Research Papers

Uncertainty Management in the Design of Multiscale Systems

[+] Author and Article Information
Ayan Sinha

The George W. Woodruff School of Mechanical Engineering, Georgia Tech, Atlanta, GA 30332-0405e-mail: sinha12@purdue.edu

Nilanjan Bera

Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India 721302 e-mail: nilanjan.bera@tatamotors.com

Janet K. Allen

The Systems Realization Laboratory, The University of Oklahoma, Norman, OK 73019e-mail: janet.allen@ou.edu

Jitesh H. Panchal

The Collective Systems Laboratory, Washington State University, Pullman, Washington 99164 e-mail: panchal@wsu.edu

Farrokh Mistree

The Systems Realization Laboratory, The University of Oklahoma, Norman, OK 73019e-mail: farrokh.mistree@ou.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received February 8, 2011; final manuscript received February 6, 2012; published online December 21, 2012. Assoc. Editor: Michael Kokkolaras.

J. Mech. Des 135(1), 011008 (Dec 21, 2012) (16 pages) Paper No: MD-11-1108; doi: 10.1115/1.4006186 History: Received February 08, 2011; Revised February 06, 2012

In this paper, the opportunities for managing uncertainty in simulation-based design of multiscale systems are explored using constructs from information management and robust design. A comprehensive multiscale design problem, the concurrent design of material and product is used to demonstrate our approach. The desired accuracy of the simulated performance is determined by the trade-off between computational cost for model refinement and the benefits of mitigated uncertainty from the refined models. Our approach consists of integrating: (i) a robust design method for multiscale systems and (ii) an improvement potential based approach for quantifying the cost-benefit trade-off for reducing uncertainty in simulation models. Specifically, our approach focuses on allocating resources for reducing model parameter uncertainty arising due to insufficient data from simulation models. Using this approach, system level designers can efficiently allocate resources for sequential simulation model refinement in multiscale systems.

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Fig. 1

Uncertainty in simulation models. A hypothetical physical phenomenon is represented by a solid curve. A simulation model with simplifications and assumptions predicts this phenomenon under model structure uncertainty (- - - curve). Running the simulation model at a small number of inputs (star points) and building a metamodel to represent the input–output relationship introduces model parameter uncertainty (— — — curve).

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Fig. 2

Simulation model refinement for uncertainty management in multiscale systems

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Fig. 3

Robust design of multiscale systems using IDEM. (This figure expands Steps 1–3 in Fig. 2).

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Fig. 4

Multiscale system for concurrent design of material and product. The various modules correspond to the hierarchy for materials design. MODULES 1 and 2 simulate the structure-processing conditions in the lowest level of the hierarchy. MODULE 3 deals with the structure-property relationship and MODULE 4 simulated the property-performance relationship.

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Fig. 5

IDEM for multiscale design of submersible vehicle.

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Fig. 6

Feasible property design space (MODULE 4). HD_EMI values are shown on the color bar.

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Fig. 7

Feasible structure design space (MODULE 3). HD_EMI values are shown on the color bar.

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Fig. 8

Feasible and robust processing design space (MODULES 1 and 2). HD_EMI values are shown on the color bar.

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Fig. 9

Schematic for improvement potential trade-off. (This figure expands Steps 5–7 shown in Fig. 2).

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Fig. 10

Robust processing spaces for 0.25, 0.20, and 0.15 convergence criteria. HD_EMI values are shown on the color bar.




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