50-m Height Research Articles

Automatic grading evaluation of winter wheat lodging based on deep learning.

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat lodging images were collected using the unmanned aerial vehicle (UAV) remote sensing platform. Based on these images, winter wheat lodging datasets were created using different time sequences and different UAV flight heights. These datasets aid in segmenting and classifying winter wheat lodging degrees and areas. The results show that MLP_U-Net has demonstrated superior detection performance in a small sample dataset. The accuracies of winter wheat lodging degree and lodging area grading were 96.1% and 92.2%, respectively, when the UAV flight height was 30m. For a UAV flight height of 50m, the accuracies of winter wheat lodging degree and lodging area grading were 84.1% and 84.7%, respectively. These findings indicate that MLP_U-Net is highly robust and efficient in accurately completing the winter wheat lodging-grading task. This valuable insight provides technical references for UAV remote sensing of winter wheat disaster severity and the assessment of losses.

Unmanned aerial vehicles (UAVs) with thermal infrared (TIR) sensors are effective for monitoring and counting threatened Vietnamese primates.

Monitoring the population size of threatened primate species with minimal disturbance is becoming an outstanding requirement for conservation and wildlife management. Drones with thermal infrared (TIR) and visible spectrum (RGB) imaging are increasingly used to survey arboreal primates, but ground-truthing is still required to assess the effectiveness of drone-based count estimates. Our pilot study aims to assess the ability of a drone with both TIR and RGB sensors to detect, count, and identify semi-wild population of four endangered species of langurs and gibbon in the Endangered Primate Rescue Center (EPRC) in northern Vietnam. We found that TIR imagery enabled higher detection rates compared to RGB imagery and obtained an accurate count with the TIR only after four drone flights. We could identify langurs species based on thermal signature at a flight height of 50m from the ground level (max tree height = 15m), via size and shape of the body. With TIR imagery, we were able to record inconspicuous behaviors such as foraging and play. While some individuals initially showed flight or avoidance behaviors when the drone was sighted, these behaviors decreased or were absent on following drone surveys. Our study suggests that monitoring and precisely counting langur and gibbon species populations could be successful with the use of thermal drones only.

Performance of Concrete Gravity Dams with Height 50m and 75m based on Incremental Dynamic Analysis

This paper discusses the performance of concrete gravity dam with a height of 50m and 75m is studied by using the incremental dynamic analysis (IDA). The IDA curves were develop based on seven ground motions which fulfil the criteria of i) the distance of near field is less than 15km, (ii) the earthquake magnitude is greater than 5.5 and (iii) the PGA is equal to or greater than 0.15g. Seven of ground motions were converted to response spectrum and scaled according to the characteristic of the soil type for development of elastic response spectrum. Referring to Eurocode 8, elastic response spectrum was developed based on soil type B. By using IDA method, the limit states of the dam which are yielding and ultimate state are identified based on the cracking schemes. The displacement of yielding state for 50m height of dam is 14.60mm and 23.18mm for 75m height of dam. Meanwhile, the ultimate state is 23.18mm and 52.05mm for 50m and 75m height of dam respectively. The results show a greater damage occurred on higher dam and allow us to study the performance of dam with various height under seismic loadings.

Seasonal Wind Energy Characterization in the Gulf of Mexico

In line with Mexico’s interest in determining its wind resources, in this paper, 141 locations along the states of the Gulf of Mexico have been analyzed by calculating the main wind characteristics, such as the Weibull shape (c) and scale (k) parameters, and wind power density (WPD), by using re-analysis MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications version 2) data with hourly records from 1980–2017 at a 50-m height. The analysis has been carried out using the R free software, whose its principal function is for statistical computing and graphics, to characterize the wind speed and determine its annual and seasonal (spring, summer, autumn, and winter) behavior for each state. As a result, the analysis determined two different wind seasons along the Gulf of Mexico;, it was found that in the states of Tamaulipas, Veracruz, and Tabasco wind season took place during autumn, winter, and spring, while for the states of Campeche and Yucatan, the only two states that shared its coast with the Caribbean Sea and the Gulf of Mexico, the wind season occurred only in winter and spring. In addition, it was found that by considering a seasonal analysis, more accurate information on wind characteristics could be generated; thus, by applying the Weibull distribution function, optimal zones for determining wind as a resource of energy can be established. Furthermore, a k-means algorithm was applied to the wind data, obtaining three clusters that can be seen by month; these results and using the Weibull parameter c allow for selecting the optimum wind turbine based on its power coefficient or efficiency.

Research on Facilities of Power Supply of Small Power Capability Consumers of Bukhara Region by using Wind and Solar Energy

This article analyzes the climatic features of the Bukhara region – the southern-eastern part of Uzbekistan. The wind and solar potential of the region and the prospects of its utilization were evaluated. In recent years, wind and solar energy has been analyzed worldwide. The potential for the use of solar radiation in the region was analyzed. Accordingly, when the solar photovoltaic batteries in the region are used (coefficient of efficiency-18%), the technical potential of the region is 41 TWh/year. Wind speeds and wind energy potential data were collected over eight years (2011-2018) and were calculated using the monthly wind speed data measured every 30 minutes at a height of 10m. During the estimation of wind energy potential we were used two parameters Weibull and Rayleigh distribution functions. The density power and energy values of the wind flow at various heights were evaluated using the extrapolation method. In accordance with that, on height of 10m it makes 41.19 W/m2 and 361.48 kWh/m2, on height of 50m 117.23 W/m2 1026.96 kWh/m2, and on height of 100m 192.76 W/m2, 1688.59 kWh/m2. The wind energy gross, technical and economic potential of the region has been evaluated. In addition, the potential and prospects of using hybrid (wind and solar) power stations for the supply of uninterrupted and reliable power supply to consumers in the region were analyzed

Evaluación de las Acciones Eólicas Transversales en Edificios de más de 50m mediante Métodos Analíticos

En el presente trabajo se evalúa la relevancia que pueden llegar a adquirir las acciones eólicas transversales cuando se diseñan edificios aislados de más de 50m mediante las diferentes metodologías de análisis que establecen tres normas internacionales, el Eurocódigo, la norma australiana y la norma japonesa. El trabajo incluye tanto las acciones transversales que resultan de las oscilaciones periódicas del viento debido al desprendimiento de los vórtices, y que debiera ser considerado inevitablemente en cualquier edificio aislado, así como las acciones eólicas que son consecuencia del acoplamiento de frecuencias entre el propio desprendimiento de los vórtices y la frecuencia fundamental en sentido transversal del edificio, pudiendo dichas fuerzas llegar a condicionar el diseño de la estructura que debe hacer frente a las acciones horizontales del edificio.

Technical and economic assessment of wind power potential of Nooriabad, Pakistan

BackgroundPakistan is a developing nation and heavily spends on the development of conventional power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of site using wind speed, wind direction, and other meteorological data collected over a period of 1 year. This type of detailed investigation provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine.MethodsThe site-specific air density, wind shear, wind power density, annual energy yield, and capacity factors have been calculated at 30 and 50 m above the ground level (AGL). The Weibull parameters have been calculated using empirical (EM), maximum likelihood (MLM), modified maximum likelihood (MMLM), energy pattern (EPFM), and graphical (GM) methods to determine the other dependent parameters. The accuracies of these methods are determined using correlation coefficient (R2) and root mean square error (RMSE) values. At last, the wind energy economic analysis has been carried out at 30- and 50-m heights.ResultsThe annual mean wind speeds were found to be 5.233and 6.55 m/s at 30- and 50-m heights, respectively, with corresponding standard deviations of 2.295 and 2.176. All methods fitted very well with the measured wind speed data except GM model. The frequency of wind speed observed that Weibull distribution gave better fitting results than Rayleigh distribution at wind site. The mean wind power densities were found to be 169.4 and 416.7 W/m2 at 30- and 50-m heights. The economic analysis showed that at Nooriabad site in Pakistan, the wind energy can be produced at US$0.02189/kWh at a hub height of 50 m.ConclusionsThe results showed that the site has potential to install utility wind turbines to generate energy at the lowest cost per kilowatt-hour at height of 50 m.

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13–15 (it is a scale to identify the phenological development stage of a plant which is here the 3- to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes.

Early sinkhole detection using a drone-based thermal camera and image processing

Accurate advance detection of the sinkholes that are occurring more frequently now is an important way of preventing human fatalities and property damage. Unlike naturally occurring sinkholes, human-induced ones in urban areas are typically due to groundwater disturbances and leaks of water and sewage caused by large-scale construction. Although many sinkhole detection methods have been developed, it is still difficult to predict sinkholes that occur in depth areas. In addition, conventional methods are inappropriate for scanning a large area because of their high cost. Therefore, this paper uses a drone combined with a thermal far-infrared (FIR) camera to detect potential sinkholes over a large area based on computer vision and pattern classification techniques.To make a standard dataset, we dug eight holes of depths 0.5–2m in increments of 0.5m and with a maximum width of 1m. We filmed these using the drone-based FIR camera at a height of 50m. We first detect candidate regions by analysing cold spots in the thermal images based on the fact that a sinkhole typically has a lower thermal energy than its background. Then, these regions are classified into sinkhole and non-sinkhole classes using a pattern classifier. In this study, we ensemble the classification results based on a light convolutional neural network (CNN) and those based on a Boosted Random Forest (BRF) with handcrafted features. We apply the proposed ensemble method successfully to sinkhole data for various sizes and depths in different environments, and prove that the CNN ensemble and the BRF one with handcrafted features are better at detecting sinkholes than other classifiers or standalone CNN.

Investigation of wind power density at different heights in the Gelibolu peninsula of Turkey

ABSTRACTThis study aims to determine the wind characteristics and wind power potential of the Gelibolu peninsula in the Çanakkale region of Turkey. For this purpose, hourly average wind data observed at the Gelibolu meteorological station were used. The Weibull probability density functions and Weibull parameters of time-series of wind speed, mean wind speed, and mean wind power potential were determined for different heights as 10, 20, 30, 40, and 50 m. According to the results obtained at 10- and 50-m heights above the ground level, the annual wind speed varied from 6.85 to 8.58 m/s in this region, respectively. The annual wind power potential of the site was determined as 407 and 800 W/m2 for 10- and 50-m heights, respectively. These results indicate that the investigated site has a reasonable wind power potential for generating electricity.

Coupled thermal-hydrological-mechanical behavior of rock mass surrounding a high-temperature thermal energy storage cavern at shallow depth

We numerically model the thermal-hydrological-mechanical (THM) processes within the rock mass surrounding a cavern used for thermal energy storage (TES). We consider a cylindrical rock cavern with a height of 50m and a radius of 10m storing thermal energy of 350°C as a conceptual TES model, and simulate its operation for thirty years. At first, the insulator performance are not considered for the purpose of investigating the possible coupled THM behavior of the surrounding rock mass; then, the effects of an insulator are examined for different insulator thicknesses. The key concerns are hydro-thermal multiphase flow and heat transport in the rock mass around the thermal storage cavern, the effect of evaporation of rock mass, thermal impact on near the ground surface and the mechanical behavior of the surrounding rock mass. It is shown that the rock temperature around the cavern rapidly increases in the early stage and, consequently, evaporation of groundwater occurs, raising the fluid pressure. However, evaporation and multiphase flow does not have a significant effect on the heat transfer and mechanical behavior in spite of the high-temperature (350°C) heat source. The simulations showed that large-scale heat flow around a cavern is expected to be conduction-dominated for a reasonable value of rock mass permeability. Thermal expansion as a result of the heating of the rock mass from the storage cavern leads to a ground surface uplift on the order of a few centimeters, and to the development of tensile stress above the storage cavern, increasing the potentials for shear and tensile failures after a few years of the operation. Finally, the analysis shows that high tangential stress in proximity of the storage cavern can some shear failure and local damage, although large rock wall failure could likely be controlled with appropriate insulators and reinforcement.

A Pre-feasibility Study of Wind Resources in Vadravadra, Gau Island, Fiji

Gau is one of the many rugged, volcanic Islands in Fiji .Averaged 10 minutely wind speed and direction data of Vadravadra settlement was supplied by Fiji Department of Energy (DoE) and the site wind characterizing was done. Considering the surface roughness, obstacle condition and terrain information of the island, a micro-scale prediction has been done using the Wind Atlas Analysis and Application Program (WAsP) to develop an annual wind atlas and also a wind resource map which shows that at 50m height, the annual wind speed over Southern Gau varies from 8.42 to 14.69 m/s. The Southern Eastern sides of Gau appear to be promising for wind electricity generation using Vergnet 275 kW wind turbines where wind power density at a height of 50m or higher is found to be around 1128 W/m2 (average) annually. The total energy produced from using eight Vergnet 275 kW turbine generators was 13.320 GWh and the COE borne was $0.55/kWh.

An assessment of wind power potential along the coast of Tamaulipas, northeastern Mexico

Herein, we present an assessment of wind power potential along the coast of Tamaulipas, northeastern Mexico. We propose a method to characterize wind variability and to estimate wind potential in coastal areas where appropriate meteorological measurements may not be available. A gridded reanalysis wind product (BMW-CERSAT), which provides data of zonal and meridional components of wind with a resolution of 1/4° (∼25 km) from years 2004–2009, is used as the reference wind field after a statistical comparison to near-surface observations. Mean wind intensity field at 50-m height and its corresponding mean power density are modeled by using the Wind Atlas Analysis and Application Program (WAsP). The wind power potential of the southern half of Tamaulipas State is classified as poor (200–300 W m−2), becoming marginal in some areas near the coast. Only the northern half of the State is classified with moderate potential (400–500 W m−2), while the lagoon zone has good potential. Estimates, however, could be 30–50% higher if wind observations are measured at a higher frequency. Results show that the wind potential along Tamaulipas is lower than that suggested by the official prevailing eolic-potential map in Mexico. We conclude that even if the assessment presented in this work is preliminary, it gives a realistic approximation of wind as a promising renewable source for electric generation along the coast of Tamaulipas.

Sea Cliff Stability in Tibau do Sul, Brazil

The city of Tibau do Sul, a popular tourist destination in Rio Grande do Norte state, is famous for its sea cliffs, which reach a maximum height of 50m. However, mass movements are causing them to retreat. The aim of this paper is to study mass movements and their causes on the coast of Tibau do Sul, in order to better understand cliff dynamics and assess the constructive potential of the surrounding land. Field work was undertaken to characterize the area and analyze slope stability using the Hoek and Bray method [1]. The effects of height, inclination and ground cohesion were analyzed for stability.

A Probabilistic Model of Extrapolated Wind Speed Data for Wind Energy Prediction at Alangarapeta, Anantapur District, Andhra Pradesh, India

The wind speed distribution determines the performance of wind energy systems for a given location and time quite well. When the wind speed probability distribution is known, the wind energy distribution can easily be obtained. Therefore, the probability distribution of wind speed is a very important piece of information needed in the assessment of wind energy potential. A proper analysis of statistical wind data is a very important step when performing a wind resource assessment campaign which supports a wind energy feasibility initiative. Wind speed data are available in most cases for only 10m and 25m height above the ground level using anemometer. Since wind speed normally increases with height above the ground, some means of estimating the wind speed at heights other than measurement height is needed. In this paper, a probabilistic model is fitted to the wind speed data recorded at Alangarapeta station at Anantapur District in state of Andhra Pradesh. Wind speeds are then extrapolated to height of 50m using power law equation with an exponent of 0.14. Data were analyzed using MINITAB statistical software to show the variability of wind speeds with time and height, and to determine the underlying probability model of the extrapolated wind speed data. The results show that wind speeds at Alangarapeta station vary cyclically over time; and they conform to the Weibull probability distribution to predict the wind energy.

Statistical Analysis of the Wind Resources at the Importance for Energy Production in Bangladesh

Wind is an important source of renewable energy. Bangladesh being a tropical country which has a lot of wind flow at different seasons of the year. In this paper, wind power system with battery storage will be presented that can play electricity to our daily uses. Rayleigh distribution functions were considered and fitted to estimate of wind speed data. In our study, we studied previously collected data on the wind resources available in Bangladesh and by analyzing this data and have necessary accessory for application like wind power generation. The wind resources were mapped with the wind power density and the annual mean wind power estimated at, 194.2556W/m² and 2351.76 Kh and the annual mean wind speed 2.34635 kph on 50m heights and RCL = 0.

Wind Energy Resource Assessment at Tindouf Region, Extreme Southwest of Algeria

In this paper, with the help of two-parameter Weibull distribution, the monthly, seasonal and annual assessment of wind energy potentiality was made using the wind speed data collected in the last decade, from 2001 to 2010 for the meteorological station of Tindouf region. The vertical extrapolation of Weibull parameters and mean wind speed at a height of 50m and the analysis of temporal energy efficiency using a wind turbine of 600kW rated power from Fuhrländer manufacturer was made. The results show that Tindouf situated in extreme Algerian southwest, in the natural region of the Sahara, on the border with Morocco (west), the non-autonomous territory of Western Sahara (south-west) and Mauritania (south), is one of windiest region in the country, swept by local winds with a good annual mean wind speed =5.67m/s and an annual mean wind power density equal to 200W/m2 at 10m, while the highest monthly wind energy produced may be reached in May, the seasonal in Spring and the annual gross electric energy produced is equal to 1.85GWh/year.

Wind energy resource assessment of Izmit in the West Black Sea Coastal Region of Turkey

Abstract The wind energy potential of Izmit (41.19 N, 30.30 E), which is located in the West Black Sea Coastal Region of Turkey, is assessed with the statistical analysis of the gathered wind data at the 50-m height measurement mast covering the period of 06/2008-06/2009. The annual average wind speed is calculated as 6 m/s and the prevailing wind direction is ENE (60°). The Weibull distribution parameters of shape and scale factor are found as 2.03 and 6.73 m/s, respectively. The measured wind speed data are compared with the data of nearby meteorological stations and the results show that there is a considerable difference between the onsite measurements and the measurements of the meteorological stations. Moreover, a turbulence analysis is carried out and the turbulence intensity is negatively correlated with the normalized height from ground level with canopy height. The energy generation performances of three different wind turbines are evaluated by using the onsite wind speed measurements and the assessment shows that the capacity factor increase by a factor of two from 17% to 34% depending on the type of the turbine. Furthermore, an economic analysis is carried out for a 50 MW wind energy project for the potential site and the proposed project benefit/cost ratio is calculated as 8.

Modeling and Simulation of a Fischer–Tropsch Slurry Bubble Column Reactor Using Different Kinetic Rate Expressions for Iron and Cobalt Catalysts

Abstract A user-friendly simulator based on a comprehensive computer model for slurry bubble column reactor (SBCR) was used to predict the performance of a conceptual commercial-scale (9-m ID and 50-m height) SBCR for Fischer–Tropsch (F–T) synthesis. Novel correlations for predicting the hydrodynamic and mass transfer parameters; a new relationship between the axial dispersion of large gas bubbles and their average diameter; and up to ten different kinetic rate expressions available in the literature for iron and cobalt-based catalysts were included in the simulator. The effects of operating conditions, including catalyst concentration, pressure, temperature, H2/CO ratio, and superficial gas velocity on the SBCR performance were predicted using the simulator for both types of catalysts. The predictions showed that the performance of the reactor was strongly dependent on the type of catalyst and the kinetic rate expression used.

Numerical analysis of the performance of a venturi-shaped roof for natural ventilation: Influence of building width

A numerical analysis with Computational Fluid Dynamics (CFD) is performed to investigate the influence of building width on the performance of a venturi-shaped roof (called Ventec roof) for natural ventilation. The specific roof configuration is intended to create an underpressure in the narrowest roof section (contraction) which can be used to partly or completely drive the natural ventilation of the building zones. In previous studies, the influence of the roof configuration on its performance was analysed in detail, however these studies were all performed for a fixed building geometry, i.e. a tower building with floor plan 20×20m² and a height of 50m. It is important to analyse the performance of the Ventec roof for different building widths. Therefore, the present paper presents CFD simulations for building (and roof) widths of 20, 40, 80, 120 and 160m. The 3D steady Reynolds-averaged Navier–Stokes (RANS) approach with the Renormalization Group (RNG) k–ε model is used. The simulations are based on grid-sensitivity analysis and on validation by comparison with wind tunnel experiments. The simulations show that the aerodynamic performance of the roof in terms of the underpressure in the contraction improves with 31% when the building width is increased from 20m to 40m, while further increasing the building width only provides relatively small additional improvements. The increased performance with increasing building width is attributed to the larger overpressure upstream of the building and to the larger underpressure and larger size (height) of the wake behind the building.