Minimum Wind Research Articles

Numerical study on wind profiles change trend of upright reticulation barriers under different configuration models

To explore how to lay the same specifications to maximize the protection benefits of mechanical sand barriers is an essential issue in the actual production process. We used the Reynolds-Averaged Navier-Stokes (RANS) method and the shear stress transport (SST) K-ε turbulence model to study the windbreak efficiency of sand barriers with different structures. Among them, the structure of the sand barriers includes rhombus 60° (cTnI = 60°, R60°), rhombus 90° (cTnI = 90°, R90°), rhombus 120° (cTnI = 120°, R120°) and parallel straight line (belt). The sand barrier was set to a porous jump model, where the surface permeability a was 2.6 × 108, and the inertial resistance coefficient c2 was 9,400. The wind velocity field results showed that the sand barrier’s blocking effect on wind velocity decreases with the increase in height. The leading edge of the 120° obstacle has the strongest weakening effect on the inlet wind speed. The minimum wind speed (0.97 m/s to 1.41 m/s) occurs near the sand barrier, and the vortex appears on both sides of the node, and the wind speed increases. The order of the blocking effect of different angles on airflow is as follows: 120° > 90°> brand >60°. Under R120° conditions, the wind speed is reduced by more than 60% at 0.05 m and 0.1 m height behind the barrier compared to the initial wind speed. This will be conducive to the design and control engineering planning of the laying angle of the gauze sand barrier in the main wind direction.

Percutaneous Osteosynthesis in Tibial Pilon Fractures: A Study Concerning a Serie of 8 Patients

Minimally invasive medial plate osteosynthesis is a reliable method of treatment for tibial pilon fractures. This technique provides a high fracture healing rate and satisfying functional outcome with minimal wound healing complications. Sagittal plan deformity remains a common complication with minimally invasive medial plate osteosynthesis. The aim of this study was to evaluate the long-term follow-up and functional and radiological outcomes of minimally invasive medial plate osteosynthesis in distal tibia fractures concerning a serie of 8 patients.

Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform

Accurate daily solar radiation prediction is a crucial task for the management and generation of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy of new machine learning methods, wavelet long short-term memory (WLSTM), wavelet multi-layer perceptron artificial neural network (WMLPANN), long short-term memory (LSTM), multi-layer perceptron artificial neural network (MLPANN), and multivariate adaptive regression splines (MARS), was assessed for modeling daily solar radiation using various input combinations of climatic data of maximum and minimum relative humidity, potential evapotranspiration, maximum and minimum temperature, precipitation and wind speed from two stations, Brownstown and Carbondale located in Illinois, USA. For accurate assessment of prediction accuracy of the proposed models, four reliable statistical metrics, root mean squared error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and One-Tailed Wilcoxon Signed-Rank Test were employed. Comparison of results, based on the RMSE values, indicated that the WLSTM method performed better than the WMLPANN, LSTM, MLPANN and MARS methods in the estimation of solar radiation values at both stations. The average RMSE values of WMLPANN, LSTM, MLPANN, and MARS approaches was decreased by 6%, 4%, 7.3%, and 13.5% using WLSTM method at Brownstown Station, by 6%, 5.2%, 13.2%, and 14.3% at Carbondale Station, respectively. The overall results during the testing phase of both stations revealed the successful application of hybridization of the LSTM model with the wavelet transform technique for improving the prediction accuracy of solar radiation based on climatic parameters.

Prediction of Extremely Severe Cyclonic Storm “Fani” Using Moving Nested Domain

The prediction of extremely severe cyclonic storms has been a long-standing and challenging issue due to their short life period and large variation in intensities over a short time. In this study, we predict the track, intensity, and structure of an extremely severe cyclonic storm (ESCS) named ‘Fani,’ which developed over the Bay of Bengal region from 27 April to 4 May 2019, using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model. Two numerical experiments were conducted using the moving nested domain method with a 3 km horizontal resolution, one with the FLUX-1 air-sea flux parameterization scheme and the other with the FLUX-2 air-sea flux parameterization scheme. The NCEP operational Global Forecast System (GFS) analysis and forecast datasets with a 25 km horizontal resolution were used to derive the initial and boundary conditions. The WRF model’s predicted track and intensity were validated with the best-fit track dataset from the India Meteorological Department (IMD), and the structure was validated with different observations. The results showed that the WRF model with the FLUX-1 air-sea parameterization scheme accurately predicted the track, landfall (position and time), and intensity (minimum sea level pressure and maximum surface wind) of the storm. The track errors on days 1 to 4 were approximately 47 km, 123 km, 96 km, and 27 km in the FLUX-1 experiment and approximately 54 km, 142 km, 152 km, and 166 km in the FLUX-2 experiment, respectively. The intensity was better predicted in the FLUX-1 experiment during the first 60 h, while it was better predicted in the FLUX-2 experiment for the remaining period. The structure, in terms of relative humidity, water vapor, maximum reflectivity, and temperature anomaly of the storm, was also discussed and compared with available satellite and Doppler Weather Radar observations.

Weather associations with physical activity, sedentary behaviour and sleep patterns of Australian adults: a longitudinal study with implications for climate change

BackgroundWeather is a potentially important influence on how time is allocated to sleep, sedentary behaviour and physical activity across the 24-h day. Extremes of weather (very hot, cold, windy or wet) can create undesirable, unsafe outdoor environments for exercise or active transport, impact the comfort of sleeping environments, and increase time indoors. This 13-month prospective cohort study explored associations between weather and 24-h movement behaviour patterns.MethodsThree hundred sixty-eight adults (mean age 40.2 years, SD 5.9, 56.8% female) from Adelaide, Australia, wore Fitbit Charge 3 activity trackers 24 h a day for 13 months with minute-by-minute data on sleep, sedentary behaviour, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) collected remotely. Daily weather data included temperature, rainfall, wind, cloud and sunshine. Multi-level mixed-effects linear regression analyses (one model per outcome) were used.ResultsNinety thousand eight hundred one days of data were analysed. Sleep was negatively associated with minimum temperature (-12 min/day change across minimum temperature range of 31.2 °C, p = 0.001). Sedentary behaviour was positively associated with minimum temperature (+ 12 min/day, range = 31.2 oC, p = 0.006) and wind speed (+ 10 min/day, range = 36.7 km/h, p< 0.001), and negatively associated with sunshine (-17 min/day, range = 13.9 h, p < 0.001). LPA was positively associated with minimum temperature (+ 11 min/day, range = 31.2 °C, p = 0.002), cloud cover (+ 4 min/day, range = 8 eighths, p = 0.008) and sunshine (+ 17 min/day, range = 13.9 h, p < 0.001), and negatively associated with wind speed (-8 min/day, range = 36.7 km/h, p < 0.001). MVPA was positively associated with sunshine (+ 3 min/day, range = 13.9 h, p < 0.001) and negatively associated with minimum temperature (-13 min/day, range = 31.2 oC, p < 0.001), rainfall (-3 min/day, range = 33.2 mm, p = 0.006) and wind speed (-4 min/day, range = 36.7 km/h, p < 0.001). For maximum temperature, a significant (p < 0.05) curvilinear association was observed with sleep (half-U) and physical activity (inverted-U), where the decrease in sleep duration appeared to slow around 23 °C, LPA peaked at 31 oC and MVPA at 27 °C.ConclusionsGenerally, adults tended to be less active and more sedentary during extremes of weather and sleep less as temperatures rise. These findings have the potential to inform the timing and content of positive movement behaviour messaging and interventions.Trial registrationThe study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123).

Weather factors, soil microbiome, and bacteria-fungi interactions as drivers of the epiphytic phyllosphere communities of romaine lettuce

Lettuce is associated with seasonal outbreaks of Shiga toxin-producing Escherichia coli (STEC) infections. Little is known about how various biotic and abiotic factors affect the lettuce microbiome, which in turn impacts STEC colonization. We characterized the lettuce phyllosphere and surface soil bacterial, fungal, and oomycete communities at harvest in late-spring and -fall in California using metagenomics. Harvest season and field type, but not cultivar, significantly influenced the microbiome composition of leaves and surface soil near plants. Phyllosphere and soil microbiome compositions were correlated with specific weather factors. The relative abundance of Enterobacteriaceae, but not E. coli, was enriched on leaves (5.2%) compared to soil (0.4%) and correlated positively with minimum air temperature and wind speed. Co-occurrence networks revealed seasonal trends in fungi-bacteria interactions on leaves. These associations represented 39%–44% of the correlations between species. All significant E. coli co-occurrences with fungi were positive, while all negative associations were with bacteria. A large proportion of the leaf bacterial species was shared with those in soil, indicating microbiome transmission from the soil surface to the canopy. Our findings provide new insight into factors that shape lettuce microbial communities and the microbial context of foodborne pathogen immigration events in the lettuce phyllosphere.

Climate Change Effects on Irrigated Corn Growth in the Lower Mississippi Delta

Abstract In this study, the possible climate change impacts on irrigated corn production in the lower Mississippi delta (LMD) region were analyzed. The observed daily maximum and minimum air temperature, wind speed, relative humidity (RH), and precipitation from 1960 to 2018 were used in the analysis. The length of the growing season, evapotranspiration (ET), and crop yield estimates from the precalibrated Root Zone Water Quality Model (RZWQM) were also used. Trend analyses were performed on growing-season averages for temperature, RH, and wind speed; growing-season totals for precipitation and ET; daily values of minimum and maximum temperature; and averages of RH and wind speed at critical corn growth stages. The last day of spring freezing (LDSF) and days with an average daily air temperature Ta of more than 35°C during corn silking were also included in the analysis. The trend analysis was performed using the modified Mann–Kendall test, Pettitt test, and Sen’s slope at a significance level of p ≤ 0.05. The results from our study pointed out increases in minimum Ta, increases in the number of days with Ta exceeding 35°C during the corn silking stage, increases in RH and decreases in ET, advancement of the LDSF by 2 weeks, and 8% reductions in corn yield that could be attributed to changes in climate. If the observed trends in climate (climate variability and change) and yield reductions continue in the region, it could be challenging to grow the corn crop in the LMD profitably. Significance Statement In recent years, growing corn and soybeans instead of cotton has been gaining popularity in the lower Mississippi delta (LMD) region. Our study is aimed to understand how climate change in the recent past (1960–2018) has affected irrigated corn production in LMD. This will help us to better understand the expected consequences of the future climate. We used observations of daily weather data from 1960 to 2018 and corn yield, water balance, and crop yield results from a computer model designed to simulate corn production. Tools were used to analyze trends and patterns in the data and results. Our analysis pointed out significant changes in weather, water balance, and yield decline for irrigated corn in the LMD.

Simulation Experiment Research of Mine Roadway Simulating Test Device with Adjustable Wind Velocity and Temperature and Humidity.

The design and development process of wind velocity sensors for mining has been a challenging task due to the complexity of a large number of field tests. To resolve this problem, this study aimed to provide a comprehensive test device for the design and development of high-precision wind velocities sensor for mining. Through a combination of experiments and computational fluid dynamics (CFD), a device that can simulate the mine roadway environment was developed. The device can control the temperature, humidity, and wind velocity parameters to fully replicate the mine roadway environment. It gives designers and developers of high-precision wind velocity sensors for mining a rational and scientific testing environment. In order to quantitatively define the uniformity of air flow in the mine highway section, the research introduced the non-uniformity determination method. The approach was expanded to assess the cross-sectional uniformity of temperature and humidity. The wind velocity within the machine can increase to 8.5 m/s by selecting the right kind of fan. The minimum wind velocity non-uniformity at this moment is 2.30%. The device's internal temperature can be raised to 38.23 °C and its humidity level can be increased to 95.09% by carefully crafting the rectifier orifice plate's structure. At this time, the lowest temperature non-uniformity is 2.22%, and the lowest humidity non-uniformity is 2.40%. The device's average wind velocity is 4.37 m/s, its average temperature is 37.7 °C, as well as its average humidity is 95%, per the emulate results. The device's non-uniformity in wind velocity, temperature, and humidity is 2.89%, 1.34%, and 2.23%, respectively. It is capable of simulating the mine roadway environment in its entirety.

Estimation of reference evapotranspiration using artificial neural network models for semi-arid region of Haryana

The study was conducted to evaluate performance of artificial neural network (ANN) models for estimating reference evapotranspiration (ET0) for semi-arid region of Haryana state. Ten years (2011-2020) daily weather data of maximum and minimum temperature, relative humidity, wind speed and sun shine hours was collected from the meteorological observatory at CCS HAU, Hisar. Multilayer perceptron feed forward back propagation ANN models were evaluated for different training algorithms (10), number of hidden layers (1-3) and number of neurons in hidden layers (1-30). Training algorithms compared in the study were heuristic techniques (GDA, GDX, RP), conjugate gradient (CGF, CGP, CGB, SCG), quasi-Newton (BFG, OSS) and Levenberg-Marquardt (LM). Results were compared against standard FAO Penman-Monteith method. The study revealed that best performance for ANN was found with LM algorithm in single layer of 13 neurons exhibiting RMSE, R, ME and RPD values 0.306, 0.986, 0.976 and 6.63, respectively. ANN models showed good performance in prediction of reference evapotranspiration.

Characterizing Coastal Wind Speed and Significant Wave Height Using Satellite Altimetry and Buoy Data

Wind speed and significant wave height are the most relevant metocean variables that support a wide range of engineering and economic activities. Their characterization through remote sensing estimations is required to compensate for the shortage of in situ observations. This study demonstrates the value of satellite altimetry to identify typical spatial patterns of wind speed and significant wave height in the northeastern region of the United States. Data from five altimetry satellite missions were evaluated against the available in situ observations with a 10 km sampling radius and a 30 min time window. An objective analysis of the collective altimeter dataset was performed to create aggregated composite maps of the wind speed and significant wave height. This asynchronous compositing of multi-mission altimeter data is introduced to compile a sufficient sampling of overpasses over the area of interest. The results of this approach allow for quantifying spatial patterns for the wind speed and significant wave height in the summer and winter seasons. The quality of altimeter estimations was assessed regarding the distance from the coast and the topography. It was found that while the altimeter data are highly accurate for the two variables, bias increases near the coast. The average minimum and maximum wind speed values detected in buoy stations less than 40 km from the coast were not matched by the aggregated altimeter time series. The method exposes the spatial and time gaps to be filled using data from future missions. The challenges of the objective analysis near the coast, especially in semi-enclosed areas, and the implications of the altimeter estimations due to the land contamination are explained. The results indicate that the combination of altimetry data from multiple satellite missions provides a significant complementary information resource for nearshore and coastal wind and wave regime estimations.

Laboratory Study on the Use of Urease-Induced Calcium Carbonate Precipitation for Stabilization of Coal Fly Ash

Coal fly ash (FA) dust negatively impacts human health and the environment. This study aimed to prevent wind erosion through the technology of enzyme-induced carbonate precipitation (EICP) to improve the surface stability of FA. In order to investigate the influence of urease activity, salt solution ratio, and polyacrylamide (PAM) concentration on the EICP process, unconfined compressive strength (UCS) test, sieving test, and wind tunnel test was carried out in the laboratory. Scanning electron microscopy (SEM) was also used to analyze the microscopic crystal morphology characteristics of mineralized products. The results showed that the wind erosion rate of the samples treated with EICP reduced significantly (the minimum wind erosion rate is 1.986 mg/(m2·min)) due to the crystal bridge function of CaCO3, while the UCS of these samples increased clearly. Appropriately increasing urease activity in the treatment solution contributed to the increased CaCO3 content and microscopic size. Excess urea concentration had a certain inhibitory effect on urease activity. The addition of PAM provided more nucleation sites for the EICP process and improved the strength of the cementation. These findings suggested that the EICP-PAM was a promising technique for the protection of FA sites from wind erosion.

Trend Analysis and Identification of the Meteorological Factors Influencing Reference Evapotranspiration

Investigating the trends of reference evapotranspiration (ETo) is fundamental importance for water resource management in agriculture, climate variability analysis, and other hydroclimate-related projects. Moreover, it would be useful for understanding the sensitivity of such trends to basic meteorological variables, as the modifications of these variables due to climate change are more easily predictable. This study aims to analyze ETo trends and sensitivity in relation to different explanatory meteorological factors. The study used a 17 year-long dataset of meteorological variables from a station located in Piazza Armerina, Sicily, a region characterized by a Mediterranean climate. First, the FAO-Penman-Monteith method was applied for estimation of ETo. Next, the Mann-Kendall test with serial autocorrelation removal by Trend-free pre-whitening (TFPW) was applied to analyze ETo trends and the basic meteorological variables on which they depend. Sen’s slope was also used to examine the magnitude of the trend of monthly ETo and its related meteorological variables. According to the obtained results, ETo only showed a downward trend of 0.790 mm per year in November, while no trend is shown in other months or on seasonal and annual time scales. Solar radiation (November and Autumn) and rainfall (Autumn) showed a downward trend. The other meteorological variables (minimum temperature, maximum temperature, mean temperature, wind speed, and relative humidity) showed an upward trend both at monthly and seasonally scale in the study area. The highest and lowest sensitivity coefficients of ETo in the study area are obtained for specific humidity and wind speed, respectively. Specific humidity and wind speed give the highest (44.59%) and lowest (0.9%) contribution to ETo trends in the study area. These results contribute to understanding the potential and possible future footprint of climate change on evapotranspiration in the study area.

Design of turbine accelerator and electromagnetic clutch for solar chimney power generation system

Solar chimney power generation system has the advantages of clean, no phase change heat transfer working medium, simple structure, low cost, and so on. It has been widely paid attention to and developed and can be combined with various forms of energy utilization, especially suitable for the rich solar energy resources, open areas, and thermal power supply is difficult to the region. To meet the minimum start-up requirements of a solar chimney power generation system, the minimum start-up wind speed of generator operation is studied on the angle between blade and turbine spindle connection (i.e., blade offset angle) and offset direction. The solar chimney power generation system is theoretically calculated and simulated to determine the minimum wind speed. Through ANSYS simulation of blades with different bias directions, the optimal bias angle is obtained as 6° by taking different downward bias angles. Appropriately reducing the area of blade pressure part reduces blade weight and saves material. Under the condition of not affecting the strength, the driving force is increased by changing the size of the blade root joint and the area of the blade tip stress area. Match accelerator and electromagnetic clutch.

Short-term wind power prediction method based on deep clustering-improved Temporal Convolutional Network

Carbon neutrality has become the global consensus, and wind power is one of the key technologies to achieve carbon neutrality in the power system. However, the randomness and fluctuation of wind energy pose a great challenge to the safe and stable operation of the power system. Accurate wind power prediction results can effectively reduce the adverse effects of wind power uncertainty on power system operation. The high proportion of wind power connected to the grid requires higher prediction accuracy. However, the existing wind power prediction methods exist the problems of inaccurate classification of numerical weather prediction (NWP) and insufficient consideration of actual characteristics. Therefore, a short-term wind power prediction method based on deep clustering-improved Temporal Convolutional Network (TCN) is proposed in this paper. First, 22 typical features of NWP are extracted (including maximum and minimum wind speed, maximum and minimum temperature, etc.). Then, a deep clustering model based on Categorical Generative Adversarial Network is constructed to classify the extracted NWP features accurately. Finally, to address the problem of partial feature loss in the training process of traditional TCN, a gating mechanism is introduced to improve the activation function of its residual block, and an improved TCN prediction model for each class is established. The actual operation data of three wind farms are used to verify the effectiveness and robustness of the proposed wind power prediction method. The results show that the proposed method can reduce the prediction error (Root Mean Squared Error) from 7.18% to 12.87% in three wind farms, compared with other traditional prediction methods.

Assessment of changes in climatic variables in hadeja-nguru wetlands

The study assessed the changes in climatic variables in Hadeja Nguru wetland. Parameters evaluated includes; variation in climatic variables over a 40-year window (1979-2019) Data on climatic variables on daily basis were obtained for a period 40years and Satellite imageries of the study area were officially downloaded from the United State Geological Survey website. Paired sample T-test and chi-square test of association were used in assessing the variation in climatic factors. Result showed that indicated sparse distribution as a major effect of climate change. Decreased production in crops, land shade, damaged of harvested crops, increase in insect population, shortening the time of germinating seeds were thought to be the consequences of reduction in rainfall, excessive rainfall, irregular rain pattern and increase in temperatures respectively, Food scarcity, reduction in income due to depletion of crops were also pinpointed as impacts of climate change. Information on climate change and its impact were obtained majorly from radio, while major mitigating strategy for changing climatic variables. Maximum and minimum temperatures, precipitation, wind speed, relative humidity and solar intensity between 1979 and 2019 were 40.07℃ and 36.46℃, 1180.36mm and 58.18mm, 2.97m/s and 2.29m/s, 0.33g/kg and 0.22g/kg and 23.68w/m2 and 20.70w/m2 respectively. There was also a significant Chi-square value (33.481a) between respondents’ effect of climate change and increase in farm sizes. It is therefore concluded that there has been fluctuations in climatic variables. There is the need to put in place right policies to protect and preserve wetlands to enhance their sustainability and resilience to climatic changes and variability.

Seasonal incidence and effect of abiotic factors on population dynamics of major insect pests on Brinjal crop

Experiment was carried out on Seasonal incidence and effect of abiotic factors on population dynamics of major insect pests on Brinjal (Solanum melongena L.) at College Farm, N. M. College of Agriculture, Navsari Agricultural University, Navsari during summer-2020. The results revealed that incidence of shoot borer infestation on shoot was commenced from second week of March (11th SMW) to third week of May (21st SMW) while, infestation of fruit damage was noticed at first picking of the brinjal fruit in the first week of April (14th SMW) with maximum in fourth week of May (21st SMW). Shoot and fruit borer infestation exhibited significant and positive correlation with maximum temperature (r= 0.8129) and minimum temperature (r= 0.8444) and wind velocity (r= 0.5871), respectively. Whitefly population was started from second week of February (9th SMW) with 0.93 whitefly per leaf, increased gradually to reach its peak in the last week of April, i.e.17th SMW (15.95 whiteflies/leaf) and positively and significantly correlated with and maximum temperature (r= 0.8062). The jassid infestation started from first week of March, i.e. 10th SMW (0.34 jassids/ leaf), initially increased slowly and reached to its peak level (11.89 jassids/leaf) at last week of April (17th SMW) and positively and significantly correlated with maximum (r= 0.7202) and minimum (r= 0.5712) temperatures. Aphid population started from the first week of March (10th SMW) with a minimum level of 0.60 aphids/leaf, increased slowly and reached to its peak level (5.04 aphids/leaf) at third week of April (16th SMW) and exhibited positive and significant correlation with maximum temperature (r= 0.7849).

Performance of DFIG fed wind turbine under fault conditions

This paper discuss the performance of doubly fed induction generator (DFIG) fed wind turbine system under fault conditions. The proposed performance analysis is demonstrated in MATLAB/Simulation software. A sailent pole rotor type induction generator and three phase converter comprises with IGBT switches are employed to propose the DFIG system. The proposed design has ability to extract utmost energy under minimum wind speed constraints. In order to achieve utmost energy, various control designs such as pitch angle control, wind turbine speed, DC bus potential, grid potential and reactive power control are presented in this paper. The simulation results obtained are helps in understanding the dynamic performance of the DFIG fed wind turbine power generation system.

EFFICIENCY OF USING AN ALTERNATIVE ENERGY SOURCE AT AGRICULTURAL ENTERPRISES

To reduce emissions of industrial gases, which are the main cause of global warming, the task is currently being set to increase natural alternative energy sources: solar, wind, water, bioenergy. (Research purpose) The research purpose is selecting the optimal parameters and justification of the efficiency of installing a wind generator at an agricultural enterprise, based on the available data for monitoring wind speed in a given area. (Materials and methods) Carried out wind load monitoring for one month in the branch of the enterprise of JSC “Chuvashkhleboprodukt” - Tyurleminsky grain receiving enterprise, Kozlovsky district of the Chuvash Republic, Tyurlem station. The horizontal component of wind movement was taken as the research material. A cup anemometer MS-13 was used to measure wind speed. The measurement time is March 6 - April 8, 2022 from 8:00 to 19:00 hours daily with an interval of one hour. Processed the data in Excel. Graphical dependences of wind speed on the time of day and days of the week were built. (Results and discussion) It was established that the average wind speed was 5 meters per second, the minimum wind speed was 0.8 meters per second, and the maximum wind speed was 19.4 meters per second. (Conclusions) It was revealed that the most attractive from the point of view of wind load are Kozlovsky, Mariinsko-Posadsky, Cheboksary and Tsivilsky districts of the Chuvash Republic. We determined that from the point of view of efficiency in the near future, we must wait for fundamentally new solutions in the development of wind energy. It was stated that the use of vortex effects in the design of wind turbines seems promising, which makes it possible not only to start generating energy in the winds at a speed of 3.0-4.0 meters per second, but also will make it possible to work from ascending heat flows, as well as rid the environment of low-frequency noise components inherent in any blade wind turbine.

PROGRESS OF FUNGAL FOLIAR DISEASES IN RELATION TO WEATHER PARAMETERS IN COTTON

Progress of fungal foliar diseases as influenced by different weather parameters in two BG II cotton hybrids (Jaadoo and RCH 2) and one straight variety (L 1060) was studied during kharif 2019-20. Disease severity was scored by adopting a standard scale of 0 to 4 in ten plants tagged randomly in each plot. Weather parameters were recorded at the meteorological observatory, RARS, Lam, Guntur.Correlation and multiple regression analysis were carried out between per cent disease index and weather parameters using MS-Excel program version 2010. Multiple regression analysis revealed certain differences in weather factors influencing the progress ofdiseases in different genotypes.Sunshine hours, the number of rainy days and wind speed are the common critical parameters contributing to the development of Alternaria and Corynespora leaf spots; minimum temperature and wind speed in case of grey mildew; RH II for rust are favourable parameters in cotton.

Estimation of the Daily Maximum Air Temperature for Baghdad City Using Multiple Linear Regression

In this paper, we relied on historical observations for the period between (2005-2020) for the Baghdad meteorological station, which is characterized by a hot, dry climate in summer and cold and rainy in winter, as it is an example of a subtropical region. The multiple linear regression equation was developed and improved to produce a formula that predicts maximum air temperature. This was done by relying on climatic elements, namely minimum air temperature, wind speed, and relative humidity, and entering them into the formula as independent inputs that have a direct impact on estimating the maximum air temperature also calculating the correlation coefficients for each of them. The bias of the model was calculated and its value was entered as the correction for errors that accompany the application of the model.