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In order to solve the problem of low transverse tensile strength of triaxial geogrid, a kind of performance-optimized multi-axial geogrid (POMG) that can bear larger transverse loads was designed. Firstly, the forming equipment and process of POMG are designed. Secondly, through the test of formability and mechanical properties, the POMG with good formability and mechanical properties is obtained, and the average tensile strength of POMG with circular and semicircular holes is the highest, reaching more than 16 KN/m. Finally, the feasibility of the process is further verified by numerical simulation, and the shape distribution and stress-strain law of POMG during the forming process are obtained, which provides further guidance for the actual production. [ABSTRACT FROM AUTHOR]

To fight the power consumption conflicts at the regional scale, rooftop solar photovoltaics (RTSPV) in rural areas is considered as a critical way. In this study, we constructed a sophisticated framework for evaluating the regional RTSPV power generation potential of rural areas. Focusing on Jiangsu Province, the rural RTSPV power generation potential was evaluated. Then, the generation potential was compared with electricity demand to describe the spatial heterogeneity of the supply and demand situation, and proposed the pathways to alleviate the regional consumption conflicts. The results showed that the total RTSPV power generation potential of rural areas in Jiangsu Province is 202.91 TWh, which can meet the electricity demand of rural areas in this province. From the perspective of supply and demand, the conflicts between rural RTSPV power generation and electricity demand will be aggravated, especially in areas of rapid economic development. The rural RTSPV power generation of Suzhou, Wuxi, and Changzhou is far low the actual demand for electricity. While for the city with significant disparities between urban and rural areas, the development of rural RTSPV can effectively alleviate the power consumption conflicts. In particular, the rural RTSPV power generation in Xuzhou can meet the demand of total electricity. [ABSTRACT FROM AUTHOR]

With the increase of solar photovoltaic (PV) permeability in power systems, high-precision PV power forecasting is critical to secure the economic operation of the smart grid. Solar irradiance is the most important factor affecting PV power. Accurately estimating the amount of solar irradiance leads to better production estimates from solar panels. Meanwhile, satellite images provide information on current and future cloud coverage, and are potentially useful in inferring solar irradiance. To accurately capture the effect of cloud on irradiance, this article develops a real-time mapping model between satellite image and solar irradiance. Based on the mapping model, a new ultrashort-term global horizontal irradiance (GHI) forecasting method combining satellite visible images and meteorological information is proposed. First, a modified method is utilized for reducing the influence of the solar zenith angle on pixel intensity and extracting the features in the image processing stage. Then, the cloud cover factors are extracted from satellite visible images by using the modified convolutional neural network. After that, the GHI forecasting model is developed, which is based on the combined use of meteorological information and cloud cover factors. Meanwhile, a cloud motion forecasting method using predicted wind speeds is developed for discussion. The better performance of the proposed method is demonstrated by comparing it with several benchmark models. [ABSTRACT FROM AUTHOR]

• A satellite image-based photovoltaic power forecasting model is proposed. • A nonlinear cloud movement forecasting method based on deep learning is proposed. • An algorithm for intra-hour cloud condition estimation is established. • An algorithm for recognizing the cloud region that blocks sunlight is designed. The rapid variation of clouds is the main factor that causes the fluctuation of photovoltaic power. 1 1 This work was supported by the National Key Research and Development Program of China under Grant 2019YFE0118400. The satellite images contain plenty of information about clouds, applicable for photovoltaic power forecast. However, in practice, two main factors obstruct the application of the satellite images: 1) the relatively low update frequency of the satellite images mismatches the photovoltaic power forecasting frequency, and 2) the cloud region that blocks the sunlight changes significantly with time. In this paper, a novel satellite image-based approach for photovoltaic power forecast is proposed to overcome these obstacles and achieve accurate forecasting results. Firstly, concerning the hourly updated satellite images, a nonlinear cloud movement forecasting model, considering the thickness and shape changes of the cloud, is presented to forecast the hourly variation of the images. Secondly, an active cloud region selection rule is derived based on the changing solar position to dynamically select the cloud region that blocks the concerned photovoltaic power station in a satellite image. Thirdly, a sequential cloud region selection algorithm is provided to estimate the intra-hour variation of the cloud to match the photovoltaic power forecasting frequency. Finally, the photovoltaic power is predicted using the XGBoost algorithm concerning the effects of the cloud and other influencing factors. Testing results show that the proposed method can achieve more accurate photovoltaic power forecasts using the low update frequency satellite images. Meanwhile, the superior performance compared with other benchmarks also verifies the effectiveness of considering cloud information obtained by the proposed method for photovoltaic power forecast. [ABSTRACT FROM AUTHOR]

In this paper, the tensile behavior of industrial polypropylene triaxial geogrid with different pre-punched hole diameters was studied by experiment and numerical analysis. The industrial polypropylene sheets with different diameters of circular holes were stretched at elevated temperature and then the tensile properties of triaxial geogrids at room temperature were evaluated. It was found that the pre-punched hole diameter of triaxial geogrid had a very close relationship with the mechanical properties. With the increase of the pre-punched hole diameter, the tensile strength of triaxial geogrid shows a trend of first increasing and then decreasing. Combined with numerical simulation, the optimal pre-punched hole diameter can be accurately obtained, and the distribution law of the width, thickness, stress, and strain of triaxial geogrid can be obtained. Under the condition of a stretching ratio of 3 and node spacing of 3 mm, it was found that the mechanical properties of industrial polypropylene triaxial geogrid was the best when the pre-punched hole diameter was 2.6 mm. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is an essential element for plant growth and development. N levels in soil may also impact plant disease occurrence. However, the physiological and microbial mechanisms between N levels in soil and plant disease occurrence are not quite clear at present. In this study, we examined the impact of seven urea levels (0 to 800 mg kg−1 soil) on the physiology of tomato (Lycopersicon esculentum) and fungal disease occurrence. Our results showed that the disease incidence and index caused by a tomato early blight pathogen (Alternaria alternata) increased with increasing N levels. The disease index and percentage of disease incidence were positively correlated with N content, indole-3-acetic acid (IAA) and salicylic acid (SA) in plants, but negatively correlated with fungal community diversity. In addition to pathogens (A. alternata) that cause known early blight, 39 other fungal taxa were also identified as plant pathogens in tomato roots, leaves, and soil, the dominant putative pathogens included Ceratobasidiaceae sp., Fusarium oxysporum , Macrophomina phaseolina , Nectriaceae sp., Podosphaera fusca and Trichoderma viride. N levels affected the distribution and dynamics of the fungal pathogen community, such as the abundance of Fusarium oxysporum increased by 33% on roots from days 25 to 35. Our results demonstrated that plants undergo complex disease risk from different pathogens under different N levels, highlighting a need for proper N management, and integration of nutrient management as a disease control approach for sustainable agricultural production. [Display omitted] • The disease incidence and index of tomato early blight increased with increasing nitrogen levels. • Disease occurrence was positively correlated with plant N nutrition. • Disease occurrence was negatively correlated with connectance and average degree of fungal co-occurrence network. • 39 fungal taxa were identified as potential plant pathogens in tomato-soil system. • The pathogens abundance varied with pathogen types, time, and plant tissue under different nitrogen levels. [ABSTRACT FROM AUTHOR]