Scale-space effect and scale hybridization in image intelligent recognition of geological discontinuities on rock slopes

Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data.Inspired by recent advances in computer vision,deep learning(DL)models have been widely utilized for image-based fracture identification.The multi-scale characteristics,image resolution and annotation quality of images will cause a scale-space effect(SSE)that makes features indistinguishable from noise,directly affecting the accuracy.However,this effect has not received adequate attention.Herein,we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques.Next,we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Combining these metrics with the scale-space theory,we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition.It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models.In light of these observations,we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation.The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD.It also facilitates the objective understanding,description and analysis of the rock behavior and stability of slopes from the perspective of image data.

LES Investigation of Drag-Reducing Mechanism of Turbulent Channel Flow with Surfactant Additives

In this work,the drag-reducing mechanism of high-Reynoldsnumber turbulent channel flow with surfactant additives is investigated by using large eddy simulation(LES)method.An N-parallel finitely extensible nonlinear elastic model with Peterlin’s approximation(FENE-P)is used to describe the rheological behaviors of non-Newtonian fluid with surfactant.To close the filtered LES equations,a hybrid subgrid scale(SGS)model coupling the spatial filter and temporal filter is applied to compute the subgrid stress and other subfilter terms.The finite difference method and projection algorithm are adopted to solve the LES governing equations.To validate the correctness of our LES method and in-house code,the particle image velocimetry(PIV)experiment is carried out and representative measured results are compared with LES results in detail.Then the flow characteristics and drag-reducing mechanism of turbulent channel flow with surfactant are investigated from the perspective of drag reduction rate,mean velocity,fluctuation of deformation rate,shear stress,transport and dissipation of turbulent kinetic energy,and turbulent coherent structures.This research can shed a light on the application of turbulent drag reduction technique in district heating,petroleum transport,etc.

An Improved k-Equation Turbulence Model

LRN （low-Reynolds number） modifications to the NR （Norris-Reynolds） k-equation turbulence model are proposed and evaluated. The k and e that render the hybrid time scale are determined using the k-transport equation together with the Bradshaw and other algebraic relations. The eddy-viscosity coefficient Cμ and the empirical damping function are constructed such as to preserve the anisotropic characteristics of turbulence for application to non-equilibrium turbulent flows. The MNR （modified NR） model is applied to calculate two well-documented flows, yielding predictions in good agreement with the DNS （direct numerical simulation） and experimental data. Comparisons demonstrate that the MNR model offers a significant improvement over the original NR model and competitiveness with the Spalart-Allmaras one-equation turbulence model. The performance evaluation dictates that unlike the original NR model, the MNR model can be employed as a single-equation model instead of associating it with the two-layer model of turbulence.

Real-Time Automatic Scaling Method of Oblique Ionogram Parameters Based on Morphological Operator and Inversion Technique

Automatic scaling ionogram can get the parameters of ionogram which are vital to ionosphere detecting. In this paper, a new method is proposed to scale F2 layer trace automatically from oblique ionogram based on morphological operator and inversion technique. This method is verified through the comparison of actual detecting data with statistical analysis. The results show that the proposed automatic scaling method has high acceptable rate and is suitable for scaling oblique ionogram with different high angle wave states. It is fast and precise to fit O-mode echoes in F2 layer without the influence from F1 layer. This method could be applied in real-time ionospheric oblique sounding research with high reliability and versatility.