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As we strive to establish a long-term presence in space, it is crucial to plan large-scale space missions and campaigns with future uncertainties in mind. However, integrated space mission planning, which simultaneously considers mission planning and spacecraft design, faces significant challenges when dealing with uncertainties; this problem is formulated as a stochastic mixed integer nonlinear program (MINLP), and solving it using the conventional method would be computationally prohibitive for realistic applications. Extending a deterministic decomposition method from our previous work, we propose a novel and computationally efficient approach for integrated space mission planning under uncertainty. The proposed method effectively combines the Alternating Direction Method of Multipliers (ADMM)-based decomposition framework from our previous work, robust optimization, and two-stage stochastic programming (TSSP).This hybrid approach first solves the integrated problem deterministically, assuming the worst scenario, to precompute the robust spacecraft design. Subsequently, the two-stage stochastic program is solved for mission planning, effectively transforming the problem into a more manageable mixed-integer linear program (MILP). This approach significantly reduces computational costs compared to the exact method, but may potentially miss solutions that the exact method might find. We examine this balance through a case study of staged infrastructure deployment on the lunar surface under future demand uncertainty. When comparing the proposed method with a fully coupled benchmark, the results indicate that our approach can achieve nearly identical objective values (no worse than 1% in solved problems) while drastically reducing computational costs.
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Robust Topology Design Optimization Based on Dimensional Decomposition Method
Abstract This paper presents an efficient approach for robust topology design optimization (RTO) which is based on polynomial dimensional decomposition (PDD) method. The level-set functions are adopted to facilitate the topology changes and shape variations. The topological derivatives of the functionals of robustness root in the concept of deterministic topological derivatives and dimensional decomposition of stochastic responses of multiple random inputs. The PDD for calculating robust topological derivatives consists of only a number of evaluations of the deterministic topological derivatives at the specified points in the stochastic space and provides effective and efficient design sensitivity analyses for RTO. The numerical examples demonstrate the effectiveness of the present method.
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- Award ID(s):
- 1635167
- PAR ID:
- 10109773
- Date Published:
- Journal Name:
- Mechanics and Engineering – Numerical Calculation and Data analysis , 2019 in Beijing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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