The development of the best properties of corn husk fibre (CHF) as a reinforcing material from coconut shell powder–polyester composite is an extremely interesting subject. This research focuses on ...characterising the mechanical properties and morphology of polyester/coconut shell powder composites filled with CHF. The variations of coconut shell powder (CSP) contents are 5% and 10%, and the corn husk fibre contents are 10%, 15%, 20% and 30% (vol%). The mixture is blended and poured into a composite mould using the hot press technique. The tensile and flexural strengths of the mixture were investigated, and the fracture surface of the composite was also analysed by scanning electron microscopy (SEM). The tensile strength of polyester/5 vol % CSP decreased dramatically from 22.459 MPa to 16.955 MPa. However, flexural strength increased from 24.233 MPa to 45.844 MPa when the corn husk fibre (CHF) content increased. Further, all polyester–10% of CSP composites have higher flexural strength values than the polyester–5% of CSP counterparts. Conversely, the tensile strength properties decrease at 10%–15% CHF content and then increase at 20%–30% CHF contents. This outcome is due to the tighter and stronger interfacial bonding between CHF–CSP–polyester and the decreasing number of fibres–pull-outs. Fractured morphology with SEM shows poor interfacial bonding between CHF–CSP–polyester and fibre pullout on composites.
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•The characterisation of a novel CSP/CHF polyester composite is investigated.•Tensile and flexural strength are investigated.•The fracture surface of the composite was analysed by SEM.•10% of CSP-filled CHF/polyester composite will be economical and satisfactory in terms of flexural strength.
The aim of this study is to analyze the number of deaths due to COVID-19 for Europe and China. For this purpose, we proposed a novel three parametric model named as Exponentiatedtransformation of ...Gumbel Type-II (ETGT-II) for modeling the two data sets of death cases due to COVID-19. Specific statistical attributes are derived and analyzed along with moments and associated measures, moments generating functions, uncertainty measures, complete/incomplete moments, survival function, quantile function and hazard function, etc. Additionally, model parameters are estimated by utilizing maximum likelihood method and Bayesian paradigm. To examine efficiency of the ETGT-II model a simulation analysis is performed. Finally, using the data sets of death cases of COVID-19 of Europe and China to show adaptability of suggested model. The results reveal that it may fit better than other well-known models.
•Comparison of four IVS methods to predict flow in two distinct watersheds using ANNs.•Two IVS methods are model-free and two are model-based, improvements are proposed for the model-based ...methods.•Performance comparison between models with and without IVS, the termination criteria used, and predefined number of inputs.•Input usefulness is not binary; the correct number of selected inputs is dependent on the desired model complexity.
Artificial neural networks (ANNs) are increasingly used for flood forecasting. The performance of these models relies on the selection of appropriate inputs. However, Input Variable Selection (IVS) is typically performed using expert knowledge or simple linear methods. This research compares and evaluates four IVS methods including two model-free methods: partial correlation (PC), partial mutual information (PMI), and two novel model-based methods: an improved input omission (IO), and improved combined neural pathway strength (CNPS). A comprehensive comparison of performance efficacy for multiple IVS methods has not been published in literature before. Each method is used for daily and hourly lead times in the Bow and Don Rivers (both in Canada), respectively. These watersheds represent different hydrological systems and were selected to highlight the performance of the IVS methods under differing conditions. This research determines that the proposed CNPS produces the strongest performing ANNs based on the robustness of the inputs selected, comparison to other IVS methods, and models developed without IVS. Additionally, this research demonstrates that standard termination criteria do not reliably identify the optimum number of inputs for the ANNs and using a model-based optimization of inputs is recommended. As a result, it is recommended that the number of inputs be determined using a systematic approach, where each input selection is informed by an IVS-based input ranking, rather than a predefined termination criterion. Lastly, this research demonstrates that input usefulness is not binary concept; the correct number of selected inputs is dependant on the desired model complexity, instead of an arbitrarily selected IVS termination criteria.
•Post-pandemic era requires supply chains to be technologically driven.•Recognized the crucial drivers for the adoption 77of IoT in supply chain management.•Identified causal links of crucial drivers ...using rough strength relation analysis.•Implications for sustainable development goals are presented.
As the post-COVID-19 pandemic era begins, the supply chain operating environment has transformed and faced disruptions. The Internet of Things (IoT) has emerged as one of the expedient technologies in the information technology domain, which solves the issues of traditional supply chain management (SCM) by providing resiliency, flexibility, and traceability. Despite the perceived advantages of IoT, it remains unknown what drivers are essential to adopt IoT in SCM. To achieve this aim, an integrated approach combining rough set theory and decision-making trial and evaluation laboratory (DEMATEL), that is rough strength relation analysis (RSRA) method, has been used to identify and investigate drivers of IoT adoption in SCM where drivers were analyzed according to their comparative importance based on expert opinions from industrial and academic backgrounds. A total of 14 drivers have been identified from the extensive literature review on previous IoT and SCM associated works, which were further analyzed to determine the most important drivers. The results show that “Efficient logistics systems”, “Business knowledge acumen”, and “Information safety assurance” are among the three most predominant driving factors. The findings may help practitioners implement IoT in supply chains to deal with disruptions, risks and vulnerabilities in the post-pandemic era.
Diesel engine connecting rod bolt withstands pre-tightening force and tensile load in working process,its strength and stiffness have great effect on the performance. An assembly model of the ...connecting rod which had considered the detail of threaded portion was built,and the strength of those bolts were analyzed with the finite element method. On this basis,the result post-processing program was complied,and the fatigue strength of the bolts was assessed by drawing the bolt material Goodman fatigue curve. The results indicate the rigidity and static strength and the fatigue strength are all within the allowable range; the maximum stress is located in the first level thread,and the most dangerous place of fatigue damage is located in the bolt head.While the static strength and rigidity meet the requirement,using fatigue strength analysis method can find the failure position correctly. Such analysis provides reference for design of bolt.
The subject of the study are short sandwich beams with special structure of the core (honeycomb). The beam is made using additive manufacturing technology. The values of elastic modules vary along ...beam. The linear shear deformation theory – the “zig-zag” hypothesis is assumed for plane cross section. The analytical model of beam is based on this hypothesis. The deflection of beam is analytically calculated. Moreover, the deflection of beam is experimentally determined on a test stand. The results of these two methods are compared.
•The impact of the preceding layer stacking on rear filament winding is considered.•An improved cubic spline function approach and a novel parabola method were proposed.•The strength analysis of gas ...cylinders based on the parabola method was carried out.•The parabola method adapts well to cylinders with varied sizes and ply schemes.
Due to the characteristics of changing angle and thickness, predicting the outer contour of the vessel dome has been a challenging task in composite pressure vessel design. The impact of preceding layer fiber stacking on subsequent filament winding has never been considered in existing methods of dome thickness calculation. So we developed an improved cubic spline function approach and a novel parabola method that takes preceding layer fiber stacking into account. The example findings show that the improved cubic spline function approach has a weakness in that the selection of several critical parameters is not clearly described and heavily reliant on actual engineering knowledge. The parabola approach described in our study is not only simple in design, but also adapts well to dome contour of gas cylinders with varied sizes and ply schemes. Moreover, the strength analysis of composite gas cylinders was carried out by finite element method. The results show high consistency with hydraulic burst test results, which clearly demonstrates that the parabola method we proposed can effectively realize the precise modeling of composite pressure vessels and will be very beneficial in the design of composite pressure vessels.
•Meso-model can simulate the behavior of composites from a preparation view.•Kernel density estimation describes the distribution of parameters more accurately.•The effect of the stitching process in ...the split type of tip shroud is enormous.•The joints of tip shroud structure are prone to crack initiation.
This paper considers the impact of the random distribution of yarn properties on the mechanical and failure behavior of ceramic matrix composites (CMCs) structures and proposes an integrated analysis method based on preform-structure. Tensile tests of CMCs tip shroud specimens were carried out, and the digital image correlation technique was used to record the deformation of the specimens in real-time during loading. Based on the actual structure, a preform model of the CMCs tip shroud was established, and the kernel density estimation method was used to obtain the distribution of the yarn cross-sectional area. The random distribution of the cross-sectional area is equivalent to material properties, simulating the dispersion of the performance of CMCs. The progressive damage method simulates the failure mode during the specimen's loading process. The peak load error is 1.5 %, and the failure modes such as transverse shear damage, stitching yarn fracture, and interlaminar separation were successfully predicted.
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