7 research outputs found
Mitigating effect of embankment to adjacent pipe with CDM columns
Pipelines are valuable infrastructures that covering a large area or expanding to long distance for the transporting function. This leads to the variety of loads and effects applied on such buried structures. A thread to pipeline integrity is the construction of the embankment on the soft soil which leads to the displacement of the pipe adjacent to the slope. This displacement will effect to the increase of internal force or causing failure of the near-by pipes. The use of concrete pile to improve the soil properties may be a solution; however, the cost for this is expensive. To propose an alternative solution for the problem, this study uses a system of cement deep mixing, CDM, columns as the solution for protecting the pipe. A simple 2D Finite Element, FE, model using Plaxis software has been established based on the equivalent soil approach which considering the CDM columns and their surrounding soil as an unified soil. The effectiveness of the proposed solution has been numerically investigated and proven. The lateral displacement of pipe and the maximum ring bending moment and other internal forces are significantly reduced with the appearance of the CDM columns. The selective parametric study has been implemented revealing the critical input variables are the distance of pipe to the slope and the length of the CDM column
Mitigating effect of embankment to adjacent pipe with CDM columns
Pipelines are valuable infrastructures that covering a large area or expanding to long distance for the transporting function. This leads to the variety of loads and effects applied on such buried structures. A thread to pipeline integrity is the construction of the embankment on the soft soil which leads to the displacement of the pipe adjacent to the slope. This displacement will effect to the increase of internal force or causing failure of the near-by pipes. The use of concrete pile to improve the soil properties may be a solution; however, the cost for this is expensive. To propose an alternative solution for the problem, this study uses a system of cement deep mixing, CDM, columns as the solution for protecting the pipe. A simple 2D Finite Element, FE, model using Plaxis software has been established based on the equivalent soil approach which considering the CDM columns and their surrounding soil as an unified soil. The effectiveness of the proposed solution has been numerically investigated and proven. The lateral displacement of pipe and the maximum ring bending moment and other internal forces are significantly reduced with the appearance of the CDM columns. The selective parametric study has been implemented revealing the critical input variables are the distance of pipe to the slope and the length of the CDM column
Impact of Parameter Mismatch on Three-Phase Dual-Active-Bridge Converters
Three-phase dual active bridge converters (DAB3) are a widely used topology in battery charging applications thanks to their numerous advantages, such as bidirectional power flow, galvanic isolation, low output current ripple, and inherent soft-switching. In such applications, three single-phase transformers are commonly employed as the AC-link to simplify manufacturing and reduce costs. These transformers’ leakage inductance can be utilized instead of the external leakage inductance to achieve high power density. However, the assumption of uniformity in these inductances is not always accurate as they can vary significantly during fabrication. This study presents a comprehensive analysis of the impact of transformer leakage inductance variation, which can deviate by up to 24% from the desired value. The effects of this variation are investigated from different perspectives, including power transfer, soft-switching range, root-mean-square (RMS) current, and the temperature rise of the transformer winding. Although the power transfer and total copper loss of transformers are changed insignificantly even under highly mismatched leakage inductance, the currents and thermal distribution among phases are considerably impacted. Based on statistical probability, a maximum leakage inductance variation threshold of 10–15% compared to the desired value is recommended to ensure the maximum acceptable temperature rise among phases. Experimental results are presented to validate the analysis
RGB-D to CAD retrieval with objectNN dataset
The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN*16] and CAD models from ShapeNet [CFG*15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy
SHREC\u2717: RgB-D to CAD Retrieval With ObjectNN Dataset
© 2017 The Eurographics Association. The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN∗16] and CAD models from ShapeNet [CFG∗15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy