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Yueming Li

Researcher at Xi'an Jiaotong University

Publications -  122
Citations -  1636

Yueming Li is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Computer science & Vibration. The author has an hindex of 19, co-authored 93 publications receiving 1112 citations.

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Vibrational and acoustic responses of a laminated plate with temperature gradient along the thickness

TL;DR: In this article, an analytical study on the dynamic characteristics of a laminated plate under temperature gradient is carried out, where theoretical formulations are derived with the first order shear deformation theory and von Karman nonlinear strain displacement relationship considering the effect of temperature gradient.
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Reduced order unsteady aerodynamic model of a rigid aerofoil in gust encounters

TL;DR: In this paper, a reduced-order model is proposed to predict gust loads using computational fluid dynamics is prohibitively expensive and unrealistic for parametric searches, and the model is parametric with respect to the gust disturbance.
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Computational fluid dynamics-based transonic flutter suppression with control delay

TL;DR: In this paper, the effects of control input time delay on closed-loop transonic computational aeroelastic analysis were investigated, and a controller for a time-delay system may be designed using the same aeroservo-elastic reduced order model, incurring in no extra costs or complications.
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Effects of creep-plastic behavior on stress development in TBCs during cooling

TL;DR: In this article, the effects of creep-plastic behavior of BC and TGO on the TC/TGO/BC interface stress development were investigated, and it was shown that if the creep in TGO is considered, the stresses will be overall relaxed no matter if BC is plastic, creep or creepplastic.
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Aeroelastic global structural optimization using an efficient CFD-based reduced order model

TL;DR: This paper presents a new optimization process using the efficient method for aeroelastic global structural optimization, which employs Genetic Algorithms (GAs) as optimization tool and shows that the most feasible and optimal solutions are effectively obtained.