Wind Profiles Research Articles

Adaptation of Wind Drag Coefficient Parameterization: Improvement of Hydrodynamic Modeling by a Wave‐Dependent Cd in Large Shallow Lakes

AbstractWind is a critical driving force in hydrodynamic and water quality modeling of large shallow lakes, and is characterized by the wind drag coefficient Cd, representing the momentum transfer at the air‐water interface. Contemporary empirical formulae for Cd estimation were derived over oceans and some of which are solely wind velocity U10 dependent. These formulae were previously found to be inadequate in inland lake models often resulting in the water velocity underestimation. To address this problem, a physical scale experiment was designed, in which Cd was measured using a wind profile and eddy covariance methodology. A new wind‐induced wave‐dependent Cd parameterization was also established and validated in two lake studies. The driving force was modified by the wave‐dependent Cd formula in a hydrodynamic model of the shallow Upper Klamath Lake (UKL), OR, USA. The experimental Cd was negatively correlated to the wind velocity up until the critical U10 = 1.6 m s−1 which was 1.0~3.1 times previous empirical extrapolations at light winds. The variation partitioning results showed that wave parameters contributed to more than 30% of Cd variation combined with wind parameters. The modified wind stress field was spatially heterogeneous and the modeled water velocity was closer to the observations at two sites. Significant main circulation and outer bank circulation were modeled accompanied by higher surface vorticity, compared to the original UKL model. Overall, the wave‐dependent Cd formula provided an improvement of the surface flow field in the UKL model and will improve the management of the lake ecosystems.

Experimental study on rooftop flow field of building based on the operation of vertical-axis wind turbines

Vertical-axis wind turbines (VAWTs) demonstrate good adaptability for harnessing wind energy on the building rooftops. However, knowledge gaps still exist in the understanding of the wind resources on the rooftop, the operation of VAWTs, and its effect on the wind field. To add new knowledge on this subject, the operation of wind turbines modeled by the NACA0018 airfoil on the rooftop of a building is experimentally studied via wind tunnel. The results indicate that flow above the rooftop of building shows an obvious speed reduction due to the blunt body effect of the turbines, and the turbulence intensities are dramatically enhanced. The different tip-speed ratios, wind directions, and installation locations of wind turbine have significant effect on flow field on the roof. Due to the flow characteristics above the rooftop, the power spectral density of the fluctuating wind speed exhibits high energy below the top position of the VAWT on the rooftop. Additionally, the wind profiles and probability distribution of the wake of VAWTs on the rooftop are analyzed and mathematically fitted for quantifying wind field characteristics rooftop turbines on the roof. For the VAWTs investigated in this study, the impacts of the operation of VAWTs on the wind field are non-negligible on the roof of building. The findings of this study will provide valuable insights for the optimal placement of VAWTs and utilization of wind energy on the rooftops.

The Wind Profile Characteristics of Super Typhoon Lekima Based on Field Measurement

Many cities in coastal areas are prone to typhoon disasters due to their location on the Pacific storm path, and the direct effect of catastrophic winds can lead to the destruction of low-rise buildings and severe damage to high-rise structures. The purpose of this study was to enhance the understanding of boundary layer wind profiles of strong typhoons in coastal areas and reduce property losses and casualties caused by wind disasters. Based on the field measurements of wind profile acoustic radar in coastal areas, the variation characteristics of the boundary layer wind profile during the passage of super typhoon Lekima were first studied in depth, and the evolution law of the typhoon boundary layer profile was summarized. Then, the effects of typhoon horizontal structure, topography, wind speed, and time distance on the characteristics of the typhoon profile were discussed, respectively. Finally, the evolution characteristics of wind profile parameters were obtained by fitting three wind profile theoretical models. Due to the strong variability of typhoon profile morphology, the theoretical model of wind profile is only applicable to the wind profile from the bottom to the low-level jet height of typhoons, while wind parameters are closely related to the spatial location of the typhoon wind field.

Multi-scale simulation of typhoon wind field at building scale utilizing mesoscale model with nested large eddy simulation

Based on the mesoscale WRF (Weather Research Forecast) and LES (Large Eddy Simulation), an integrated numerical simulation framework aiming to resolve typhoon wind fields around urban building blocks has been developed. The holistic multi-scale simulation spans four scales: macroscale (∼1000 km), mesoscale (∼100 km), microscale (∼1 km), and building scale (0.1–100m). By interactively sharing the meteorological data among different scales, this simulation framework addressed the challenge of downscaling typhoon effects in urban environments. Typhoon weather reanalysis and urban wind field simulation were conducted by the WRF model with nested computational domains incorporating high-resolution topography data. For the downscaled LES model with urban buildings, the wind profiles produced by the WRF model were used at the interface with provided turbulent fluctuations in the flow. K11 building is a 270-m-tall building located in a densely built-up area of Hong Kong. Wind velocity and pressure fields surrounding the K11 building in Hong Kong during Typhoon Kammuri were obtained by the proposed framework, and the simulation results were validated by comparing with the meteorological data and full-scale measurements at the top of the K11 building. The interactions of the wind fields and building clusters resulting in vortex shedding, separation, and channeling effects have been resolved at high resolutions. Probabilistic distributions and higher-order statistics of wind pressure, i.e., skewness and kurtosis, were presented to highlight non-Gaussian characteristics of the simulated local wind pressure on the K11 building. Based on the simulated wind pressure, wind-induced vibration of the K11 building during the typhoon was analyzed and compared to the measurements.

Wind Profile Reconstruction Based on Convolutional Neural Network for Incoherent Doppler Wind LiDAR

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results.

Geospatial optimisation of wind and solar energy generation: A framework for optimised resource allocation

Wind and solar power generation profiles exhibit a high degree of power output variability and uncertainty compared to conventional fossil fuel generation plants. However, it is generally accepted that this variability and uncertainty can be mitigated by geographic siting and dispersion of the renewable energy plants, particularly with the view to leverage aggregation effects. In this context, this paper proposes a framework for the geospatial optimisation of wind and solar PV generation for scenarios with a high penetration of wind and solar renewable energy. The proposed optimisation framework represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, the wind and solar resources are categorised in terms of the statistical properties of the associated spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time of Use periods. It is thereby recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time of Use periods are more important than others. The proposed framework is implemented for a number of baseline case studies and optimisation case studies, using wind and solar resource data sets covering South Africa. Overall, it is concluded that this approach can leverage geospatial site optimisation to substantially reduce the risks associated with high residual load events. Furthermore, the proposed framework is highly flexible in that the formulation of the minimum and maximum allocation constraints allows application for real-world scenarios whereby stringent capacity allocation constraints can be applied.

In-event surrogate model training for regional transmission tower-line system dynamic responses prediction in realistic hurricane wind fields

Surrogate models have shown improved accuracy in predicting infrastructure responses during dynamic loadings. However, training a surrogate model for complex loading inputs across the entire hazard region remains challenging. This study provides insight into the training of surrogate models to estimate the responses of transmission tower-line structures in a coupled high-dimensional and high-resolution wind field and presents innovative methods for addressing these challenges. Four data- and physics-based spatial-temporal decoupling sampling methods are employed and cross-compared to obtain the most representative in-event wind profiles for training the surrogate model. Long Short-Term Memory (LSTM) is utilised as the surrogate model framework to predict the dynamic responses of the structure during the 2017 Hurricane Harvey. The accuracy and robustness of two transmission tower-line structure configuration surrogate models are validated by comparing the predictions with finite element analyses by using randomly distributed temporal and geospatial wind profiles throughout the hurricane. Finally, a single LSTM surrogate model is developed, trained by applying the full reference wind speed range of Hurricane Harvey for the regional-scale structural performance evaluation of the transmission tower-line system. The results demonstrate that the proposed surrogate model training methodology is general and can be applied to regional-scale structural performance evaluations.

The Day‐To‐Day Variability of the Dayside Equatorial Ionospheric Plasma Drifts and Neutral Winds Observed by ICON

AbstractIn this study, we utilize ICON observations from 2019 to 2022 to analyze the variability of vertical plasma drift and its relationship with the neutral winds. The results reveal that there are 19% of downward plasma drifts at 13–17 LT, which changes with seasons and longitudes. The downward plasma drift occurs less frequently compared to the contemporaneous counter electrojet during solstices. We identify the relationship between vertical plasma drifts and north foot magnetic zonal and meridional wind profiles at 90–300 km altitudes. As the vertical plasma drifts become small or downward, the zonal winds display diverse variations at the altitudes; that is, the disturbances are eastward at 95–120 km altitudes, westward at 120–160 km altitudes, and eastward above 180 km altitudes, while the meridional winds present weak changes in all altitudes. Additionally, we discuss the possible roles of the E‐ and F‐region dynamos on the vertical plasma drifts.

Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power Control (DPC) approach has garnered considerable attention due to its simple and highly adaptable algorithm. This approach has been increasingly recognized in numerous applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board.

Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data

Abstract. Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment: the US Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis and the possibility to observe these variables in real-time via measurements. The wind speed at 10 ma.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the symmetric extremal dependence index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5.

Influence of radiosonde observations on the sharpness and altitude of the midlatitude tropopause in the ECMWF IFS

Abstract. Initial conditions of current numerical weather prediction systems insufficiently represent the sharp vertical gradients across the midlatitude tropopause. Data assimilation may provide a means to improve tropopause structure by correcting the erroneous background forecast towards the observations. In this paper, the influence of assimilating radiosonde observations on tropopause structure, i.e., the sharpness and altitude, is investigated in the ECMWF's Integrated Forecasting System. We evaluate 9729 midlatitude radiosondes launched during 1 month in autumn 2016. About 500 of these radiosondes, launched on request during the North Atlantic Waveguide Downstream Impact Experiment (NAWDEX) field campaign, are used to set up an observing system experiment (OSE) that comprises two assimilation forecast experiments, one run with and one without the non-operational soundings. The influence on the tropopause is assessed in a statistical, tropopause-relative evaluation of observation departures of temperature, static stability (N2), wind speed, and wind shear from the background forecast and the analysis. Temperature is overestimated by the background at the tropopause (warm bias, ∼ 1 K) and underestimated in the lower stratosphere (cold bias, −0.3 K) leading to an underestimation of the abrupt increase in N2 at the tropopause. The increments (differences in analysis and background) reduce these background biases and improve tropopause sharpness. Profiles with sharper tropopause exhibit stronger background biases but also an increased positive influence of the observations on temperature and N2 in the analysis. Wind speed is underestimated in the background, especially in the upper troposphere (∼ 1 m s−1), but the assimilation improves the wind profile. For the strongest winds the background bias is roughly halved. The positive influence on the analysis wind profile is associated with an improved vertical distribution of wind shear, particularly in the lower stratosphere. We furthermore detect a shift in the analysis tropopause altitude towards the observations. The evaluation of the OSE highlights that the diagnosed tropopause sharpening can be primarily attributed to the radiosondes. This study shows that data assimilation improves wind and temperature gradients across the tropopause, but the sharpening is small compared with the model biases. Hence, the analysis still systematically underestimates tropopause sharpness which may negatively impact weather and climate forecasts.

Extending the wind profile beyond the surface layer by combining physical and machine learning approaches

Abstract. Accurate estimation of the wind profile, especially in the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. Nevertheless, the Monin–Obukhov similarity theory fails above the surface layer over a heterogeneous underlying surface, causing an unreliable wind profile to be obtained from conventional extrapolation methods. To solve this problem, we propose a novel method called the PLM-RF method that combines the power-law method (PLM) with the random forest (RF) algorithm to extend wind profiles beyond the surface layer. The underlying principle is to treat the wind profile as a power-law distribution in the vertical direction, with the power-law exponent (α) determined by the PLM-RF model. First, the PLM-RF model is constructed based on the atmospheric sounding data from 119 radiosonde (RS) stations across China and in conjunction with other data such as surface wind speed, land cover type, surface roughness, friction velocity, geographical location, and meteorological parameters from June 2020 to May 2021. Afterwards, the performance of the PLM-RF, PLM, and RF methods over China is evaluated by comparing them with RS observations. Overall, the wind speed at 100 m from the PLM-RF model exhibits high consistency with RS measurements, with a determination coefficient (R2) of 0.87 and a root mean squared error (RMSE) of 0.92 m s−1. By contrast, the R2 and RMSE of wind speed results from the PLM (RF) method are 0.75 (0.83) and 1.37 (1.04) m s−1, respectively. This indicates that the estimates from the PLM-RF method are much closer to observations than those from the PLM and RF methods. Moreover, the RMSE of the wind profiles estimated by the PLM-RF model is relatively large for highlands, while it is small for plains. This result indicates that the performance of the PLM-RF model is affected by the terrain factor. Finally, the PLM-RF model is applied to three atmospheric radiation measurement sites for independent validation, and the wind profiles estimated by the PLM-RF model are found to be consistent with Doppler wind lidar observations. This confirms that the PLM-RF model has good applicability. These findings have great implications for the weather, climate, and renewable energy sector.

The Variations of Gravity Wave Activities During the Formation of the Large Horizontal Wind Shears in the Static State Lower Thermosphere

AbstractLarge horizontal winds and shears across the mesopause region have been observed by sounding rockets and satellites but with limited simultaneous temperature profiles to date. This feature is critical to the diagnosis of the insitu dynamic conditions. Theories suggest this feature may be related to variations of the atmosphere static stability, Brunt‐Väisälä frequency square (N2), and the wave activity in the mesosphere and lower thermosphere (MLT) that cannot be resolved by the models. Since 2020, the Na Doppler lidar at Utah State University has attained several hundred hours of wind and temperature profiles in the MLT up to ∼110 km altitude. This paper focuses on the variations of these large winds and shears (>40 m/s/km), along with their potential relation to the activities of the various scales of gravity waves in the static state upper mesosphere, measured coordinately by the Advanced Mesospheric Temperature Mapper operating alongside the lidar. The investigation reveals that larger shears tend to occur in the meridional direction than the zonal direction and are observed more frequently in winter. The shears are comparable in both directions in summer and more large shears were observed in zonal direction. In addition, small shears occur when the medium‐scale waves are completely or partially blocked by the mean wind, though no convincing evidence relates small‐scale wave activities with these large winds and shears. Furthermore, small and decreasing N2 in the upper mesopause region are associated with insignificant magnitude of the shears.

Three-dimensional Atmospheric Dynamics of Jupiter from Ground-based Doppler Imaging Spectroscopy in the Visible

We present three-dimensional (3D) maps of Jupiter’s atmospheric circulation at cloud-top level from Doppler-imaging data obtained in the visible domain with JIVE, the second node of the JOVIAL network, which is mounted on the Dunn Solar Telescope at Sunspot, New Mexico. We report on 12 nights of observations between 2018 May 4 and May 30, representing a total of about 80 hr. First, the average zonal wind profile derived from our data is compatible with that derived from cloud-tracking measurements performed on Hubble Space Telescope images obtained in 2018 April from the Outer Planet Atmospheres Legacy program. Second, we present the first ever 2D maps of Jupiter’s atmospheric circulation from Doppler measurements. The zonal velocity map highlights well-known atmospheric features, such as the equatorial hot spots and the Great Red Spot (GRS). In addition to zonal winds, we derive meridional and vertical velocity fields from the Doppler data. The motions attributed to vertical flows are mainly located at the boundary between the equatorial belts and tropical zones, which could indicate active motion in theses regions. Qualitatively, these results compare well to recent Juno data that have unveiled the 3D structure of Jupiter’s wind field. To the contrary, the motions attributed to meridional circulation are very different from what is obtained by cloud tracking, except at the GRS. Because of limitations with data resolution and processing techniques, we acknowledge that our measurements of the vertical or meridional flows of Jupiter are still to be confirmed.

Space Domain Awareness Observations Using the Buckland Park VHF Radar

There is increasing interest in space domain awareness worldwide, motivating investigation of the use of non-traditional sensors for space surveillance. One such class of sensor is VHF wind profiling radars, which have a low cost relative to other radars typically applied to this task. These radars are ubiquitous throughout the world and may potentially offer complementary space surveillance capabilities to the Space Surveillance Network. This paper updates an initial investigation on the use of Buckland Park VHF wind profiling radars for observing resident space objects in low Earth orbit to further investigate the space surveillance capabilities of the sensor class. The radar was operated during the Australian Defence “SpaceFest” 2019 activity, incorporating new beam scheduling and signal processing functionality that extend upon the capabilities described in the initial investigation. The beam scheduling capability used two-line element propagations to determine the appropriate beam direction to use to observe transiting satellites. The signal processing capabilities used a technique based on the Keystone transform to correct for range migration, allowing the development of new signal processing modes that allow the coherent integration time to be increased to improve the SNR of the observed targets, thereby increasing the detection rate. The results reveal that 5874 objects were detected over 10 days, with 2202 unique objects detected, representing a three-fold increase in detection rate over previous single-beam direction observations. The maximum detection height was 2975.4 km, indicating a capability to detect objects in medium Earth orbit. A minimum detectable RCS at 1000 km of −10.97 dBm2 (0.09 m2) was observed. The effects of Faraday rotation resulting from the use of linearly polarised antennae are demonstrated. The radar’s utility for providing total electron content (TEC) measurements is investigated using a high-range resolution mode and high-precision ephemeris data. The short-term Fourier transform is applied to demonstrate the radar’s ability to investigate satellite rotation characteristics and monitor ionospheric plasma waves and instabilities.

Impact of Assimilation of the Tropical Cyclone Strong Winds Observed by Synthetic Aperture Radar on Analyses and Forecasts

Abstract A few high-wind observations have been obtained from satellites over the ocean around tropical cyclones (TCs), but the impact of data assimilation of such observations over the sea on forecasting has not been clear. The spaceborne synthetic aperture radar (SAR) provides high-resolution and wide-area ocean surface wind speed data around the center of a TC. In this study, the impact of data assimilation of the ocean surface wind speed of SAR (OWSAR) on regional model forecasts was investigated. The assimilated data were estimated from SAR on board Sentinel-1 and RADARSAT-2. The bias of OWSAR depends on wind speed, the observation error variance depends on wind speed and incidence angle, and the spatial observation error correlation depends on the incidence angle. The observed OWSAR is screened using the variational quality control method with the Huber norm. In the case of Typhoon Hagibis (2019), OWSAR assimilation modified the TC low-level inflow, which also modified the TC upper-level outflow. The propagation of this OWSAR assimilation effect from the surface to the upper troposphere was given by a four-dimensional variational method that searches for the optimal solution within strong constraints on the time evolution of the forecast model. Statistical validation confirmed that errors in the TC intensity forecast decreased over lead times of 15 h, but this was not statistically significant. The validation using wind profiler observations showed that OWSAR assimilation significantly improved the accuracy of wind speed predictions from the middle to the upper level of the troposphere. Significance Statement The purpose of this study was to demonstrate the impact of the assimilation of ocean surface wind speed by synthetic aperture radar (SAR) on regional model predictions. In the case of tropical cyclones, ocean surface wind speed assimilation modified inflows in the lower layer and outflows in the upper layer. The results indicate that the SAR assimilation improves the accuracy of wind speed forecasts in the middle to upper troposphere.

Refined Short‐Term Forecasting Atmospheric Temperature Profiles in the Stratosphere Based on Operators Learning of Neural Networks

AbstractThe efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere.

Influence of inflow conditions on simplified heavy vehicle wake

In the current study, the impact of various inflow conditions, including turbulent wind profiles and turbulent intensity, on the wake flow topology of a simplified ground transportation system (GTS) model was investigated using the improved delayed detached eddy simulation. The reliability and accuracy of the numerical method adopted in this paper were verified against the results comprising the aerodynamic drag and the wake flow structure of the GTS model obtained from the large eddy simulation and the experimental data. The research results indicate that turbulent winds characterized by logarithmic and uniform velocity profiles resulted in significantly different wake flow topologies yet exhibit the same dominant frequency. The turbulent intensity also plays a crucial role in the wake of the GTS model. It is observed that an increase in turbulence intensity corresponds with a rise in the aerodynamic drag. Specifically, when the turbulence intensity is set at 15%, there is a 3.68% increase in the aerodynamic drag of the GTS model compared to a case where the turbulence intensity was only 5%. In addition, the turbulent intensity is critical to the dominant frequency characteristics of the wake region of the GTS model. These results demonstrate that both the velocity profiles and the turbulence intensities significantly influence the wake flow topology and aerodynamic drag of the GTS model, providing a valuable reference for establishing appropriate inflow conditions and exploring the formation mechanism of flow topology in the wake of the GTS model.

Wind Profiles in Peninsular Malaysia: A Comprehensive Upper Air Analysis

Understanding the atmospheric properties and patterns is crucial in empowering Malaysia's national aerospace blueprint, national space, and legislation. While various policies have been enacted and implemented, there is a lack of information on the wind profile, specifically the upper air across Peninsular Malaysia. Realising the need to establish a standard guideline for national reference, future research, space-aerospace application, and legislation, this study was performed to develop the first wind profile analysis of upper air in Peninsular Malaysia. Relevant data from the Malaysian Meteorology Department was collected for analysis and evaluation. Specifically, a meteorology balloon attached with a sounding radiosonde was used to record data at 0000UTC (0800 h LT) and 1200UTC (2000 h LT) on the 15th day of each month for 7 years (from 2015 to 2021) at the KLIA and Kuantan Meteorology Stations for the wind profiling analysis. The daily overall data collection was recorded accurately once the balloon's rising rate stabilises from vertical air current (katabatic or anabatic winds). Subsequently, the collected data were evaluated in terms of the minimum, maximum, and average wind speeds for each year and time. Finally, the average wind speed of each year and time were combined to generate the Peninsular Malaysia wind profile. Based on the results, the projected wind profile for both stations identified three peaks of discrete sine wave flow type with low-speed wind profile in Peninsular Malaysia. The three peaks amplified the highest air velocity, whereby the 1st and 2nd peaks were located at the troposphere layer from 9,000 m to 12,000 m altitude (average wind speed of 10.8 ms-1) and 12,000 m to 18,000 m altitude (average wind speed of 13.7 ms-1). The 3rd peak was located at the stratosphere layer from 18,000 m to 32,000 m altitude (average wind speed of 15.2 ms-1). Since East Malaysia is located on the same equatorial line, the wind profile is hypothetically the same and exhibits only slight differences. In short, the established wind profile of upper air in Peninsular Malaysia in this study would facilitate other future studies and assist long-term planning of Malaysia's airspace legislation.

Wind resource assessment at mountainous wind farm: Fusion of RANS and vertical multi-point on-site measured wind field data

RANS (Reynolds-averaged Navier-Stokes) has become one of the mainstream methods to determine the wind field in the wind resource assessment of the complex terrain. However, before the simulation of wind field by RANS, defining the inlet boundary condition often depends on user's experience, and lacks of theoretical guidance, which brings great uncertainties to the accuracy of simulation results. In this paper, based on the vertical multi-point wind speed data (including wind direction) obtained from the on-site measurement and the wind speed data simulated by RANS, a data fusion method called the RANS-VMM (Vertical Multi-point on-site Measurement) method is proposed. In this method, through the polar coordinate diagram (PCD) that can express the wind direction and wind profile index at the same time, the vertical multi-point measured wind speed of wind measurement tower and the RANS simulation results are fused via multiple iterations, which can reconstruct the wind field distribution with spatial variability in complex mountainous areas, thus improving the accuracy of wind resource assessment in the complex terrain. The evaluation and verification of the proposed method are conducted through an real wind farm with complex terrain, and the results indicate that the RANS-VMM method can improves the accuracy of wind resource assessment in the complex terrain.