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

Serani, Andrea; Diez, Matteo; Wackers, Jeroen; Visonneau, Michel; Stern, Frederick

doi:10.2514/6.2019-2218

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…The difference between exact value of the objective function at the given sample point is computed by the CFD tool and the approximate value of the objective function at the given sample point is predicted by the RBF surrogate model. This RBF surrogate model is constructed with other subset of training set [30]. The constructed model is valid if the difference is small enough.…”

Section: -Construct Surrogate Model By Rbf Methodsmentioning

confidence: 99%

“…The optimization tools consisted of Karhunen-Loève expansion of a free-form deformation, URANS-based CFD simulations, metamodels, and multi-objective particle swarm. Another similar attempt was made by Serani et al [30] that developed a high-fidelity stochastic shape optimization problem. They modified a DTMB5415 model in calm water and wavy condition by combining stochastic shape optimization via design-space assessment approaches.…”

Section: Introductionmentioning

confidence: 98%

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Automated CFD-based optimization of inverted bow shape of a trimaran ship: Proposing an applicable and efficient optimization platform

Nazemian

Ghadimi

2020

Scientia Iranica

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This paper investigates the improvement of bow region of a trimaran ship hull, proposing a CFD-based automated approach to reduce total resistance. Two main goals are pursued; to create and develop a useful optimization platform for ship hull modification, and then investigate the influence of different inverted bow on hydrodynamic performance of trimaran ship. A wave-piercing bow trimaran hull is the baseline design. Ship bow is redesigned by Arbitrary Shape Deformation (ASD) technique that defines the input variables for optimization process. The objective function is the drag force and this study is conducted at cruise speed. To accomplish this task, two optimization methods are sequentially applied. A Latin Hypercube Sampling tool distributes design points and an RBF-based surrogated model is constructed to investigate system behavior. Final optimum design in Design of Experiment (DOE) study is introduced to direct optimization SHERPA algorithm. Integration of CFD solver, geometric parametrization, and optimizer tool is managed by HEEDS MDO package with a multi-connection approach. Optimization results show successful optimization along with 10.2% resistance reduction. Comparison between initial and optimized hull demonstrates that the proposed optimization platform can be used for ship hull optimization in industrial application with significantly reduced computational time and effort.

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Section: -Construct Surrogate Model By Rbf Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Automated CFD-based optimization of inverted bow shape of a trimaran ship: Proposing an applicable and efficient optimization platform

Nazemian

Ghadimi

2020

Scientia Iranica

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“…The proposed method is demonstrated for the hull-form optimisation of the DTMB 5415 model (see Fig. 3), an early and open to public version of the USS Arleigh Burke destroyer DDG 51, extensively used as an international benchmark for shape optimisation problems [23,24]. Table 1 summarises the main characteristics of the hull and test conditions.…”

Section: Test Casementioning

confidence: 99%

Physics-Informed Feature-to-Feature Learning for Design-Space Dimensionality Reduction in Shape Optimisation

Khan

Serani

Diez

et al. 2021

AIAA Scitech 2021 Forum

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High-dimensional parametric design problems cause optimisers and physics simulations to suffer from the curse-of-dimensionality, resulting in high computational cost. In this work, to release this computational burden, we adopted a two-step feature-to-feature learning methodology to discover a lower-dimensional latent space, based on the combination of geometry-and physics-informed principal component analysis and the active subspace method. At the first step, statistical dependencies implicit in the design parameters encode important geometric features of the underline shape. During the second step, functional features of designs are extracted in term of previously learned geometric features. Afterwards, both geometric and functional features are augmented together to create a functionally-active subspace, whose basis not only captures the geometric variance of designs but also induces the variability in the designs' physics. As the new subspace accumulates both the functional and geometric variance, therefore, it can be exploited for efficient design exploration and the construction of improved surrogate models for designs' physics prediction. The validation and experimental studies presented in this work show the beneficial effects of the current approach in comparison to a conventional single-step feature learning.

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“…7 shows the 5.720 m length replica of the DTMB 5415 model, used for towing tank experiments, as seen at CNR-INM [41]. The DTMB 5415 has been also used as a benchmark in NATO STO Task Groups AVT-204 Assess the Ability to Optimize Hull Forms of Sea Vehicles for Best Performance in a Sea Environment [43], AVT-252 Stochastic Design Optimization for Naval and Aero Military Vehicles [44]. Moreover, it was used as a test case in variable-accuracy multi-disciplinary design optimization studies, coupling the hydrodynamic analysis with the rigid body equation of motion through multi-disciplinary analysis [45].…”

Section: Engineering Problem: Ship Design Optimizationmentioning

confidence: 99%

Dense conjugate initialization for deterministic PSO in applications: ORTHOinit+

Leotardi

Serani

Diez

et al. 2021

Applied Soft Computing

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This paper describes a class of novel initializations in Deterministic Particle Swarm Optimization (DPSO) for approximately solving costly unconstrained global optimization problems. The initializations are based on choosing specific dense initial positions and velocities for particles. These choices tend to induce in some sense orthogonality of particles' trajectories, in the early iterations, in order to better explore the search space. Our proposal is inspired by both a theoretical analysis on a reformulation of PSO iteration, and by possible limits of the proposals reported in Campana et al. (2010); Campana et al. (2013). We explicitly show that, in comparison with other initializations from the literature, our initializations tend to scatter PSO particles, at least in the first iterations. The latter goal is obtained by imposing that the initial choice of particles' position/velocity satisfies specific conjugacy conditions, with respect to a matrix depending on the parameters of PSO. In particular, by an appropriate condition on particles' velocities, our initializations also resemble and partially extend a general paradigm in the literature of exact methods for derivative-free optimization. Moreover, we propose dense initializations for DPSO, so that the final approximate global solution obtained is possibly not too sparse, which might cause troubles in some applications. Numerical results, on both Portfolio Selection and Computational Fluid Dynamics problems, validate our theory and prove the effectiveness of our proposal, which applies also in case different neighborhood topologies are adopted in DPSO.

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

Cited by 14 publications

References 24 publications

Automated CFD-based optimization of inverted bow shape of a trimaran ship: Proposing an applicable and efficient optimization platform

Automated CFD-based optimization of inverted bow shape of a trimaran ship: Proposing an applicable and efficient optimization platform

Physics-Informed Feature-to-Feature Learning for Design-Space Dimensionality Reduction in Shape Optimisation

Dense conjugate initialization for deterministic PSO in applications: ORTHOinit+

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