Sloshing reduced-order model based on neural networks for aeroelastic analyses-Reference-Cited by-同舟云学术

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Sloshing reduced-order model based on neural networks for aeroelastic analyses

Published:2022-08 Issue: Volume:127 Page:107708
ISSN:1270-9638
Container-title:Aerospace Science and Technology
language:en
Short-container-title:Aerospace Science and Technology

Author:

Saltari Francesco^ORCID,Pizzoli Marco,Gambioli Francesco,Jetzschmann Christina,Mastroddi Franco^ORCID

Funder

Horizon 2020

Publisher

Elsevier BV

Subject

Aerospace Engineering

Reference31 articles.

1. Sloshing wing dynamics – project overview sloshing wing dynamics – project overview;Gambioli,2020

2. Effect of fuel sloshing in the external tank on the flutter of subsonic wings;Firouz-Abadi;J. Aerosp. Eng.,2014

3. Modeling of fuel sloshing and its physical effects on flutter;Farhat;AIAA J.,2013

4. Sloshing reduced-order models for aeroelastic analyses of innovative aircraft configurations;Colella;Aerosp. Sci. Technol.,2021

5. A two-dimensional computational model of fuel sloshing effects on aeroelastic behaviour;Hall,2013

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1. Loads-based optimal fuel-usage strategy by using a neural-network-based reduced-order model for vertical sloshing;Aerospace Science and Technology;2024-09

2. Comparison of Artificial Neural Networks and Genetic Algorithms for Predicting Liquid Sloshing Parameters;Journal of Marine Science and Application;2024-05-24

3. A fast prediction model of blade flutter in turbomachinery based on graph convolutional neural network;Aerospace Science and Technology;2024-05

4. Artificial neural network-based temperature prediction of a lunar orbiter in thermal vacuum test: Data-driven reduced-order models;Aerospace Science and Technology;2024-02

5. Research on aerodynamic shape optimization of reentry vehicle based on hybrid scale multi-fidelity neural network model;Aerospace Science and Technology;2023-11

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