Astronomical adaptive optics: a review

Rao, Changhui; Zhong, Libo; Guo, Youming; Li, Min; Zhang, Lanqiang; Wei, Kai

doi:10.1186/s43074-024-00118-7

Cited by 9 publications

References 184 publications

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A Smart Slippery Surface with Reversible Dynamic Temperature Responsiveness for Long‐Term Controllable Anti‐Icing

Huai,

Zhang,

Wang

et al. 2024

Adv Materials Technologies

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Recently, slippery liquid‐infused porous surface (SLIPS) is widely utilized for anti‐icing. Nevertheless, a major challenge hindering the practical applications of SLIPS is the depletion of lubricating oil, significantly deteriorating its deicing performance. Given that super‐slippery effects are not required during most non‐icing periods, this research introduces a novel dynamic wettability‐adjustable SLIPS anti‐icing coating inspired by the nepenthes. This coating‐comprising modified diatomaceous earth and soft silicone rubber‐releases lubricating oil only when temperatures fall below −2 °C, thus exhibiting super‐slippery characteristics and effectively preventing icing. The porous modified diatomaceous earth retains lubricating oil for prolonged periods and facilitates its controlled release under external forces. The high elasticity and strength of the soft silicone rubber endow the coating with thermal expansion and contraction capabilities, enabling the active absorption and release of lubricating fluid in response to temperature variations. This coating, prepared using a straightforward strategy, demonstrates an ice adhesion strength and an oil loss rate of less than 23 kPa and 13%, respectively, after 50 icing/de‐icing cycles. These findings highlight the remarkable performance of the coating, providing valuable insights for achieving long‐term anti‐icing on the external surfaces of equipment in extreme working conditions.

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A Smart Slippery Surface with Reversible Dynamic Temperature Responsiveness for Long‐Term Controllable Anti‐Icing

Huai,

Zhang,

Wang

et al. 2024

Adv Materials Technologies

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An optimization method for deformable mirror configuration in multi-conjugate adaptive optics systems

Li,

Liu,

Zhang

et al. 2024

A&A

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Multi-conjugate adaptive optics (MCAO) is a crucial technology for achieving high-resolution imaging over a wide field of view with modern ground-based optical telescopes. The configuration of deformable mirrors (DMs) is a key component in the analysis and optimization of MCAO performance. Currently, the search for the optimal DM configuration often relies on iterative and time-consuming Monte Carlo simulations. This issue arises from the lack of an appropriate optimization method for DM configurations. The primary objective of this paper is to establish an optimization method for DM configurations in MCAO systems. We established a quantitative criterion for evaluating DM configurations by analyzing their correction capabilities for turbulence aberrations at different altitudes. Then, we optimized the DM configurations based on this criterion. This method provides a new theoretical foundation and practical tool for the design and performance optimization of MCAO systems. Based on the pupil phase structure function, we established a DM configuration evaluation criterion, namely the non-conjugate correction index (NCCI). Using NCCI as the optimal criterion, combined with the particle swarm optimization algorithm, we searched for the optimal solution across different DM configuration spaces. We conducted simulations based on the turbulence profiles of typical telescope sites. We validated our proposed theoretical model against Monte Carlo simulation models and find that the NCCI error ranges from 0.05 to 0.1. For optimizing DM conjugate heights, the results of our optimization algorithm differ by less than 1 km from those obtained via Monte Carlo simulations. Regarding the performance of the DM optimization algorithm, the average convergence accuracy error is less than 0.1 km, and the average convergence speed is approximately ten iterations. Additionally, our optimization method runs in just a few minutes; Monte Carlo simulations, in comparison, require several dozen hours.

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