Sharon L. Padula

D-optimal designs for sensor and actuator locations

Active control of noise and vibration is now possible in automobiles, aircraft, and many other devices. Where to place actuators, to control noise and vibration, and sensors, to measure the performance of the actuators, is a central question. Given a truss structure, we seek the k most effective locations to control and/or sense vibrations. A discrete D-optimal design has been proposed as a solution to this location problem. We develop a simple static tabu search and test its performance on an 80 node truss structure built at NASA-Langley Research Center. We show that our tabu search approach dominates the traditional approaches to finding D-optimal designs.

Quelling Cabin Noise in Turboprop Aircraft via Active Control

Cabin noise in turboprop aircraft causes passenger discomfort, airframe fatigue, and employee scheduling constraints due to OSHA standards for exposure to high levels of noise. The noise levels in the cabins of turboprop aircraft are typically 10 to 30 decibels louder than commercial jet noise levels. However, unlike jet noise the turboprop noise spectrum is dominated by a few low frequency tones. Active structural acoustic control is a method in which the control inputs (used to reduce interior noise) are applied directly to a vibrating structural acoustic system. The control concept modeled in this work is the application of in-plane force inputs to piezoceramic patches bonded to the wall of a vibrating cylinder. The goal is to determine the force inputs and locations for the piezoceramic actuators so that (1) the interior noise is effectively damped; (2) the level of vibration of the cylinder shell is not increased; and (3) the power requirements needed to drive the actuators are not excessive. Computational experiments for data taken from a computer generated model and from a laboratory test article at NASA Langley Research Center are provided.

Aircraft Morphing program

In the last decade smart technologies have become enablers that cut across traditional boundaries in materials science and engineering. Here we define smart to mean embedded actuation, sensing, and control logic in a tightly coupled feedback loop. While multiple successes have been achieved in the laboratory, we have yet to see the general applicability of smart devices to real aircraft systems. The NASA Aircraft Morphing program is an attempt to couple research across a wide range of disciplines to integrate smart technologies into high payoff aircraft applications. The program bridges research in seven individual disciplines and combines the effort into activities in three primary program thrusts. System studies are used to assess the highest-payoff program objectives, and specific research activities are defined to address the technologies required for development of smart aircraft systems. In this paper we address the overall program goals and programmatic structure, and discuss the challenges associated with bringing the technologies to fruition.

Aerospace applications of optimization under uncertainity

The multidisciplinary optimization (MDO) branch at NASA Langley Research Center develops new methods and investigates opportunities for applying optimization to aerospace vehicle design. We describe MDO branch experiences with three applications of optimization under uncertainty: (1) improved impact dynamics for airframes, (2) transonic airfoil optimization for low drag, and (3) coupled aerodynamic/structures optimization of a 3-D wing. For each case, a brief overview of the problem and references to previous publications are provided. The three cases are aerospace examples of the challenges and opportunities presented by optimization under uncertainty. We illustrate a variety of needs for this technology, summarize promising methods, and uncover fruitful areas for new research

Actuator Selection for the Control of Multi-Frequency Noise in Aircraft Interiors

In this paper we study the selection of actuator locations for a constrained multi-frequency active noise control optimization problem. Data regarding the effectiveness of each actuator is gathered from the fuselage of a DC-9 aircraft. A constraint is placed limiting the force that can be applied to each actuator. Tabu search is used to select locations for the actuators and a quadratically constrained complex least squares problem must be solved to determine the control forces for the selected actuators. S.l INTRODUCTION Tabu search and, more generally, adaptive memory programming continues to have startling success in efficiently generating high-quality solutions to difficult practical optimization problems. (4) provides forty-two vignettes, each of which describes a different application of tabu search by researchers and practitioners. The applications include standard problems such as the p-median problem, job shop scheduling and the quadratic assignment problem, as well as unusual applications including polymer chemistry, forest management, and the control of flexible space structures. Here, we study an optimization problem that arises in active noise control of interior noise in a turboprop aircraft. In Section 8.2, we give a brief introduc-

Probabilistic Analysis and Design of a Raked Wing Tip for a Commercial Transport

An approach for conducting reliability-based design and optimization (RBDO) of a Boeing 767 raked wing tip (RWT) is presented. The goal is to evaluate the benefits of RBDO for design of an aircraft substructure. A finite-element (FE) model that includes eight critical static load cases is used to evaluate the response of the wing tip. Thirteen design variables that describe the thickness of the composite skins and stiffeners are selected to minimize the weight of the wing tip. A strain-based margin of safety is used to evaluate the performance of the structure. The randomness in the load scale factor and in the strain limits is considered. Of the 13 variables, the wing-tip design was controlled primarily by the thickness of the thickest plies in the upper skins. The report includes an analysis of the optimization results and recommendations for future reliability-based studies.