Reduced-Order Modeling Applied to the Aviation Environmental Design Tool for Rapid Noise Prediction
Publication: Journal of Aerospace Engineering
Volume 31, Issue 5
Abstract
The aviation environmental design tool (AEDT) is simulation software for estimating the environmental ramifications of aircraft. Because the AEDT would be repeatedly used for applications such as airport noise assessment, a computational cost may arise as a concern for aviation environmental research. To expedite noise evaluation using the AEDT, this research devised a rapid approximation of the AEDT via reduced-order modeling (ROM) because the technique is effective in approximating large-dimensional data. In particular, proper orthogonal decomposition (POD) and ordinary Kriging were leveraged for orthonormal basis extraction and basis coefficient prediction, respectively. For demonstration, a reduced-order AEDT model was developed by associating two AEDT outputs—departure and approach noise—with five atmospheric parameters—elevation, temperature, pressure, relative humidity, and headwind—for single departure and arrival flights using straight tracks. With the help of POD, the reduced-order AEDT noise model was formed based on the first three leading basis vectors, and the three corresponding basis coefficients were estimated by separate Kriging models according to atmospheric variation. After the model construction, the reduced-order AEDT noise model was verified using the training and testing data sets and exhibited reliable approximation capability. Overall, the employed POD-based ROM with Kriging was able to tremendously accelerate the AEDT noise simulation, thus encouraging the use of the AEDT for aviation environmental impact studies, particularly those involving many airports operating a multitude of flights.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research was supported by the Engineering Research Center Program and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (No. 2012R1A5A1048294) and the Ministry of Education (No. NRF-2016R1D1A1B03930126), respectively.
References
Abebe, M., K. Lee, and B.-S. Kang. 2016. “Surrogate-based multi-point forming process optimization for dimpling and wrinkling reduction.” Int. J. Adv. Manuf. Technol. 85 (1–4): 391–403. https://doi.org/10.1007/s00170-015-7897-1.
Ahearn, M., et al. 2017. “Aviation environmental design tool (AEDT): Technical manual, version 2c service pack 2.” Accessed August 13, 2017. https://aedt.faa.gov/Documents/AEDT2cSP2_TechManual.pdf.
Allaire, D., G. Noel, K. Willcox, and R. Cointin. 2014. “Uncertainty quantification of an aviation environmental toolsuite.” Reliability Eng. Syst. Saf. 126: 14–24. https://doi.org/10.1016/j.ress.2014.01.002.
Allaire, D., and K. Willcox. 2010. “Surrogate modeling for uncertainty assessment with application to aviation environmental system models.” AIAA J. 48 (8): 1791–1803. https://doi.org/10.2514/1.J050247.
Bai, Z. 2002. “Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems.” Appl. Numer. Math. 43 (1–2): 9–44. https://doi.org/10.1016/S0168-9274(02)00116-2.
Bernardo, J. E., M. Kirby, and D. Mavris. 2014. “Development of a rapid fleet-level noise computation model.” J. Aircr. 52 (3): 721–733. https://doi.org/10.2514/1.C032503.
Couckuyt, I., F. Declercq, T. Dhaene, H. Rogier, and L. Knockaert. 2010. “Surrogate-based infill optimization applied to electromagnetic problems.” Int. J. RF Microwave Comput. Aided Eng. 20 (5): 492–501. https://doi.org/10.1002/mmce.20455.
Couckuyt, I., T. Dhaene, and P. Demeester. 2014. “ooDACE toolbox: A flexible object-oriented Kriging implementation.” J. Mach. Learn. Res. 15: 3183–3186.
Cressie, N. 2015. Statistics for spatial data. New York, NY: Wiley.
Du, K.-L., and M. N. Swamy. 2006. Neural networks in a softcomputing framework. London, UK: Springer.
Fukunaga, K. 2013. Introduction to statistical pattern recognition. New York, NY: Academic Press.
Giunta, A. A. 1997. “Aircraft multidisciplinary design optimization using design of experiments theory and response surface modeling methods.” Ph.D. thesis, Virginia Polytechnic Institute and State Univ.
Jolliffe, I. T. 1986. “Principal component analysis and factor analysis.” In Principal component analysis, 115–128. New York, NY: Springer.
Kang, K. T., and S. Lee. 2017. “Modeling and assessment of jet interaction database for continuous-type side jet.” J. Spacecraft Rockets 54 (4): 916–929. https://doi.org/10.2514/1.A33807.
Lee, K., T. Nam, C. Perullo, and D. N. Mavris. 2011. “Reduced-order modeling of a high-fidelity propulsion system simulation.” AIAA J. 49 (8): 1665–1682. https://doi.org/10.2514/1.J050887.
LeVine, M. J., A. Kaul, J. E. Bernardo, M. R. Kirby, and D. Mavris. 2013. “Methodology for calibration of ANGIM subjected to atmospheric uncertainties.” In Proc., Aviation Technology, Integration, and Operations Conf. Reston, VA: American Institute of Aeronautics and Astronautics.
Liang, Y., H. Lee, S. Lim, W. Lin, K. Lee, and C. Wu. 2002. “Proper orthogonal decomposition and its applications—Part I: Theory.” J. Sound Vibr. 252 (3): 527–544. https://doi.org/10.1006/jsvi.2001.4041.
McKay, M. D., R. J. Beckman, and W. J. Conover. 1979. “Comparison of three methods for selecting values of input variables in the analysis of output from a computer code.” Technometrics 21 (2): 239–245. https://doi.org/10.1080/00401706.1979.10489755.
Mifsud, M., S. Shaw, and D. MacManus. 2010. “A high-fidelity low-cost aerodynamic model using proper orthogonal decomposition.” Int. J. Numer. Methods Fluids 63 (4): 468–494.
Myers, R. H., D. C. Montgomery, and C. M. Anderson-Cook. 2016. Response surface methodology: Process and product optimization using designed experiments. New York, NY: Wiley.
Pinnau, R. 2008. “Model reduction via proper orthogonal decomposition.” In Model order reduction: Theory, research aspects and applications, edited by W. H. Schilders, H. A. van der Vorst, and J. Rommes, 95–109. New York, NY: Springer.
Rasmussen, C. E., and C. K. Williams. 2006. In Vol. 1 of Gaussian processes for machine learning. Cambridge, MA: MIT Press.
Raveh, D. E. 2001. “Reduced-order models for nonlinear unsteady aerodynamics.” AIAA J. 39 (8): 1417–1429. https://doi.org/10.2514/2.1473.
Schilders, W. H., H. A. Van der Vorst, and J. Rommes. 2008. In Vol. 13 of Model order reduction: Theory, research aspects and applications. Berlin, Germany: Springer.
Simpson, T. W., T. M. Mauery, J. J. Korte, and F. Mistree. 1998. “Comparison of response surface and Kriging models for multidisciplinary design optimization.” In Proc., 7th AIAA/USAF/NASA/ISSMO Symp. on Multidisciplinary Analysis and Optimization, Multidisciplinary Analysis Optimization Conf. Reston, VA: American Institute of Aeronautics and Astronautics.
Sirovich, L. 1987. “Turbulence and the dynamics of coherent structures. I: Coherent structures.” Q. Appl. Math. 45 (3): 561–571. https://doi.org/10.1090/qam/910462.
Zanella, P. 2017. “Sensitivity analysis for noise and emissions based on parametric departure and approach tracks.” In Proc., 17th AIAA Aviation Technology, Integration, and Operations Conf. Reston, VA: American Institute of Aeronautics and Astronautics.
Information & Authors
Information
Published In
Copyright
©2018 American Society of Civil Engineers.
History
Received: Apr 5, 2017
Accepted: Dec 28, 2017
Published online: Jun 9, 2018
Published in print: Sep 1, 2018
Discussion open until: Nov 9, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.