URANS analysis of a free-running destroyer sailing in irregular stern-quartering waves at sea state 7

Serani, Andrea; Diez, Matteo; Walree, Frans van; Stern, Frederick

doi:10.1016/j.oceaneng.2021.109600

Cited by 19 publications

(10 citation statements)

References 22 publications

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“…Though the models were successful in some DoF, surge and sway produced undesirable results and predictions of all DoF became worse as time increased. Diez et al (2021) proposed a dynamic mode decomposition method for the forecasting of ship motions but similar to D 'Agostino et al (2021), the predictions were close initially but largely drifted as time progressed. The previous work with neural networks has showcased their efficacy when representing simplified ship motions but expansion to a fully free-running ship with implications for nowcasting and forecasting has not been completely developed.…”

Section: Introductionmentioning

confidence: 82%

“…Though temporal responses are critical for real-time ship motion forecasting, understanding the probability distribution of a particular response is desirable to produce a statistical description of the ship's operability. Comparisons of probability distributions also provide a useful method of validating models and have been leveraged in previous work validating CFD tools in Serani et al (2021) and neural network models in Silva and Maki (2021a,c). Fig.…”

Section: Course-keeping In Irregular Wavesmentioning

confidence: 99%

“…This contrasts with classical seakeeping evaluations, where the vessel is assumed to travel at a constant speed and heading. Previous studies have shown success in predicting 6-DoF motions with URANS simulations (Serani et al, 2021), while others have employed a potential flow time-domain solution (Lin et al, 2006), or a hybrid formulation in White et al (2021) where a URANS double-body formulation and potential flow are combined in a tightly-coupled solver to reduce the computational cost of a volume-of-fluid (VOF) URANS simulation, yet still model some of the important viscous features.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks

Silva¹,

Maki²

2021

Preprint

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Critical evaluation and understanding of ship responses in the ocean is important for not only the design and engineering of future platforms but also the operation and safety of those that are currently deployed. Simulations or experiments are typically performed in nominal sea conditions during ship design or prior to deployment, to evaluate different hullforms and develop operational profiles. Though it is necessary to analyze the response for nominal conditions, the results may not be reflective of the instantaneous state of the vessel and the ocean environment while deployed. Short-term temporal predictions of ship responses given the current wave environment and ship state would enable enhanced decision-making onboard and reduce the overall risk during operations for both manned and unmanned vessels, especially as the marine industry trends towards more autonomy. However, the current state-of-the-art in numerical hydrodynamic simulation tools are too computationally expensive to be employed for real-time ship motion forecasting and the computationally efficient tools are too low fidelity to provide accurate responses. Thus, a methodology is needed to provide fast and efficient predictions with levels of accuracy closer to the higher-fidelity tools.A methodology is developed with long short-term memory (LSTM) neural networks to represent the motions of a free running David Taylor Model Basin (DTMB) 5415 destroyer operating at 20 knots in Sea State 7 stern-quartering irregular seas. Case studies are performed for both course-keeping and turning circle scenarios. An estimate of the vessel's encounter frame is made with the trajectories observed in the training dataset. Wave elevation time histories are given by artificial wave probes that travel with the estimated encounter frame and serve as input into the neural network, while the output is the 6-DOF temporal ship motion response. Overall, the neural network is able to predict the temporal response of the ship due to unseen waves accurately, which makes this methodology suitable for system identification and real-time ship motion forecasting. The methodology, the dependence of model accuracy on wave probe and training data quantity and the estimated encounter frame are all detailed.

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Section: Introductionmentioning

confidence: 82%

Section: Course-keeping In Irregular Wavesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks

Silva¹,

Maki²

2021

Preprint

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“…The assessment and optimization of the design performance in realistic operating and environmental conditions, which are always permeated with stochasticity, require computational efforts that can be unattainable for most users. To give an example, recent research showed how an accurate Unsteady Reynolds Averaged Navier-Stokes simulation to evaluate the statistically significant performance of ship maneuvering in irregular waves may require up to 1M CPU hours on HPC systems [2].…”

Section: Introductionmentioning

confidence: 99%

A Derivative-Free Line-Search Algorithm for Simulation-Driven Design Optimization Using Multi-Fidelity Computations

et al. 2022

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The paper presents a multi-fidelity extension of a local line-search-based derivative-free algorithm for nonsmooth constrained optimization (MF-CS-DFN). The method is intended for use in the simulation-driven design optimization (SDDO) context, where multi-fidelity computations are used to evaluate the objective function. The proposed algorithm starts using low-fidelity evaluations and automatically switches to higher-fidelity evaluations based on the line-search step length. The multi-fidelity algorithm is driven by a suitably defined threshold and initialization values for the step length, which are associated to each fidelity level. These are selected to increase the accuracy of the objective evaluations while progressing to the optimal solution. The method is demonstrated for a multi-fidelity SDDO benchmark, namely pertaining to the hull-form optimization of a destroyer-type vessel, aiming at resistance minimization in calm water at fixed speed. Numerical simulations are based on a linear potential flow solver, where seven fidelity levels are used selecting systematically refined computational grids for the hull and the free surface. The method performance is assessed varying the steplength threshold and initialization approach. Specifically, four MF-CS-DFN setups are tested, and the optimization results are compared to its single-fidelity (high-fidelity-based) counterpart (CS-DFN). The MF-CS-DFN results are promising, achieving a resistance reduction of about 12% and showing a faster convergence than CS-DFN. Specifically, the MF extension is between one and two orders of magnitude faster than the original single-fidelity algorithm. For low computational budgets, MF-CS-DFN optimized designs exhibit a resistance that is about 6% lower than that achieved by CS-DFN.

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“…Recent work in the context of computational fluid dynamics (CFD) optimization showed how a hull-form optimization based on accurate unsteady Reynolds Averaged Navier-Stokes (URANS) solutions under realistic stochastic conditions may require up to 500k CPU hours on high performance computing (HPC) systems, even if computational cost reduction methods are used [39]. Similarly, an accurate URANS-based statistically significant evaluation of ship maneuvering performance in irregular waves may require up to 1M CPU hours on HPC systems [34]. Furthermore, multidisciplinary design optimization (MDO) problems, involving several interconnected disciplines, usually require additional computational efforts especially for global optimization problems [20] and stochastic conditions [11].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Fidelity Active Learning Method for Global Design Optimization Problems with Noisy Evaluations

Pellegrini¹,

Wackers²,

Broglia³

et al. 2022

Preprint

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A multi-fidelity (MF) active learning method is presented for design optimization problems characterized by noisy evaluations of the performance metrics. Namely, a generalized MF surrogate model is used for design-space exploration, exploiting an arbitrary number of hierarchical fidelity levels, i.e., performance evaluations coming from different models, solvers, or discretizations, characterized by different accuracy. The method is intended to accurately predict the design performance while reducing the computational effort required by simulation-driven design (SDD) to achieve the global optimum. The overall MF prediction is evaluated as a low-fidelity trained surrogate corrected with the surrogates of the errors between consecutive fidelity levels. Surrogates are based on stochastic radial basis functions (SRBF) with least squares regression and inthe-loop optimization of hyperparameters to deal with noisy training data. The method adaptively queries new training data, selecting both the design points and the required fidelity level via an active learning approach. This is based on the lower confidence bounding method, which combines performance prediction and associated uncertainty to select the most promising design regions. The fidelity levels are selected considering the benefit-cost ratio associated with their use in the training. The method's performance is assessed and discussed using four analytical tests and three SDD problems based on computational fluid dynamics (CFD) simulations, namely the shape optimization of

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URANS analysis of a free-running destroyer sailing in irregular stern-quartering waves at sea state 7

Cited by 19 publications

References 22 publications

Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks

Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks

A Derivative-Free Line-Search Algorithm for Simulation-Driven Design Optimization Using Multi-Fidelity Computations

A Multi-Fidelity Active Learning Method for Global Design Optimization Problems with Noisy Evaluations

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