbrances, the choice of new material has led to the innovative unconventional choice of a Wolfrom cycloidal speed reducer.

Provided solution is innovative respect current state of the art for machine currently used in construction yards: in terms of adopted transmission layout; in terms of chosen materials, resulting in an innovative solution.

Current research has strong implications on the adoption of polimeric materials for the construction of reliable transmission for harsh industrial environment as the proposed case study (concrete mixer for construction yard).

Proposed transmission system is absolutely original and innovative respect current state of art also considering proposed materials and consequently production methods. This is an example of transmission designed to be built with polymeric materials by optimizing chosen topology respect to chosen material.

This paper proposes the combination of rapid prototyping and physical modelling as a set-based concept evaluation method in the early stage of new product development.

The concept evaluation method is applied in a case study of a new metal additive manufacturing process for aluminium, where a set of four extruder concepts has been modelled and evaluated. Rapid prototyping was used to produce plastic models of the different designs, and plasticine feedstock material was used to physically model the metal flow during operation. Finally, the selected concept has been verified in full-scale for processing of aluminium feedstock material.

The proposed method led to several valuable insights on critical factors that were unknown at the outset of the development project. Overall, these insights enabled concept exploration and concept selection that led to a substantially better solution than the original design.

This method can be applied for other projects where numerical approaches are not applicable or capable, and where the costs or time required for producing full-scale prototypes are high.

Employing this method can enable a more thorough exploration of the design space, allowing new solutions to be discovered.

The proposed method allows a design team to test and evaluate multiple concepts at lower cost and time than what is usually required to produce full-scale prototypes. It is, therefore, concluded to be a valuable design strategy for the early development stages of complex products or technologies.

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.