Scalable Parallel Distance Field Construction for Large-Scale Applications

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan‐Liu; Kolla, Hemanth; Chen, Jacqueline H.

doi:10.1109/tvcg.2015.2417572

Cited by 12 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The University of California, Davis has realized in large-scale volume rendering, real-time illumination, multivariate feature extraction etc. (Jadamec, Kreylos, Chang, Fischer, & Yikilmaz, 2018;Yu, Xie, Ma, Kolla, & Chen, 2015). In addition, in terms of scientific visualization analysis, World Wind, Skyline, Open Scene Graph (OSG) and Google Earth can simulation and visualization of ocean and atmospheric environment (Sarthou, Mas, Jacquin, Moreno, & Salamon, 2015).…”

Section: Analysis Methodsmentioning

confidence: 99%

Data science for oceanography: from small data to big data

Qian

Huang

Yang³

et al. 2021

Big Earth Data

View full text Add to dashboard Cite

The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven. Based on the types and distribution of oceanographic data, this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean science. The ocean science has not fully entered the era of big data. There are two ways to expand the amount of oceanographic data to better understanding and management of the ocean. On the data level, fully exploit the potential value of big and small ocean data, and transform the limited, small data into rich, big data, will help to achieve this. On the application level, oceanographic data are of great value if realize the federation of the core data owners and the consumers. The oceanographic data will provide not only a reliable scientific basis for climate, ecological, disaster and other scientific research, but also provide an unprecedented rich source of information that can be used to make predictions of the future.

show abstract

Section: Analysis Methodsmentioning

confidence: 99%

Data science for oceanography: from small data to big data

Qian

Huang

Yang³

et al. 2021

Big Earth Data

View full text Add to dashboard Cite

show abstract

“…voxel grid) [41]. More sophisticated approaches are based on parallel implementation of quadtrees and octrees [37,42]. In the proposed approach, sparse block grid data structure is proposed [36].…”

Section: Signed Distance Field Representationmentioning

confidence: 99%

Modelling and simulation of controlled depth abrasive water jet machining (AWJM) for roughing passes of free-form surfaces

Özcan

Tunç

Jan

et al. 2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Controlled depth abrasive waterjet machining (AWJM) is an unconventional and promising process for materials introducing challenges in conventional machining for high value manufacturing (HVM) industries such as aerospace and automotive. In such applications, waterjet acts as a flexible cutting tool compared to conventional milling. The kerf profile, i.e. the removed material volume, nonlinearly depends on the process parameters such as water pressure, jet traverse speed, abrasive rate, and stand-offdistance unlike the deterministic case in mechanical milling. Thus, prediction of kerf profile, i.e. width, depth, and shape, is of great importance for accurate and efficient process development and tool path generation in AWJM. In this paper, a novel analytical model is proposed for prediction of kerf profile, relying on conservation of energy and momentum, where the material removal is related to the machinability number of the workpiece material, so that the requirement of calibration tests is eliminated. Then, the proposed AWJM process model is used in a framework to predict 3D in-process workpiece (IPW) geometry, which is represented using signed distance field (SDF) approach. The process model is verified by experimental results with an average error of 15%, where the machine profiles are measured by confocal optical microscopy. In some of the experiments, undulated material removal was observed which is due to inconsistent abrasive supply of the system, which is considered to be the major source of the kerf depth prediction errors. The IPW simulation model is verified by variable jet traverse speed AWJM experiments, considering the significant effect of jet traverse speed on the kerf depth. The machined specimens are sectioned along the jet traverse direction and the cross section of the test pieces is visually compared with the simulations. The machined profiles introduced some amount of undulated profile, which may be attributed to the non-consistent abrasive supply in the system. It is shown that the proposed IPW simulation approach demonstrates a reasonable accuracy to plan controlled depth AWJM processes.

show abstract

“…There have been significant algorithmic advances for the fast generation of octrees on modern supercomputers. For instance, octrees have been used for voxelization of 3D objects to accelerate raytracing [64], signed distance calculation [67], compression [50], and fast rendering [34]. Building upon these successes, octrees have become one of the more common mesh generation tools for large-scale PDE simulations, with scalable and adaptive capabilities [21,24,29,32,49,58].…”

Section: Related Workmentioning

confidence: 99%

Scalable adaptive PDE solvers in arbitrary domains

Saurabh,

Ishii,

Fernando

et al. 2021

Preprint

View full text Add to dashboard Cite

Efficiently and accurately simulating partial differential equations (PDEs) in and around arbitrarily defined geometries, especially with high levels of adaptivity, has significant implications for different application domains. A key bottleneck in the above process is the fast construction of a 'good' adaptively-refined mesh. In this work, we present an efficient novel octree-based adaptive discretization approach capable of carving out arbitrarily shaped void regions from the parent domain: an essential requirement for fluid simulations around complex objects. Carving out objects produces an incomplete octree. We develop efficient top-down and bottom-up traversal methods to perform finite element computations on incomplete octrees. We validate the framework by (a) showing appropriate convergence analysis and (b) computing the drag coefficient for flow past a sphere for a wide range of Reynolds numbers (O (1−10 6 )) encompassing the drag crisis regime. Finally, we deploy the framework on a realistic geometry on a current project to evaluate COVID-19 transmission risk in classrooms.

show abstract

Scalable Parallel Distance Field Construction for Large-Scale Applications

Cited by 12 publications

References 31 publications

Data science for oceanography: from small data to big data

Data science for oceanography: from small data to big data

Modelling and simulation of controlled depth abrasive water jet machining (AWJM) for roughing passes of free-form surfaces

Scalable adaptive PDE solvers in arbitrary domains

Contact Info

Product

Resources

About