This paper reports the results of a recently completed real-time adaptive drag minimization wind tunnel investigation of a highly flexible wing wind tunnel model equipped with the Variable Camber Continuous Trailing Flap (VCCTEF) technology at the University of Washington Aeronautical Laboratory (UWAL). The wind tunnel investigation is funded by NASA SBIR Phase II contract with Scientific Systems Company, Inc. (SSCI) and University ofWashington (UW) as a subcontractor. The wind tunnel model is a sub-scale Common Research Model (CRM) wing constructed of foam core and fiberglass skin and is aeroelastically scaled to achieve a wing tip deflection of 10% of the wing semi-span which represents a typical wing tip deflection for a modern transport such as Boeing 787. The jig-shape twist of the CRM wing is optimized using a CART3D aero-structural model to achieve the minimum induced drag for the design cruise lift coefficient of 0.5. The wing is equipped with two chordwise cambered segments for each of the six spanwise flap sections for a total of 12 individual flap segments that comprise the VCCTEF system. Each of the 12 flap segments is actively controlled by an electric servo-actuator. The real-time adaptive drag optimization strategy includes an on-board aerodynamic model identification, a model excitation, and a real-time drag optimization. The on-board aerodynamic model is constructed parametrically as a function of the angle of attack and flap positions to model the lift and drag coefficients of the wing. The lift coefficient models include a linear model and a second-order model. The drag coefficient models include a quadratic model and a higher-order up to 6th-order model to accurately model the drag coefficient at high angles of attack. The onboard aerodynamic model identification includes a recursive least-squares (RLS) algorithm and a batch least-squares (BLS) algorithm designed to estimate the model parameters. The model excitation method is designed to sample the input set that comprises the angle of attack and the flap positions. Three model excitation methods are developed: random excitation method, sweep method, and iterative angle-of-attack seeking method. The real-time drag optimization includes a generic algorithm developed by SSCI and several optimization methods developed by NASA which include a second-order gradient Newton-Raphson optimization method, an iterative gradient optimization method, a pseudo-inverse optimization method, an analytical optimization method, and an iterative refinement optimization method. The first wind tunnel test entry took place in September 2017. This test revealed major hardware issues and required further redesign of the flap servo mechanisms. The second test entry took place in April 2018. However, the test was not successful due to the issues with the onboard aerodynamic model identification RLS algorithm which incorrectly identified model parameters. This test also provides an experimental comparison study between the VCCTEF and a variable camber discrete trailing edge flap (VCDTEF) without the elastomer transition mechanisms. The experimental result confirms the benefit of the VCCTEF which produces lower drag by 5% than the VCDTEF. The third and final test entry took place in June 2018 after the issues with the RLS algorithm have been identified and corrected. Additional improvements were implemented. These include the BLS algorithm, the iterative angle-of-attack seeking method, the iterative gradient optimization method, and the pseudo-inverse optimization method. The test objectives were successfully demonstrated as the real-time drag optimization identifies several optimal solutions at off-design lift coefficients. The iterative gradient optimization method is found to achieve up to 4.7% drag reduction for the off-design lift coefficient of 0.7. The pseudo-inverse optimization method which does not require the drag coefficient model is found to be quite effective in reducing drag. Up to 9.4% drag reduction for the off-design lift coefficient of 0.7 is achieved with the pseudo-inverse optimization method. The wind tunnel investigation demonstrates the potential of real-time drag optimization technology. Several new capabilities are developed that could enable future adaptive wing technologies for flexible wings equipped with drag control devices such as the VCCTEF. The authors wish to acknowledge NASA Advanced Air Transport Technologies project for the funding support of this work.
|Original language||English (US)|
|Title of host publication||AIAA Aviation 2019 Forum|
|Publisher||American Institute of Aeronautics and Astronautics Inc, AIAA|
|Number of pages||47|
|State||Published - 2019|
|Event||AIAA Aviation 2019 Forum - Dallas, United States|
Duration: Jun 17 2019 → Jun 21 2019
|Name||AIAA Aviation 2019 Forum|
|Conference||AIAA Aviation 2019 Forum|
|Period||6/17/19 → 6/21/19|
Bibliographical noteFunding Information:
The authors wish to acknowledge NASA Advanced Air Transport Technologies project for the funding support of this work. The authors also acknowledge the funding for the wind tunnel experiment under NASA SBIR Phase II Contract NNX15CA16C
The real-time drag optimization wind tunnel test is funded by a NASA SBIR Phase II contract with Scientific Systems Company, Inc. (SSCI) and University of Washington (UW) as a sub-contractor. This wind tunnel test comprises three different test entries which took place in the UWAL in 2017 and 2018. Unlike the first two wind tunnel test campaigns, the VCCTEF is actively controlled by servo-actuator mechanisms in the real-time drag optimization wind tunnel test. As a result, the wind tunnel model is a much more complex mechanism than the previous wind tunnel models. The first wind tunnel test entry took place in September 2017. This test revealed major hardware issues and required further redesign of the flap actuators. The second test entry took place in April 2018 when the real-time adaptive drag optimization was first conducted. However, the test objectives were not met due to the issues with the onboard aerodynamic model identification algorithm which incorrectly identified model parameters due to incorrect settings in the software algorithm. As a result, the real-time drag optimization failed to find optimal solutions. The third and final test entry took place in June 2018 after the issues with the onboard aerodynamic model identification algorithm have been identified and corrected. The test objectives were successfully demonstrated this time as the real-time drag optimization was able to identify several optimal solutions.
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