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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.

In this work, the most fundamental aspects of an aeronautical sloshing problem have been studied using an alternative and simplified model. This model consists of a single degree of freedom version of the original problem which keeps the essence of the fluid structure interaction and also the most relevant physical aspects of the industrial case. Two independent methodologies have been used: first an experimental rig has been designed to measure and visualize different magnitudes of the problem and also a smoothed particle hydrodynamics formulation has been adapted to obtain a local representation of the flow interaction. Two very different fluids in terms of viscosity have been tested, and the differences in terms of the characteristics of the sloshing regimes, free surface fragmentation and relative kinetic energy have been described and compared. Apart from the comparison of the results obtained by both methodologies in terms of tank acceleration, sloshing forces and free surface evolution, a deep study of the sloshing force has been performed. This study focuses on a deeper understanding of the different aspects that constitute the sloshing force, such as its synchronization with the tank movement, the relation to the movement of the liquid’s center of mass and the physical projection of the force on the pressure and viscous parts. Additionally, a reconstruction of the sloshing force as a sum of the pressure signal recorded by a finite number of pressure sensors has been also performed.

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.

A thorough understanding of the effects of sloshing on aircraft dynamic loadscan be exploited for the future design of aircraft to be able to reduce their structural mass. Indeed, the high vertical accelerations caused by the vibrations of the structure can lead to the fragmentation of the fuel free surface. Fluid impacts on the tank roof are potentially a new source of damping for the structure that have hardly been considered before when computing the dynamic loads of the wings. This work aims at applying recently developed reduced-ordermodels for vertical sloshing to a representative aeroelastic testbed, to investigate their effects on the wing’s response under pre-critical and post-critical conditions. The vertical sloshing dynamics is considered comparing three different reduced order models based, respectively, on neural networks, equivalent mechanical model, and surrogate model then integrated into theaeroelastic system.

Damping induced by vertically sloshing liquids at large amplitudes is of interest in the understanding of the dynamic response of aircraft wings containing fuel. This work presents the experimental investigation of sloshinginduced damping in two small rectangular tanks (L = 100 mm, h = 37.5 mm and 50 mm) under vertical excitation at various frequencies (3.3 to 10.3 Hz) and amplitudes (up to 1.57 tank heights). The focus is placed on large amplitudes that lead to violent vertical interactions between the liquid and the tank and the nonlinear damping effects they cause. The sloshing-induced dissipative energy in dimensional form shows a deviation from linearity with increasing amplitude of excitation, which is attributed to force-displacement hysteresis sub-cycle formations. When increasing the frequency of excitation, it is shown that the damping increases initially, and then remains constant. These findings are confirmed by a kinematics-based ballistic-harmonic model. Optical flow and a new technique based on pixel intensity standard deviation are employed in correlation with an analysis of the hysteresis cycles in order to offer insight into the sloshing force variation within one liquid cycle of oscillation.&nbsp