Capturing transition and non-transition flows with a new shear stress transport model

A new Shear Stress Transport(SST) k-ω model is devised to integrate salient features of both the non-transitional SST k-ω model and correlation-based γ-Reθtransition model. An exceptionally simplified approach is applied to extend the New SST(NSST) model capabilities toward transition/non-transition predictions. Bradshaw’s stress-intensity factor ■ can be parameterized with the wall-distance dependent Reynolds number ■; however, as the Reyis replaced by a ‘‘flow-structure-adaptive” parameter Rμ=...     

A new target tracking filter based on deep learning

At present, current filters can basically solve the filtering problem in target tracking, but there are still many problems such as too many filtering variants, too many filtering forms, loosely coupled with the target motion model, and so on. To solve the above problems, we carry out crossapplication research of artificial intelligence theory and methods in the field of tracking filters. We firstly analyze the computation graphs of typical a-β and Kalman. Through analysis, it is concluded that ...     

Multiframe weak target track-before-detect based on pseudo-spectrum in mixed coordinates

Traditional multiframe Track-Before-Detect(TBD) may incur adverse integration loss resulting from model mismatch in sensor coordinates. Its suboptimal integration strategy may cause target envelope degradation. To address these issues, a pseudo-spectrum-based multiframe TBD in mixed coordinates is proposed firstly. The data search for energy integration is conducted based on an accurate model in the x-y plane while target energy is integrated based on pseudo-spectrum in sensor coordinates. The a...     

A transition prediction method for flow over airfoils based on high-order dynamic mode decomposition

This article presents a novel approach for predicting transition locations over airfoils, which are used to activate turbulence model in a Reynolds-averaged Navier-Stokes flow solver. This approach combines Dynamic Mode Decomposition (DMD) with e^N criterion. The core idea is to use a spatial DMD analysis to extract the modes of unstable perturbations from a steady flowfield and substitute the local Linear Stability Theory (LST) analysis to quantify the spatial growth of Tollmien–Schlichting (TS) waves. Transition is assumed to take place at the stream-wise location where the most amplified mode’s N-factor reaches a prescribed threshold and a turbulence model is activated thereafter. To improve robustness, the high-order version of DMD technique (known as HODMD) is employed. A theoretical derivation is conducted to interpret how a spatial high-order DMD analysis can extract the growth rate of the unsteady perturbations. The new method is validated by transition predictions of flows over a low-speed Natural-Laminar-Flow (NLF) airfoil NLF0416 at various angles of attack and a transonic NLF airfoil NPU-LSC-72613. The transition locations predicted by our HODMD/e^N method agree well with experimental data and compare favorably to those obtained by some existing methods (LST/e^N or $\gamma -\stackrel{￣}{R{e}_{\theta \text{t}}}$). It is shown that the proposed method is able to predict transition locations for flows over different types of airfoils and offers the potential for application to 3D wings as well as more complex configurations.     

An intelligent approach for flight risk prediction under icing conditions

Flight risk prediction is significant in improving the flight crew’s situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions, especially aircraft icing conditions. The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed, commanded bank angle, and commanded vertical velocity) is developed to provide the crew with practi...     

Large eddy simulations of a turbulent mixing layer periodically excited with fundamental and third harmonic frequency

A better understanding of the mixing behavior of excited turbulent mixing layers is critical to a number of aerospace applications. Previous studies of excited turbulent mixing layers focused on single frequency excitation or the excitation with fundamental and its second harmonic frequency. There is a lack of detailed studies on applying low and higher frequency excitation. In this study, we have performed large-eddy simulations of periodically excited turbulent mixing layers. The excitation consists of a fundamental frequency and its third harmonic. We have used phase-averaging to identify the vortex structure and strength in the mixing layer, and we have studied the vortex dynamics. Two different vortex paring mechanisms are observed depending on the phase shift between the two excitation frequencies. The influence of these two mechanisms on the mixing of a passive scalar is also studied. It is found that exciting the mixing layer with these low and high frequencies has initially an adverse influence on the mixing process; however, it improves the mixing further downstream of the splitter plate with the excitation using a phase shift of $\mathrm{\Delta }\varphi =\mathrm{\pi }$ showing the best mixing performance. The present works shed lights on the fundamental vortex dynamics, and has great potential for aeronautical, automotive and combustion engineering applications.     

Influences of milling and grinding on machined surface roughness and fatigue behavior of GH4169 superalloy workpieces

Surface topography of superalloy GH4169 workpieces machined by milling and grinding is different significantly. Meanwhile, surface roughness, as one of the main indicators of machined surface integrity, has a great influence on the fatigue behavior of workpieces. Based on analyzing the formation mechanism and characteristics of surface roughness utilizing different machining processes and parameters, the machined surface roughness curve can be decoupled into two parts utilizing frequency spectrum analysis, which are kinematic surface roughness curve and stochastic surface roughness curve. The kinematic surface roughness curve is influenced by machining process,parameters, geometry of the cutting tool or wheel, the maximum height of which is expressed as R'_z.By subtracting the kinematic part from the measurement curve, the stochastic surface roughness curve and its maximum height R'_zcan be obtained, which is influenced by the defects of cutting tool edge or abrasive grains, built-up edges(BUE), cracks, high frequency vibration and so on. On the other hand, the results of decoupling analysis of surface roughness curves indicate that Raand Rz values of milling GH4169 are 2–5 times and 1–3 times as high as those of grinding, while R'_zvalue of milling is 13.85%–37.7% as high as that of grinding. According to the results of fatigue life tests of specimens machined by milling and grinding, it can be concluded that fatigue behavior of GH4169 decreases with the increase of R'_zmonotonically, even utilizing different machining processes.     

Time delay compensation in lateral-directional flight control systems at high angles of attack

The previous studies of time delay compensation in flight control systems are all based on the conventional aerodynamic derivative model and conducted in longitudinal motions at low angles of attack. In this investigation, the effects of time delay on the lateral-directional stability augmentation system in high-α regime are discussed based on the $\dot{\beta }$ model, which is proposed in our previous work and proved as a more accurate aerodynamic model to reveal the lateral-directional unsteady aerodynamic characteristics at high angles of attack. Both the $\dot{\beta }$ model and the quasi-steady model are used for simulating the effects of time delay on the flying qualities in high-α maneuvers. The comparison between the simulation results shows that the flying qualities are much more sensitive to the mismatch of feedback gains than the state errors caused by time delay. Then a typical adaptive controller based on the conventional dynamic derivative model and a gain-prediction compensator based on $\dot{\beta }$ model are designed to address the time delay in different maneuvers. The simulation results show that the gain-prediction compensator is much simpler and more efficient at high angles of attack. Finally, the gain-prediction compensator is combined with a linearized $\dot{\beta }$ model reference adaptive controller to compensate the adverse effects of very large time delay, which exhibits excellent performance when addressing the extreme conditions at high angles of attack.