Applications in engineering science最新文献

Round metal bars are available at various sizes at commercial markets and in industries. These bars are used in various branches of industry, for instance in automobile, aerospace, power and manufacturing engineering as well as paper industry, printing and packaging industry and construction industry. It is often found that round bars as available in open market are not often straight at various sections along the length. As the round bars usually fulfil responsible functions, their surface irregularities should be precisely measured. In the present investigation, straightness of bars is measured section-wise across the length of the bar in order to understand the actual scenario of production quality. An attempt has been made by choosing various different types of materials of different sizes to ascertain the actual availability of bar sizes at commercial market and also to understand the level of straightness of these bars along the axis of the bar. 3D surface plot is also used here to find out whether such bars as commercially available can be utilised for precision industrial applications or not. Finite element analysis is also performed to compare the deformations of different solid round metal bars. Results show that commercially available aluminium bars are showing nearly straight having less deformations compared to bars of other materials.

In this paper which is the continuation of the isothermal viscoelastic model presented in Gomez-Constante and Palade (2023), we incorporate temperature changes into our model. To achieve this, we present a temperature dependent Helmholtz potential from where the model will be derived using the idea of evolving natural configurations. To simplify the analysis, we assume that the temperature and the invariants of deformation are decoupled so the Helmholtz potential can be expressed as the product of two independent functions. The model thus derived is consistent with fundamental thermodynamical postulates and constrains. To show its qualitative behavior we chose to compare the isothermal model in Gomez-Constante and Palade (2023) with this paper non-isothermal version of the model and show how they behave in the classical Couette flow between infinite parallel plates and analyze their differences. We also present a simple extensional flow simulation for the non-isothermal version of the model to complete the analysis. Ideas towards the following steps towards a generalization of the model are also presented and discussed.

It is known in the literature that in the case of compressible fluids, higher values than the fluid temperature are displayed on temperature sensors, partly due to the accumulation point flow. Depending on the operating point, this can be several degrees Celsius. One possibility of consideration is the so-called recovery factor. There are various theoretical approaches and models that have been transferred on the basis of measurement on the flat plate. In some cases, the recovery factor is only defined as a function of the Prandtl number. A test bench has been developed that can be used to determine the recovery factor of cylindrical, quere-flowed temperature sensors. Up to Ma numbers of about 0.5, sensors with different diameters, and thus different Reynolds numbers, were measured and the recovery factors were calculated. There is a pronounced dependence on both the Ma number and the Re number and the recovery factor is not constant. An empirical equation based on the measurement results is given, with which the recovery factor can be determined as a function of the fluid, the Mach- and Reynolds-numbers and thus a more accurate calculation of the real fluid temperature is possible.

It is well-known that a Tesla valve allows fluids to flow unidirectionally without moving parts; however, how Tesla valves interact with active matters and the potential applications of Tesla valves in biology remain largely unexplored. Here, we present a computational study on the potential use of Tesla valves for filtering and sorting microscale active swimmers such as bacteria. We investigated the behavior of microscale swimmers passing through the Tesla valve at different linear and angular velocities using numerical simulations and quantified the diodicity of the Tesla valve for active swimmers. Our results demonstrate that the Tesla valve can effectively filter and sort microscale swimmers based on their swimming behavior. The findings of this study suggest that Tesla valves could have potential applications in microscale sorting and chromatography, with significant implications for biomedical and environmental engineering.

This paper investigates the viability of stabilizing expansive brown clays in the Northern region of Jordan using a local bituminous oil shale ash from El-Lajjun, Jordan. A comprehensive experimental programme was designed to i. examine the impact of oil shale ash dose by weight on the strength gain. ii. Evaluate oil shale ash stabilized clays' consolidation and volume change behaviour during loading and unloading. Results revealed that adding 20 % bituminous OSA remarkably improved expansive brown clays' engineering properties and mechanical behaviour. An increase of 140 % in the unconfined compressive strength and a reduction of 19.3 % in the permeability coefficient was obtained at 20 % OSA. Also, the Unconfined Compressive Strength tests revealed that the strength of OSA-treated expansive clays is significantly affected by the curing time. Increases of 35.1 % and 65.7 % in the unconfined compressive strength were achieved after 7 days and 21 days of curing, respectively. Furthermore, the addition of OSA showed an effect on compressibility behaviour, reducing settlement. Regarding the consolidation parameters, the inclusion of 20 % OSA reduced the compression index (Cc) and swelling index (Cs) by 42.67 % and 72.6 %, respectively.

This research aims to propose a numerical methodology that employs a non-increasing sequence to approximate the solution for a large class of ordinary differential equations like the ones described in complex non-linear heat transfer through porous fins. It also aims to compare this proposed methodology with an earlier one using a non-decreasing sequence of elements and to provide an upper-bound estimation for the error. The comparison between the non-increasing and non-decreasing sequences showed excellent agreement when applied to an example of convection and radiation in porous fins.

This research aimed to determine the performance of bamboo-reinforced concrete precast panels (BRCPP) subjected to axial compressive loads, validated using numerical simulations. Axial compressive load capacity, crack, and failure patterns were investigated and compared with steel-reinforced concrete precast panels (SRCPP) as a control. Eight panels were made with details of four BRCPP and SRCPP, having dimensions of 1200 × 400 × 50 mm³ each. The four SRCPPs consisted of two panels, produced in laboratories and two manufactured by local companies. The variations in bamboo reinforcement spacing were 150 mm and 180 mm, while the gap between steels was 180 mm, and panels were tested under axial compressive load. The results showed that the axial compressive load capacity of BRCPP with a bamboo reinforcement size of 12 × 12 mm² at a distance of 150 mm was sufficient to meet the specifications of SRCPP panels produced by the local industry. Based on experimental, theoretical, and numerical simulation analysis, the performance of BRCPP showed suitability, making it a considerable choice, specifically for precast panels. The special advantage of BRCPP encompassed eco-friendliness and sustainability.

Cracks and cavities belong to two basic forms of damage to the concrete structure, which may reduce the load-bearing capacity and tightness of the structure and lead to failures and catastrophes in construction structures. One of the most prevalent faults in concrete constructions is cracking, which, if left unchecked, can greatly diminish the longevity of the building. For the past few years, scientists have worked to understand the complex mechanics behind concrete cracking. It has been found that the primary initiators of plastic cracking include surface finishing, capillary action, bleeding, evaporation, and settlement of solid particles. A number of techniques and measurements have been developed to measure the influence of the various processes that result in concrete shrinkage and cracking. Cracking due to shrinkage and plastic segregation are now also referred to as plastic cracking. Numerous cracking prevention techniques have been suggested, such as fogging, using fewer particles, and using fibres. This cutting-edge study updates the significant growth of research on the subject to what is currently considered cutting-edge and state-of-the-art.

Background

The determination of area tumor presents the chief challenge in brain tumor therapy and assessment. Without ionizing radiation, the medical Magnetic Resonance Imaging (MRI) tool has appeared as an essential diagnostic technique for brain cancers. Using 2D MRI images, manual segmentation of brain tumor size is a slow, error-prone task which the performance is extremely depends on operator's experience. In that respect, a consistent totally automated segmentation approach for the brain tumor detection is effectively needed to get a proficient dimension of the tumor size.

Results

In this paper, an effusively computerized scheme for brain tumor detection is proposed by the use of deep convolutional networks. The proposed method was appraised on Brain Tumor Image Segmentation (BRATS 2020) datasets, including 1352 affected by brain tumor.

Conclusion

Cross-validation technique has revealed that our process can attain talented segmentation competently reaching higher accuracy compared to other previous studies.

The shear capacity of stud shear connectors was the main parameter affecting the mechanical performance of steel-concrete composite structures. In this paper, three artificial neural networks (ANN) were developed to evaluate the shear capacity of stud shear connectors in steel-high strength concrete composite structures. The models was applied to high strength concrete, covering the compressive strength of concrete in 61.19 MPa∼200 MPa. Based on the correlation analysis, the main influential parameters, including the compressive strength of concrete, the diameter, height, yield strength, number, and pretension force of stud shear connectors, were selected as input variables to the models. The proposed models were trained and tested with 100-group test data gathered from previous studies. By comparing with existing empirical models, it was proved that the proposed Elman network and RBF network had high applicability and reliability for predicting the shear capacity of stud shear connectors in steel-high strength concrete composite structures. Subsequently the parametric sensitive analysis was carried out based on the BP network.