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Numerical Optimization in Microfluidics

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Abstract

Numerical modelling can illuminate the working mechanism and limitations of microfluidic devices. Such insights are useful in their own right, but one can take advantage of numerical modelling in a systematic way using numerical optimization. In this chapter we will discuss when and how numerical optimization is best used.

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Notes

  1. 1.

    Animations available at https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method. Cited 30 April 2017.

  2. 2.

    A value three orders of magnitude larger than the machine precision is a good starting point, but the optimal value is problem dependent and it is thus a good idea to study, when numerical noise dies out and 2nd order effects sets in.

  3. 3.

    https://github.com/xuy/pyipopt. Cited 30 April 2017.

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Acknowledgements

This work is supported by the Villum Foundation (Grant No. 9301) and the Danish Council for Independent Research (DNRF122).

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Authors and Affiliations

  1. Department of Micro- and Nanotechnology, Technical University of Denmark, Ørsteds Plads, 2800, Kongens Lyngby, Denmark

    Kristian Ejlebjerg Jensen

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  1. Kristian Ejlebjerg Jensen
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Correspondence to Kristian Ejlebjerg Jensen .

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Editors and Affiliations

  1. Cen. de Est. de Fenómenos de Transporte, Facu. de Engenharia da Univ. do Porto Cen. de Est. de Fenómenos de Transporte, Porto, Portugal

    Francisco José Galindo-Rosales

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Jensen, K.E. (2018). Numerical Optimization in Microfluidics. In: Galindo-Rosales, F. (eds) Complex Fluid-Flows in Microfluidics. Springer, Cham. https://doi.org/10.1007/978-3-319-59593-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-59593-1_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-59593-1

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