Stochastic optimization methods for ship resistance and operational efficiency via CFD

Diez, Matteo; Campana, Emilio F.; Stern, Frederick

doi:10.1007/s00158-017-1775-4

Cited by 48 publications

(19 citation statements)

References 41 publications

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“…No objective function evaluation nor gradient is required by the method. The method has been successfully applied for deterministic [1,7,8] and stochastic [9,10] hull form optimization of monohulls and catamarans in calm water and waves, respectively. Similarly, Poole et al [11] have applied POD to airfoil shape optimization via singular value decomposition (SVD) of an airfoil geometric-data library.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Shape Optimization via Design-Space Augmented Dimensionality Reduction and RANS Computations

Serani

Diez

Wackers

et al. 2019

AIAA Scitech 2019 Forum

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The paper presents how to efficiently and effectively solve stochastic shape optimization problems by combing Reynolds-averaged Navier-Stokes (RANS) equation solvers with designspace augmented dimensionality reduction (ADR). This study has been conducted within the NATO Science and Technology Organization, Applied Vehicle Technology, Task Group AVT-252 "Stochastic Design Optimization for Naval and Aero Military Vehicles." The application pertains to the robust and the reliability-based robust design optimization of a destroyer hullform for resistance in calm water and waves and seakeeping performance, under stochastic environmental and operating conditions (speed, sea state, heading). The current work extends previous research by the authors, presented at earlier AIAA conferences [1-3], where only potential flow solvers were used. In the present work, the expected value of the total resistance is reduced respectively by 4.4 and 3% in calm water and waves. An 8% improvement of the seakeeping performance is also achieved. Design-space assessment by ADR is demonstrated to be a viable option in solving the curse of dimensionionality in shape optimization, especially when high-fidelity CPU-expensive solvers are used.

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Section: Introductionmentioning

confidence: 99%

Stochastic Shape Optimization via Design-Space Augmented Dimensionality Reduction and RANS Computations

Serani

Diez

Wackers

et al. 2019

AIAA Scitech 2019 Forum

Self Cite

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“…The stochastic (reliability-based and robust) design optimization of a high-speed catamaran in irregular head waves was solved [1]. Realistic environment conditions are associated to the North Pacific Ocean, whereas operating conditions include stochastic sea state and speed.…”

Section: Catamaran Problemmentioning

confidence: 99%

“…Four design variables (N = 4) control the catamaran hull global modifications, based on the Karhunen-Loève expansion (KLE) of the shape modification vector [49] based on free-form deformation. Further details can be found in [1]. The reference non-dominated solution set R is taken from [6].…”

Section: Catamaran Problemmentioning

confidence: 99%

“…In this context, the simulation-based design (SBD) paradigm has demonstrated the ability to support the design decision-making process. The recent development of high-performance computing systems has led the SBD to integrate optimization algorithms and uncertainty quantification (UQ) methods, shifting the SBD paradigm to automatic deterministic and stochastic SBD optimization (SBDO) [1] with potential global solutions to the design problem. The computational costs associated with the solution of the SBDO problem with high-fidelity solvers remain a limiting factor for the implementation of the SBDO in industries and, in particular, small-and medium-sized enterprises.…”

Section: Introductionmentioning

confidence: 99%

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Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches

et al. 2020

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The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts.

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“…Nowadays, Computational Fluid Dynamics (CFD) represents a standard design practice in solving the ship hydrodynamics problems worldwide [2,3]. Thus, various CFD studies investigated the added resistance and ship motion in various sea state conditions [4,5], in seakeeping [6] or to find the optimal hull form for resistance reduction and operational efficiency [7,8]. The speed performance of eight commercial ships including resistance and propulsion characteristics was evaluated using CFD results [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison between Model Test and Three CFD Studies for a Benchmark Container Ship

Chirosca

Rusu

2021

JMSE

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An alternative to experiments is the use of numerical model tests, where the performances of ships can be evaluated entirely by computer simulations. In this paper, the free surface viscous flow around a bare hull model is simulated with three Computational Fluid Dynamics (CFD) software packages (FINE Marine, ANSYS CFD and SHIPFLOW) and compared to the results obtained during the experimental tests. The bare hull model studied is the Duisburg Test Case (DTC), developed at the Institute of Ship Technology, Ocean Engineering and Transport Systems (ISMT) for benchmarking and validation of the numerical methods. Hull geometry and model test results of resistance, conducted in the experimental facility at SVA Postdam, Nietzschmann, in 2010, are publicly available. A comparative analysis of the numerical approach and experimental results is performed, related to the numerical simulation of the free surface viscous flow around a typical container ship. Further, a comparative analysis between the results provided by NUMECA, ANSYS and SHIPFLOW is performed. Regarding the solution obtained, a satisfactory agreement between the towing test results and the computation results can be noticed. The minimum mean error was obtained through the SHIPFLOW case, 2.011%, which proved the best solution for the case studied.

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Stochastic optimization methods for ship resistance and operational efficiency via CFD

Cited by 48 publications

References 41 publications

Stochastic Shape Optimization via Design-Space Augmented Dimensionality Reduction and RANS Computations

Stochastic Shape Optimization via Design-Space Augmented Dimensionality Reduction and RANS Computations

Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches

Comparison between Model Test and Three CFD Studies for a Benchmark Container Ship

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