A decomposition-based uncertainty quantification approach for environmental impacts of aviation technology and operation. Issue 3 (3rd August 2017)

• Record Type: Journal Article
• Authors: Amaral, Sergio; Allaire, Douglas; De La Rosa Blanco, Elena; Willcox, Karen E.
• Editors: Ghanem, Roger; Du, Xiaoping
• Is Part Of: AI EDAM. Volume 31:Issue 3(2017)
• Page Start: 251
• Page End: 264
• Publication Date: 2017-08-03
• Subjects: Aviation Environmental Impact, -- Decomposition, -- Global Sensitivity Analysis, -- Uncertainty Quantification; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060417000154
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: As a measure to manage the climate impact of aviation, significant enhancements to aviation technologies and operations are necessary. When assessing these enhancements and their respective impacts on the climate, it is important that we also quantify the associated uncertainties. This is important to support an effective decision and policymaking process. However, such quantification of uncertainty is challenging, especially in a complex system that comprises multiple interacting components. The uncertainty quantification task can quickly become computationally intractable and cumbersome for one individual or group to manage. Recognizing the challenge of quantifying uncertainty in multicomponent systems, we utilize a divide-and-conquer approach, inspired by the decomposition-based approaches used in multidisciplinary analysis and optimization. Specifically, we perform uncertainty analysis and global sensitivity analysis of our multicomponent aviation system in a decomposition-based manner. In this work, we demonstrate how to handle a high-dimensional multicomponent interface using sensitivity-based dimension reduction and a novel importance sampling method. Our results demonstrate that the decomposition-based uncertainty quantification approach can effectively quantify the uncertainty of a feed-forward multicomponent system for which the component models are housed in different locations and owned by different groups.