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A decomposition-based uncertainty quantification approach for environmental impacts of aviation technology and operation

Published online by Cambridge University Press:  03 August 2017

Sergio Amaral ,
Douglas Allaire ,
Elena De La Rosa Blanco  and
Karen E. Willcox
Sergio Amaral
Affiliation:
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Douglas Allaire
Affiliation:
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Elena De La Rosa Blanco
Affiliation:
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Karen E. Willcox*
Affiliation:
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
*
Reprint requests to: Karen E. Willcox, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA. E-mail: [email protected]
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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.

Keywords

Aviation Environmental ImpactDecompositionGlobal Sensitivity AnalysisUncertainty Quantification
Type
Special Issue Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 251 - 264
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

Amaral, S., Allaire, D., & Willcox, K. (2014). A decomposition-based approach to uncertainty analysis of feed-forward multicomponent systems. International Journal for Numerical Methods in Engineering 100(13), 982–1005.CrossRefGoogle Scholar
Amaral, S., Allaire, D., & Willcox, K. (2016). Optimal l2-norm empirical importance weights for the change of probability measure. Statistics and Computing Journal 100(13), 982–1005.Google Scholar
Arnst, M., Ghanem, R., Phipps, E., & Red-Horse, J. (2012a). Dimension reduction in stochastic modeling of coupled problems. International Journal for Numerical Methods in Engineering 92(11), 940–968.Google Scholar
Arnst, M., Ghanem, R., Phipps, E., & John Red-Horse, J. (2012b). Measure transformation and efficient quadrature in reduced-dimensional stochastic modeling of coupled problems. International Journal for Numerical Methods in Engineering 92(12), 1044–1080.Google Scholar
Arnst, A., Ghanem, R., Phipps, E., & Red-Horse, J. (2014). Reduced chaos expansions with random coefficients in reduced-dimensional stochastic modeling of coupled problems. International Journal for Numerical Methods in Engineering 97(5), 352–376.Google Scholar
Borgonovo, E. (2007). A new uncertainty importance measure. Reliability Engineering & System Safety 92(6), 771–784.Google Scholar
Borgonovo, E., Castaings, W., & Tarantola, S. (2011). Moment independent importance measures: new results and analytical test cases. Risk Analysis 31(3), 404–428.Google Scholar
Braun, R., & Kroo, I. (1997). Development and application of the collaborative optimization architecture in a multidisciplinary. SIAM Journal of Optimization 80, 98–116.Google Scholar
Chaudhuri, A., & Willcox, K. (2016). Multifidelity uncertainty propagation in coupled multidisciplinary systems. Proc. 18th AIAA Non-Deterministic Approaches Conf., San Diego, CA.Google Scholar
Constantine, P., Phipps, E., & Wildey, T. (2014). Efficient uncertainty propagation for network multiphysics systems. International Journal for Numerical Methods in Engineering 99(3), 183–202.Google Scholar
Cumpsty, N., Alonso, J., Eury, S., Maurice, L., Nas, B.-O., Ralph, M., & Sawyer, R. (2010). Report of the independent experts on fuel burn reduction technology goals. Proc. Int. Civil Aviation Organization (ICAO), Committee on Aviation Environmental Protection (CAEP), Montreal: International Civil Aviation Organization.Google Scholar
Drela, M. (2010). Simultaneous optimization of the airframe, powerplant, and operation of transport aircraft. Proc. 2nd Aircraft Structural Design Conf., Hamilton Place, London.Google Scholar
International Civil Aviation Organization. (2010 a). Report of the Committee on Aviation Environmental Protection, 8th Meeting (Technical Report CAEP/8-IP/30). Montreal: Author.Google Scholar
International Civil Aviation Organization. (2010 b). 37th Session of the ICAO Assembly, Resolution A37-19: Consolidated Statement of Continuing ICAO Policies and Practices Related to Environmental Protection Climate Change (Technical Report). Montreal: Author.Google Scholar
Kim, H.-M., Michelena, N., Papalambros, P., & Tao Jiang, T. (2003). Target cascading in optimal system design. Journal of Mechanical Design 125(3), 474–480.Google Scholar
Koopmann, J., & Ahearn, M. (2012). Aviation Environmental Design Tool (AEDT) 2a: Technical Manual (Technical Report). Washington, DC: Federal Aviation Administration.Google Scholar
Kroo, I. (2004). Distributed multidisciplinary design and collaborative optimization. In VKI Lecture Series on Optimization Methods & Tools for Multicriteria/Multidisciplinary Design, pp. 1–22, November 15–19.Google Scholar
Lee, D., Fahey, D., Forster, P., Newton, P., Wit, R., Lim, L., Owen, B., & Sausen, R. (2009). Aviation and global climate change in the 21st century. Atmospheric Environment 43(22), 3520–3537.Google Scholar
Liu, Y., Yin, X., Arendt, P., Chen, W., & Huang, H.Z. (2010). A hierarchical statistical sensitivity analysis method for multilevel systems with shared variables. Journal of Mechanical Design 132(3), 031006.Google Scholar
Martin, J., & Simpson, T. (2006). A methodology to manage system-level uncertainty during conceptual design. Journal of Mechanical Design 128(4), 959–968.CrossRefGoogle Scholar
Noel, G., Allaire, D., Jacobson, S., Willcox, K., & Cointin, R. (2009). Assessment of the aviation environmental design tool. Proc. 8th USA/Europe Air Traffic Management Research and Development Seminar (ATM2009), Vol. 29, Napa, CA.Google Scholar
Nuic, A. User Manual for the Base of Aircraft Rata (BADA) Revision 3.10. (2010). Brussels: Eurocontrol.Google Scholar
Rahman, S. (2014). A generalized ANOVA dimensional decomposition for dependent probability measures. SIAM/ASA Journal on Uncertainty Quantification 2, 670–697.CrossRefGoogle Scholar
Roof, C., Hansen, A., Fleming, G., Thrasher, T., Nguyen, A., Hall, C., Grandi, F., Kim, B., Usdrowski, S., & Hollingsworth, P. (2007). Aviation Environmental Design Tool (AEDT) System Architecture (Doc. AEDT-AD-01). Washington, DC: Federal Aviation Administration.Google Scholar
Rutherford, D., & Zeinali, M. (2009). Efficiency Trends for New Commercial Jet Aircraft 1960 to 2008. Washington, DC: International Council on Clean Transportation.Google Scholar
Saltelli, A. (2005). Global sensitivity analysis: an introduction. Proc. 4th Int. Conf. Sensitivity Analysis of Model Output (SAMO04), pp. 27–43, Sante Fe, NM.Google Scholar
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Hoboken, NJ: Wiley.Google Scholar
Sankararaman, S., & Mahadevan, S. (2012). Likelihood-based approach to multidisciplinary analysis under uncertainty. Journal of Mechanical Design 134(3), 031008, 1–12.Google Scholar
Smith, R. (2013). Uncertainty Quantification: Theory, Implementation, and Applications. Philadelphia, PA: Society for Industrial and Applied Mathematics.Google Scholar
Sobieszczanski-Sobieski, J., Agte, J., & Sandusky, R. (2000). Bilevel integrated system synthesis. AIAA Journal 38(1), 164–172.Google Scholar
Yao, W., Chen, X., Luo, W., van Tooren, M., & Guo, J. (2011). Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles. Progress in Aerospace Sciences 47(6), 450–479.Google Scholar
Yin, X., & Chen, W. (2008). A hierarchical statistical sensitivity analysis method for complex engineering systems design. Journal of Mechanical Design 130(7), 071402.Google Scholar