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01 Dec 2018TL;DR: This work argues that semantic technologies and in particular semantic models or ontologies is promising modelling paradigm for DTs and plans to augment the PI System developed by OSIsoft with ontologies and show how the resulting solution can help in simplifying analytical and machine learning routines forDTs.
Abstract: Digital twins (DTs) are a powerful mechanism for representing complex industrial assets such as oil platforms as digital models. These models can facilitate temporal analyses and computer simulations of assets. In order to enable this, DTs should be able to capture characteristics of an asset as specified by the manufacturer, its state during the run time, as well as how the asset interacts with other assets in a complex system. We argue that semantic technologies and in particular semantic models or ontologies is promising modelling paradigm for DTs. Semantic models allow to capture complex systems in an intuitive fashion, can be written in standardised ontology languages, and come with a wide range of off-the-shelf systems to design, maintain, query, and navigate semantic models. In this work we report our preliminary results on developing a system that would support semantic-based DTs. In particular, we plan to augment the PI System developed by OSIsoft with ontologies and show how the resulting solution can help in simplifying analytical and machine learning routines for DTs.
31 citations
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TL;DR: In this article, the Harmonic Polynomial Cell (HPC) method was extended to 3D to study potential-flow problems in marine hydrodynamics, and the comparison with some existing boundary element based methods including the Fast Multipole Accelerated Boundary Element Methods showed that the HPC method is very competitive in terms of both accuracy and efficiency.
Abstract: A new numerical 2D cell method has been proposed by Shao & Faltinsen [1] based on representing the velocity potential in each cell by harmonic polynomials. The method was named the Harmonic Polynomial cell (HPC) method. The method was later extended to 3D to study potential-flow problems in marine hydrodynamics [2]. With the considered number of unknowns that are typical in marine hydrodynamics, the comparisons with some existing boundary element based methods including the Fast Multipole Accelerated Boundary Element Methods showed that the HPC method is very competitive in terms of both accuracy and efficiency. The HPC method has also been applied to study fully-nonlinear wave-body interactions [1, 2], for example, sloshing in tanks, nonlinear waves over different sea-bottom topographies and nonlinear wave diffraction by a bottom-mounted vertical circular cylinder. However, no current effects were considered. In this paper, we study the fully-nonlinear time-domain wave-body interaction considering the current effects. In order to validate and verify the method, a bottom-mounted vertical circular cylinder which has been studied extensively in the literature will first be examined. Comparisons are made with published numerical results and experimental results. As a further application, the HPC method will be used to study multiple bottom-mounted cylinders. An example of the wave diffraction of two bottom-mounted cylinders is also presented.
31 citations
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TL;DR: In this article, the authors employed a wire mesh sensor to investigate high flow rate horizontal air-water flow and revealed some recurrent liquid structures (one may call pseudo-slugs) analogous to huge waves of (vertical) churn flow.
30 citations
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TL;DR: Proban is a general purpose probabilistic analysis program that covers calculation needs for example in structural reliability, and calculates probability, distribution, first passage probability, crossing rate, and related sensitivity measures.
30 citations
Authors
Showing all 1935 results
Name | H-index | Papers | Citations |
---|---|---|---|
Sergio A. Jimenez | 84 | 416 | 28486 |
Hao Yu | 81 | 981 | 27765 |
Clifford Nass | 65 | 195 | 22615 |
Odd M. Faltinsen | 50 | 247 | 11374 |
Otilia Mó | 46 | 382 | 8641 |
Zefeng Zhou | 38 | 84 | 8653 |
Asgeir J. Sørensen | 35 | 221 | 4459 |
Michael Havbro Faber | 33 | 260 | 4372 |
Deborah Greaves | 31 | 194 | 3141 |
Alessandro Toffoli | 30 | 117 | 2494 |
Yang Miang Goh | 27 | 53 | 2051 |
Narasi Sridhar | 27 | 202 | 3017 |
Elzbieta M. Bitner-Gregersen | 26 | 109 | 2234 |
Jørgen Amdahl | 26 | 150 | 2157 |
Christopher D. Taylor | 25 | 143 | 2840 |