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Generally, the experimental aerodynamics is related to wind tunnel experiments. The wind tunnel design topic is very old but the development in computational fluid dynamics led to improvement in the wind tunnel design. This paper describes the design and optimization of low speed wind tunnel using CFD techniques. The new optimum wind tunnel will replace the old one featuring poor air quality and small area with lower wind speed at the test section. A computational domain was generated and adopted using ANSYS mesh generator and the solution domain was analysed by simulation technique using FLUENT CFD code in ANSYS Workbench package. The pressure drop calculations comparison between analytical, computational and experimental is included for different sections in the wind tunnel. The contraction cone was optimized using the response surface technique. The results identified that the pressure drop and turbulence level are modified as compared to the old wind tunnel.

We examine the dependence on wind speed of the share of the mean turbulent kinetic energy among the three velocity components in the near-neutral surface layer. To contrast the general behaviour and the local effects, four datasets are considered, corresponding to different surfaces and environmental conditions. For high wind speeds (i.e., wind speed \\[\\approx {10}\\,{\\hbox {ms}^{-1}}\\]), the shares are well-defined and about the same for all sites. As wind speed decreases (becoming \\[\\approx {1}\\,{\\hbox { ms}^{-1}}\\]), large record-to-record variability occurs giving, on average, an almost isotropic state for the horizontal velocity components. Through spectral analysis, we relate this behaviour to the low-frequency, submeso motions and to the lack of conditions required by Reynolds averaging. The implications for modelling are also discussed, showing that the wind speed, or a related quantity, must be accounted for, besides stability, in second-order closures.

A wind tunnel is needed for a lot of research and model testing in the field of engineering design. Commercial wind tunnels are large and expensive, making them unsuitable for small-scale aerodynamic model testing. This work aims to experimentally investigate the effects of flow, noise, and vibration on constructing and designing a low-speed wind tunnel structure. The flow uniformity in the wind tunnel has been tested by measuring the velocity profiles inside the empty test section with a pitot-static tube at various fan frequencies. The experiment results showed a good flow uniformity of more than 90% across the test section area, and the maximum wind velocity achieved was about 25.1 m/s. Due to the stability of the flow near the exit test section, the vibration measurement revealed that the entrance portion has larger vibration fluctuations than the exit part. Furthermore, as the axial fan frequency increases, the noise level increases. At 40 Hz, the noise level enters the hazardous zone, which has an impact on the person who performs the measurement process. The resonance of the wind tunnel structure is an important measurement test that affects vibration measurement.

This paper presents wind tunnel measurements of the NREL S826 airfoil at Reynolds number Re 100,000 for angles of attack in a range of -10° to 25° the corresponding Large Eddy Simulation (LES) for selected angles of attack. The measurements have been performed at the low speed wind tunnel located at Fluid Mechanics laboratory of the Technical University of Denmark (DTU). Lift coefficient is obtained from the forge gauge measurements while the drag is measured according to the integration of the wake profiles downstream of the airfoil. The pressure distribution is measured by a set of pressure taps on the airfoil surface. The lift and drag polars are obtained from the LES computations using DTU's inhouse CFD solver, EllipSys3D, and good agreement is found between the measurement and the simulations. At high angles of attack, the numerical computations tend to over-predict the lift coefficients, however, there is a better agreement between the drag measurements and computations. It is concluded that LES computations are able to capture the lift and drag polars as well as the pressure distribution around the airfoil with an acceptable accuracy.

Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ∼10×10 km2 domains with≤50×50 m2 pixel resolution and 1 %–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50×50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10 m wind speed can infer source rates with an error of 0.07–0.17 t h-1+5 %–12 % depending on instrument precision (1 %–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h-1+8 %–12 %) but a simpler physical basis. For comparison, point sources larger than 0.3 t h-1 contribute more than 75 % of methane emissions reported to the US Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but detrimental for source quantification.

The Weather Research and Forecasting (WRF) model presents a high surface wind speed bias over plains and valleys that constitutes a limitation for the increasing use of the model for several applications. This study attempts to correct for this bias by parameterizing the effects that the unresolved topographic features exert over the momentum flux. The proposed parameterization is based on the concept of a momentum sink term and makes use of the standard deviation of the subgrid-scale orography as well as the Laplacian of the topographic field. Both the drag generated by the unresolved terrain and the possibility of an increase in the speed of the flow over the mountains and hills, where it is herein shown that WRF presents a low wind speed bias, are considered in the scheme. The surface wind simulation over a complex-terrain region that is located in the northeast of the Iberian Peninsula is improved with the inclusion of the new parameterization. In particular, the underestimation of the wind speed spatial variability resulting from the mentioned biases is corrected. The importance of selecting appropriate grid points to compare with observations is also examined. The wind speed from the nearest grid point is not always the most appropriate one for this comparison, nearby ones being more representative. The new scheme not only improves the climatological winds but also the intradiurnal variations at the mountains, over which the default WRF shows limitations in reproducing the observed wind behavior. Some advantages of the proposed formulation for wind-resource evaluation are also discussed.

Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM1), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM1 at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM1 concentrations (coefficient of determination (R2) of 0.58), with model performance depending on the individual PM1 species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), low temperatures, low wind speeds, or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ∼5 µg/m3 for MLHs below <500 m a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM1 concentrations of as much as ∼9 µg/m3. Northeasterly winds are found to contribute ∼5 µg/m3 to predicted PM1 concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM1 concentrations in summer are temperatures above ∼25 ∘C (contributions of up to ∼2.5 µg/m3), dry spells of several days (maximum contributions of ∼1.5 µg/m3), and wind speeds below ∼2 m/s (maximum contributions of ∼3 µg/m3), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures.

Delhi, India, routinely experiences some of the world's highest urban particulate matter concentrations. We established the Delhi Aerosol Supersite study to provide long-term characterization of the ambient submicron aerosol composition in Delhi. Here we report on 1.25 years of highly time-resolved speciated submicron particulate matter (PM1) data, including black carbon (BC) and nonrefractory PM1 (NR-PM1), which we combine to develop a composition-based estimate of PM1 (“C-PM1” = BC + NR-PM1) concentrations. We observed marked seasonal and diurnal variability in the concentration and composition of PM1 owing to the interactions of sources and atmospheric processes. Winter was the most polluted period of the year, with average C-PM1 mass concentrations of ∼210 µg m−3. The monsoon was hot and rainy, consequently making it the least polluted (C-PM1 ∼50 µg m−3) period. Organics constituted more than half of the C-PM1 for all seasons and times of day. While ammonium, chloride, and nitrate each were ∼10 % of the C-PM1 for the cooler months, BC and sulfate contributed ∼5 % each. For the warmer periods, the fractional contribution of BC and sulfate to C-PM1 increased, and the chloride contribution decreased to less than 2 %. The seasonal and diurnal variation in absolute mass loadings were generally consistent with changes in ventilation coefficients, with higher concentrations for periods with unfavorable meteorology – low planetary boundary layer height and low wind speeds. However, the variation in C-PM1 composition was influenced by temporally varying sources, photochemistry, and gas–particle partitioning. During cool periods when wind was from the northwest, episodic hourly averaged chloride concentrations reached 50–100 µg m−3, ranking among the highest chloride concentrations reported anywhere in the world. We estimated the contribution of primary emissions and secondary processes to Delhi's submicron aerosol. Secondary species contributed almost 50 %–70 % of Delhi's C-PM1 mass for the winter and spring months and up to 60 %–80 % for the warmer summer and monsoon months. For the cooler months that had the highest C-PM1 concentrations, the nighttime sources were skewed towards primary sources, while the daytime C-PM1 was dominated by secondary species. Overall, these findings point to the important effects of both primary emissions and more regional atmospheric chemistry on influencing the extreme particle concentrations that impact the Delhi megacity region. Future air quality strategies considering Delhi's situation in both a regional and local context will be more effective than policies targeting only local, primary air pollutants.

The doldrums are regions of low wind speeds and variable wind directions in the deep tropics that have been known for centuries. Although the doldrums are often associated with the Intertropical Convergence Zone (ITCZ), the exact relationship remains unclear. This study re‐examines the relationship between low‐level convergence and the Atlantic doldrums. By analyzing the frequency distribution of low wind speed events in reanalysis and buoy data, we show that the doldrums are largely confined between the edges of the ITCZ marked by enhanced surface convergence. While the region between the edges is a region of high time‐mean precipitation, low wind speed events occur in the absence of precipitation. Based on these results, we hypothesize that low wind speed events occur in regions of low level divergence rather than convergence. Plain Language Summary The doldrums, an area between the trade winds formerly feared by mariners because of its low wind speeds and variable wind directions, have largely disappeared from mention in the scientific literature. The most commonly given explanation for the existence of the doldrums, according to which the weaker surface winds result from the upward circulation of the trade winds, can only be true when averaged over timescales of days or weeks. In this study, we re‐examine this region and its relationship to the convergence of the trade winds. We show that although low wind speed events occur in the region where the trade winds meet and precipitation rates are high on average, they occur precisely when there is no precipitation. This leads us to the hypothesis that these regions of low wind speeds are characterized by sinking rather than rising air. Key Points The doldrums are confined to the area of time‐mean convergence of the Atlantic Intertropical Convergence Zone (ITCZ) The frequency distribution of low wind speed events peaks between the edges of the ITCZ, which are characterized by increased convergence Low wind speed events within the ITCZ occur when precipitation is absent, suggesting they coincide with local low‐level divergence

The paper shows wind shear impact on PM10 vertical profiles in Kraków, southern Poland. The data used consist of background data for two cold seasons (September 2018 to April 2019 and September 2019 to April 2020) and data for several case studies from November 2019 to March 2020. The data are composed of PM10 measurements, model data, and wind speed and direction data. The background model data come from operational forecast results of the AROME model. PM10 concentration in the vertical profile was measured with a sightseeing balloon. Significant spatial variability of the wind field was found. The case studies represent the conditions with much lower wind speed and a much higher PM10 level than the seasonal average. The inversions were much more frequent than on average too. Wind shear turned out to be the important factor in terms of PM10 vertical profile modification. It is generated due to the relief impact, i.e. the presence of a large valley, blocked on one side with the hills. The analysis of PM10 profiles from all flights allows us to distinguish three vertical zones of potential air pollution hazards within the valley (about 100 m deep) and the city of Kraków: (1) up to about 60 m a.g.l. – the zone where during periods of low wind speed, air pollution is potentially the highest and the duration of such high levels is the longest, i.e. the zone with the worst aerosanitary conditions; (2) about 60–100 m a.g.l. – transitional zone where the large decrease in PM10 levels with height is observed; (3) above 100–120 m a.g.l. – the zone where air quality is significantly better than in zone 1, either due to the increase in the wind speed or due to the wind direction change and advection of different, clean air masses.