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Amalia Moutsopoulou, Markos Petousis, Georgios E. Stavroulakis, Anastasios Pouliezos and Nectarios Vidakis
In this study, we created an accurate model for a homogenous smart structure. After modeling multiplicative uncertainty, an ideal robust controller was designed using µ-synthesis and a reduced-order H-infinity Feedback Optimal Output (Hifoo) controller, ...
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Weilong Guang, Peng Wang, Jinshuai Zhang, Linjuan Yuan, Yue Wang, Guang Feng and Ran Tao
Predicting the flow situation of cavitation owing to its high-dimensional nonlinearity has posed great challenges. To address these challenges, this study presents a novel reduced order modeling (ROM) method to accurately analyze and predict cavitation f...
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Zihao Yuan, Ruinan Mu, Haifeng Zhao and Ke Wang
In this work, a dynamic model is proposed to simulate the drilling and steering process of an autonomous burrowing mole to access scientific samples from the deep subsurface of the Moon. The locomotive module is idealized as a rigid rod. The characterist...
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Nikhil Iyengar, Dushhyanth Rajaram and Dimitri Mavris
Uncertainties in the atmosphere and flight conditions can drastically impact the performance of an aircraft and result in certification delays. However, uncertainty propagation in high-fidelity simulations, which have become integral to the design proces...
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Heng Liu and Veronica Eliasson
Geometrical shock dynamics (GSD) is a model capable of efficiently predicting the position, shape, and strength of a shock wave. Compared to the traditional Euler method that solves the inviscid Euler equations, GSD is a reduced-order model derived from ...
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Asaf Asher, Rivka Gilat and Slava Krylov
Configuration-dependent spectral behavior of initially curved circular microplates loaded by a distributed nonlinear electrostatic force is investigated. The structures under consideration are distinguished by two interesting features. The first is that ...
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José Felix Zapata Usandivaras, Annafederica Urbano, Michael Bauerheim and Bénédicte Cuenot
Improving the predictive capabilities of reduced-order models for the design of injector and chamber elements of rocket engines could greatly improve the quality of early rocket chamber designs. In the present work, we propose an innovative methodology t...
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Chiara Gastaldi and Muzio M. Gola
A method called PCR (Platform Centered Reduction) is designed to more effectively perform complex iterative and nonlinear calculations required for the dynamic response of turbine blades damped by dry friction contacts between rigid dampers and airfoil-t...
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Nicola Demo, Marco Tezzele, Andrea Mola and Gianluigi Rozza
In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed ...
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Yang Zhang, Xudong Zheng and Qian Xue
This paper proposes a machine-learning based reduced-order model that can provide fast and accurate prediction of the glottal flow during voice production. The model is based on the Bernoulli equation with a viscous loss term predicted by a deep neural n...
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