Uncertainty management in the design of multiscale systems
By: Sinha, Ayan
.
Contributor(s): Bera, Nilanjan.
Publisher: New York ASME 2013Edition: Vol.135(1), Jan.Description: 1-16p.Subject(s): Mechanical EngineeringOnline resources: Click here
In:
Journal of mechanical designSummary: 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.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2024-0639 |
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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