Correction: Sloshing Wing Dynamics - 2nd Year Project Overview

Gambioli, Francesco; Chamos, Apostolos; Levenhagen, Jens; Behruzi, Philipp; Mastroddi, Franco; Malan, Arnaud G.; Longshaw, Stephen; Skillen, Alex; Cooper, Jonathan E.; González, Leo M.; Marrone, S.

doi:10.2514/6.2022-1341.c1

Cited by 13 publications

(30 citation statements)

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“…The main goal of the project (Ref. [4]) is to define a holistic approach (both experimental and numerical) to quantify the energy-dissipation effects associated with the liquid movement inside aircraft fuel tanks, as the wing undergoes dynamic excitations. This goal is divided into four specific objectives:…”

Section: Project Organizationmentioning

confidence: 99%

Sloshing Wing Dynamics - 2nd Year Project Overview

Gambioli

Chamos

Levenhagen

et al. 2022

AIAA SCITECH 2022 Forum

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The SLOshing Wing Dynamics (SLOWD) project aims to investigate the modelling of fuel sloshing physics to reduce the design loads on aircraft structures. This goal will be achieved through investigating the damping effect of sloshing on the dynamics of flexible wing-like structures carrying liquid (fuel) via the development of experimental set-ups complemented by novel numerical and analytical tools. The primary focus of the project is the application of modelling capabilities to the wing design of large civil passenger aircraft (subject to EASA CS-25 type certification), which are designed to withstand the loads occurring from atmospheric gusts, turbulence and landing impacts. The timeframe of the project is three years, starting in September 2019. This paper reviews the current progress that has been made and outlines goals for the rest of the project.

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Section: Project Organizationmentioning

confidence: 99%

Sloshing Wing Dynamics - 2nd Year Project Overview

Gambioli

Chamos

Levenhagen

et al. 2022

AIAA SCITECH 2022 Forum

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“…This investigation successfully demonstrated the presence of additional damping induced by sloshing (see Refs. [19][20][21]). Following this result, Single-Degree-Of-Freedom (SDOF) experiments were developed as part of the SLOWD project to isolate the vertical component of tank motion in order to the study the dissipative behavior of sloshing.…”

Section: Introductionmentioning

confidence: 99%

Sloshing reduced-order model trained with Smoothed Particle Hydrodynamics simulations

Martinez-Carrascal,

Pizzoli,

Saltari

et al. 2023

Nonlinear Dyn

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The main goal of this paper is to provide a Reduced Order Model (ROM) able to predict the liquid induced dissipation of the violent and vertical sloshing problem for a wide range of liquid viscosities, surface tensions and tank filling levels. For that purpose, the Delta Smoothed Particle Hydrodynamics (δ-SPH) formulation is used to build a database of simulation cases where the physical parameters of the liquid are varied. For each simulation case, a bouncing ball-based equivalent mechanical model is identified to emulate sloshing dynamics. Then, an interpolating hypersurface-based ROM is defined to establish a mapping between the considered physical parameters of the liquid and the identified ball models. The resulting hypersurface effectively estimates the bouncing ball design parameters while considering various types of liquids, producing results consistent with SPH test simulations. Additionally, it is observed that the estimated bouncing ball model not only matches the liquid induced dissipation but also follows the liquid center of mass and presents the same sloshing force and phase-shift trends when varying the tank filling level. These findings provide compelling evidence that the identified ROM is a practical tool for accurately predicting critical aspects of the vertical sloshing problem while requiring minimal computational resources.

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“…This research activity is framed in the European project H2020 SLOshing Wing Dynamics (SLOWD) aiming with an heuristic approach (combining experimental and numerical data) at characterising sloshing dynamics for its use in future aircraft design (Ref. [1]).…”

Section: Introductionmentioning

confidence: 99%

“…Previous experimental fluid-structure interaction campaigns in which a tank system was positioned at the tip of a beam, representing a wing, showed how this type of chaotic response induces a very damped response of the structure (Refs. [11][12][13][14]). Indeed, turbulence, impacts and the continuous generation of the free surface cause additional dissipation of energy.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Neural-Network-Based Nonlinear Reduced-Order Model for Vertical Sloshing

Pizzoli

Saltari

Coppotelli

et al. 2022

AIAA SCITECH 2022 Forum

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In this paper, a nonlinear reduced order model based on neural networks is introduced in order to model vertical sloshing for use in fluid-structure interaction simulations. A box partially filled with water, representative of a wing tank, is first tested to identify a neural network model and then attached to a cantilever beam to test the effectiveness of the neural network in predicting the sloshing forces when coupled with the structure. The experimental set-up is equipped with accelerometers and load cells at the interface between the tank and an electrodynamic shaker, which provides vertical acceleration to the tank. Accelerations and interface forces measured during the experimental tests are employed to identify a recurrent network able to return the vertical sloshing forces when the tank is set on vertical motion. The identified model is then experimentally tested and assessed by its integration on the tip of a cantilever beam. The free response of the experimental setup are compared with those obtained by simulating an equivalent virtual model in which the identified reduced-order model is integrated to account for the effects of vertical sloshing.

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Correction: Sloshing Wing Dynamics - 2nd Year Project Overview

Cited by 13 publications

References 0 publications

Sloshing Wing Dynamics - 2nd Year Project Overview

Sloshing Wing Dynamics - 2nd Year Project Overview

Sloshing reduced-order model trained with Smoothed Particle Hydrodynamics simulations

Experimental Validation of Neural-Network-Based Nonlinear Reduced-Order Model for Vertical Sloshing

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