Four-dimensional dynamic flow measurement by holographic particle image velocimetry

The ultimate goal of holographic particle image velocimetry (HPIV) is to provide space- and time-resolved measurement of complex flows. Recent new understanding of holographic imaging of small particles, pertaining to intrinsic aberration and noise in particular, has enabled us to elucidate fundamental issues in HPIV and implement a new HPIV system. This system is based on our previously reported off-axis HPIV setup, but the design is optimized by incorporating our new insights of holographic particle imaging characteristics. Furthermore, the new system benefits from advanced data processing algorithms and distributed parallel computing technology. Because of its robustness and efficiency, for the first time to our knowledge, the goal of both temporally and spatially resolved flow measurements becomes tangible. We demonstrate its temporal measurement capability by a series of phase-locked dynamic measurements of instantaneous three-dimensional, three-component velocity fields in a highly three-dimensional vortical flow--the flow past a tab

Wind turbine blade end-of-life options: an eco-audit comparison

Wind energy has developed rapidly over the last two decades to become one of the most promising economical and green sources of renewable energy, responding to concerns about use of fossil fuels and increasing demand for energy. However, attention is now turning to what happens to end-of-life wind turbine waste, and there is scrutiny of its environmental impact. In this study, we focus on one aspect of this, the blades. We analyse and compare end-of-life options for wind turbine blade materials (mainly glass fibre reinforced plastic and carbon fibre reinforced plastic) in terms of environmental impact (focusing on energy consumption), using our own data together with results gathered from the literature. The environmental impacts of each end-of-life option are discussed, looking at processing energy consumption, the recycling benefits and the effect of blade technology development trends. There is considerable variability in the results, and lack of consensus on predictions for the future. We therefore analyse the results using a range of different scenarios to show how the ‘optimal’ solutions are influenced by trends in blade composition and end-of-life process development. The most environmentally favourable process is dependent on whether the materials used for the blades are glass fibre composite or carbon fibre composite. The extent to which process improvement might affect the viability of different end-of-life processes has been assessed by looking at ‘crossover’ points for when the environmental impact becomes favourable. This analysis gives new insight into areas where research into process technologies could be targeted to enable significant end-of-life environmental benefits.China Scholarship Council Jesus College Cambridg

Neural Inheritance Relation Guided One-Shot Layer Assignment Search

Layer assignment is seldom picked out as an independent research topic in neural architecture search. In this paper, for the first time, we systematically investigate the impact of different layer assignments to the network performance by building an architecture dataset of layer assignment on CIFAR-100. Through analyzing this dataset, we discover a neural inheritance relation among the networks with different layer assignments, that is, the optimal layer assignments for deeper networks always inherit from those for shallow networks. Inspired by this neural inheritance relation, we propose an efficient one-shot layer assignment search approach via inherited sampling. Specifically, the optimal layer assignment searched in the shallow network can be provided as a strong sampling priori to train and search the deeper ones in supernet, which extremely reduces the network search space. Comprehensive experiments carried out on CIFAR-100 illustrate the efficiency of our proposed method. Our search results are strongly consistent with the optimal ones directly selected from the architecture dataset. To further confirm the generalization of our proposed method, we also conduct experiments on Tiny-ImageNet and ImageNet. Our searched results are remarkably superior to the handcrafted ones under the unchanged computational budgets. The neural inheritance relation discovered in this paper can provide insights to the universal neural architecture search.Comment: AAAI202

Generating Giant and Tunable Nonlinearity in a Macroscopic Mechanical Resonator from Chemical Bonding Force

Nonlinearity in macroscopic mechanical system plays a crucial role in a wide variety of applications, including signal transduction and processing, synchronization, and building logical devices. However, it is difficult to generate nonlinearity due to the fact that macroscopic mechanical systems follow the Hooke's law and response linearly to external force, unless strong drive is used. Here we propose and experimentally realize a record-high nonlinear response in macroscopic mechanical system by exploring the anharmonicity in deforming a single chemical bond. We then demonstrate the tunability of nonlinear response by precisely controlling the chemical bonding interaction, and realize a cubic elastic constant of \mathversion{bold}$2 \times 10^{18}~{\rm N}/{\rm m^3}$, many orders of magnitude larger in strength than reported previously. This enables us to observe vibrational bistate transitions of the resonator driven by the weak Brownian thermal noise at 6~K. This method can be flexibly applied to a variety of mechanical systems to improve nonlinear responses, and can be used, with further improvements, to explore macroscopic quantum mechanics