Genetic Spatial Optimization of Active Elements on an Aeroelastic Delta Wing

Abstract

This work outlines a cohesive approach for the design and implementation of a genetically optimized, active aeroelastic delta wing. Emphasis was placed on computational efficiency of model development and efficient means for optimizing sensor and actuator geometries. Reduced-order models of potential-flow aerodynamics were developed for facilitation of analysis and design of the aeroelastic system in the early design phase. Using these methods, models capturing “95% of the physics with 8% of the modeling effort” can be realized to evaluate various active and passive design considerations. The aeroelastic delta wing model was employed in determining the most effective locations and sizes for transducers required to provide flutter control. The basic design presented is based upon an analytical model of the structure. A comparison of optimization strategies led to the use of a genetic algorithm to determine the optimal transducer locations, sizes, and orientations required to provide effective flutter control based upon an open-loop performance metric. The genetic algorithm and performance metric essentially provided loop shaping through the adaptive structure design. An experimental model was then developed based upon the optimal transducer designs. Wind tunnel tests were performed to demonstrate closed-loop performance for flutter control. Results from this study indicate that a single sensor/actuator pair can be designed to extend the flutter boundary and selectively couple to only those modes required to control the response.

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“…And once nonlinear aeroelastic models have reached a state of maturity sufficient for their consideration in the design process, then active and adaptive control can potentially provide for even greater flight vehicle performance. The discussion of active and adaptive control is beyond the scope of this paper, but the reader may wish to consult the work of Heeg [113], Lazarus, et al [114,115], Ko, et al [68][69][70], Block and Strganac [67], Vipperman, et al [116], Bunton and Denegri [8], Clark et al [117], Frampton et al [118], Rule et al [119], Richards et al [120] and Platanitis and Strganac [121].…”

Reduced-Order Techniques for Sensitivity Analysis and Design Optimization of Aerospace Systems

“…1,2 Methods such as polynomial regression, Kriging, multivariate adaptive regression splines, radial basis functions, artificial neural networks, and support vector machines are all common approaches to providing more economic evaluations of a function. [3][4][5][6][7][8][9][10][11][12][13][14][15][16] The effectiveness of these techniques is closely tied to topics such as design of experiments (DOE), both from general statistics as well as the specific circumstances of computational experiments, as can be seen in various example applications. 15,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] Multidisciplinary analysis and optimization (MDA/MDO), including formal optimization, coupled system theory, and sensitivity analysis, is also relevant to this research work.…”

Structural acoustic control of plates with variable boundary conditions: Design methodology

“…This discrete integration method was previously used to optimize patch locations for suppressing flutter on a delta wing. 24 In this approach, a grid of 4-mm-square piezoceramic elements was distributed across the surface of the plate. The electromechanical coupling and capacitance were calculated at the center of each element and multiplied by the element's area to obtain approximate values for each element.…”

“…The HSVs are useful in the design of performance metrics that quantify the level of controllability and observability related to a system path and have been used in the actuator/sensor design. 27,26,[28][29][30] The overall performance metric involves not only the actuator/sensor path but also the disturbance/performance path of the system ͑see Fig. 3͒.…”