MDO Techniques and Novel Aircraft Control Systems
James L. Rogers and Sharon L. Padula
Multidisciplinary Optimization Branch, NASA Langley Research Center
July 2001
TA 706-32-21-01
Research Objective. To develop methods for design of novel aircraft control systems, especially those with large numbers of shape change effectors rather than conventional flaps. These innovative control system concepts enable new mission scenarios by reducing both airframe noise and radar cross-section. This approach is applicable to revolutionary aircraft concepts for which a computational fluid dynamics (CFD) model exists.
Approach. The approach is demonstrated on the Lockheed Martin ICE model shown in the
figure. Given a low fidelity model of the ICE configuration the process consists of three steps. First, use automatic differentiation of the PMARC CFD code to predict the change in pitch, roll and yaw moments with respect to a change in height of any single effector. The predicted sensitivity derivatives can be processed by MATLAB to produce six color contour plots of control effectiveness. Second, study the contour plots and identify a subset of 350 candidate effectors. Use the sensitivity derivatives in MATLAB plus an estimate of the maximum attainable shape change to predict the global pitch, roll and yaw moments for any combination of multiple effectors. Third, use a genetic algorithm (GA) to find the best set of effectors from the 350 candidate locations. The goal is to minimize the number of effectors required to complete all maneuvers specified by the controls expert.Accomplishment Description. One excellent combination for the effectors is illustrated in the
figure. The green stars indicate single effector locations for which sensitivity derivatives are available. The black circles indicate the selected locations on the upper surface of the right wing. The GA evaluated 150,000 combinations of effectors out of about 3x1099 possible combinations. The GA approach rapidly provides the controls expert with promising designs for further evaluation.Significance. Control system design involving large numbers of distributed effectors is extremely challenging. Typically, design is postponed until wind tunnel tests provide control effectiveness data. Now, control system designers can influence aircraft configuration decisions before test data is available. Contour plots provide information for manual selection of effectors while the GA provides an automatic placement capability.
Future Plans. The GA optimization method is appropriate whenever discrete choices need to be made. For example, a GA linked to a detailed finite element model can select the best locations for piezoelectric actuators in morphing aircraft. Similarly, a GA can be linked to spreadsheets used for conceptual design of micro air vehicles. These and several other multidisciplinary applications of discrete optimization will be explored next year.
Figure:
MDO Techniques and Novel Aircraft Control SystemsNASA POC: Thomas A. Zang
Telephone: (757) 864-2307E-Mail: t.a.zang@larc.nasa.gov
Page Curator: D. H. Rudy
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Date last updated: April 20, 2006