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Design for Extremes: A Contour Method for Defining Requirements Based on Multivariate Extremes

Published online by Cambridge University Press:  26 July 2019

Andreas F. Haselsteiner*
Affiliation:
University of Bremen, Germany;
Rafael Reisenhofer
Affiliation:
University of Vienna, Austria
Jan-Hendrik Ohlendorf
Affiliation:
University of Bremen, Germany;
Klaus-Dieter Thoben
Affiliation:
University of Bremen, Germany;
*
Contact: Haselsteiner, Andreas, Florian University of Bremen, Production Engineering: Mechanical and Process Engineering, Germany, a.haselsteiner@uni-bremen.de

Abstract

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The design of various products is driven by requirements that describe extremes. In marine structural design, joint extremes of environmental variables like wave height and wind speed are used to define load cases. Similarly, in ergonomic design minimum and maximum values of anthropometric variables are considered to make sure a product is suitable for a wide range of users. Here, we present a method that supports designers to define requirements using joint extreme values: the requirements contour method. The method is based on structural engineering's environmental contour method and uses a dataset and statistical methods to specify a region in the variable space that must be considered in the design process. That region's enclosure is the requirements contour and holds the joint extremes. After formally describing the method, we give an illustrative example of its usage: we use it to define requirements for the design of an ergonomic handle for a power tool. The requirements contour method is a field-independent approach to design for extremes. In the tradition of design for X, we think that a design project can benefit from applying methods that focus on different 'X's.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Chai, W. and Leira, B. J. (2018), “Environmental contours based on inverse SORM”, Marine Structures 60, pp. 3451.Google Scholar
GL, DNV (2017), “Recommended practice DNVGL-RP-C205: Environmental conditions and environmental loads”, Technical report.Google Scholar
Eckert-Gallup, A. and Martin, N. (2016), “Kernel density estimation (KDE) with adaptive bandwidth selection for environmental contours of extreme sea states, in ‘OCEANS 2016 MTS/IEEE Monterey”, IEEE, Monterey, CA”, USA, pp. 15.Google Scholar
Einmahl, J. H. J., Haan, L. D. and Krajina, A. (2013), “Estimating extreme bivariate quantile regions”, Extremes 16, pp. 121145.Google Scholar
Gero, J. S. (1990), “Design prototypes : A knowledge representation schema for design”, AI Magazine 11(4).Google Scholar
Gordon, C. C., Blackwell, C. L., Bradtmiller, B., Parham, J. L., Barrientos, P., Paquette, S. P., Corner, B. D., Carson, J. M., Venezia, J. C., Rockwell, B. M., Mucher, M. and Kristensen, S. (2014), “2012 Anthropometric survey of U.S. army personnel : methods and summary statistics, Technical report, U.S. Army Natick Soldier Research”, Development and Engineering Center.Google Scholar
Haselsteiner, A. F. (2018), “compute-hdc: An open-source implementation of the highest density contour method in Matlab (version 1.1.0)’. URL : https://github.com/ahaselsteiner/compute-hdc/releases/tag/1.1.0Google Scholar
Haselsteiner, A. F., Ohlendorf, J.-H. and Thoben, K.-D. (2017), “Environmental contours based on kernel density estimation”, in Proc. 13th German Wind Energy Conference (DEWEK 2017), Bremen, Germany.Google Scholar
Haselsteiner, A. F., Ohlendorf, J.-H., Wosniok, W. and Thoben, K.-D. (2017), “Deriving environmental contours from highest density regions”, Coastal Engineering 123, pp. 4251.Google Scholar
Haver, S. (1985), “Wave climate off northern Norway”, Applied Ocean Research 7(2), 8592.Google Scholar
Haver, S. (1987), “On the joint distribution of heights and periods of sea waves”, Ocean Engineering 14(5), 359376.Google Scholar
Huseby, A. B., Vanem, E. and Natvig, B. (2013), “A new approach to environmental contours for ocean engineering applications based on direct Monte Carlo simulations”, Ocean Engineering 60, pp. 124135.Google Scholar
Hyndman, R. J. (1996), “Computing and graphing highest density regions”, The American Statistician 50(2), 120126.Google Scholar
Commission, International Electrotechnical (2009), “Wind turbines - part 3: Design requirements for offshore wind turbines”, Technical Report IEC 61400–3:2009-02.Google Scholar
Jonathan, P., Ewans, K. and Flynn, J. (2014), “On the estimation of ocean engineering design contours”, Journal of Offshore Mechanics and Arctic Engineering Vol. 136 No. 4, pp. 41101-1 to 041101-8.Google Scholar
Jürgens, H., Matzdorff, I. and Windberg, J. (1998), “Internationale antropometrische Daten als Voraussetzung für die Gestaltung von Arbeitsplätzen und Maschinen, in Arbeitswissenschaftliche Erkenntnisse: Forschungsergebnsise für die Praxis, Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Dortmund, Germany.Google Scholar
Kuo, T. C., Huang, S. H. and Zhang, H. C. (2001), “Design for manufacture and design for ‘X’: Concepts, applications, and perspectives”, Computers and Industrial Engineering 41(3), 241260.Google Scholar
Li, L., Gao, Z. and Moan, T. (2015), “Joint environmental data at five European offshore sites for design of combined wind and wave energy devices”, Journal of Offshore Mechanics and Arctic Engineering 137 No. 031901.Google Scholar
Madsen, H. O., Krenk, S. and Lind, N. C. (2006), Methods of structural safety, Dover Publications, Mineola, New York, USA.Google Scholar
Manuel, L., Nguyen, P. T. T., Canning, J., Coe, R. G., Eckert-Gallup, A. C. and Martin, N. (2018), “Alternative approaches to develop environmental contours from metocean data”, Journal of Ocean Engineering and Marine Energy 4(4), 293310.Google Scholar
Matthiesen, S. and Germann, R. (2018), “Meaningful prediction parameters for evaluating the suitability of power tools for usage”, in Procedia CIRP, Vol. 70, pp. 241246.Google Scholar
Molenbroek, J. F. (2000), “Making an anthropometric size system interactively”, in Proc. of the Human Factors and Ergonomics Society Annual Meeting, Vol. 44, pp. 766769.Google Scholar
Molenbroek, J. F., Kroon-Ramaekers, Y. M. and Snijders, C. J. (2003), “Revision of the design of a standard for the dimensions of school furniture”, Ergonomics 46(7), 681694.Google Scholar
Naess, A. and Moan, T. (2013), “Random environmental process”, in Stochastic dynamics of marine structures, Cambridge University Press, Cambridge, United Kingdom, pp. 191208.Google Scholar
Ochi, M. (1998), “Ocean waves: The stochastic approach, Cambridge University Press, Cambridge”, United Kingdom.Google Scholar
Serinaldi, F. (2015), “Dismissing return periods!”, Stochastic Environmental Research and Risk Assessment 29(4), 11791189.Google Scholar
Silverman, B. W. (1998), Density estimation for statistics and data analysis, CRC press, London, UK.Google Scholar
Vanem, E. (2018), “A simple approach to account for seasonality in the description of extreme ocean environments”, Marine Systems & Ocean Technology 13(2-4), 6373.Google Scholar
Wang, C.-Y. and Cai, D.-C. (2016), “Hand tool handle design based on hand measurements”, in MATEC Web of Conferences (IMETI 2016), Vol. 119, pp. 01044-1 to 01044-5.Google Scholar
Winterstein, S. R., Ude, T. C., Cornell, C. A., Bjerager, P. and Haver, S. (1993), “Environmental parameters for extreme response: Inverse FORM with omission factors”, in Proc. 6th International Conference on Structural Safety and Reliability (ICOSSAR 93), Innsbruck, Austria.Google Scholar