Turbulence parameters measured by the Beijing Mesosphere–Stratosphere–Troposphere radar in the troposphere/lower stratosphere with three models: Comparison and analyses

Based on the quality-controlled observational spectral width data of the Beijing Mesosphere–Stratosphere–Troposphere (MST) radar in the altitudinal range of 3–19.8 km from 2012 to 2014, this paper analyzes the relationship between the proportion of negative turbulent kinetic energy (N-TKE) and the horizontal wind speed/horizontal wind vertical shear domain, and gives the distributional characteristics of atmospheric turbulence parameters obtained by using different calculation models. Three calculation models of the spectral width method were used in this study—namely, the H model (Hocking, 1985), N-2D model (Nastrom, 1997) and D-H model (Dehghan and Hocking, 2011). The results showed that the proportion of N-TKE in the H model increases with the horizontal wind speed and/or the vertical shear of horizontal wind speed, up to 80 %. When the horizontal wind speed is greater than 40 m·s−1, the proportion of N-TKE in the H model is greater than 60 %, and thus the H model is not applicable. When the horizontal wind speed is greater than 20 m s−1, the proportion of N-TKE in the N-2D model and D-H model increases with the horizontal wind speed, independent of the vertical shear of the horizontal wind speed, and the maximum values are 2 % and 4 %, respectively. However, it is still necessary to consider the applicability of the N-2D model and D-H model in some weather processes with strong winds. The distributional characteristics with height of the turbulent kinetic energy dissipation rate 𝜀 and the vertical eddy diffusion coefficient Kz derived by the three models are consistent with previous studies. Still, there are differences in the values of turbulence parameters. Also, the range resolution of the radar has little effect on the differences in the range of turbulence parameters' values. The median values of 𝜀 in the H model, N-2D model and D-H model are 10−3.2–10−2.8 m2 s−3, 10−2.8–10−2.4 m2 s−3 and 10−3.0–10−2.5 m2 s−3, respectively. The median values of Kz in these three models are 100.18–100.67 m2 s−1, 100.57–100.90 m2 s−1 and 100.44–100.74 m2 s−1.

Comparison of Four-Dimensional Flow Magnetic Resonance Imaging and Particle Image Velocimetry to Quantify Velocity and Turbulence Parameters

Although recent advances of four-dimensional (4D) flow magnetic resonance imaging (MRI) has introduced a new way to measure Reynolds stress tensor (RST) in turbulent flows, its measurement accuracy and possible bias have remained to be revealed. The purpose of this study was to compare the turbulent flow measurement of 4D flow MRI and particle image velocimetry (PIV) in terms of velocity and turbulence quantification. Two difference flow rates of 10 and 20 L/min through a 50% stenosis were measured with both PIV and 4D flow MRI. Not only velocity through the stenosis but also the turbulence parameters such as turbulence kinetic energy and turbulence production were quantitatively compared. Results shows that 4D flow MRI velocity measurement well agreed with the that of PIV, showing the linear regression slopes of two methods are 0.94 and 0.89, respectively. Although turbulence mapping of 4D flow MRI was qualitatively agreed with that of PIV, the quantitative comparison shows that the 4D flow MRI overestimates RST showing the linear regression slopes of 1.44 and 1.66, respectively. In this study, we demonstrate that the 4D flow MRI visualize and quantify not only flow velocity and also turbulence tensor. However, further optimization of 4D flow MRI for better accuracy might be remained.

A Method of Solving Three Temperature Problem of Turbine With Adiabatic Wall Temperature

The aeroengine turbine cavity with pre-swirl structure makes the turbine component obtain better cooling effect, but the complex design of inlet and outlet makes it difficult to determine the heat transfer reference temperature of turbine disk. For the pre-swirl structure with two air intakes, the driving temperature difference of heat transfer between disk and cooling air cannot be determined either in theory or in test, which is usually called three-temperature problem. In this paper, the three-temperature problem of a rotating cavity with two cross inlets are studied by means of experiment and numerical simulation. By substituting the adiabatic wall temperature for the inlet temperature and summarizing its variation law, the problem of selecting the reference temperature of the multi-inlet cavity can be solved. The results show that the distribution of the adiabatic wall temperature is divided into the high jet area and the low inflow area, which are mainly affected by the turbulence parameters λT, the rotating Reynolds number Reω, the high inlet temperature Tf,H* and the low radius inlet temperature Tf,L* of the inflow, while the partition position rd can be considered only related to the turbulence parameters λT and the rotating Reynolds number Reω of the inflow. In this paper, based on the analysis of the numerical simulation results, the calculation formulas of the partition position rd and the adiabatic wall temperature distribution are obtained. The results show that the method of experiment combined with adiabatic wall temperature zone simulation can effectively solve the three-temperature problem of rotating cavity.

A Discussion: Issue Improving the Accuracy of Turbine Blade Heat Transfer Simulation

To be able to set uniform inlet boundary conditions in simulation, there must be an inlet extension at the first guide vane. In the inlet extension, turbulence experiences strong numerical dissipation, which has not been paid attention to. In the current paper, the influence of the numerical dissipation of turbulence on accuracy in predicting heat transfer was discussed. Two cases, where the numerical dissipation of turbulence was neglected, were analysed. In the first case, wrong conclusion about effect of turbulence scale on heat transfer was drawn: blade heat transfer increases with inlet turbulence scale under the same inlet turbulence intensity. The mechanism for the wrong conclusion is that turbulence with larger scale numerically dissipates more slowly in the inlet extension so that turbulence intensity at blade leading edge is greater under turbulence with larger scale, it is the turbulence intensity not turbulence scale itself really affects heat transfer. In the second case, when the numerical dissipation of turbulence is neglected and turbulence parameters at measuring plane of inlet are directly as input for turbulence boundary condition, flow transition is postponed downstream and heat transfer error is greater, however, when the numerical dissipation of turbulence is considered and turbulence parameters at measuring plane are regard as benchmark and matched by adjusting parameters of inlet turbulence boundary condition, the result shows better agreement with experiment. Thus, the correct way to set turbulence boundary condition is to match turbulence parameters at measuring plane by adjusting parameters of inlet turbulence boundary.

Method of Estimation of Turbulence Integral Parameters

The optical effects of turbulence are directly related to turbulence integral parameters, which are integrals of the refractive index structure constant over a whole path with different path-weighting functions (PWFs). We describe a method that utilizes measurable turbulence integral parameters, such as angle-of-arrival fluctuations and scintillation, to estimate turbulence integral parameters that cannot be measured directly. The estimates of the turbulence integral parameters are based on the linear combination of the PWFs of those measurable quantities. New measurable quantities and their PWFs under different propagation conditions were studied. Some interesting and meaningful results have been obtained. This method shows the prospect of characterizing anisoplanatism in adaptive optics and allows for the estimation of some optical turbulence parameters under non-ideal conditions, such as an isoplanatic angle in a finite distance.

Analysis of Alfvénic flows with Solar Orbiter: particle and magnetic observations down to kinetic scales.

<p>The magnetic and velocity fluctuations of the solar wind may be strongly correlated. This characterizes the  ‘Alfvenic’ flows. Using the observations of the Proton Alfa sensor (PAS/SWA) and the magnetometer (MAG) onboard Solar Orbiter, we analyze a period of 100 hours of such alfvenic flows, at different scales. Several parameters of the turbulence are computed (V-B correlation, various spectral indexes, cross-helicity, residual energy). We explore how these parameters may vary with time and characterize different turbulent states of the flow. More specifically, using the unprecedented time resolution of PAS during burst mode, especially its capability to measure 3D distribution functions at time scale below the proton gyroperiod, we study the connection of the turbulence to the dissipation domain and analyze the fine structure of the distribution functions and their evolutions at sub-second scales. The goal is to investigate whether some characteristics of the distributions, as their more or less pronounced temperature anisotropy, may be related to the turbulence parameters and the degree of V-B correlation.</p>