Tire-Pavement Noise Simulation and Analysis

Ph.DDOCTOR OF PHILOSOPH

CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.Comment: 7 pages, 4 figure

An active learning approach for the prediction of hydrodynamic roughness properties

Realistic surfaces of flow-related equipment are often hydraulically rough due to wear or fouling. Predicting the skin friction exerted by such rough surfaces is a challenging task since the topography of these surfaces is inherently irregular and complex. Recent developments in data-driven methods and increasing affordability of high-fidelity direct numerical simulations (DNS) have created new possibilities for estimation of drag on irregular rough surfaces. In the present work we aim to demonstrate a viable approach to efficiently train a predictive model for the estimation of drag for irregular roughness based on its height probability density function (PDF) and the surface height power spectrum (PS). An active learning (AL) framework is employed to efficiently navigate the construction of a training database. Training data is generated by conducting direct numerical simulations of a flow over artificially generated rough surfaces in minimal channels in order to minimize the computational effort. An ensemble neural network (ENN) model is trained based on the database. The ENN model shows promising potential in predicting the skin friction as well as estimating the epistemic (model) uncertainty. Furthermore, the model – trained on artificial surfaces – is tested on five realistic surface scans, showing that a maximum error of 8.7% between the predicted roughness function ∆U+ and the DNS results is achieved. Overall, the AL framework shows a great potential as a basis for future research towards a universal predictive tool for any arbitrary roughness

Study on Temperature Force Control Mechanism of CRTSⅡ Slab Track: Control Conditions of Temperature Cracking

Diseases such as track slab arching and joint concrete crushing of China Railway Track System (CRTS)II slab track were caused by huge temperature force, which seriously threatens driving safety of trains. In this study, a longitudinal weak connection scheme of CRTSII slab track was proposed to adjust the temperature force in track slab and reduce diseases of longitudinal continuous track slab. This paper focuses on the cracking characteristics of the longitudinal heterogeneous concrete composite structure. The equation which was originally developed to calculate crack width and structure stress under temperature loads, was put forward to consider deformation difference of different elastic modulus. The influence law of various parameters was analyzed. The reinforcement stress and crack width of CRTSII slab track after longitudinal connection weakening were calculated, and the reasonable limit value of tensile force of connection reinforcement and the minimum value of bond resistance of reinforcement in joint position were obtained. The result shows that, in order to reduce the bond resistance between the joint material and the reinforcement, the elastic modulus of the elastic material should be less than 5000 MPa; in order to ensure that the reinforcement does not produce large stress, the elastic modulus of the joint should be greater than 1000 MPa

A comparison of hydrodynamic and thermal properties of artificially generated against realistic rough surfaces

The mathematical roughness generation approaches enjoy outstanding flexibility in delivering desired roughness geometries to perform systematic research. However, whether an mathematically (artificially) generated roughness can be considered an adequate surrogate of a realistic surface in terms of its influence on the flow remains nonetheless an open question. Motivated by this, the present study discusses the possibility of reproducing flow properties over realistic roughness with artificial roughness. To this end, six types of artificial rough surfaces are generated through imitation of the realistic height probability density function (PDF) and the roughness power spectrum (PS) preserving the stochastic nature of the roughness structure. The flow properties of the artificial surfaces are assessed using direct numerical simulations (DNS) in a fully-developed turbulent channel flow at Re_ = 500−2000. An excellent match in terms of global flow properties, mean velocity and temperature profiles, Reynolds stresses as well as equivalent sand grain sizes is found compared to their original counterpart with exception of a strongly anisotropic sample (surface anisotropy ratio $≈ 1.7$). Additionally, some artificial surfaces are generated by matching only the PS, and it was shown that only at adequately low effective slopes this can lead to similar flow properties. Overall, the results suggest that artificial roughness generated using the employed method by mimicking realistic PDF and PS can be applied as a full-fledged surrogate for realistic roughness under the premise of surface isotropy

Nestin and CD133: valuable stem cell-specific markers for determining clinical outcome of glioma patients

<p>Abstract</p> <p>Aim</p> <p>Gliomas represent the most frequent neoplasm of the central nervous system. Unfortunately, surgical cure of it is practically impossible and their clinical course is primarily determined by the biological behaviors of the tumor cells. The aim of this study was to investigate the correlation of the stem cell markers Nestin and CD133 expression with the grading of gliomas, and to evaluate their prognostic value.</p> <p>Methods</p> <p>The tissue samples consisted of 56 low- (WHO grade II), 69 high- (WHO grade III, IV) grade gliomas, and 10 normal brain tissues. The expression levels of Nestin and CD133 proteins were detected using SABC immunohistochemical analysis. Then, the correlation of the two markers' expression with gliomas' grading of patients and their prognostic value were determined.</p> <p>Results</p> <p>Immunohistochemical analysis with anti-Nestin and anti-CD133 antibodies revealed dense and spotty staining in the tumor cells and their expression levels became significantly higher as the glioma grade advanced (<it>p </it>< 0.05). There was a positive correlation between the two markers' expression in different gliomas tissues (rs = 0.89). The low expression of the two markers significantly correlated with long survival of the glioma patients (<it>p </it>< 0.05). The survival rate of the patients with Nestin+/CD133+ expression was the lowest (<it>p </it>< 0.01), and the multivariate analysis confirmed that conjoined expression of Nestin+/CD133+ and Nestin-/CD133- were independent prognostic indicators of gliomas (both <it>p </it>< 0.01, Cox proportional hazard regression model).</p> <p>Conclusion</p> <p>These results collectively suggest that Nestin and CD133 expression may be an important feature of human gliomas. A combined detection of Nestin/CD133 co-expression may benefit us in the prediction of aggressive nature of this tumor.</p

Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness -- a data driven approach

The purpose of the present work is to examine two possibilities; firstly, predicting equivalent sand-grain roughness size $k_s$ based on the roughness height probability density function and power spectrum leveraging machine learning as a regression tool, and secondly, extracting information about relevance of different roughness scales to skin-friction drag by interpreting the output of the trained data-driven model. The model is an ensemble neural network consisting of 50 deep neural networks. The data for the training of the model is obtained from direct numerical simulations (DNSs) of turbulent flow in plane channels over 85 irregular multi-scale roughness samples at friction Reynolds number Re$_\tau=800$. The 85 roughness samples are selected from a repository of 4200 samples, covering a wide parameter space, through an active learning (AL) framework. The selection is made in several iterations, based on the informativeness of samples in the repository, quantified by the variance of ENN predictions. This AL framework aims to maximize the generalizability of the predictions with a certain amount of data. This is examined using three different testing data sets with different types of roughness, including 21 surfaces from the literature. The model yields an overall mean error of 5\% to 10\% on different testing data sets. Subsequently, a data interpretation technique, known as layer-wise relevance propagation, is applied to measure the contributions of different roughness wave-lengths to the predicted $k_s$. High-pass filtering is then applied to the roughness PS to exclude the wave-numbers identified as drag-irrelevant. The filtered rough surfaces are investigated using DNS, and it is demonstrated that, despite significant impact of filtering on the roughness topographical appearance and statistics, the skin-friction coefficient of the original roughness is successfully preserved.Comment: 24 pages, 11 figures, submitted to JF

Shear response behavior of STF/kevlar composite fabric in picture frame test

The picture frame test was applied to compare Kevlar neat and STF/Kevlar composite fabrics. The digital image correlation markers method was applied to measure the shear deformation behavior of the fabric in real-time under three loading rates: 100, 500, and 1000 mm/min. A theoretical model was applied to evaluate the effect of STF on the shear deformation stiffness of the fabric and cells and on the energy absorption during shear deformation. The results show that the STF/Kevlar composite fabric has a larger load-carrying capacity than the neat fabric in the picture frame test, and has obvious loading rate dependence. The yarn cell of the fabric undergoes slip deformation and reaches a shear-locked state; the shear modulus and the cell spring torsion coefficient of the STF/Kevlar composite fabric are significantly higher than those of neat fabric. The shear thickening behavior of STF occurs at higher loading rates, and the composite fabric has the highest shear deformation stiffness and shear energy absorption level