Experimental characterisation of sloshing tank dissipative behaviour in vertical harmonic excitation

Saltari, Francesco; Pizzoli, Marco; Coppotelli, Giuliano; Gambioli, Francesco; Cooper, Jonathan E.; Mastroddi, Franco

doi:10.1016/j.jfluidstructs.2021.103478

Cited by 33 publications

(57 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental setup presented in this section replicates that employed in Ref. [11] to characterize dissipation caused by vertical sloshing when a tank is set in harmonic motion. The experimental case study is shown in Fig.…”

Section: Experimental Test Case Of the Isolated Sloshing-tankmentioning

confidence: 99%

“…Figure 2(a) shows, for only one pair of operating parameters taken as an example (acceleration and excitation frequency equal to 3g and 17Hz respectively), the force data used for the dissipation evaluation performed in Ref. [11]. Specifically, Fig.…”

Section: Experimental Test Case Of the Isolated Sloshing-tankmentioning

confidence: 99%

“…By increasing the level of vertical acceleration some modes can become unstable depending on the oscillation frequency (Refs. [3,[9][10][11][12]). By reaching an even higher value of vertical acceleration, Rayleigh-Taylor instabilities are triggered (Ref.…”

Section: Introductionmentioning

confidence: 99%

“…(Ref. [11,12]). As a consequence, this phenomenon can not simply be described by means of linear viscoelastic models in which the loss of energy depends only on the oscillation frequency [18,19], such as fractional derivatives and finite states for the damping (see Ref.…”

Section: Introductionmentioning

confidence: 99%

“…An experiment aimed at characterizing the dissipative behavior of vertical sloshing based on harmonic motion, by varying the frequency and amplitude of the motion has already been carried out in Ref. [11]. The same configuration is used for this study to collect experimental data for the identification of a nonlinear reduced-order model (ROM) that takes advantage of neural network properties.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Neural-network-based reduced order modeling for nonlinear vertical sloshing with experimental validation

Pizzoli

Saltari

Coppotelli

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, a nonlinear reduced order model based on neural networks is introduced in order to model vertical sloshing in presence of Rayleigh-Taylor instability of the free surface for use in fluid-structure interaction simulations. A box partially filled with water, representative of a wing tank, is first set on vertical harmonic motion via a controlled electrodynamic shaker. Accelerometers and load cells at the interface between the tank and an electrodynamic shaker are employed to train a neural-network-based reduced order model for vertical sloshing. The model is then investigated for its capacity to consistently simulate the amount of dissipation associated with vertical sloshing under different fluid dynamics regimes. The identified tank is then experimentally attached at the free end of a cantilever beam to test the effectiveness of the neural network in predicting the sloshing forces when coupled with the overall structure. The experimental free response and random seismic excitation responses are then compared with that obtained by simulating an equivalent virtual model in which the identified nonlinear reduced-order model is integrated to account for the effects of violent vertical sloshing.

show abstract

Section: Experimental Test Case Of the Isolated Sloshing-tankmentioning

confidence: 99%

Section: Experimental Test Case Of the Isolated Sloshing-tankmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Neural-network-based reduced order modeling for nonlinear vertical sloshing with experimental validation

Pizzoli

Saltari

Coppotelli

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Neural network-based reduced-order modeling for nonlinear vertical sloshing with experimental validation

et al. 2023

View full text Add to dashboard Cite

In this paper, a nonlinear reduced-order model based on neural networks is introduced in order to model vertical sloshing in presence of Rayleigh–Taylor instability of the free surface for use in fluid–structure interaction simulations. A box partially filled with water, representative of a wing tank, is first set on vertical harmonic motion via a controlled electrodynamic shaker. Accelerometers and load cells at the interface between the tank and an electrodynamic shaker are employed to train a neural network-based reduced-order model for vertical sloshing. The model is then investigated for its capacity to consistently simulate the amount of dissipation associated with vertical sloshing under different fluid dynamics regimes. The identified tank is then experimentally attached at the free end of a cantilever beam to test the effectiveness of the neural network in predicting the sloshing forces when coupled with the overall structure. The experimental free response and random seismic excitation responses are then compared with that obtained by simulating an equivalent virtual model in which the identified nonlinear reduced-order model is integrated to account for the effects of violent vertical sloshing.

show abstract

Sloshing reduced-order model trained with Smoothed Particle Hydrodynamics simulations

Martinez-Carrascal,

Pizzoli,

Saltari

et al. 2023

Nonlinear Dyn

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Experimental characterisation of sloshing tank dissipative behaviour in vertical harmonic excitation

Cited by 33 publications

References 16 publications

Neural-network-based reduced order modeling for nonlinear vertical sloshing with experimental validation

Neural-network-based reduced order modeling for nonlinear vertical sloshing with experimental validation

Neural network-based reduced-order modeling for nonlinear vertical sloshing with experimental validation

Sloshing reduced-order model trained with Smoothed Particle Hydrodynamics simulations

Contact Info

Product

Resources

About