Continuous Ultrasonic Reactors: Design, Mechanism and Application

Ultrasonic small scale flow reactors have found increasing popularity among researchers as they serve as a very useful platform for studying and controlling ultrasound mechanisms and effects. This has led to the use of these reactors for not only research purposes, but also various applications in biological, pharmaceutical and chemical processes mostly on laboratory and, in some cases, pilot scale. This review summarizes the state of the art of ultrasonic flow reactors and provides a guideline towards their design, characterization and application. Particular examples for ultrasound enhanced multiphase processes, spanning from immiscible fluid–fluid to fluid–solid systems, are provided. To conclude, challenges such as reactor efficiency and scalability are addressed.

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Flow reactors integrated with in-line monitoring using benchtop NMR spectroscopy

Reaction Chemistry & Engineering ◽

10.1039/c8re00083b ◽

2018 ◽

Vol 3 (4) ◽

pp. 399-413 ◽

Cited By ~ 34

Author(s):

Patrick Giraudeau ◽

François-Xavier Felpin

Keyword(s):

Nmr Spectroscopy ◽

State Of The Art ◽

Emerging Technology ◽

The State ◽

Flow Reactors

The state-of-the-art flow reactors integrated with in-line benchtop NMR are thoroughly discussed with highlights on the strengths and weaknesses of this emerging technology.

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Neural Machine Translation with Joint Representation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6344 ◽

2020 ◽

Vol 34 (05) ◽

pp. 8285-8292

Author(s):

Yanyang Li ◽

Qiang Wang ◽

Tong Xiao ◽

Tongran Liu ◽

Jingbo Zhu

Keyword(s):

Machine Translation ◽

English Translation ◽

Large Scale ◽

State Of The Art ◽

Statistical Machine Translation ◽

The State ◽

Small Scale ◽

Neural Machine Translation ◽

Joint Representation

Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units, e.g., alignment, the recent Neural Machine Translation (NMT) systems resort to the attention which partially encodes the interaction for efficiency. In this paper, we employ Joint Representation that fully accounts for each possible interaction. We sidestep the inefficiency issue by refining representations with the proposed efficient attention operation. The resulting Reformer models offer a new Sequence-to-Sequence modelling paradigm besides the Encoder-Decoder framework and outperform the Transformer baseline in either the small scale IWSLT14 German-English, English-German and IWSLT15 Vietnamese-English or the large scale NIST12 Chinese-English translation tasks by about 1 BLEU point. We also propose a systematic model scaling approach, allowing the Reformer model to beat the state-of-the-art Transformer in IWSLT14 German-English and NIST12 Chinese-English with about 50% fewer parameters. The code is publicly available at https://github.com/lyy1994/reformer.

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A Review of Grooved Dynamic Gas Bearings

Applied Mechanics Reviews ◽

10.1115/1.4044191 ◽

2019 ◽

Vol 72 (1) ◽

Cited By ~ 5

Author(s):

Lili Gu ◽

Eliott Guenat ◽

Jürg Schiffmann

Keyword(s):

High Speed ◽

State Of The Art ◽

Integrated Design ◽

The State ◽

Small Scale ◽

Second Phase ◽

Foil Bearings ◽

Turbulence Effects ◽

Physical Phenomena ◽

Gas Bearing

Abstract This paper offers an extensive review of publications dealing with the modeling, the design, and the experimental investigation of grooved dynamic gas-lubricated bearings. Recent years have witnessed a rise in small-scale and high-speed turbomachinery applications. Besides the well-known gas foil bearings, grooved bearings offer attractive advantages, which unveil their potential in particular at small scale due to the structural simplicity as well as satisfying predictability. This paper starts with a general background of the application of gas-lubricated bearings and introduces and compares the different gas bearing topologies. Further, the state-of-the-art modeling of grooved gas-lubricated bearings is introduced, systematically assessing the advantages and inconveniences of two major approaches, i.e., the narrow groove theory (NGT) and direct discretization method. Since the NGT method is an elegant and efficient approach to model the complex effects of periodic grooves, a critical section is dedicated to the NGT. In a second phase, different models to include additional physical phenomena such as real gas lubrication, rarefaction, or turbulence effects are reviewed. The paper concludes with a critical assessment of the state-of-the-art and indicates potential fields of research that would allow to shed more light into the understanding of these bearings, as well as with some thoughts on the integrated design methodologies of gas bearing supported rotors.

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The state of the art of small-scale pellet-based heating systems and relevant regulations in Sweden, Austria and Germany

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2003.11.002 ◽

2004 ◽

Vol 8 (3) ◽

pp. 201-221 ◽

Cited By ~ 74

Author(s):

Frank Fiedler

Keyword(s):

State Of The Art ◽

The State ◽

Small Scale ◽

Heating Systems

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The State-of-the-Art Technology of H-Type Darrieus Wind Turbine Rotors

Journal of Energy Resources Technology ◽

10.1115/1.4044559 ◽

2019 ◽

Vol 142 (3) ◽

Cited By ~ 2

Author(s):

Siddhant Jain ◽

Ujjwal K. Saha

Keyword(s):

State Of The Art ◽

Technological Development ◽

Turbulence Models ◽

Vertical Axis ◽

Scientific Work ◽

The State ◽

Small Scale ◽

Curvature Effects ◽

Blade Vortex Interaction ◽

Darrieus Rotor

Abstract There has been a resurgence of interest in the development of small-scale vertical-axis wind turbines (VAWTs) in the past few decades as is evident from the plethora of published scientific work. This attention may be attributed to the desperate need for cheaper, cleaner, and off-grid mechanisms for generating electric power. In such hasty scenario, VAWTs are being considered as one of the potential solution for small-scale power generation. Among the VAWTs, H-type Darrieus rotors have undergone extensive exploration. In this review work, an attempt has been made to assemble all the major areas of research done in the field of H-type Darrieus rotor development. These areas include the aerodynamic models, computational fluid dynamics (CFD) methods and turbulence models, self-starting and dynamic stalling behavior, blade-vortex interaction and wake studies, and blade designs and optimization studies, besides topics of special interest like blade curvature effects and skewed flows. Overall, the work gives a comprehensive review of the state-of-the-art technology of the H-type Darrieus rotor. Finally, recommendations have been made for each of the areas keeping in view of the technological development.

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Dynamic Electronic Toll Collection via Multi-Agent Deep Reinforcement Learning with Edge-Based Graph Convolutional Networks

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/635 ◽

2019 ◽

Author(s):

Wei Qiu ◽

Haipeng Chen ◽

Bo An

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Road Network ◽

State Of The Art ◽

The State ◽

Small Scale ◽

Electronic Toll Collection ◽

Multi Agent ◽

Toll Collection ◽

Edge Based

Over the past decades, Electronic Toll Collection (ETC) systems have been proved the capability of alleviating traffic congestion in urban areas. Dynamic Electronic Toll Collection (DETC) was recently proposed to further improve the efficiency of ETC, where tolls are dynamically set based on traffic dynamics. However, computing the optimal DETC scheme is computationally difficult and existing approaches are limited to small scale or partial road networks, which significantly restricts the adoption of DETC. To this end, we propose a novel multi-agent reinforcement learning (RL) approach for DETC. We make several key contributions: i) an enhancement over the state-of-the-art RL-based method with a deep neural network representation of the policy and value functions and a temporal difference learning framework to accelerate the update of target values, ii) a novel edge-based graph convolutional neural network (eGCN) to extract the spatio-temporal correlations of the road network state features, iii) a novel cooperative multi-agent reinforcement learning (MARL) which divides the whole road network into partitions according to their geographic and economic characteristics and trains a tolling agent for each partition. Experimental results show that our approach can scale up to realistic-sized problems with robust performance and significantly outperform the state-of-the-art method.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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