Experimental Study on Dry Friction Damping Characteristics of the Steam Turbine Blade Material with Nonconforming Contacts

An experiment system has been established to study the dry friction damping dynamic characteristics of the steam turbine blade material 1Cr13. The friction dynamic characteristics of the specimens with nonconforming contact surfaces are measured under different parameters. The experiment results are compared with that of the macroslip hysteresis model and the Mindlin microslip friction model in detail. The results show that the experimental result of the tangential contact stiffness is in good agreement with that of the theory result based on the fractal theory and the Hertz contact theory by Jiang et al., 2009. The dimensionless equivalent stiffness and equivalent damping obtained by the macroslip hysteresis model agree well with the experimental results when relative motion is relatively large. However, the results of the macroslip hysteresis model differ a lot from the experimental results when relative motion is relatively small. Compared with the macroslip hysteresis model, the Mindlin microslip friction model can predict the dimensionless equivalent stiffness and equivalent damping accurately during the whole measurement range. The linear regularities of dimensionless equivalent stiffness and equivalent damping are obtained, which decrease the difficulty of building the vibration analysis model of the blade with sufficient accuracy

Preparation of Aligned Ultra-long and Diameter-controlled Silicon Oxide Nanotubes by Plasma Enhanced Chemical Vapor Deposition Using Electrospun PVP Nanofiber Template

Well-aligned and suspended polyvinyl pyrrolidone (PVP) nanofibers with 8 mm in length were obtained by electrospinning. Using the aligned suspended PVP nanofibers array as template, aligned ultra-long silicon oxide (SiOx) nanotubes with very high aspect ratios have been prepared by plasma-enhanced chemical vapor deposition (PECVD) process. The inner diameter (20–200 nm) and wall thickness (12–90 nm) of tubes were controlled, respectively, by baking the electrospun nanofibers and by coating time without sacrificing the orientation degree and the length of arrays. The micro-PL spectrum of SiOx nanotubes shows a strong blue–green emission with a peak at about 514 nm accompanied by two shoulders around 415 and 624 nm. The blue–green emission is caused by the defects in the nanotubes

Study on Impeller Fracture Model Based on Vibration Characteristics and Fractal Theory

During the operation of centrifugal compressor, failure easily occurs in the presence of complicated external forces. The failure process characterizes with strong nonlinearity, and hence it is difficult to be described by conventional methods. In this paper, firstly, the cracks in different positions are described using crack fractal theory. The basic failure modes of the impeller are summarized. Secondly, a three-dimensional finite element model of the impeller is constructed. Then the von Mises stress under the centrifugal force is calculated, and the corresponding impeller failure process is simulated by “element life and death technology” in ANSYS. Finally, the impeller failure mechanism is analyzed. It can be found that the static stress is not the main cause of the impeller failure, and the dynamic characteristics of the impeller are not perfect because of the pitch vibration modes which appeared in the investigated frequency range. Meanwhile, the natural frequency of the impeller also cannot avoid the frequency of the excitation force

Fractal Theory and Contact Dynamics Modeling Vibration Characteristics of Damping Blade

The contact surface structure of dry friction damper is complicate, irregular, and self-similar. In this paper, contact surface structure is described with the fractal theory and damping blade is simplified as 2-DOF cantilever beam model with lumped masses. By changing the position of the damper, lacing and shroud structure are separately simulated to study vibration absorption effect of damping blade. The results show that both shroud structure and lacing could not only dissipate energy but also change stiffness of blade. Under the same condition of normal pressure and contact surface, the damping effect of lacing is stronger than that of shroud structure. Meanwhile, the effect on changing blade stiffness of shroud structure is stronger than that of lacing. This paper proposed that there is at least one position of the blade, at which the damper dissipates the most vibration energy during a vibration cycle

Efficient depositing aluminum alloy using thick strips through severe deformation-based friction rolling additive manufacturing: processing, microstructure, and mechanical properties

Solid-state additive manufacturing is a promising technology for production of high-strength aluminum alloy components. However, most solid-state AM technologies use thin foils or powder as feeding materials, limiting the deposition thickness of each layer and thus restricting deposition efficiency. This study is the first to utilize 2-mm-thick 5052-H32 aluminum alloy strips as feed material for a severe deformation-based friction rolling additive manufacturing (FRAM) method. In this study, the effect of process parameters such as rising height, transverse speed, and rotational speed, on the forming quality, microstructure, and mechanical properties of the resulting part were investigated. The results indicated that macroscopic forming defects can be avoided by selecting appropriate process parameters. A non-planar lamellar microstructure of fine and ultra-fine equiaxed grains densely embedded in each other was obtained without porosity or unbound defects; although the deposited material was not composed of completely equiaxed grains, the anisotropy of mechanical properties was small. The ultimate tensile strength and elongation in both the longitudinal and build directions of deposited material were higher than those of the raw feeding strips owing to the non-planar lamellar microstructure and grain refinement, with maximum values of 215 MPa and 32%, respectively. Through this microstructure analysis, the mechanism behind the formation of the lamellar microstructure was elucidated. The concepts investigated here regarding defect-free, high strength, and high elongation lamellar materials by FRAM with thick strips as feeding material, provides a methodology for future preparation of laminated composites using FRAM

Impact of BRDF Spatiotemporal Smoothing on Land Surface Albedo Estimation

Surface albedo, as a key parameter determining the partition of solar radiation at the Earth’s surface, has been developed into a satellite-based product from various Earth observation systems to serve numerous global or regional applications. Studies point out that apparent uncertainty can be introduced into albedo retrieval without consideration of surface anisotropy, which is a challenge to albedo estimation especially from observations with fewer angular samplings. Researchers have begun to introduce smoothed anisotropy prior knowledge into albedo estimation to improve the inversion efficiency, or for the scenario of observations with signal or poor angular sampling. Thus, it is necessary to further understand the potential influence of smoothed anisotropy features adopted in albedo estimation. We investigated the albedo variation induced by BRDF smoothing at both temporal and spatial scales over six typical landscapes in North America using MODIS standard anisotropy products with high quality BRDF inversed from multi-angle observations in 500 m and 5.6 km spatial resolutions. Components of selected typical landscapes were assessed with the confidence of the MCD12 land cover product and 30 m CDL (cropland data layer) classification maps followed by an evaluation of spatial heterogeneity in 30 m scale through the semi-variogram model. High quality BRDF of MODIS standard anisotropy products were smoothed in multi-temporal scales of 8 days, 16 days, and 32 days, and in multi-spatial scales from 500 m to 5.6 km. The induced relative and absolute albedo differences were estimated using the RossThick-LiSparseR model and BRDFs smoothed before and after spatiotemporal smoothing. Our results show that albedo estimated using BRDFs smoothed temporally from daily to monthly over each scenario exhibits relative differences of 11.3%, 12.5%, and 27.2% and detectable absolute differences of 0.025, 0.012, and 0.013, respectively, in MODIS near-infrared (0.7–5.0 µm), short-wave (0.3–5.0 µm), and visible (0.3–0.7 µm) broad bands. When BRDFs of investigated landscapes are smoothed from 500 m to 5.6 km, variations of estimated albedo can achieve up to 36.5%, 37.1%, and 94.7% on relative difference and absolute difference of 0.037, 0.024, and 0.018, respectively, in near-infrared (0.7–5.0 µm), short wave (0.3–5.0 µm), and visible (0.3–0.7 µm) broad bands. In addition, albedo differences caused by temporal smoothing show apparent seasonal characteristic that the differences are significantly higher in spring and summer than those in autumn and winter, while albedo differences induced by spatial smoothing exhibit a noticeable relationship with sill values of a fitted semi-variogram marked by a correlation coefficient of 0.8876. Both relative and absolute albedo differences induced by BRDF smoothing are demonstrated to be captured, thus, it is necessary to avoid the smoothing process in quantitative remote sensing communities, especially when immediate anisotropy retrievals are available at the required spatiotemporal scale

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately. Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observations from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three-component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China. The results showed that T11, T33, and total backscattered power (SPAN) are highly correlated with forest canopy height. In addition, this study showed that determining the weights of different polarization observation variables contributes positively to the accurate estimation of forest canopy height. The forest canopy height-weighted inversion method proposed in this paper was shown to be superior to the multiple regression model, with a 26% improvement in r and a 0.88 m reduction in the root-mean-square error (RMSE)