Wind Speed Research Articles

Assessing the quality of atmospheric motion vectors using wind profiler radar observations over Cochin (10.04°N, 76.33°E)

ABSTRACT Atmospheric Motion Vectors (AMVs) are nearly continuous wind data estimated using satellites, derived from tracking cloud movements and water vapour gradients via sequential geostationary or polar satellite imagery. AMVs have been an integral part of Numerical Weather Prediction (NWP) since the early years, and hence, ensuring their quality is of utmost importance. This work utilizes the observations from the 205 MHz Stratosphere-Troposphere (ST) wind profiler radar placed at the Advanced Centre for Atmospheric Radar Research (ACARR) in Cochin (10.04 ∘ N, 76.33 ∘ E), India, to validate 3 years (2017–2019) of AMV data from the Indian Ocean Data Coverage (IODC) mission Climate Data Records (CDR) from Meteosat-8. The AMVs are classified into different atmospheric levels based on their pressure: lower, middle, and upper, and compared with collocated radar wind measurements. A detailed analysis was performed only on upper-level winds as filtering out low-quality AMVs significantly reduced the number of collocations in lower and middle levels. A strong agreement was observed between satellite and radar upper-level wind measurements with biases of 0.79 ± 5.9 m s − 1 and 3.07 ± 35.6 ∘ in wind speed and direction, respectively. Seasonal variability is seen in the wind speed discrepancies, such as more in winter and summer than in spring and autumn, and it can be attributed to the larger vertical shear during summer and winter. The maximum error in upper-level AMV height assignment is quantified to ± 4 km. The observed differences may arise because satellite-derived AMV heights tend to be overestimated (underestimated) in low (high) wind speed conditions.

The bleeding weather: association of environmental changes with intracranial aneurysms’ rupture

BackgroundVarious studies have attempted to link aneurysmal subarachnoid hemorrhage (aSAH) with environmental factors, yielding inconsistent results.MethodsWe retrospectively collected clinical and demographic data from all patients who presented to our hospital with aSAH between January 1st, 2015, and December 31st, 2021.ResultsOur study found a significant variation in the occurrence of aneurysm ruptures based on the day of the week, with more ruptures occurring on Mondays compared to Fridays. Additionally, we discovered that the amount of dust in the air prior to the ictus influenced the severity of the aSAH. In contrast, other environmental parameters such as atmospheric pressure, temperature, humidity, rain, wind speed, and cloud cover did not significantly impact the prevalence or severity of aSAH.ConclusionsTo our knowledge, this is the first study to associate the severity of aSAH with atmospheric dust levels. Another key finding is the increased incidence of aneurysm ruptures on Mondays compared to Fridays.

Spatiotemporal covariability between air pollution and meteorological variables over Khyber Pakhtunkhwa, Pakistan.

This study analyzed spatiotemporal covariability of O3, SO2, NO2, CO, and PM2.5 with meteorological variables (rainprecipitationrate, specific humidity, pressure, temperature, wind speed, latent heat flux, and solar radiation) using satellite data in Khyber Pakhtunkhwa province, Pakistan. Inverse Distance Weighted interpolation, ordinary least square regression, Pearson correlation, Generalized Linear, and Generalized Additive models were applied. Results revealed highest annual average pollutants as; NO₂ (3.87 ± 0.73) × 1015 molecules/cm2, PM2.5 (37.91 ± 17.75) µg/m3, SO2 (6.81 ± 8.27) × 1014, CO (1.34 ± 0.52) × 1018molecules/cm2, and O3 (7.73 ± 0.10) × 1018molecules/cm2. Seasonally NO2 peaked in summer and spring, SO₂ in autumn, CO in spring, PM2.5 in winter while O₃ in spring with minor seasonal variations. Annual spatial distribution of SO2, PM2.5, and CO were highest in central and southern areas while O3 in the central and NO2 in the central and southeastern. Wind speed was negatively correlated with NO2 annually and in winter, summer, and autumn. Temperature positively influenced NO2 and PM2.5 annually and seasonally, while O3 positively correlated with rain and specific humidity but negatively with solar radiation and temperature in spring. In autumn, O3 exhibited a positive association with rain and negative with solar radiation. SO2 indicated positive correlations with solar radiation annually and temperature in spring, while CO showed weak associations except for a positive correlation with specific humidity in summer. GAM models slightly better captured pollutant dynamics by explaining both linear and nonlinear relationships. These findings provide crucial insights for targeted air quality management strategies and pollutant mitigation.

Forewarning the seasonal dynamics of corn leafhopper and mollicutes through neural networks.

The corn leafhopper (CL), Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae), has become the most important corn pest in Brazil and other corn-producing countries. This highly efficient insect vector transmits corn stunting pathogens resulting in significant yield losses in corn fields. This study aimed to investigate the relationship between CL abundance and pathogen infection in adult CL with weather variables, day of the year (DOY), and corn season in four Brazilian corn-producing areas using artificial neural networks (ANN). We developed three ANN models, using monitoring data from 2019 to 2023, for year-round forewarning of CL populations and infection of corn stunt spiroplasma (CSS) and maize bushy stunt phytoplasma (MBSP) in CL adults. The best-fit models demonstrated strong correlations in the validation set for CL abundance (0.71), and substantial classification agreement for both CSS (0.81) and MBSP (0.81). The final inputs for the models included relative humidity, air temperature, wind speed, DOY, corn season, and CL abundance. The presence of corn plants and DOY are manageable factors for achieving CL and mollicute control. This can be made by eliminating volunteer plants, reducing planting windows, and avoiding late-plantings. Our results are suitable for further predictions and offer essential guidance to be incorporated into the IPM of D. maidis and to better understand CSS and MBSP infection on a large-scale. Lastly, ANN is a reliable machine-learning algorithm to predict vector population dynamics and the infection of phytopathogens in D. maidis.

National serosurvey and risk mapping reveal widespread distribution of Coxiella burnetii in Kenya

Coxiella burnetii, the causative agent of Q fever, is an emerging pathogen that has the potential to cause severe chronic infections in animals and humans worldwide. The detrimental impact on public health is projected to be higher in the low- and middle-income countries given their lower capacity to sustain effective surveillance and response measures. We implemented a national serosurvey of cattle in Kenya to map the spatial distribution of the pathogen. The study used serum samples that were collected from randomly selected cattle in different ago-ecological zones across the country. These samples were screened for the pathogen using PrioCHECK Ruminant Q Fever AB Plate ELISA kit. The laboratory findings were analyzed using INLA package to identify risk factors for C. burnetii exposure from herd- and animal-level factors, area, and bioclimatic datasets accessed from online databases. A total of 6,593 cattle were recruited for the study; of these, 7.9% (95% CI; 7.2–8.5) were seropositive. Outputs from the multivariable analysis revealed that the animal age and some of the geographical variables including wind speed, area under shrubs and “petric calcisols” type of soil were significantly associated with C. burnetii seropositivity. Being a calf, weaner or subadult was associated with lower odds of exposure compared to being an adult by 0.24 (credibility interval: 2.5% and 97.5%), 0.41 (0.30–0.55) and 0.51 (0.38–0.69), respectively. In addition, a unit increase in the wind speed increased the odds of C. burnetii seropositivity by 1.27 (1.05–1.52) while an increase on the land area under shrubs was associated with lower odds of exposure (0.67 [0.47–0.69]). The effect of petric calcisols was non-linear; an increase of the land area with this soil type was associated with an exponential increase in C. burnetii seropositivity. This study provides new data on C. burnetii seroprevalence, information of its risk factors and a prevalence map that can be used for C. burnetii risk surveillance and control. The identification of environmental risk factors for C. burnetii exposure, and the increasing awareness of the zoonotic potential of the pathogen, calls for the need to enhance the existing collaborations for the surveillance and control of C. burnetii in line with the One Health framework. The evidence generated on the potential role of environmental factors can also be used to design nature-based interventions, such as replacement of vegetation in denuded areas, to reduce potential for the aerosolization of the pathogen. Livestock vaccination in the hotspots would also reduce animal infections and hence the contamination of the environment.

Impact of meteorological factors and atmospheric particulate matter on background radiation.

The interactions between meteorological factors, atmospheric particulate matter (PM), and background radiation were investigated in this study. Three databases that recorded these data in Taipei were used and multiple linear regression was applied to analyze the data. It turned out the distributions of meteorological factors, PM2.5 and PM10 concentrations, and background radiation differed significantly between periods of sunny and rainy hours. Background radiation was positively correlated with temperature and relative humidity, but negatively correlated with wind speed on sunny and rainy days. In particular, background radiation significantly increased with PM2.5 and PM10 concentrations on sunny days or nights. However, on rainy days or nights, the background radiation significantly increased with precipitation, regardless of the PM concentration. The effects of PM2.5, PM10 and precipitation on background radiation were found to last up to 1, 5 and 4h, respectively. In conclusion, meteorological factors and PM have significantly different effects on background radiation on sunny and rainy hours.

An Evaluation of the Power System Stability for a Hybrid Power Plant Using Wind Speed and Cloud Distribution Forecasts

An Evaluation of the Power System Stability for a Hybrid Power Plant Using Wind Speed and Cloud Distribution Forecasts

Impacts of Terrain Slope and Surface Roughness Variations on Turbulence Generation in the Nighttime Stable Boundary Layer

AbstractTerrain slopes with and without upslope large surface roughness impact downstream shear‐generated turbulence differently in the nighttime stable boundary layer (SBL). These differences can be identified through variations in the relationship between turbulence and wind speed at a given height, known as the HOckey STick (HOST) transition, as compared to the HOST relationship over flat terrain. The transport of cold surface air from elevated uniform terrain reduces downstream air temperature not much air stratification. As terrain slope rises, the increasing cold and heavy air enhances downstream hydrostatic imbalance, resulting in increasing turbulence for a given wind speed. That is, the rate of turbulence increase with wind speed from downslope flow is independent of terrain slope. Upslope large surface roughness elements enhance vertical turbulent mixing, elevating cold surface air from the terrain. Horizontal transport of this elevated, cold, turbulent air layer reduces the downstream upper warm air temperature. Benefiting from the progressive reduction of downstream stable stratification with increasing height in the SBL, wind shear can effectively generate strong turbulence. In addition to the turbulence enhancement from the cold downslope flow, the rate of turbulence increase with wind speed is elevated. This study demonstrates key physical mechanisms for turbulence generation captured by the HOST relationship. It also highlights the influence of terrain features on these mechanisms through deviations from the HOST relationship over flat terrain.

CFD-based design of air wall water blocking for underground garage entrances and exits.

In the increasingly perfect underground garage construction process, the underground garage entrance water blocking problem is getting more and more attention. This paper proposes a kind of air wall water-blocking device applied to underground garage. The device is installed on the side of the straight and curved paths at the entrances and exits of the spiral underground garage. It utilizes two fans to blow the water to the drain on the other side to achieve unobstructed access to stop the water. In this paper, CFD simulation is firstly carried out on the straight road of the spiral underground garage to verify the feasibility of the program. The accuracy of the simulation results was verified by building a straight road model and conducting experiments. After that, an equal-scale 3D model of the spiral underground garage was built. Orthogonal experiments on the effects of inlet water flow velocity, fan wind speed at the straight road and fan wind speed at the curved road on the water blocking effect were carried out by using CFD simulation. A preliminary range of water velocity of 2.250 m/min to 6.786 m/min was obtained experimentally, and this range of water velocity was used as an input parameter for the simulation. The results show that the speed of water flow at the entrance of the garage and the wind speed of the fan at the straight road have a greater effect on the water blocking effect than the wind speed of the fan at the curved road. When the wind speed of the fan at the straight road is 25m/s, proper adjustment of the wind speed of the fan at the curved road can realize a good water-blocking effect of the spiral underground garage entrance within the range of water flow rate of 2.250m/min ~ 6.786m/min. Therefore, in the practical application of using the air wall water blocking scheme to realize the underground garage entrance water blocking strategy is to give priority to improve the wind speed of the fan at the straight road to improve the effect of water blocking.

Short-term wind speed prediction with adaptive signal processing based hybrid statistical models

Short-term wind speed prediction with adaptive signal processing based hybrid statistical models

Weather sensing with structured light

Environmental conditions, such as temperature and wind speed, heavily influence the complex and rapidly varying optical distortions propagating optical fields experience. The continuous random phase fluctuations commonly make deciphering the exact origins of specific optical aberrations challenging. The generation of eddies is a major contributor to atmospheric turbulence, similar in geometric structure to optical vortices that sit at the center of beams that carry Orbital Angular Momentum (OAM). Decomposing the received optical fields into OAM provides a unique spatial similarity that can be used to analyze turbulent channels. In this work, we present a mode decomposition assisted machine learning approach that reveals trainable features in the distortions of vortex beams that allow for effective environmental monitoring. This technique can be used reliably with Support Vector Machine regression models to measure temperature variations of 0.49 °C and wind speed variations of 0.029 ms−1 over a 36 m experimental turbulent free-space channel with controllable and verifiable temperature and wind speed with a short 3 s measurement. These findings could indicate the presence of an underlying physical relationship between environmental conditions that lead to specific eddy formation and the OAM spiral spectra. Therefore, this relationship could be used to develop next generation optical weather sensors.

Validation of BARRA2 and comparison with MERRA-2 and ERA5 using historical wind power generation

Atmospheric reanalyses are a popular source of wind speed data for energy modelling but are known to exhibit biases. Such biases can have a significant impact on the validity of techno-economic energy assessments that include simulated wind power. This study assesses the Australian BARRA-R2 (Bureau of Meteorology Atmospheric Regional Reanalysis for Australia, version 2) atmospheric reanalysis, and compares it with MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, V2) and ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis, fifth generation). Simulated wind power is compared with observed power from 54 wind farms across Australia using site-specific wind turbine specifications. We find that all of the reanalyses replicate wind speed patterns associated with the passage of weather systems. However, modelled power can diverge significantly from observed power at times. Assessed by bias, correlation and error, BARRA-R2 gave the best results, followed by MERRA-2, then ERA5. Annual bias can be readily corrected by wind speed scaling; however, linear scaling will not narrow the error distribution, or reduce the associated error in the frequency distribution of wind power. At the level of a wind farm, site-specific factors and microscale wind behaviour are contributing to differences between simulated and observed power. Although the performance of all the reanalyses is good at times, variability is high and site-dependent. We recommend the use of confidence intervals that reflect the degree of uncertainty in wind power simulation, and the degree of confidence required in the energy system model.

Flow acceleration statistics: a new paradigm for wind-driven loads, towards probabilistic turbine design

Abstract. A method is developed to identify load-driving events based on filtered flow acceleration, regardless of the event-generating mechanism or specific temporal signature. Low-pass filtering enables calculation of acceleration statistics per characteristic turbine response time; this circumvents the classic problem of small-scale noise dominating observed accelerations or extremes, while providing a way to deal with different turbines and controllers. Not only is the flow acceleration physically meaningful, but its use also removes the need for de-trending. Through consideration of the 99th percentile (P99) of filtered acceleration per each 10 min period, we avoid assumptions about distributions of fluctuations or turbulence and derive statistics of load-driving accelerations for offshore conditions from “fast” (10 and 20 Hz) measurements spanning more than 15 years. These statistics depend on low-pass-filter frequency (the reciprocal of turbine response time) but vary in a nontrivial manner with height due to the influence of the atmospheric boundary layer's capping inversion, as well as the surface. We find long-term probability distributions of 10 min P99 of filtered accelerations, which drive loads ranging from fatigue to ultimate; this also includes joint distributions of the P99 with the 10 min mean wind speed (U) or standard deviation of horizontal wind speed fluctuations (σs). The long-term mean and mode of the P99 of streamwise acceleration, conditioned on σs and U, are found to vary monotonically with σs and U, respectively; this corroborates the International Electrotechnical Commission (IEC) 61400-1 prescriptions for fatigue design load cases. An analogous relationship is also seen between lateral (directional) acceleration and the standard deviation of direction, particularly for submesoscale fluctuations. The largest (extreme) P99 of filtered accelerations are seen to be independent of 10 min mean speeds and have only limited connection to 10 min σs; traditional 10 min statistics cannot be translated into extreme load-driving acceleration statistics. From measurement heights of 100 and 160 m, time series of the 10 most extreme acceleration events per 1 m s−1 wind speed bin were further investigated; events of diverse character were found to arise from numerous mechanisms, ranging from nonturbulent to turbulent flow regimes and also depending on the filter scale. Different behaviors were noted in the lateral and streamwise directions at different heights, although a small fraction of these events exhibited extreme amplitudes for both horizontal acceleration components and/or were observed at both heights within a given 10 min window. Via fits to the tails of the marginal P99 distributions, curves of offshore extreme P99 of filtered acceleration for return periods up to 50 years were calculated for three characteristic turbine response times (filter scales) at the observation heights of 100 and 160 m. To drive aeroelastic simulations, Mann-model parameters were also calculated from the time series of the most extreme events, allowing constrained simulations embedding the recorded events. To facilitate this for typical industrial measurements that lack three-dimensional anemometry, a new technique for obtaining Mann-model turbulence parameters was also created; this was employed to find the parameters corresponding to the background flow behind the extremes identified and their time series. Further, a method was created to use the extreme acceleration statistics in stochastic simulations for application to loads, including interpretation within the context of the IEC 61400-1 standard. Preliminary parallel work has documented aeroelastic simulations conducted using the extreme event time series identified here, as well as Monte Carlo simulations based on the extreme statistics and new method for stochastic generation of acceleration events.

Forecasting particulate matter concentration in Shanghai using a small-scale long-term dataset

This study applies machine learning techniques, specifically stacked generalization, to develop a 1-day-ahead air pollution forecasting model for Shanghai, one of the largest metropolitan areas in the world, taking advantage of the essential information on air quality that can be inferred from a small but long-term dataset of meteorological and pollutant concentration variables (PM10 and PM2.5), consisting of only 4240 samples. We conducted a comprehensive analysis of daily-averaged meteorological observation data, including temperature (T), relative humidity (RH), wind speed (WS) and direction (WD) and precipitation (P) from 74 automatic weather stations over a 12-year period, from 2011 to 2021, and satellite-retrieved aerosol optical depth (AOD) at 550 nm and planetary boundary layer height (PBLH) for the same time interval. In addition, principal component analysis (PCA) was used to identify the most prevalent synoptic weather patterns in the Shanghai region to assess the origin of pollution advection sources. Thanks to the long-term trends information used for model training, combined with machine learning stacking generalization techniques, the developed model improved the prediction results of alternative methods for pollution forecasting with limited observations, obtaining RMSE and R2 values of 11.93μgm-3 and 0.72, respectively. Moreover, it was able to forecast most of the pollution peaks, such as those in January 2019 and November 2020, showing itself as a useful tool for policy making and alerting for health risks. The results of this study highlight the need for cohesive strategies that tackle both air quality and climate change to promote sustainable urban growth and environmental robustness.

Can Solar‐Powered Irrigation Systems Naturally Meet Crop Water Requirements? Proof of Concept From a Case Study in Sub‐Saharan Africa

ABSTRACTIrrigation scheduling is crucial for ensuring precise water delivery to crops. However, in many sub‐Saharan African irrigation schemes, water is applied without considering crop water needs, resulting in low crop water productivity and low yields. Solar‐powered irrigation systems can automatically meet these needs by utilizing solar radiation, which drives both evapotranspiration and solar panel power production for pumping. This study aimed to integrate irrigation scheduling into a solar‐driven irrigation system and assess the impact of meteorological variables on reference evapotranspiration (ETo) in Ghana. A 50‐watt solar panel powered a 12‐V submersible pump, with a flow meter installed on the outlet pipe for hourly volume of water pumped (VWP) data readings. These data were used to examine correlations between solar radiation (Rs) and ETo, as well as between Rs and VWP. Partial correlation analyses were used to assess the relative influences of Rs, wind speed (U2), relative humidity (RH) and air temperature (Tair) on ETo across 10 locations in Ghana's agroecological zones. The study revealed a strong linear correlation between the hourly Rs and ETo (R2 > 0.9) and between the hourly Rs and VWP (R2 = 0.8). The VWP was sufficient to meet crop‐water demand year‐round. Solar radiation was consistently the primary meteorological factor influencing ETo in Ghana.

Flow Patterns Providing Maximum Speed-Up Ratio and Maximum Speed-Up Area of Pedestrian-Level Winds

Wind speed increases in pedestrian-level spaces around high-rise buildings tend to cause uncomfortable and even unsafe wind conditions for pedestrians. Especially, instantaneous strong winds can have a significant impact on the body sensation of pedestrians, and they are usually related to complex flow patterns around buildings. A detailed examination of flow patterns corresponding to instantaneous strong wind events around high-rise buildings is essential to understanding the physical mechanism of this phenomenon. To quantitatively evaluate the pedestrian-level wind environment around high-rise buildings, two important indices, speed-up ratio and speed-up area, have usually been introduced. In this study, a Large Eddy Simulation (LES) was conducted for square-section building models with different heights, represented by H (=100 m, 200 m, and 400 m in full-scale) or aspect ratios, represented by H/B0 (=2, 4, and 8), where B0 (=50 m in full-scale) represents the building width. Two instantaneous strong wind events providing a “maximum speed-up ratio” and a “maximum speed-up area” of pedestrian-level wind are investigated based on a conditional average method. The results indicate that these two instantaneous strong wind events usually do not occur simultaneously. Flow patterns around buildings for the two events are also different: the contribution of downwash tends to be larger for strong wind events providing “maximum speed-up area” showing more three-dimensional characteristics.

Temporal and spatial heterogeneity of tropospheric O3 and NO2 and health impact analysis in Shaanxi, Gansu, and Ningxia regions of China.

O3 and precursor pollution in the Shaanxi-Gansu-Ningxia region of China is becoming increasingly severe, and the regional pollution characteristics are also more prominent. To investigate the causes of O3 and NO2 pollution and the health impacts of O3, the spatial and temporal heterogeneity of O3 and NO2 pollution and the health and economic losses in the Shaanxi-Gansu-Ningxia region were analyzed by using a detector with optimal parameters and the BenMAP (Environmental Benefits Mapping and Analysis Program-community Edition) model. The results show that O3 in the troposphere of Shaanxi, Gansu, and Ningxia showed a "bimodal" distribution from 2005 to 2022, reaching a maximum value of 32.6 DU in 2010, and the change of NO2 increased first and then decreased in the troposphere, reaching a peak value of 5.2 × 1015 molec·cm-2 in 2011. The seasonal variations of O3 and NO2 were the highest in winter, the second highest in spring and fall, and the lowest in summer. The high-value area of O3 was mainly located in the northwest of Gansu. The concentration gradually decreased from northwest to southeast. In contrast, the high-value area of NO2 was concentrated in the east of Guanzhong Plain and the north of Yulin City, and the overall distribution was high in the east and low in the west. Among the interactions of the nine factors, the interactions of temperature and wind speed, precipitation and wind speed had the highest explanatory power for O3 changes, with 0.951 and 0.96, respectively, and the interactions of temperature and wind speed, and precipitation and sunshine hours had the highest explanatory power for NO2 changes, with 0.834 and 0.844, respectively; the interactions among pollutants were weaker than the interactions among meteorological factors. Cardiovascular disease is the leading cause of premature death in the population. The number of premature deaths in the three provinces gradually decreases from 2018 to 2020, and the proportion of health economic loss to GDP also gradually decreases, from 1.55%, 0.82%, and 3.99% to 0.2%, 0.34%, and 2.86%, respectively. This study can provide theoretical references for the control and health impacts of O3 and NO2 in Shaanxi, Gansu, and Ningxia regions of China.

Machine learning predicts pedestrian wind flow from urban morphology and prevailing wind direction

Abstract Pedestrian-level wind plays a critical role in shaping the urban microclimate and is significantly influenced by urban form and geometry. The most common method for determining spatial wind speed patterns in cities relies on numerical computational fluid dynamics (CFD) simulations, which resolve Navier-Stokes equations around buildings. While effective, these simulations are computationally intensive and require specialised expertise, limiting their broader applicability. To address these limitations, this study proposes a more cost-effective alternative while achieving 90% performance in capturing the mean and maintaining spatial wind patterns captured by CFD. We developed a machine learning (ML) approach with U-net architecture to predict time mean wind speed patterns from prevailing wind directions and three-dimensional urban morphology, which are increasingly available for global cities. The model is trained and tested using a comprehensive dataset of 512 numerical simulations of urban neighbourhoods, representing diverse morphological configurations in cities worldwide. We find that the ML algorithm accurately predicts complex wind patterns, achieving a normalised mean absolute error of less than 10%, which is comparable to wind anemometer measurement in a low wind speed environment. In predicting wind statistics, the ML model also surpasses that of regression models based solely on statistical representations of urban morphology. The R2 values measuring grid-level agreement between ML and CFD range from 0.94-0.99 and 0.65-0.95 for the idealised and whole datasets, respectively. However, we find that grid-based R2 is not an effective metric for evaluating the 2D model performance due to localised biases arising from faster wind speed grid regions, which is revealed by the wind probability density function. These findings demonstrate that complex pedestrian wind patterns can be effectively predicted using an image-based ML approach, offering the potential to emulate physics-based LES models, which are computationally expensive, thereby significantly reducing computing costs.

Simulation and experimental study of shell-kernel separation device for household sunflower based on CFD-DEM coupling

In order to improve the separation efficiency of shell and kernel in a household-type sunflower seed sheller. The coupled research method of Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) was employed to simulate the shell-kernel separation process in the air channel. The main function of this shell-kernel separation device is to separate sunflower seed shells from sunflower kernels using airflow. This enables the dehulling of sunflower seeds, preparing them for subsequent processing or consumption.The air channel structure was optimized by analyzing pressure and velocity cloud images to enhance the separation efficiency. Comparison was made between the results of physical validation experiments and simulation experiments, including the shell and kernel separation rate, loss rate, and wind speed at the outlet. The P-values of the T-tests were less than 0.05 and the relative errors were all less than 3.00%, which proved the accuracy of the simulation experiments. Using shell kernel separation rate and loss rate as evaluation indexes, and air channel angle and wind speed as research objects, a single-factor experiment was conducted and the optimal range of each factor was determined. Based on the results of the single-factor experiments, orthogonal experiments were conducted, and the optimal combination of operating parameters for the device was determined using a comprehensive scoring method. A bench-scale validation experiment was conducted to verify the optimal combination of operational parameters. The results showed that, under the conditions of a wind speed of 6 m/s and an airway angle of 31°, the shell kernel separation rate reached 89.91%, with a loss rate of 6.24%.

Linear regression with Fibonacci-derived polynomials for temperature prediction model

This research work explores the integration of Fibonacci-derived polynomial and linear equations from Fibonacci numbers into a machine learning framework for predictive modeling of environmental datasets, such as wind speed, temperature, and humidity. The research aims to evaluate the effectiveness of combining deterministic mathematical equations with traditional machine learning techniques to improve prediction accuracy and uncover hidden patterns in natural datasets. This novel approach was implemented on a number of models which showed a good result for further research. The linear and Fibonacci-derived polynomial equations showed features of machine learning and since the Fibonacci pattern have been adopted in natural science, this technique of using the deterministic mathematical equation for feature engineering became fitting for natural dataset, the option of working with time series and environmental dataset have shown encouraging result and an above average accuracy.