Estimates Of Turbulence Intensity Research Articles

Deep Learning–Based Summertime Turbulence Intensity Estimation Using Satellite Observations

Abstract Turbulence is what we want to avoid the most during flight. Numerical weather prediction (NWP) model–based methods for diagnosing turbulence have offered valuable guidance for pilots. NWP-based turbulence diagnostics show high accuracy in detecting turbulence in general. However, there is still room for improvements such as capturing convectively induced turbulence. In such cases, observation data can be beneficial to correctly locate convective regions and help provide corresponding turbulence information. Geostationary satellite data are commonly used for upper-level turbulence detection by utilizing its water vapor band information. The Geostationary Operational Environmental Satellite (GOES)-16 carries the Advanced Baseline Imager (ABI), which enables us to observe further down into the atmosphere with improved spatial, temporal, and spectral resolutions. Its three water vapor bands allow us to observe different vertical parts of the atmosphere, and from its infrared window bands, convective activity can be inferred. Such multispectral information from ABI can be helpful in inferring turbulence intensity at different vertical levels. This study develops U-Net based machine learning models that take ABI imagery as inputs to estimate turbulence intensity at three vertical levels: 10–18, 18–24, and above 24 kft (1 kft ≈ 300 m). Among six different U-Net-based models, U-Net3+ model with a filter size of three showed the best performance against the pilot report (PIREP). Two case studies are presented to show the strengths and weaknesses of the U-Net3+ model. The results tend to be overestimated above 24 kft, but estimates of 10–18 and 18–24 kft agree well with the PIREP, especially near convective regions. Significance Statement Turbulence is directly related to aviation safety as well as cost-effective aircraft operation. To avoid turbulence, turbulence diagnostics are calculated from numerical weather prediction (NWP) model outputs and are provided to pilots. The goal of this study is to develop a satellite data–driven machine learning model that estimates turbulence intensity in three different vertical layers to provide additional information along with the NWP-based turbulence diagnostics. Validation results against pilot reports show that the machine learning model performs comparable to NWP-based turbulence diagnostics. Furthermore, results with different channel selections reveal that using multiple water vapor channels can help extract additional information for estimating turbulence intensity at lower levels.

In Situ Calibration of Underwater Glider Flight Model Using Acoustic Doppler Current Profilers

Abstract The angle of attack (AOA) is the difference between the underwater glider’s path and pitch angle and is necessary to accurately estimate dead-reckoned position and depth-averaged velocity. The AOA is also important for any sensor measurements that are affected by the glider’s velocity through water, such as ocean turbulence measurement. A glider flight model is generally used to accurately estimate AOA and glider’s actual velocity based on the knowledge of lift and drag coefficients optimized for each glider. This paper examines the AOA of a Slocum glider using an acoustic Doppler current profiler (ADCP) to demonstrate a regression method to estimate these coefficients. Since the current shear was sufficiently small on average, it was reasonable to assume that the ADCP velocity at the nearest bin could capture the glider’s motion during flight and was used to calculate AOA. The lift and drag coefficients were optimized so the flight model estimated the observed pitch–AOA relationship derived from the ADCP and the glider’s pitch observations. The resultant coefficients also satisfied the vertical and horizontal constraints of glider motion and gave unbiased estimates of turbulence intensity derived from the flight model and ADCP. Our method was also applied to a SeaExplorer glider to derive the lift and drag coefficients for the first time. The observed pitch–AOA relationship was reasonably captured by the flight model with the resultant coefficients, suggesting that our method to estimate the lift and drag coefficient of underwater gliders can be applied to any type of underwater glider equipped with an ADCP.

Evaluating phase-detection-based approaches for interfacial velocity and turbulence intensity estimation in a highly-aerated hydraulic jump

Estimation of turbulence intensity within a highly-aerated turbulent flow is challenging. A possible way is to record bubble arrival information using intrusive phase-detection probes, but the derivation of velocity variation is subject to the correlation and de-correlation of the signals, especially in highly-turbulent and rapidly-varied flows with complex bubble transport such as in hydraulic jumps. Although attempts have been devoted to the improvement of data processing, it is difficult to assess the existing approaches in strong hydraulic jumps due to the lack of alternative measurement techniques applicable to the internal air-water flow region. In this letter, a substantial amount of work is devoted to manual analysis of instantaneous interfacial velocity in strong hydraulic jumps, and the velocity variation results are compared with the results of full-signal cross-correlation and adaptive-window cross-correlation approaches, to evaluate their performance in approximating the velocity turbulence. The manual results are validated in terms of the time-averaged velocity. The interfacial turbulence intensity is suggested to be greater than the water-phase velocity turbulence, typically between 20% and 40% in the unidirectional jet-shear region. Overall the manual results agree better with the calculation of adaptive-window cross-correlation technique. The relevance of the signal decomposition processing is also discussed, and it is emphasized that the phase-detection-based approaches are subject to the limitations of one-dimensional measurements in a complex three-dimensional flow.

The potential role of turbulence in modulating the migration of demersal zooplankton

AbstractDespite suggestions that turbulence can affect the migration of zooplankton, field observations of such effects are scarce. This is especially the case for bottom‐associated (demersal) zooplankton that reside in the typically turbulent near‐bottom environment. Using moored sensors deployed at two coastal sites in the North Pacific and the Red Sea, we present observations of the effects of turbulence on the nocturnal emergence of demersal zooplankton. A cabled observatory consisting of a plankton camera, an acoustic current profiler and environmental sensors, was deployed near bottom in 20 m of water near Oshima Island, Japan. Observations were also obtained from a second site near a coral reef in 16 m of water in the Red Sea. Acoustic backscatter data obtained from current profilers at both sites provided a proxy for zooplankton density. Combined with simultaneous estimates of turbulence intensity, the observations suggest that the nocturnal emergence of demersal zooplankton was hindered by elevated levels of turbulence. While our findings are inferred from acoustic data, agreement between the two different sites supports our hypothesis that demersal zooplankton may remain near the bed during times of strong turbulence.

Experimental Evaluation of Theoretical Formulations for the Correction of Spectral Widths of MST Radar Spectra

The turbulent kinetic energy (TKE) per unit mass estimated from the spectral width of very high frequency/ultra high frequency (VHF/UHF) radars often produce unrealistic negative values, particularly in the presence of strong winds, due to excess correction of nonturbulent factors. An experiment has been conducted with the newly acquired capabilities of the Advanced Indian MST Radar (AIR), in which several antenna configurations, providing different beamwidths, have been employed to evaluate various existing theoretical formulations for the estimation of nonturbulent factors. These formulations include traditional single-beamwidth, symmetric dual-beamwidth, and asymmetric beamwidth methods. The large variation is seen in the observed spectral widths with different antenna apertures with broader beams showing larger biases due to beam and shear broadening even in moderate winds. Wind-driven biases in spectral width are found to be larger for beams pointed perpendicular to the wind direction than those pointed parallel, particularly at heights of strong wind. After employing the above-mentioned correction methods, all profiles of mean spectral widths obtained with different antenna configurations converge and produce nearly equal values, indicating that the corrected spectral width ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{2}_{\mathrm{ turb}}$ </tex-math></inline-formula> ) values may represent realistic estimates of turbulence intensity. Comparison of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{2}_{\mathrm{ turb}}$ </tex-math></inline-formula> estimated using east and south beams indicates that the turbulence (on average) is isotropic. Among all the correction methods, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{2}_{\mathrm{ turb}}$ </tex-math></inline-formula> obtained by employing the asymmetric dual-beamwidth method has relatively larger bias than other methods. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{2}_{\mathrm{ turb}}$ </tex-math></inline-formula> values are used to estimate the vertical eddy diffusion coefficients and eddy dissipation rates to have direct comparisons with those available in the literature.

Lidar-based Estimation of Turbulence Intensity for Controller Scheduling

Lidar-assisted wind turbine control is a promising technology and various concepts have been developed. This paper aims to add another concept to the list by describing how turbulence intensity can be estimated and used for controller scheduling to reduce structural loads. The turbulence intensity estimation is applied to lidar data from aero-elastic simulations and good agreement with the turbulence intensity calculation from wind fields is obtained. Further, a controller scheduling scheme is proposed to adjust the power level based on the estimated turbulence intensity. In a first simulation study, the scheduling scheme is able to reduce the power and extreme loads on the tower during severe turbulence conditions while keeping a similar level of power production and fatigue loads for normal turbulent conditions.

On-site airflow measurement of a laboratory fume hood using customized large-scale image-based velocimetry

This study demonstrates the feasibility of conducting on-site large-scale image-based measurements for indoor airflows characterisation. To illustrate the potential of our method, we chose to study the suction flow generated by a laboratory fume hood in operating conditions. As a matter of fact, laboratory fume hoods are frequently subject to routine checks during which air speed measurements by hot-wire anemometry are performed. However, classical point-to-point hot-wire anemometry may be not sufficient to detect and locate potential leakages. To improve these controls, we developed and tested a new method based on particle image velocimetry principles, which is non-intrusive and authorizes a good spatio-temporal analysis. To face large-scale and on-site issues, we had to make some adaptations. For this reason, we used tracers like bubbles or smoke which have good scattering properties. We also developed our own low-cost light system. To compute velocities from image sequences, we developed an optical flow algorithm based on a large-scale flow model instead of using traditional correlation. The tested method gave good results with a good agreement with sparse hot-wire anemometry measurements but with a wider spatial distribution. In addition, the method provided turbulence intensity estimation and a good monitoring of dynamic flow features, which is important for the detection of leakages.

Estimates of Turbulence Intensity and Power Density of an Asymmetric Tidal Current under Wind Forcing Variability

Estimates of Turbulence Intensity and Power Density of an Asymmetric Tidal Current under Wind Forcing Variability

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.

Turbulence in breaking mountain waves and atmospheric rotors estimated from airborne in situ and Doppler radar measurements.

Atmospheric turbulence generated in flow over mountainous terrain is studied using airborne in situ and cloud radar measurements over the Medicine Bow Mountains in southeast Wyoming, USA. During the NASA Orographic Clouds Experiment (NASA06) in 2006, two complex mountain flow cases were documented by the University of Wyoming King Air research aircraft carrying the Wyoming Cloud Radar.The structure of turbulence and its intensity across the mountain range are described using the variance of vertical velocity σw2 and the cube root of the energy dissipation rate ɛ 1/3 (EDR). For a quantitative analysis of turbulence from the cloud radar, the uncertainties in the Doppler wind retrieval have to be taken into account, such as the variance of hydrometeor fall speed and the contamination of vertical Doppler velocity by the horizontal wind. A thorough analysis of the uncertainties shows that 25% accuracy or better can be achieved in regions of moderate to severe turbulence in the lee of the mountains, while only qualitative estimates of turbulence intensity can be obtained outside the most turbulent regions.Two NASA06 events exhibiting large‐amplitude mountain waves, mid‐tropospheric wave breaking, and rotor circulations are examined. Moderate turbulence is found in a wave‐breaking region with σw2 and EDR reaching 4.8 m2 s−2 and 0.25 m2/3 s−1, respectively. Severe turbulence is measured within the rotor circulations with σw2 and EDR respectively in the ranges of 7.8–16.4 m2 s−2 and 0.50–0.77 m2/3 s−1. A unique result of this study is the quantitative estimation of the intensity of turbulence and its spatial distribution in the interior of atmospheric rotors, provided by the radar‐derived turbulence fields.

Design of Mini Wind Tunnel Based on Coanda Effect

An experimental investigation and CFD treatment were employed to design mini-wind tunnel based on Coanda effect for model tests and basic research. The inlet source flow is efficiently creating smooth steady airflow with acceptable noise, achieving the possibility of placing the test target closer to the source of flow with reasonable estimates of turbulence intensity. The design aims at achieving flow uniformity in the working section midplane, preventing separation in the contraction and minimizing the boundary–layer thickness. Intensive measurements after construction demonstrate the significance of the design process and validate the CFD predictions. The results are represented in graphic form to indicate the aspects of the contraction ratio. The numerical and experimental results show the uniformity of velocity distribution inside the working section. Tracing of separation and backflow is crucial allowing a variety of realistic demonstrations to be performed. The numerical solution provides a powerful tool to demonstrate the rate of boundary–layer growth inside the working section and validate against the empirical correlations with insignificant wall–friction drag. Assessment study to address large–scale wind tunnel based on coanda effect would be considered.

Performance analysis of the first method for long-term turbulence intensity estimation at potential wind energy sites

The paper presents a validation test of a recent algorithm implemented by the author to correlate turbulence intensity (TI) data recorded at two meteorological masts and based on the conditional probability to measure simultaneous events of wind speed, direction and TI. Two testing sites, located about 5 km apart from each other in a hilly terrain, in the South of Australia, are considered in this work. Three years of concurrent data (2005–2008) are analyzed to estimate a long-term (LT) representative TI. A complete examination of the scores is carried out by spanning dimension and temporal period of the data samples used in the correlation analysis. Root mean square error, committed by the method to approximate mean value of TI measured in each of the three years, can be correlated with number of used months by exponential decay functions. The intermonthly variations stronger affect the accuracy of the results than the yearly ones. However, the average errors are always moderate and good performances are achieved for all the considered wind speed thresholds and also when examining different periods of the year. The tested methodology represents an important step through standardization of Measure-correlate-predict (MCP) technique for TI assessment.

Estimation of turbulence intensity and shear factor for diurnal and nocturnal periods with an URANS flow solver coupled with WRF

Mesoscale results using the WRF model were downscaled from 3 km to 250 m resolution in a one-way coupling with VENTOS®/M. The results were compared against field measurements at one site comprising 4 meteorological masts, each with two sets of cup anemometers and wind vanes. The results showed that the addition of VENTOS®/M to the model chain improved the wind speed RMSE. Regarding the prediction of wind direction ambivalent results were obtained. Special attention was given to the prediction of turbulence intensity, particularly in reproducing its inverse proportionality with increasing wind speed (cf. IEC 61400-1 standard). The typical use of computational models in wind resource assessment, i.e., relying on decoupled methodologies and neutrally-stratified regimes, does not allow the representation of turbulence intensity for all wind speeds. The results obtained with VENTOS®/M were in agreement with the measured turbulence characteristics at both high and low wind speeds. Such was achieved without the coupling of any turbulence related field, relying solely on the turbulence model embedded in VENTOS®/M and its respective wall boundary conditions, based on Monin-Obukhov similarity theory. The behaviour under different stratification regimes was verified by analysing diurnal and nocturnal events separately.

Performance of LIDAR- and radar-based turbulence intensity measurement in comparison with anemometer-based turbulence intensity estimation based on aircraft data for a typical case of terrain-induced turbulence in association with a typhoon

The Hong Kong Observatory (HKO) provides low-level turbulence alerting service for the Hong Kong International Airport (HKIA) through the windshear and turbulence warning system (WTWS). In the WTWS, turbulence intensities along the flight paths of the airport are estimated based upon correlation equations established between the surface anemometer data and the turbulence data from research aircraft before the opening of the airport. The research aircraft data are not available on day-to-day basis. The remote sensing meteorological instruments, such as the Doppler light detection and ranging (LIDAR) and radar, may be used to provide direct measurements of turbulence intensities over the runway corridors. The performances of LIDAR- and radar-based turbulence intensity data are studied in this paper based on actual turbulence intensity measurements made on 423 commercial jets for a typical case of terrain-induced turbulence in association with a typhoon. It turns out that, with the tuning of the relative operating characteristic (ROC) curve between hit rate and false alarm rate, the LIDAR-based turbulence intensity measurement performs better than the anemometer-based estimation of WTWS for turbulence intensity at moderate level or above. On the other hand, the radar-based measurement does not perform as well when compared with WTWS. By combining LIDAR- and radar-based measurements, the performance is slightly better than WTWS, mainly as a result of contribution from LIDAR-based measurement. As a result, the LIDAR-based turbulence intensity measurement could be used to replace anemometer-based estimate for non-rainy weather conditions. Further enhancements of radar-based turbulence intensity meas- urement in rain would be necessary.

In Vivo Validation of Numerical Prediction for Turbulence Intensity in an Aortic Coarctation

This paper compares numerical predictions of turbulence intensity with in vivo measurement. Magnetic resonance imaging (MRI) was carried out on a 60-year-old female with a restenosed aortic coarctation. Time-resolved three-directional phase-contrast (PC) MRI data was acquired to enable turbulence intensity estimation. A contrast-enhanced MR angiography (MRA) and a time-resolved 2D PCMRI measurement were also performed to acquire data needed to perform subsequent image-based computational fluid dynamics (CFD) modeling. A 3D model of the aortic coarctation and surrounding vasculature was constructed from the MRA data, and physiologic boundary conditions were modeled to match 2D PCMRI and pressure pulse measurements. Blood flow velocity data was subsequently obtained by numerical simulation. Turbulent kinetic energy (TKE) was computed from the resulting CFD data. Results indicate relative agreement (error ≈10%) between the in vivo measurements and the CFD predictions of TKE. The discrepancies in modeled vs. measured TKE values were within expectations due to modeling and measurement errors.

Expansion of fluidized solids' layer at the critical impeller speed marking the onset of a complete suspension

Experimental measurements of the expansion of suspended solids' layer at the critical suspension state have been done in using two representatives of axial-type turbines differing in blades inclination. Results obtained have been correlated by means of theoretical equations which are derived from turbulent mechanical balances. Experimental results have been exploited for the estimation of turbulence intensity near the slurry-pure liquid interface.

The Structure of the Doppler-difference Signal and the Analysis of its Autocorrelation Function

The Doppler-difference optical arrangement is nowadays the one most commonly used for fluid flow investigations with a laser anemometer. In this review, the essential characteristics of the Doppler-difference signal and of the associated autocorrelation function, obtained in general by integrating over many successive particle transits, are considered for both laminar and turbulent flows. Experimental conditions under which it is known that the autocorrelation function can be analysed to yield reliable estimates of mean velocity and turbulence intensity are specified; the stated restrictions are sufficient but may not all be necessary. In particular, it is shown that the effect on the autocorrelation function of the total velocity-component in the plane normal to the included bisector of the beam-axes is usually inseparable from the contribution due to the component u normal to the fringe system.The various data-reduction methods which have been proposed are classified and briefly reviewed. For laminar flows data-processing is relatively straightforward, but we are led to the conclusion that unknown flows having relatively high levels of turbulence (greater than about 15%) can only be treated successfully if there are very many fringes within a beam diameter, or if frequency-shifting techniques are used to achieve an equivalent effect. Under these circumstances, the relationship between the autocorrelation function and the distribution of the u-component of velocity reduces immediately to that of a simple Fourier cosine transform. When the measuring volume contains only a small number of fringes, certain approximations must always be made if interpretation of the data is to be possible in the general case, and it is shown that the Fourier transform relationship again arises in a natural way. However, the estimation of turbulence intensity in low-turbulence flows presents special difficulties, since truncation of the data at a finite number of channels causes broadening of the distribution in the transform plane. In these cases, it is often possible to assume that the velocity-distribution is approximately Gaussian in form, and curve-fitting methods can be used. An alternative procedure which uses information already available in the transform plane is described. Finally, the analysis of the autocorrelation function arising from a single transit in the presence of background radiation is briefly considered.