A simplified shape optimization strategy for blended-wing-body underwater gliders

Li, Chengshan; Wang, Peng; Dong, Huachao; Wang, Xinjing

doi:10.1007/s00158-018-2005-4

Cited by 24 publications

(5 citation statements)

References 38 publications

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“…One is the tutorial case called pitzDaily in OpenFOAM. The other two cases are a tow-dimensional (2D) NACA0012 airfoil and a three-dimensional (3D) blended-wing-body airfoil from Li C. et al [37].…”

Section: Numerical Resultsmentioning

confidence: 99%

Improving Initial Guess for the Iterative Solution of Linear Equation Systems in Incompressible Flow

Lin

et al. 2020

Mathematics

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The pressure equation, generated while solving the incompressible Navier–Stokes equations with the segregated iterative algorithm such as PISO, produces a series of linear equation systems as the time step advances. In this paper, we target at accelerating the iterative solution of these linear systems by improving their initial guesses. We propose a weighted group extrapolation method to obtain a superior initial guess instead of a general one, the solution of the previous linear equation system. In this method, the previous solutions that are used to extrapolate the predicted solutions are carefully organized to address the oscillatory solution on each grid. The proposed method uses a weighted average of the predicted solutions as the new initial guess to avoid over extrapolating. Three numerical test results show that the proposed method can accelerate the iterative solution of most linear equation systems and reduce the simulation time up to 61.3%.

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Section: Numerical Resultsmentioning

confidence: 99%

Improving Initial Guess for the Iterative Solution of Linear Equation Systems in Incompressible Flow

Lin

et al. 2020

Mathematics

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“…Blended-Wing-Body underwater gliders (BWBUGs) [43] play an important role in scientific and commercial fields due to their advantages of low cost and capability for long gliding range and have got considerable amount of attention in recent years. Inside the glider, there are several pressure-resistant cabins to guarantee that the pricey instruments and equipment installed operate properly in the deep-sea environment.…”

Section: Engineering Applicationsmentioning

confidence: 99%

Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems

Dong

Wang

et al. 2022

Complex Intell. Syst.

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Aiming at the constrained optimization problem where function evaluation is time-consuming, this paper proposed a novel algorithm called data-driven Harris Hawks constrained optimization (DHHCO). In DHHCO, Kriging models are utilized to prospect potentially optimal areas by leveraging computationally expensive historical data during optimization. Three powerful strategies are, respectively, embedded into different phases of conventional Harris Hawks optimization (HHO) to generate diverse candidate sample data for exploiting around the existing sample data and exploring uncharted region. Moreover, a Kriging-based data-driven strategy composed of data-driven population construction and individual selection strategy is presented, which fully mines and utilizes the potential available information in the existing sample data. DHHCO inherits and develops HHO's offspring updating mechanism, and meanwhile exerts the prediction ability of Kriging, reduces the number of expensive function evaluations, and provides new ideas for data-driven constraint optimization. Comprehensive experiments have been conducted on 13 benchmark functions and a real-world expensive optimization problem. The experimental results suggest that the proposed DHHCO can achieve quite competitive performance compared with six representative algorithms and can find the near global optimum with 200 function evaluations for most examples. Moreover, DHHCO is applied to the structural optimization of the internal components of the real underwater vehicle, and the final satisfactory weight reduction effect is more than 18%.

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“…Generally, the surrogate models can be fitted by many mature methods, such as response surface method 39 (RSM), Kriging method 27 and radial basis function (RBF) neural network method. 51 In this paper, we employ the above three methods to fit the surrogate models, respectively, and the surrogate models with the highest fitting accuracy are used for the subsequent optimization calculation.…”

Section: Surrogate Modelsmentioning

confidence: 99%

“…Yang et al 26 analyzed the effects of hull deformation and seawater density variation on the glider motion and proposed corresponding compensation method. Li et al 27 studied a simplified shape optimization method of blended-wing-body underwater glider to improve its hydrodynamic performance, combining the CFD method and the Kriging method. Yoon and Kim 28 studied a method to optimize the glider trajectory to maximize its advance speed when the water depth was limited.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective optimization for control parameters of underwater gliders considering effect of uncertain input errors

Niu

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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In actual application, the energy utilization rate of underwater glider directly affects the total voyage range. When underwater glider is used for executing exploration mission for a fixed point, the position that the glider resurfaces should be accurate enough. In this paper, we employ a multi-objective optimization method to determine the control parameters values that can maximize the position accuracy that the glider resurfaces and the energy utilization rate simultaneously. Especially, the optimization of this paper considers the effect of uncertain input errors. The control parameters include the net buoyancy adjustment amount and the movable mass block translation amount. The input errors include the control parameters errors, the motion depth error and the current. Based on the dynamic model of an underwater glider, we propose the calculation model and evaluation flow that are used for analyzing the glider position accuracy and energy utilization rate, considering the effect of uncertain input errors. Besides, a combinatorial experimental design method is proposed to calculate the performance evaluation parameters under different control parameters values. Then the radial basis function neural network is employed to establish the surrogate models of performance evaluation parameters to participate in the optimization calculation, which can improve the optimization efficiency. After optimization calculation based on the non-dominated sorting genetic algorithm II, we obtain a Pareto optimal set consisting of 257 sets of non-dominated solutions. Finally, the selection rule of optimal control parameters values is given, and the optimization results are validated under 3 sets of solutions. This research may be valuable for the improvement of the glider work quality.

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A simplified shape optimization strategy for blended-wing-body underwater gliders

Cited by 24 publications

References 38 publications

Improving Initial Guess for the Iterative Solution of Linear Equation Systems in Incompressible Flow

Improving Initial Guess for the Iterative Solution of Linear Equation Systems in Incompressible Flow

Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems

Multi-objective optimization for control parameters of underwater gliders considering effect of uncertain input errors

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