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Research Papers: Design Automation

Risk-Adaptive Set-Based Design and Applications to Shaping a Hydrofoil

[+] Author and Article Information
J. O. Royset

Professor
Naval Postgraduate School,
Monterey, CA 93943
e-mail: joroyset@nps.edu

L. Bonfiglio, G. Vernengo

Mechanical Engineering,
MIT,
Cambridge, MA 95616

S. Brizzolara

Aerospace and Ocean Engineering,
Virginia Tech,
Blacksburg, VA 24061

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 29, 2016; final manuscript received August 2, 2017; published online August 30, 2017. Assoc. Editor: Irem Tumer. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Mech. Des 139(10), 101403 (Aug 30, 2017) (8 pages) Paper No: MD-16-1799; doi: 10.1115/1.4037623 History: Received November 29, 2016; Revised August 02, 2017

The paper presents a framework for set-based design under uncertainty and demonstrates its viability through designing a super-cavitating hydrofoil of an ultrahigh speed vessel. The framework achieves designs that safely meet the requirements as quantified precisely by superquantile measures of risk (s-risk) and reduces the complexity of design under uncertainty. S-risk ensures comprehensive and decision-theoretically sound assessment of risk and permits a decoupling of parametric uncertainty and surrogate (model) uncertainty. The framework is compatible with any surrogate building technique, but we illustrate it by developing for the first time risk-adaptive surrogates that are especially tailored to s-risk. The numerical results demonstrate the framework in a complex design case requiring multifidelity simulation.

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Figures

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Fig. 1

S-risk Rα(gk(x, V)) is the average of the worst (1−α)100% outcomes (shaded)

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Fig. 2

Time-averaged vapor content contours for the benchmark design; flow is left to right

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Fig. 3

Design variables x=(x1,...,x15). Arrows indicate allowed movement of control points; some are fixed and illustrated by squares. Three control points move together with x15.

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Fig. 4

Low- and high-fidelity estimates of drag-over-lift (meter)

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Fig. 5

Histogram of errors in low-fidelity estimates of drag-over-lift (meter)

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Fig. 6

D2 (triangles for control points) and benchmark (inverted triangles for control points); axis scale in meter

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Fig. 7

D4 (circles for control points) and benchmark (inverted triangles for control points); axis scale in meter

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Fig. 8

Time-averaged vapor content (left column) and pressure coefficients (right column) contours. From top to bottom: benchmark design, D2, and D4. Flow conditions as in Fig. 2.

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