Application of nonlinear stiffness mechanism on energy harvesting from vortex-induced vibrations

This study investigates the potential of vortex-induced vibration (VIV) as a renewable energy source, achieved when fluid flow interacts with a bluff body, inducing self-sustained oscillations through vortex shedding in the wake. While VIV research has traditionally focused on understanding its mechanisms and mitigating detrimental effects, interest in VIV energy harvesting has surged as a means to convert marine hydrokinetic (MHK) energy into usable electrical power. The nonlinear effects of two linear oblique springs on VIV energy harvesting are explored using the wake oscillator model, encompassing bistable and Duffing hardening stiffness. The study examines the response and energy harvesting performance while considering the impact of undeformed spring length, structural damping, and initial conditions on VIV energy conversion. Findings show that nonlinear stiffness application in the VIV system can broaden the synchronization bandwidth or reduce the VIV initiation flow speed. Bistable stiffness may broaden the synchronization velocity range, while Duffing hardening stiffness efficiently reduces the VIV initiation speed with small energy harvesting loss. Combining both stiffness types with appropriate control strategies presents a promising approach for achieving a broad synchronization VIV bandwidth and low initiation flow speed. Key parameters, such as the nondimensional parameter defining spring system obliquity and the ratio between undeformed spring length and cylinder diameter, significantly influence VIV response and energy harvesting. Moreover, optimal structural damping is vital to maximize energy harvesting efficiency, and understanding and controlling initial conditions are crucial for optimizing VIV synchronization bandwidth and energy harvesting efficiency for both bistable and Duffing hardening stiffness. This study provides valuable insights into VIV system dynamics and energy conversion potential with nonlinear springs, offering promising avenues for enhancing energy harvesting efficiency and inspiring further applications of nonlinear effects in VIV energy converters

Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models

Comparison and assessment of NDVI time series for seasonal wetland classification

Satellite-based wetland mapping faces challenges due to the high spatial heterogeneity and dynamic characteristics of seasonal wetlands. Although normalized difference vegetation index (NDVI) time series (NTS) shows great potential in land cover mapping and crop classification, the effectiveness of various NTS with different spatial and temporal resolution has not been evaluated for seasonal wetland classification. To address this issue, we conducted comparisons of those NTS, including the moderate-resolution imaging spectroradiometer (MODIS) NTS with 500 m resolution, NTS fused with MODIS and Landsat data (MOD_LC8-NTS), and HJ-1 NDVI compositions (HJ-1-NTS) with finer resolution, for wetland classification of Poyang Lake. Results showed the following: (1) the NTS with finer resolution was more effective in the classification of seasonal wetlands than that of the MODIS-NTS with 500-m resolution and (2) generally, the HJ-1-NTS performed better than that of the fused NTS, with an overall accuracy of 88.12% for HJ-1-NTS and 83.09% for the MOD_LC8-NTS. Future work should focus on the construction of satellite image time series oriented to highly dynamic characteristics of seasonal wetlands. This study will provide useful guidance for seasonal wetland classification, and benefit the improvements of spatiotemporal fusion models

A Unifying Approach to Classifying Wetlands in the Ontonagon River Basin, Michigan, Using Multi-temporal Landsat-8 OLI Imagery

Accurate spatial information is critical to the assessment and protection of wetlands in the context of human intervention and global climate change. However, it is challenging to map and monitor wetland vegetation classes with satisfactory results because of their highly seasonal dynamics, spatial heterogeneity, and spectral similarity. This paper examines the effectiveness of various classification approaches commonly employed in wetland mapping, including the support vector machine (SVM) algorithm, maximum likelihood classifier (MLC), classification and regression tree (CART) and other remote sensing indices, by using multi-temporal Landsat-8 Operational Land Imager (OLI) spectral data and end-member fraction data as well as terrain data. These different mapping approaches were compared in the Ontonagon River drainage basin in upper Michigan, USA, where easily-confused wetland types such as forested wetland, palustrine scrub/shrub wetland, and palustrine emergent wetland are extensively distributed. The results show that multi-temporal data sets can reduce the classification omission caused by the highly seasonal dynamic of wetlands. The spatial heterogeneity can partly be characterized by using end-member fraction maps. Classification of the fraction maps had better results than that using the original spectral data, which implies that the selection of data inputs could be more important than the selection of classifiers. While each algorithm has its own capability of discriminating specific wetland types, CART showed relatively better classification results than the others due to its compatibility and lack of assumptions about data normal distribution. Finally, the authors developed a decision tree method (called DTM), which adopted the satisfactory resultant classification of specific wetland types using MLC and SVM classification, to update the wetland map of Ontonagon River Basin with acceptable accuracy (overall accuracy of 89% and kappa coefficients of 0.89)

Optimum Discrimination on the Subsection Taxonomy of Wild Tree Peony Species in China Using Pollen Characteristics

Pollen characteristics have some significance for plant taxonomy classification. We intend to explore a more concise index value for the subsection taxonomy with the pollen morphology and pollen viability determined from all nine wild tree peony species, including 18 populations native to China. We observed the pollen morphologic characters by scanning electron microscopy and measured the pollen viability in vitro. The results showed that the pollen polar length is the decisive characteristic in distinguishing the two subsections in section Moutan belonging to Paeoniaceae. The pollen polar length of five species belonging to subsect. Vaginatae is longer than 43 μm, while that of the four species belonging to subsect. Delavayanae is shorter than 43 μm. Meanwhile, the differences in pollen viability between the two subsections also play an auxiliary role in the classification of tree peony. The germination rate of the populations in subsect. Vaginatae is mostly greater than those of the populations in subsect. Delavayanae. The pollen germination rate of populations in subsect. Vaginatae is commonly more than 50%, while that of populations in subsect. Delavayanae less than 50%. Our study established taxonomic evidence between subsections in section Moutan and compared the pollen viability of subsect. Vaginatae and subsect. Delavayanae