• Structural Engineering and Mechanics
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• v.34 no.5
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• pp.549-561
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• 2010

In this paper, free vibration analyses of a parallel placed twin pipe system simulated by simply supported-simply supported and fixed-fixed Euler-Bernoulli beams resting on Winkler elastic soil are presented. The motion of the system is described by a homogenous set of two partial differential equations, which is solved by a simulation method called the Differential Transform Method (DTM). Free vibrations of an elastically connected twin pipe system are realized by synchronous and asynchronous deflections. The results of the presented theoretical analyses for simply supported Euler-Bernoulli beams are compared with existing ones in open literature and very good agreement is demonstrated.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2642-2649
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• 2023

A real-time unmeasured dynamic response prediction process for the nuclear power plant pressure pipeline is proposed and its performance is tested in the test-loop system (KAERI). The aim of the process is to predict unmeasurable or unreachable dynamic responses such as acceleration, velocity, and displacement by using a limited amount of directly measured physical responses. It is achieved by combining a well-constructed finite element model and robust inverse force identification algorithm. The pressure pipeline system is described by using the displacement-pressure vibro-acoustic formulation to consider fully filled liquid effect inside the pipeline structure. A robust multiphysics modal projection technique is employed for the real-time sensor synchronized prediction. The inverse force identification method is also derived and employed by using Bathe's time integration method to identify the full-field responses of the target system from the modal domain computation. To validate the performance of the proposed process, an experimental test is extensively performed on the nuclear power plant pressure pipeline test-loop under operation conditions. The results show that the proposed identification process could well estimate the unmeasured acceleration in both frequency and time domain faster than 32,768 samples per sec.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2381-2398
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• 2024

The construction of virtual indoor spaces is crucial for the development of metaverses, virtual production, and other 3D content domains. Traditional methods for creating these spaces are often cost-prohibitive and labor-intensive. To address these challenges, we present a pipeline for generating digital twins of real indoor environments from RGB-D camera-scanned data. Our pipeline synergizes space structure estimation, 3D object detection, and the inpainting of missing areas, utilizing deep learning technologies to automate the creation process. Specifically, we apply deep learning models for object recognition and area inpainting, significantly enhancing the accuracy and efficiency of virtual space construction. Our approach minimizes manual labor and reduces costs, paving the way for the creation of metaverse spaces that closely mimic real-world environments. Experimental results demonstrate the effectiveness of our deep learning applications in overcoming traditional obstacles in digital twin creation, offering high-fidelity digital replicas of indoor spaces. This advancement opens for immersive and realistic virtual content creation, showcasing the potential of deep learning in the field of virtual space construction.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.245-250
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• 2023

Digital twin technology, which copies information from real space into virtual space, is being used in a variety of fields.Interest in digital twins is increasing, especially in advanced manufacturing fields such as Industry 4.0-based smart manufacturing. Operating a digital twin system generates a large amount of data, and the data generated has different characteristics depending on the technology field, so it is necessary to efficiently manage resources and use an optimized digital twin platform technology. Research on digital twin pipelines has continued, mainly in the advanced manufacturing field, but research on high-speed pipelines suitable for data in the plant field is still lacking. Therefore, in this paper, we propose a pipeline design method that is specialized for digital twin data in the plant field that is rapidly poured through Apache Kafka. The proposed model applies plant information on a Revit basis. and collect plant-specific data through Apache Kafka. Equipped with a lightweight CFD engine, it is possible to create a digital twin model that is more suitable for the plant field than existing digital twin technology for the manufacturing field.