Mixed displacement-pressure-phase field framework for finite strain fracture of nearly incompressible hyperelastic materials

The favored phase field method (PFM) has encountered challenges in the finite strain fracture modeling of nearly or truly incompressible hyperelastic materials. We identified that the underlying cause lies in the innate contradiction between incompressibility and smeared crack opening. Drawing on the stiffness-degradation idea in PFM, we resolved this contradiction through loosening incompressible constraint of the damaged phase without affecting the incompressibility of intact material. By modifying the perturbed Lagrangian approach, we derived a novel mixed formulation. In numerical aspects, the finite element discretization uses the classical Q1/P0 and high-order P2/P1 schemes, respectively. To ease the mesh distortion at large strains, an adaptive mesh deletion technology is also developed. The validity and robustness of the proposed mixed framework are corroborated by four representative numerical examples. By comparing the performance of Q1/P0 and P2/P1, we conclude that the Q1/P0 formulation is a better choice for finite strain fracture in nearly incompressible cases. Moreover, the numerical examples also show that the combination of the proposed framework and methodology has vast potential in simulating complex peeling and tearing problem

Planetary gearboxes performance degradation analysis and prediction

Gearbox is the core component of various machines and vehicles, so it is necessary to monitor the performance degradation of gearbox for improving the reliability of mechanical equipment and vehicle. The use of the health condition monitoring on core components such as gearbox can reduce the economic losses due to its failure In this paper, some parameters are extracted from data of run-to-failure test, for example, kurtosis, RMS and energy. The performance degradation of the gearbox is analyzed and predicted by the monitoring the parameters. In this paper, some forecasting methods such as ARMA model, moving average line and feed forward neural network are compared by calculate absolute error

Rolling bearing fault diagnosis using modified K-means cluster analysis

Rolling bearing is essential component of most rotating machinery, fault diagnosis of rolling bearing is significant for enhance the reliability of mechanical device. It is becoming a hot research topic recent years. There are some disadvantages for existing methods, like computing complex, long spending time and so on. In order to overcome these shortcomings of existing methods, this paper present a modified K-means cluster analysis which is used to bearing fault diagnostics. And the data of Case Western Reserve University are used to validate effectiveness of the proposed method

Modeling of Complex Life Cycle Prediction Based on Cell Division

Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction

Preparation and properties of PVA/SS/AgNPs composite nanofibres

144-148Polyvinyl alcohol (PVA), PVA/silk sericin (SS) and PVA/SS/AgNPs (nano-silver) solutions have been prepared, and the nanofibres are produced by electrospinning technology. The nanofibres are then tested using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The results show that the morphology of 7-11 wt% PVA nanofibres is fine and smooth. With the increase in SS and AgNPs contents, the average diameter of PVA/SS and PVA/SS/AgNPs composite nanofibres is increased. The FTIR spectra and XRD patterns of PVA/SS/AgNPs composite nanofibres with different mass ratios have the similar regular curves. The intensity of the infrared peak of PVA/SS/AgNPs composite nanofibres weakens at 837, 1087, 1648 and 2914 cm-1 with the increase in AgNPs; the intensity of the diffraction peak gradually increases at 13.1o and weakens at 19.09 °. This may be due to the reason that Ag interacts with PVA and SS molecules. The findings are of great significance for the development of nano-scale antibacterial fibres

Preparation and properties of PVA/SS/AgNPs composite nanofibres

Polyvinyl alcohol (PVA), PVA/silk sericin (SS) and PVA/SS/AgNPs (nano-silver) solutions have been prepared, and the nanofibres are produced by electrospinning technology. The nanofibres are then tested using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The results show that the morphology of 7-11 wt% PVA nanofibres is fine and smooth. With the increase in SS and AgNPs contents, the average diameter of PVA/SS and PVA/SS/AgNPs composite nanofibres is increased. The FTIR spectra and XRD patterns of PVA/SS/AgNPs composite nanofibres with different mass ratios have the similar regular curves. The intensity of the infrared peak of PVA/SS/AgNPs composite nanofibres weakens at 837, 1087, 1648 and 2914 cm-1 with the increase in AgNPs; the intensity of the diffraction peak gradually increases at 13.1o and weakens at 19.09 °. This may be due to the reason that Ag interacts with PVA and SS molecules. The findings are of great significance for the development of nano-scale antibacterial fibres

Radial basic function-based analysis of dynamic deflection of invisible layer profiles in the flexible pavement

This study proposes a radial basic function (RBF) neural network model which can simulate the dynamic deflection process of invisible individual layers in the full-scale flexible pavement along with an increase of load repetitions. The training and testing data is formed through empirical and conceptual judgment on the final profiles of the four pavement layers in the test. The independent and dependent variables are defined as the known top and invisible layer deflections respectively. Then, the RBF model produces the numerical results between layer dynamic deflections. Finally, several parameters are suggested to study the response of the invisible pavement layers. The RBF model shows that the implicit dynamic relationship between pavement layer deflections could be modeled by a static state of the flexible pavement. Furthermore, some working features of the pavement might be revealed from its dynamic response

Fossilized skin reveals coevolution with feathers and metabolism in feathered dinosaurs and early birds

Feathers are remarkable evolutionary innovations that are associated with complex adaptations of the skin in modern birds. Fossilised feathers in non-avian dinosaurs and basal birds provide insights into feather evolution, but how associated integumentary adaptations evolved is unclear. Here we report the discovery of fossil skin, preserved with remarkable nanoscale fidelity, in three non-avian maniraptoran dinosaurs and a basal bird from the Cretaceous Jehol biota (China). The skin comprises patches of desquamating epidermal corneocytes that preserve a cytoskeletal array of helically coiled α-keratin tonofibrils. This structure confirms that basal birds and non-avian dinosaurs shed small epidermal flakes as in modern mammals and birds, but structural differences imply that these Cretaceous taxa had lower body heat production than modern birds. Feathered epidermis acquired many, but not all, anatomically modern attributes close to the base of the Maniraptora by the Middle Jurassic

Site selection of LNG terminal based on cloud matter element model and principal component analysis

With the development of liquefied natural gas(LNG) port, as one of the crucial LNG port sitting process, the LNG terminal site’s condition assessment method has always received attention from experts, scholars concern more and more about the method’s practicality and reliability. In the traditional condition assessment method, due to the characteristics of the complex and extensive factors in the comprehensive assessment of the LNG terminal site, the assessment system is not comprehensive enough, or the assessment is too complex, the indexes are not easy to quantify, such problems are emerging. In view of the above reasons, the principal component analysis(PCA) method is used to transform the multi-indicators that affect the comparison of terminal sites into a few comprehensive indicators. A comprehensive evaluation model of the LNG terminal site based on cloud matter element theory and subjective and objective comprehensive weighting method was constructed. By the subjective and objective comprehensive weighting method, the comprehensive weight of each index is determined and the LNG terminal site comprehensive assessment standard cloud element model is constructed with the combination of cloud model and matter-element theory. The cloud matter-element correlation function is established to determine the degree of association between the matter element to be evaluated and the standard cloud matter element model. In order to eliminate random errors and improve the credibility of the results, the algorithm is used for multiple calculations and analysis to achieve the purpose of simultaneously giving the evaluation results and coefficients of credible degree. Finally, the reliability and rationality of the method are verified by an example