On the parametrisation of motion kinematics for experimental aerodynamic optimisation

Busch, Christoph; Gehrke, Alexander; Mülleners, Karen

doi:10.1007/s00348-021-03367-5

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Fifty offspring are generated each generation. The algorithm usually converges after approximately 35 generations, using the convergence speed criterion based on the generational distance defined by Busch, Gehrke & Mulleners (2012), for a total of 1800 evaluations. This justifies the need for surrogate modelling, prior to the optimisation, since the 1800 evaluations cannot be achieved by CFD.…”

Section: Optimisation Of Flapping Wing Motions Using Neural Networkmentioning

confidence: 99%

Discovering optimal flapping wing kinematics using active deep learning

Corban,

Bauerheim,

Jardin

2023

J. Fluid Mech.

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This paper focuses on the discovery of optimal flapping wing kinematics using a deep learning surrogate model for unsteady aerodynamics and multi-objective optimisation. First, a surrogate model of the unsteady forces experienced by a 3-D flapping wing is built, based on deep neural networks. The model is trained on a dataset of randomly generated kinematics simulated using direct numerical simulation (DNS). Once trained, the neural networks can quickly predict the unsteady lift and torques experienced by the wing, using sparse information on the kinematics. This fast surrogate model allows multi-objective optimisation to be performed. The resulting Pareto front consists of new kinematics that may be very different from the kinematics of the initial dataset. A few arbitrarily chosen kinematics on the Pareto front are thus simulated using DNS and used to enhance the database. The new dataset is used to train again the networks, and this active deep learning/optimisation framework is performed until convergence, obtained after only two iterations. Overall, this method reduced the cost of optimisation by 83 %. Results reveal two distinct families of motions. Kinematics promoting high efficiency are characterised by large stroke amplitudes and relatively low angles of attack, as observed for fruit flies, honeybees or hawkmoths. For those, lift production is driven by quasi-steady effects and the formation of a stable leading edge vortex. Kinematics promoting high lift are characterised by small stroke amplitudes and high angles of attack, reminiscent of mosquitoes. Lift production is driven by the rapid generation of vorticity at the trailing edge.

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Section: Optimisation Of Flapping Wing Motions Using Neural Networkmentioning

confidence: 99%

Discovering optimal flapping wing kinematics using active deep learning

Corban,

Bauerheim,

Jardin

2023

J. Fluid Mech.

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Experimental optimization of a fish robot’s swimming modes: a complex multiphysical problem

Abbaszadeh,

Hoerner,

Leidhold

2024

Exp Fluids

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Multiphysical optimization is particularly challenging when involving fluid–solid interactions with large deformations. While analytical approaches are commonly computational inexpensive but lack of the necessary accuracy for many applications, numerical simulations can provide higher accuracy but become very fast extremely costly. Experimental optimization approaches promise several benefits which can allow to overcome these issues in particular for application which bear complex multiphysics such as fluid–structure interactions. Here, we propose a method for an experimental optimization using genetic algorithms with a custom optimizer software directly coupled to a fully automatized experiment. Our application case is a biomimicking fish robot. The aim of the optimization is to determine the best swimming gaits for high propulsion performance in combination with low power consumption. The optimization involves genetic algorithms, more precise the NSGA-II algorithm and has been performed in still and running water. The results show a negligible impact of the investigated flow velocity. A subsequent spot analysis allows to derive some particular characteristics which leads to the recommendation to perform two different swimming gaits for cruising and for sprinting. Furthermore, we show that Exp-O techniques enable a massive reduction in the evaluation time for multiphysical optimization problems in realistic scenarios.

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Self-exploring automated experiments for discovery, optimization, and control of unsteady vortex-dominated flow phenomena

Mulleners

2024

Phys. Rev. Fluids

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Typical unsteady vortex-dominated flows like those involved in bio-inspired propulsion, airfoil separation, bluff body wakes, and vortex-induced vibrations can be prohibitively expensive to simulate and impossible to measure comprehensively. These examples are governed by nonlinear interactions, and often involve moving boundaries, high-dimensional parameter spaces, and multiscale flow structures. The classical way to get around these challenges has been to reduce the experimental complexity by using canonical motions or simplified unsteady inflow conditions. A paradigm shift is emerging in the form of self-exploring automated experiments that combine the automation of the experimental pipeline with data-science tools to increase experimental throughput and expedite scientific discovery. Such automated experiments can explore and exploit higher-dimensional parameter spaces and cover more realistic and technically relevant unsteady conditions compared to what is traditionally feasible with supervised canonical experiments. This alternative approach can yield robust and generalizable models and control solutions, as well as the discovery of rare and extreme events. Here, we provide a perspective on the transformative potential of self-exploring automated experiments for the discovery, optimization, and control of unsteady vortex-dominated flow phenomena. Published by the American Physical Society 2024

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On the parametrisation of motion kinematics for experimental aerodynamic optimisation

Cited by 3 publications

References 25 publications

Discovering optimal flapping wing kinematics using active deep learning

Discovering optimal flapping wing kinematics using active deep learning

Experimental optimization of a fish robot’s swimming modes: a complex multiphysical problem

Self-exploring automated experiments for discovery, optimization, and control of unsteady vortex-dominated flow phenomena

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