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Sökning: WFRF:(Wang Q) > Bokkapitel

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1.
  • Shen, H., et al. (författare)
  • Wearable carbon nanotube devices for sensing
  • 2017
  • Ingår i: Industrial applications of carbon nanotubes. - : Elsevier. - 9780323415316 - 9780323414814 ; , s. 179-199
  • Bokkapitel (refereegranskat)abstract
    • The application of carbon nanotubes (CNTs) for wearable sensing device fabrication is an interesting area, attracting sustained scientific and technological interest. Acquisition of information from wearable technologies is considered a cornerstone in person-centered health informatics, because of its independence, currency, and timeliness. The unique physical and chemical properties (such as high aspect ratio, ultralight weight, high mechanical strength, high electrical conductivity, and high thermal conductivity) of CNTs make them an attractive nanomaterial for wearable sensor fabrication. Extensive efforts will continuously be made in both academia and industry in the research and development of wearable CNT sensors to enhance their function, flexibility, energy saving, and longevity. We will discuss recent advances in the design and application of wearable devices based on CNT materials for sensing, especially in the fields of medical equipment, motion detection, and environmental sensor.
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2.
  • Wang, Q., et al. (författare)
  • Design Optimization and Performance Prediction of Compact Heat Exchangers
  • 2014
  • Ingår i: Emerging Topics in Heat Transfer: Enhancement and Heat Exchangers. - 9781845648183 - 9781845648190 ; , s. 301-319
  • Bokkapitel (refereegranskat)abstract
    • Applications of genetic algorithms (GAs) and artificial neural networks (ANNs) in thermal engineering have received much attention. Compact heat exchangers (CHEs) have special merits of high-performance and compactness and are widely used in various applications of energy and power engineering. This chapter demonstrates optimization design of a plate-fin heat changer and performance prediction of three kinds of tube-fin heat exchangers by a GA and an ANN, respectively. The successful applications of optimization and prediction of CHEs suggest that GAs and ANNs have strong ability of intelligent design and prediction of CHEs.
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