Significance of wave data source selection for vessel response prediction and fatigue damage estimation

Schirmann, Matthew L.; Collette, Matthew; Gose, James W.

doi:10.1016/j.oceaneng.2020.107610

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The workpiece is an immutable state of things, which can be regarded as a physical object. Agents can promote, control, and affect the implementation of the process [ 20 , 21 ].…”

Section: Bt and Rm Technologymentioning

confidence: 99%

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Jiang

Lei

2022

Computational Intelligence and Neuroscience

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To better track the source of goods and maintain the quality of goods, the present work uses blockchain technology to establish a system for trusted traceability queries and information management. Primarily, the analysis is made on the shortcomings of the traceability system in the field of agricultural products at the present stage; the study is conducted on the application of the traceability system to blockchain technology, and a new model of agricultural product traceability system is established based on the blockchain technology. Then, a study is carried out on the task scheduling problem of resource clusters in cloud computing resource management. The present work expands the task model and uses the deep Q network algorithm in deep reinforcement learning to solve various optimization objectives preset in the task scheduling problem. Next, a resource management algorithm based on a deep Q network is proposed. Finally, the performance of the algorithm is analyzed from the aspects of parameters, structure, and task load. Experiments show that the algorithm is better than Shortest Job First (SJF), T e t r i s ∗ , Packer, and other classic task scheduling algorithms in different optimization objectives. In the traceability system test, the traceability accuracy is 99% for the constructed system in the first group of samples. In the second group, the traceability accuracy reaches 98% for the constructed system. In general, the traceability accuracy of the system proposed here is above 98% in 8 groups of experimental samples, and the traceability accuracy is close for each experimental group. The resource management approach of the traceability system constructed here provides some ideas for the application of reinforcement learning technology in the construction of traceability systems.

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“…The workpiece is an immutable state of things, which can be regarded as a physical object. Agents can promote, control, and affect the implementation of the process [ 20 , 21 ].…”

Section: Bt and Rm Technologymentioning

confidence: 99%

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Jiang

Lei

2022

Computational Intelligence and Neuroscience

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“…Bernasconi et al amalgamated finite element analysis with augmented reality, proffering a twin model dedicated to monitoring fatigue crack evolution in metal composite bonded connections founded on measured strain data [16]. Schirmann et al [17] instituted a digital twin system for estimating hull structure fatigue, predicated on measured wave response data, serving as an evaluation tool for cumulative impairment endured by the ship.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Digital Twin of Ship Structure Deformation Field Based on the Inverse Finite Element Method

Wei,

Li,

Jiang

et al. 2024

JMSE

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Digital twins, an innovative technology propelled by data and models, play a seminal role in the digital transformation and intelligent upgrade of ships. This study introduces a digital twin methodology for the real-time monitoring of ship structure deformation fields, based on finite discrete strain data, and a visualization tool framework is developed using virtual reality technology. First, the inverse Finite Element Method (iFEM) is employed to derive the deformation field of the ship structure in real time using sensor strain data. Secondly, the deformation field data obtained based on the iFEM algorithm is converted into general visualization data conducive to interpretation within virtual reality (VR) applications. Lastly, a digital twin software tool is built to enable synchronous responses and interactions between the virtual scene and the physical scene, directly superposing particular virtual objects (data acquired by sensors, computer-aided design (CAD) virtual models, and deformation field cloud images) onto the physical scene in real time. The digital twin tool embodies a virtual reality visualization system framework integrating the physical data measurement, reconstruction, analysis, expression, storage, rendering, and interaction of deformation field data. Through practical application, the flexibility, effectiveness, and compatibility of the developed prototype tool are verified. According to the results, the system can enhance the efficiency of scientific communication, model validation, and interdisciplinary sharing during the analysis and evaluation of the mechanical properties of ship structures.

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“…Several approaches have been proposed to predict the behavior of objects, including the development of semantic models [32,33]. There are numerous studies in the literature focused on predicting and preventing target failure [34][35][36][37][38]. A similar case is presented in [39], where a predictive method based on fatigue tests is developed to enable control over the target.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Health Management Using IR Thermography: The Case of a Digital Twin of a NiTi Endodontic File

Ruffa

Lugarà

Fulco

et al. 2023

Sensors

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Prognostic and health management technologies are increasingly important in many fields where reducing maintenance costs is critical. Non-destructive testing techniques and the Internet of Things (IoT) can help create accurate, two-sided digital models of specific monitored objects, enabling predictive analysis and avoiding risky situations. This study focuses on a particular application: monitoring an endodontic file during operation to develop a strategy to prevent breakage. To this end, the authors propose an innovative, non-invasive technique for early fault detection based on digital twins and infrared thermography measurements. They developed a digital twin of a NiTi alloy endodontic file that receives measurement data from the real world and generates the expected thermal map of the object under working conditions. By comparing this virtual image with the real one acquired by an IR camera, the authors were able to identify an anomalous trend and avoid breakage. The technique was calibrated and validated using both a professional IR camera and an innovative low-cost IR scanner previously developed by the authors. By using both devices, they could identify a critical condition at least 11 s before the file broke.

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Significance of wave data source selection for vessel response prediction and fatigue damage estimation

Cited by 8 publications

References 14 publications

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Real-Time Digital Twin of Ship Structure Deformation Field Based on the Inverse Finite Element Method

Prognostic Health Management Using IR Thermography: The Case of a Digital Twin of a NiTi Endodontic File

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