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Ship Technology Research is a peer-reviewed journal published by Taylor & Francis, covering Ship Hydrodynamics and Maneuverability, Fluid Dynamics Simulations and Interactions, Structural Integrity and Reliability Analysis.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Ship Technology Research 12 (3): 45–58.
Formats any DOI in Ship Technology Research style. No sign-up. |
| Publishes research in | Ship Hydrodynamics and Maneuverability Fluid Dynamics Simulations and Interactions Structural Integrity and Reliability Analysis Maritime Transport Emissions and Efficiency Cavitation Phenomena in Pumps |
| ISSN | 0937-7255 |
| Citation impact (2-yr) | 1.14 |
| h-index | 26 |
| i10-index | 102 |
| Total citations | 3,339 |
| Top institutions publishing here | Hamburg University of Technology |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
AbstractDuisburg Test Case (DTC) is a hull design of a typical 14000 TEU container ship, developed at the Institute of Ship Technology, Ocean Engineering and Transport Systems (ISMT) for benchmarking and validation of numerical methods. Hull geometry and model test results of resistance, propulsion and roll damping are publicly available. The paper presents existing data…
This paper generalises existing approaches for free-surface wave damping via momentum sinks for flow simulations based on the Navier-Stokes equations. It is shown in 2D flow simulations that, to obtain reliable wave damping, the coefficients in the damping functions must be adjusted to the wave parameters. A scaling law for selecting these damping coefficients is…
A digital twin is a digital asset that simulates the behaviours of a physical counterpart. Digital twin ship literature identifies that the concept is already being applied to specialised problems, but no clear guide exists for creating broader interdisciplinary digital twins. Relevant dimensions of product data modelling and previous attempts at standardizing ship data elucidate…
The paper presents a novel and publicly available set of high-quality sensory data collected from a ferry over a period of two months and overviews existing machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied in both real-time and predictive settings. Performance results for the real-time models are shown. The presented…
This paper presents a data-driven model for time series prediction of ship motion. Prediction based on past time series of data is a powerful function in modern ship support systems. For a large amount of ship sensor data, neural network (NN) is considered as a proper tool in modelling the prediction system. Efforts are made…
