Multifidelity optimization under uncertainty for a tailless aircraft
This paper presents a multifidelity method for optimization under uncertainty for aerospace problems. In this work, the effectiveness of the method is demonstrated for the robust optimization of a tailless aircraft that is based on the Boeing Insitu ScanEagle. Aircraft design is often affected by un...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
American Institute of Aeronautics and Astronautics
2018
|
| Online Access: | http://hdl.handle.net/1721.1/115165 https://orcid.org/0000-0002-2281-3067 https://orcid.org/0000-0003-2156-9338 |
| _version_ | 1826211013828018176 |
|---|---|
| author | Jasa, John Martins, Joaquim R. R. A. Chaudhuri, Anirban Willcox, Karen E |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Jasa, John Martins, Joaquim R. R. A. Chaudhuri, Anirban Willcox, Karen E |
| author_sort | Jasa, John |
| collection | MIT |
| description | This paper presents a multifidelity method for optimization under uncertainty for aerospace problems. In this work, the effectiveness of the method is demonstrated for the robust optimization of a tailless aircraft that is based on the Boeing Insitu ScanEagle. Aircraft design is often affected by uncertainties in manufacturing and operating conditions. Accounting for uncertainties during optimization ensures a robust design that is more likely to meet performance requirements. Designing robust systems can be computationally prohibitive due to the numerous evaluations of expensive-to-evaluate high-fidelity numerical models required to estimate system-level statistics at each optimization iteration. This work uses a multifidelity Monte Carlo approach to estimate the mean and the variance of the system outputs for robust optimization. The method uses control variates to exploit multiple fidelities and optimally allocates resources to different fidelities to minimize the variance in the estimates for a given budget. The results for the ScanEagle application show that the proposed multifidelity method achieves substantial speed-ups as compared to a regular Monte-Carlo-based robust optimization. |
| first_indexed | 2024-09-23T14:59:03Z |
| format | Article |
| id | mit-1721.1/115165 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:59:03Z |
| publishDate | 2018 |
| publisher | American Institute of Aeronautics and Astronautics |
| record_format | dspace |
| spelling | mit-1721.1/1151652022-09-29T11:54:06Z Multifidelity optimization under uncertainty for a tailless aircraft Jasa, John Martins, Joaquim R. R. A. Chaudhuri, Anirban Willcox, Karen E Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Chaudhuri, Anirban Willcox, Karen E This paper presents a multifidelity method for optimization under uncertainty for aerospace problems. In this work, the effectiveness of the method is demonstrated for the robust optimization of a tailless aircraft that is based on the Boeing Insitu ScanEagle. Aircraft design is often affected by uncertainties in manufacturing and operating conditions. Accounting for uncertainties during optimization ensures a robust design that is more likely to meet performance requirements. Designing robust systems can be computationally prohibitive due to the numerous evaluations of expensive-to-evaluate high-fidelity numerical models required to estimate system-level statistics at each optimization iteration. This work uses a multifidelity Monte Carlo approach to estimate the mean and the variance of the system outputs for robust optimization. The method uses control variates to exploit multiple fidelities and optimally allocates resources to different fidelities to minimize the variance in the estimates for a given budget. The results for the ScanEagle application show that the proposed multifidelity method achieves substantial speed-ups as compared to a regular Monte-Carlo-based robust optimization. United States. Air Force. Office of Scientific Research. Multidisciplinary University Research Initiative (FA9550-15-1-0038) 2018-05-02T16:33:01Z 2018-05-02T16:33:01Z 2018-01 2018-04-17T13:44:25Z Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/115165 Chaudhuri, Anirban, et al. "Multifidelity Optimization Under Uncertainty for a Tailless Aircraft." 2018 AIAA Non-Deterministic Approaches Conference, American Institute of Aeronautics and Astronautics, 8-12 January, 2018, Kissimmee, Florida, AIAA, 2018. © 2018 American Institute of Aeronautics and Astronautics Inc. https://orcid.org/0000-0002-2281-3067 https://orcid.org/0000-0003-2156-9338 https://doi.org/10.2514/6.2018-1658 2018 AIAA Non-Deterministic Approaches Conference Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Institute of Aeronautics and Astronautics MIT Web Domain |
| spellingShingle | Jasa, John Martins, Joaquim R. R. A. Chaudhuri, Anirban Willcox, Karen E Multifidelity optimization under uncertainty for a tailless aircraft |
| title | Multifidelity optimization under uncertainty for a tailless aircraft |
| title_full | Multifidelity optimization under uncertainty for a tailless aircraft |
| title_fullStr | Multifidelity optimization under uncertainty for a tailless aircraft |
| title_full_unstemmed | Multifidelity optimization under uncertainty for a tailless aircraft |
| title_short | Multifidelity optimization under uncertainty for a tailless aircraft |
| title_sort | multifidelity optimization under uncertainty for a tailless aircraft |
| url | http://hdl.handle.net/1721.1/115165 https://orcid.org/0000-0002-2281-3067 https://orcid.org/0000-0003-2156-9338 |
| work_keys_str_mv | AT jasajohn multifidelityoptimizationunderuncertaintyforataillessaircraft AT martinsjoaquimrra multifidelityoptimizationunderuncertaintyforataillessaircraft AT chaudhurianirban multifidelityoptimizationunderuncertaintyforataillessaircraft AT willcoxkarene multifidelityoptimizationunderuncertaintyforataillessaircraft |