Comparison of the influence of 2D and 3D geometry of the main chamber on plasma parameters in the SOL of ASDEX Upgrade

In this paper the influence of toroidally asymmetric wall features on plasma solutions for ASDEX Upgrade is investigated by using the 3D scrape-off-layer simulation code EMC3-EIRENE. A comparison of simulation results in a 2D case with a toroidally symmetric first wall and divertor and a 3D case that differs from the 2D setup by including the 3D structure of the poloidal rib-limiters on the low field side of ASDEX Upgrade, highlights notable differences in the main chamber neutral particle distributions, ionisation sources and plasma flow patterns. Both neutral particle distribution and ionisation sources extend poloidally further upwards at the outer mid-plane in the 3D case and the plasma flow is globally influenced by the 3D wall features. Both simulations are conducted with identical input parameters to isolate the influence of wall geometry from other factors. By analysing the transport of neutrals from different poloidal locations it was possible to explain the observed discrepancies by different transport paths for recycled neutrals from the divertor region, only accessible in the 3D version of the wall geometry. Together with observed differences in fall-off lengths for plasma flow and electron temperature at the outer mid-plane, presented results are of key importance for interpreting global impurity migration experiments.

Study On The Single Point Incremental Sheet Forming of AISI 321 Variable Wall Angle Geometry

For rapid prototyping, design validation and small batch productions process with low tooling cost is preferred. Single Point Incremental Forming (SPIF) is a die-less sheet metal forming process which requires only low cost forming tool driven by CNC machine in a toolpath to form required geometry at room temperature from sheet blank clamped in a low cost and low stiffness clamping system. In this study, effect of process parameters such as tool radius, feed rate and lubrication are considered on the formability of the truncated profile of AISI 321 Variable Wall Geometry (VWA). Set parameters conditions with 2 level layers are optimized using numerical and statistical approach. Experimentation on the same setup is carried out by selecting the most, least and mid favorable solutions optimized on the basis of forming forces and stresses in the sheet. Geometrical accuracy, sheet thinning, and forming forces are compared analytically, numerically and experimentally addressing the inadequacy of analytically models for Variable Wall Angle Geometries.

Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM

As-built Building Information Models (BIMs) are becoming increasingly popular in the Architectural, Engineering, Construction, Owner and Operator (AECOO) industry. These models reflect the state of the building up to as-built conditions. The production of these models for existing buildings with no prior BIM includes the segmentation and classification of point cloud data and the reconstruction of the BIM objects. The automation of this process is a must since the manual Scan-to-BIM procedure is both time-consuming and error prone. However, the automated reconstruction from point cloud data is still ongoing research with both 2D and 3D approaches being proposed. There currently is a gap in the literature concerning the quality assessment of the created entities. In this research, we present the empirical comparison of both strategies with respect to existing specifications. A 3D and a 2D reconstruction method are implemented and tested on a real life test case. The experiments focus on the reconstruction of the wall geometry from unstructured point clouds as it forms the basis of the model. Both presented approaches are unsupervised methods that segment, classify and create generic wall elements. The first method operates on the 3D point cloud itself and consists of a general approach for the segmentation and classification and a class-specific reconstruction algorithm for the wall geometry. The point cloud is first segmented into planar clusters, after which a Random Forests classifier is used with geometric and contextual features for the semantic labelling. The final wall geometry is created based on the 3D point clusters representing the walls. The second method is an efficient Manhattan-world scene reconstruction algorithm that simultaneously segments and classifies the point cloud based on point feature histograms. The wall reconstruction is considered an instance of image segmentation by representing the data as 2D raster images. Both methods have promising results towards the reconstruction of wall geometry of multi-story buildings. The experiments report that over 80% of the walls were correctly segmented by both methods. Furthermore, the reconstructed geometry is conform Level-of-Accuracy 20 for 88% of the data by the first method and for 55% by the second method despite the Manhattan-world scene assumption. The empirical comparison showcases the fundamental differences in both strategies and will support the further development of these methods.

Using convolutional neural networks for hygrothermal predictions to extrapolate to other external climates

When simulating the hygrothermal behaviour of a building component, many uncertainties are involved (e.g. exterior and interior climates, material properties, configuration geometry). In contrast to a deterministic assessment, a probabilistic analysis enables including these uncertainties, and thus allows a more reliable assessment of the hygrothermal performance. This easily involves thousands of simulations, which easily becomes computationally inhibitive. To overcome this time-efficiency issue, a convolutional neural network, a type of metamodel mimicking the original model with a strongly reduced calculation time, can replace the hygrothermal model. This was proven in a previous study for a massive masonry wall, where variability of exterior and interior climate, brick material properties and wall geometry was included. However, the question rises whether it is possible to train the network on a limited number of climates, and afterwards use the network to predict accurately for other climates as well. This paper thus focuses on this aspect, and results show that, as long as the range of the new climate data falls within the range of the climate data the network was trained on, the network is able to predict accurately for new climates as well.

SEMI-AUTOMATED TOPOLOGY ADJUSTMENT OF PARTIAL WALL GEOMETRY

The reconstruction of Building Information Modeling objects for as-built modeling is currently the subject of ongoing research. A popular method is to extract building information from point cloud data to create a set of parametric objects. The automation of this process is highly desired by the industry but is currently hindered by occlusions, clutter and the complexity of the building geometry. To create an as-built BIM, it is vital to not only accurately reconstruct the building’s structure but also to compute the topology between the objects. More specifically, we target the topology of the reconstructed partial wall geometry as this forms the basis for other objects.In this work, a novel method is presented to automatically adjust the topology of wall geometry in an as-built BIM. We present a semi-automated method that procedurally evaluates the configuration of reconstructed objects and adjusts them to create a more faithful BIM. A wall connection evaluation algorithm is proposed that takes as input the centrelines of partial wall geometry and a set of floor and ceilings mesh segments and outputs the topologically adjusted objects. The method is tested on a variety of scenes and shows promising results to reliably compute the topology of as-built models. The generated geometry is similar to the geometric modification proposed by expert modelers. A key advantage is that the algorithm operates directly in Revit and Rhino and can be used for new models as well as for updating existing models.

Effect of Shear wall Geometry on Ten Storey Building with Raft and Fixed base Foundation

:Raft foundation is widely used for high-rise buildings where presence of shear wall is very common. Shear Wall resists a major portion of the lateral load of high-rise buildings. So, presence of shear wall on mat foundation causes significant change in pattern and intensity of loading on mat foundation. The present work involved an investigation of effect of shear wall geometry in different seismic zones with and without the presence of raft foundation. Multi storey building with ten storey is analysed for the storey drift, storey displacement and base shear. The analysis of building is done by response spectrum analysis. The different shapes of shear walls C,L,F,I with same plan area is considered. The effect of shear wall geometry is studied in zone II, zone III, zone IV, zone V. Loads and load combinations selected based on IS 456-2000 and IS 875-1987 code. Three types of soil conditions are considered typeI ,typeII, typeIII.Raft foundation is designed by meshing the slab into equal quadrilaterals.Assigning of area springs to the each quadrilateral.Meshing is done inorder to convert the infinite solution into finite solution.The different shapes of the shear walls is analysed in zone V with the raft foundation in type III.The results are compared with and without the raft foundation ,conclusions are drawn that the best shape of the shear wall suits in different seismic zones.