A Comparison of Different Linearized Formulations for Progressive Flooding Simulations in Full-Scale

Braidotti, Luca; Degan, Germano; Bertagna, Serena; Bucci, Vittorio; Marinò, Alberto

doi:10.1016/j.procs.2021.01.159

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…However, it is important to note that the model does not account for dynamic ship motions and focuses solely on the quasi-steady changes in heel, trim, and sinkage. Further details and insights into the model can be found in [83,84,97] NAPA. A well-known software suite for ship design and operation, the flow rates are calculated from Bernoulli's equation, with user-defined discharge coefficients for each opening.…”

Section: Numerical Methods and Toolsmentioning

confidence: 99%

“…A new calculation technique for onboard progressive flooding simulation, implementing a linearised assessment of floodwater levels, was presented in 2019 by Braidotti and Mauro [83], with different linearisation formulations being investigated in 2021 [84]. A sensitivity analysis of the method was performed on a box-shaped barge undergoing progressive flooding [85], concluding that many of the input parameters significantly affect the flooding simulation.…”

Section: Recent Development: 2018-2023mentioning

confidence: 99%

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A Review of Methods for Modelling Flooding, Its Progression and Outcome in Damaged Ships

Rodrigues

2024

JMSE

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The timely and precise prediction of flooding progression and its eventual outcome in ships with breached hulls can lead to dramatic improvements in maritime safety through improved guidance for both emergency response and ship design. The traditional approach to assessing damage-induced flooding in both these stages, which also fully complies with statutory rules, is through static calculations. On the other hand, the application of models that simulate the flooding progression and the behaviour of flooded ships from, or close to, first principles allows for increased accuracy of the modelling of the phenomenon. This increase in accuracy can then be used to support advanced design for safety procedures. Furthermore, it can considerably enhance a ship’s capability for damage identification and inference-based logic for emergency decision support systems and marine accident response in general. This paper conducts a review of selected state-of-the-art methods, procedures, and case studies in recent years which aimed to model progressive flooding and damage ship behaviour and provide some explanations of fundamentals. Applications related to damage identification, the prediction of outcome/situation awareness, and flooding emergency response are also briefly discussed. The paper concludes with a brief reflection on salient gaps in the context of accelerating the development of these methods and their applicability.

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Section: Numerical Methods and Toolsmentioning

confidence: 99%

Section: Recent Development: 2018-2023mentioning

confidence: 99%

A Review of Methods for Modelling Flooding, Its Progression and Outcome in Damaged Ships

Rodrigues

2024

JMSE

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“…All the progressive flooding simulations were carried out using a quasi-static technique based on the solution of a linearized differential-algebraic equation system [18,19]. The method represents a good compromise between accuracy and computational effort [20]; hence, it was considered adequate for the generation of large databases of progressive flooding simulations.…”

Section: Database-generation Methodsmentioning

confidence: 99%

Effect of Database Generation on Damage Consequences’ Assessment Based on Random Forests

Braidotti

Prpić-Oršić

Valčić

2021

JMSE

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Recently, the application of machine learning has been explored to assess the main damage consequences without employing flooding sensors. This method can be the base of a new generation of onboard decision support systems to help the master during the progressive flooding of the ship. In particular, the application of random forests has been found suitable to assess the final fate of the ship and the damaged compartments’ set and estimate the time-to-flood. Random forests have to be trained using a database of precalculated progressive flooding simulations. In the present work, multiple options for database generation were tested and compared: three based on Monte Carlo (MC) sampling based on different probability distributions of the damage parameters and a parametric one. The methods were tested on a barge geometry to highlight the main effects on the damage consequences’ assessment in order to ease the further development of flooding-sensor-agnostic decision support systems for flooding emergencies.

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“…Several accidents occurred to RoPax and the disaster of the Costa Concordia foster the development of flooding simulation codes while highlighting the need for improving decision support during flooding casualties. Among other vessels types, large cruise vessels are a very challenging environment [1]. The complex internal subdivision together with the short freeboard at the bulkhead deck and short metacentric radius leads to a difficult assessment of the consequences of a flooding scenario without the help of proper tools.…”

Section: Introductionmentioning

confidence: 99%

Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning

Braidotti

Valčić

Prpić-Oršić

2021

JMSE

Self Cite

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Recently, progressive flooding simulations have been applied onboard to support decisions during emergencies based on the outcomes of flooding sensors. However, only a small part of the existing fleet of passenger ships is equipped with flooding sensors. In order to ease the installation of emergency decision support systems on older vessels, a flooding-sensor-agnostic solution is advisable to reduce retrofit cost. In this work, the machine learning algorithms trained with databases of progressive flooding simulations are employed to assess the main consequences of a damage scenario (final fate, flooded compartments, time-to-flood). Among the others, several classification techniques are here tested using as predictors only the time evolution of the ship floating position (heel, trim and sinkage). The proposed method has been applied to a box-shaped barge showing promising results. The promising results obtained applying the bagged decision trees and weighted k-nearest neighbours suggests that this new approach can be the base for a new generation of onboard decision support systems.

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A Comparison of Different Linearized Formulations for Progressive Flooding Simulations in Full-Scale

Cited by 10 publications

References 16 publications

A Review of Methods for Modelling Flooding, Its Progression and Outcome in Damaged Ships

A Review of Methods for Modelling Flooding, Its Progression and Outcome in Damaged Ships

Effect of Database Generation on Damage Consequences’ Assessment Based on Random Forests

Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning

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