Zobrazeno 1 - 10
of 131
pro vyhledávání: '"NIKOLAOS DERVILIS"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The
Externí odkaz:
https://doaj.org/article/0c13ab1041f74e619a3b84be0c658379
Autor:
Antonios Kamariotis, Eleni Chatzi, Daniel Straub, Nikolaos Dervilis, Kai Goebel, Aidan J. Hughes, Geert Lombaert, Costas Papadimitriou, Konstantinos G. Papakonstantinou, Matteo Pozzi, Michael Todd, Keith Worden
Publikováno v:
Data-Centric Engineering, Vol 5 (2024)
To maximize its value, the design, development and implementation of structural health monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-m
Externí odkaz:
https://doaj.org/article/8c51d08b3c0d4881b5590d36578b78ab
Publikováno v:
Data-Centric Engineering, Vol 5 (2024)
Population-based structural health monitoring (PBSHM) systems use data from multiple structures to make inferences of health states. An area of PBSHM that has recently been recognized for potential development is the use of multitask learning (MTL) a
Externí odkaz:
https://doaj.org/article/216eb2b0c94d4dd4a2fb3d9d49c86814
Autor:
Paulo Gonzaga, Henrik Toft, Keith Worden, Nikolaos Dervilis, Lars Bernhammer, Nevena Stevanovic, Alejandro Gonzales
Publikováno v:
Wind Energy, Vol 25, Iss 6, Pp 1060-1076 (2022)
Abstract Offshore wind power has been in the spotlight among renewable energy sources. The current trends of increased power ratings and longer blades come together with the aim to reduce energy costs by design optimisation. The standard approach to
Externí odkaz:
https://doaj.org/article/8d9cb3cf56a646f6b4fe6598e068f318
Publikováno v:
Frontiers in Energy Research, Vol 11 (2023)
Non-linear analysis is of increasing importance in wind energy engineering as a result of their exposure in extreme conditions and the ever-increasing size and slenderness of wind turbines. Whilst modern computing capabilities facilitate execution of
Externí odkaz:
https://doaj.org/article/95d001d35ebf4d38bd5179da332015f3
Autor:
Marcus Haywood-Alexander, Nikolaos Dervilis, Keith Worden, Robin S. Mills, Purim Ladpli, Timothy J. Rogers
Publikováno v:
Sensors, Vol 23, Iss 1, p 185 (2022)
Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully defined relationship between central frequency and propagation characteristi
Externí odkaz:
https://doaj.org/article/1f9d88a1366847e9a2d420a3dd8a2f55
Publikováno v:
Data-Centric Engineering, Vol 2 (2021)
A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modeling applications. T
Externí odkaz:
https://doaj.org/article/bbebbe1be9dd4a148205c44b4487121a
Autor:
Keith Worden, Lawrence A. Bull, Paul Gardner, Julian Gosliga, Timothy J. Rogers, Elizabeth J. Cross, Evangelos Papatheou, Weijiang Lin, Nikolaos Dervilis
Publikováno v:
Frontiers in Built Environment, Vol 6 (2020)
One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring healt
Externí odkaz:
https://doaj.org/article/5339ca2873814118bdb2ef8f179d6bfc
Publikováno v:
Frontiers in Built Environment, Vol 3 (2017)
The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter estimation for machine learning applications. The
Externí odkaz:
https://doaj.org/article/c1395490a7d14dcf92cfda947c2fc5de
Machine learning has affected the way in which many phenomena for various domains are modelled, one of these domains being that of structural dynamics. However, because machine-learning algorithms are problem-specific, they often fail to perform effi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::923ce2d3ccbe1f35cdf3ac010afe5f22
http://arxiv.org/abs/2307.09862
http://arxiv.org/abs/2307.09862
