Comparative numerical study of aerodynamic heating and performance of transonic hyperloop pods with different noses

Niu, Jilei; Yang, Shuai; Yu, Qiujun; Cao, Xiaoling; Yuan, Yanping; Yang, Xiaofeng

doi:10.1016/j.csite.2021.101701

Cited by 12 publications

(1 citation statement)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For hypersonic vehicles (HV) flying at high Mach numbers [1], the surrounding airflow compresses against and generates friction with their surfaces. This leads to the rapid generation of immense thermal energy, causing a sudden surge in the HV's surface temperature-a phenomenon known as "aerodynamic heating" [2]. This intense aerodynamic heating, often referred to as the "thermal barrier" problem, can compromise flight stability, diminish the structural integrity of the HV, reduce the material's strength limit, and potentially jeopardize the stability and reliability of the internal sophisticated electronic components.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Global Fractional Order Fast Terminal Sliding Mode Model-Free Intelligent PID Control for Hypersonic Vehicle’s Ground Thermal Environment

Lv,

Zhang,

Bai

et al. 2023

Aerospace

View full text Add to dashboard Cite

In this paper, an adaptive neural network global fractional order fast terminal sliding mode model-free intelligent PID control strategy (termed as TDE-ANNGFOFTSMC-MFIPIDC) is proposed for the hypersonic vehicle ground thermal environment simulation test device (GTESTD). Firstly, the mathematical model of the GTESTD is transformed into an ultra-local model to ensure that the control strategy design process does not rely on the potentially inaccurate dynamic GTESTD model. Meanwhile, time delay estimation (TDE) is employed to estimate the unknown terms of the ultra-local model. Next, a global fractional-order fast terminal sliding mode surface (GFOFTSMS) is introduced to effectively reduce the estimation error generated by TDE. It also eliminates arrival time, accelerates the convergence speed of the sliding phase, guarantees finite time arrival, avoids the singularity phenomenon, and bolsters robustness. Then, as the upper bound of the disturbance error is unknown, an adaptive neural network (ANN) control is designed to approximate the upper bound of the estimation error closely and mitigate the chattering phenomenon. Furthermore, the stability of the control system and the convergence time are proven by the Lyapunov stability theorem and are calculated, respectively. Finally, simulation results are conducted to validate the efficacy of the proposed control strategy.

show abstract

Section: Introductionmentioning

confidence: 99%