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We present direct numerical simulation (DNS) and modelling of incompressible, turbulent, generalized Couette–Poiseuille flow. A particular example is specified by spherical coordinates 
$(Re,\theta,\phi )$, where 
$Re = 6000$ is a global Reynolds number, 
$\phi$ denotes the angle between the moving plate, velocity-difference vector and the volume-flow vector and 
$\tan \theta$ specifies the ratio of the mean volume-flow speed to the plate speed. The limits 
$\phi \to 0^\circ$ and 
$\phi \to 90^\circ$ give alignment and orthogonality, respectively, while 
$\theta \to 0^\circ,\ \theta \to 90^\circ$ correspond respectively to pure Couette flow in the 
$x$ direction and pure Poiseuille flow at angle 
$\phi$ to the 
$x$ axis. Competition between the Couette-flow shear and the forced volume flow produces a mean-velocity profile with directional twist between the confining walls. Resultant mean-speed profiles relative to each wall generally show a log-like region. An empirical flow model is constructed based on component log and log-wake velocity profiles relative to the two walls. This gives predictions of four independent components of shear stress and also mean-velocity profiles as functions of 
$(Re,\theta,\phi )$. The model captures DNS results including the mean-flow twist. Premultiplied energy spectra are obtained for symmetric flows with 
$\phi =90^\circ$. With increasing 
$\theta$, the energy peak gradually moves in the direction of increasing 
$k_x$ and decreasing 
$k_z$. Rotation of the energy spectrum produced by the faster moving velocity near the wall is also observed. Rapid weakening of a spike maxima in the Couette-type flow regime indicates attenuation of large-scale roll structures, which is also shown in the 
$Q$-criterion visualization of a three-dimensional time-averaged flow.
Flow physics vary in different regimes across the full Mach number range, with our knowledge being particularly poor about the hypersonic regime. An Eulerian realization of the particles on demand method, a kinetic model formulated in the comoving reference frame, is proposed to simulate hypersonic compressible flows. The present model allows for flux evaluation in different reference frames, in this case rescaled and shifted by local macroscopic quantities, i.e. fluid speed and temperature. The resulting system of coupled hyperbolic equations is discretized in physical space with a finite volume scheme ensuring exact conservation properties. Regularization via Grad expansion is introduced to implement distribution function and flux transformation between different reference frames. It is shown that the proposed method possesses Galilean invariance at a Mach number up to 
$100$. Different benchmarks including both inviscid and viscous flows are reproduced with the Mach number up to 
$198$ and pressure ratio up to 
$10^5$. Finally, the new model is demonstrated to be capable of simulating hypersonic reactive flows, including one-dimensional and two-dimensional detonations. The developed methodology opens up possibilities for the simulation of the full range of compressible flows, without or with chemical reactions, from the subsonic to hypersonic regimes, leading to enhanced understanding of flow behaviours across the full Mach number range.
There is a growing consensus on brain networks that it is not immutable but rather a dynamic complex system for adapting environment. The neuroimaging research studying how brain regions work collaboratively with dynamic methods had demonstrated its effectiveness in revealing the neural mechanisms of schizophrenia.
To investigate altered dynamic brain functional topology in first-episode untreated schizophrenia patients (SZs) and establish classification models to find objective brain imaging biomarkers.
Resting-state-functional magnetic resonance data for SZs and matched healthy controls were obtained(Table1).
Power-264-template was used to extract nodes and sliding-window approach was carried out to establish functional connectivity matrices. Functional topology was assessed by eigenvector centrality(EC) and node efficiency and its time-fluctuating was evaluated with coefficient of variation(CV). Group differences of dynamic topology and correlation analysis between Positive and Negative Syndrome Scale(PANSS) scores and topology indices showing group differences, which also were used in establishing classification models, was examed.
The CV of node efficiency in angular and paracingulate gyrus was larger in SZs. There are 13 nodes assigned into several brain networks displaying altered CV of EC between groups(Figure1.A). Fluctuation of EC of the node in DMN, which was lower in SZs, showed negative correlation with PANSS total scores(Figure1.B). Dynamic functional topology of above nodes was used to train classification models and demonstrated 80% and 71% accuracy for support vector classification(SVC) and random forest(RF), respectively(Figure2).
Dynamic functional topology illustrated a capability in identifying SZs. Aberrated dynamics of DMN relevant to severity of patient’s symptoms could reveal the reason why it contributed to classification.
No significant relationships.
In this paper, a pulsed spark discharge plasma actuator array is deployed to control laminar–turbulent transition in a Mach 3.0 flat-plate boundary layer, and the subtle flow structures are visualized by nanoparticle planar laser scattering (NPLS) technique. Results show that the onset location of turbulence can be brought upstream by plasma actuation, corresponding to forced boundary-layer transition. Hairpin vortex packets evolved from the thermal bulbs play a vital role in the breakdown of laminar flow. With the help of a machine learning tool, all the relevant structures induced by plasma actuation are extracted from NPLS images, and a conceptual model of the hairpin vortex generation is proposed, including three stages: production and lift-up of the high-vorticity region, formation of the 
$\varLambda$ vortex and evolution of the hairpin vortex.
Though schizophrenia (SZ) and obsessive-compulsive disorder (OCD) are conceptualized as distinct clinical entities, they do have notable symptom overlap and a tight association. Graph-theoretical analysis of the brain connectome provides more indicators to describe the functional organization of the brain, which may help us understand the shared and disorder-specific neural basis of the two disorders.
To explore the static and dynamic topological organization of OCD and SZ as well as the relationship between topological metrics and clinical variables.
Resting state functional magnetic resonance imaging data of 31 OCD patients, 49 SZ patients, and 45 healthy controls (HC) were involved in this study (Table 1). Using independent component analysis to obtain independent components (ICs) (Figure 1), which were defined as nodes for static and dynamic topological analysis.
Static analysis showed the global efficiency of SZ was higher than HC. For nodal degree centrality, OCD exhibited decreased degree centrality in IC59 (located in visiual network) (P = 0.03) and increased degree centrality in IC38 (located in salience network) (P = 0.002) compared with HC. Dynamic analysis showed OCD exhibited decreased dynamics of degree centrality in IC38 (P = 0.003) compared with HC, which showed a negative correlation with clinical scores in OCD. While SZ showed decreased dynamics of degree centrality in IC76 (located in sensory motor network) compared with OCD (P=0.009), which showed a positive correlation with clinical scores in SZ (Figure 2).
These changes are suggestive of disorder-specific alternation of static and dynamic brain topological organization in OCD and SZ.
Obsessive-compulsive disorder (OCD) and schizophrenia (SZ) are both severe psychiatric disorders. Though these two disorders have distinct typical symptoms, there are partial polygenic overlap and comorbidity between the two disorders. However, few studies have explored the shared and disorder-specific brain function underlying the neural pathophysiology of the two disorders, especially in the aspect of dynamics.
To explore the abnormal characteristics of the dynamic functional connectivity (dFC) in OCD and SZ as well as the association between dFC metrics and symptom severity.
The resting state functional magnetic resonance imaging data of 31 patients with OCD, 49 patients with SZ, and 45 healthy controls were analyzed using independent component analysis to obtain independent components (ICs) and assigned them into eight brain networks (Figure 1), then used the sliding-window approach to generate dFC matrices. Using k-means clustering, we obtained three reoccurring dFC states (Figure 2), and state transition metrics were obtained
In a sparsely connected state (state 1), SZ showed both increased fractional time and mean dwell time than controls (P=0.047 and P=0.033) and OCD (P=0.001 and P=0.003). In a state characterized by negative FC between networks (state 2), OCD showed both increased fractional time and mean dwell time than controls (P=0.032 and P=0.013) and SZ (P=0.005 and P=0.003). Moreover, the fractional time of state 2 was positively correlated with anxiety scores in OCD (r=0.535, P=0.021, FDR corrected) (Figure 3).
OCD and SZ patients showed distinct alternations of brain functional dynamics.
No significant relationships.
