Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

High-Efficiency Fog Collector: Water Unidirectional Transport on Heterogeneous Rough Conical Wires

Abstract: An artificial periodic roughness-gradient conical copper wire (PCCW) can be fabricated by inspiration from cactus spines and wet spider silks. PCCW can harvest fog on periodic points of the conical surface from air and transports the drops for a long distance without external force, which is attributed to dynamic as-released energy generated from drop deformation in drop coalescence, in addition to both gradients of geometric curve (inducing Laplace pressure) and periodic roughness (inducing surface energy difference). It is found that the ability of fog collection can be related to various tilt-angle wires, thus a fog collector with an array system of PCCWs is further designed to achieve a continuous process of efficient water collection. As a result, the effect of water collection on PCCWs is better than previous results. These findings are significant to develop and design materials with water collection and water transport for promising application in fogwater systems to ease the water crisis.

Optimization of multiple tuned mass dampers to suppress machine tool chatter

Abstract: Chatter is more detrimental to machining due to its instability than forced vibrations. This paper presents design and optimal tuning of multiple tuned mass dampers (TMDs) to increase chatter resistance of machine tool structures. Chatter free critical depth of cut of a machine is inversely proportional to the negative real part of frequency response function (FRF) at the tool–workpiece interface. Instead of targeting reduction of magnitude, the negative real part of FRF of the machine is reduced by designing single and multiple TMD systems. The TMDs are designed to have equal masses, and their damping and stiffness values are optimized to improve chatter resistance using minimax numerical optimization algorithm. It is shown that multiple TMDs need more accurate tuning of stiffness and natural frequency of each TMD, but are more robust to uncertainties in damping and input dynamic parameters in comparison with single TMD applications. The proposed tuned damper design and optimization strategy is experimentally illustrated to increase chatter free depth of cuts.

Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network

Abstract: 3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to obtain. Unsupervised learning methods ease the burden of manual annotation by exploiting unlabeled data without supervision. In this article, we propose a new unsupervised learning method using convolutional neural networks under an end-to-end framework, Volume Tweening Network (VTN), for 3D medical image registration. We propose three innovative technical components: (1) An end-to-end cascading scheme that resolves large displacement; (2) An efficient integration of affine registration network; and (3) An additional invertibility loss that encourages backward consistency. Experiments demonstrate that our algorithm is 880x faster (or 3.3x faster without GPU acceleration) than traditional optimization-based methods and achieves state-of-the-art performance in medical image registration.

Nickel nanocone-array supported silicon anode for high-performance lithium-ion batteries.

Abstract: The development of Li-ion batteries with high capacity, long lifespan, and fast charge/discharge rates is of great technological importance for future portable electronics, power tools, electric and hybrid vehicles, and renewable energies. [ 1–3 ] Silicon is a particularly appealing anode material for Li-ion batteries because of its high theoretical capacity of 4200 mAh g − 1 [ 4 ] and it has been the subject of extensive research efforts. [ 5 , 6 ] However, lithium alloying and de-alloying with Si are accompanied by an enormous volume change ( > 300%), which induces severe pulverization and electrical disconnection from the current collector. [ 7 ] This structural and electronic degradation thereby leads to drastic and fast capacity fading and hindrance of practical implementation. To overcome these drawbacks, one effective approach is to design powder-based composites (e.g., a Si-metal active/inactive matrix concept), [ 8 , 9 ] preferably Si-carbon composites which have been investigated for many years. [ 10–13 ] Previous research has shown improvement of the electrochemical performance of Si-based anodes but only to a limited extent. [ 6 ]

A Self-Pumping Dressing for Draining Excessive Biofluid around Wounds

Abstract: Excessive biofluid around wounds often causes infection and hinders wound healing. However, the intrinsic hydrophilicity of the conventional dressing inevitably retains excessive biofluid at the interface between the dressing and the wound. Herein, a self-pumping dressing is reported, by electrospinning a hydrophobic nanofiber array onto a hydrophilic microfiber network, which can unidirectionally drain excessive biofluid away from wounds and finally accelerate the wound healing process. The hydrophilic microfiber network offers a draining force to pump excessive biofluid through the hydrophobic nanofiber array, which can further keep those pumped biofluids from rewetting the wounds. In the proof of concept, the self-pumping dressing unidirectionally drains the biofluid from murine dorsum wounds, thereby resulting in faster wound healing than conventional dressings. This unique self-pumping dressing has enormous potential to be a next-generation dressing for healing wounds clinically.