X-ray computed tomography (CT) has emerged as the most prevalent technique to obtain three-dimensional morphological information of granular geomaterials. A key challenge in using the X-ray CT technique is to faithfully reconstruct particle morphology based on the discretized pixel information of CT images. In this work, a novel framework based on the machine learning technique and the level set method is proposed to segment CT images and reconstruct particles of granular geomaterials. Within this framework, a feature-based machine learning technique termed Trainable Weka Segmentation is utilized for CT image segmentation, i.e., to classify material phases and to segregate particles in contact. This is a fundamentally different approach in that it predicts segmentation results based on a trained classifier model that implicitly includes image features and regression functions. Subsequently, an edge-based level set method is applied to approach an accurate characterization of the particle shape. The proposed framework is applied to reconstruct three-dimensional realistic particle shapes of the Mojave Mars Simulant. Quantitative accuracy analysis shows that the proposed framework exhibits superior performance over the conventional watershed-based method in terms of both the pixel-based classification accuracy and the particle-based segmentation accuracy. Using the reconstructed realistic particles, the particle-size distribution is obtained and validated against experiment sieve analysis. Quantitative morphology analysis is also performed, showing promising potentials of the proposed framework in characterizing granular geomaterials.
High precision design wave height is required in extreme marine environments in typhoon-affected sea areas. A new model is built under typhoon effect to calculate the design wave heights. The new model has multiple undetermined parameters, and it is able to fit observed data more flexibly and accurately. In addition, the distribution functions of this new model are based on the maximum entropy principle. Therefore, they can avoid the apriority, which means arbitrarily assigning Poisson distribution to describe the distribution of typhoon occurrence frequency and assigning Gumbel distribution or Pearson-III distribution to describe the distribution of extreme events in the process of applying the compound distribution to deduce the design elevations. The observed data of 18-year (1984–2001) extreme wave heights and the corresponding typhoon events in Maidao are used to test the model. Test results show that the new model is theoretically more stable and more precise when predicting the design wave heights under the typhoon-affected sea areas. 相似文献
A series of numerical simulations about a small scale(aspect ratio:63.2) flexible pipe undergoing forced harmonious oscillation and vortex-induced vibration(VIV) have been taken into account.The wake hydrodynamics and pipe deformation were accomplished by ANSYS MFX solution strategy designed for fluid-structure interaction(FSI) problem with well-performed LES model.The configuration of structured mesh,multi-domain design,different mesh stiffness admeasured by User Fortran ensured that the numerical task was competent to deal with large deformation related to this case.The introduction of instantaneous amplitude definition and modeless component decomposition method(Chen and Kim,2008) was helpful to reveal much more information from modal analysis.Most results from numerical simulation are generally consistent with those from model test(Choi and Hong,2000) via the comparison between them.As supplementary to model test,visualization of the vortex wake was also provided.It has been proved that the forced oscillation doesn't only excite a complicated dumbbell-like wake pattern around the outer thimble,but also results in inner flow inside the PVC pipe.The velocity of the inner flow increases with the frequency of forced oscillation. 相似文献
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