Sharon L. Padula’s research while affiliated with Old Dominion University and other places

The Multidisciplinary Optimization (MDO) Branch at NASA Langley Research Center develops new methods and investigates opportunities for applying optimization to aerospace vehicle design. This paper describes 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 and 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. The present paper will illustrate a variety of needs for this technology, summarize promising methods, and uncover fruitful areas for new research.

This paper explains why high resolution design spaces encourage traditional airfoil optimization algorithms to generate noisy shape modifications, which lead to inaccurate linear predictions of aerodynamic coefficients and potential failure of descent methods. By using auxiliary drag constraints for a simultaneous drag reduction at all design points and the least shape distortion to achieve the targeted drag reduction, an improved algorithm generates relatively smooth optimal airfoils with no severe off-design performance degradation over a range of flight conditions, in high resolution design spaces parameterized by cubic B-spline functions. Simulation results using FUN2D in Euler flows are included to show the capability of the robust aerodynamic shape optimization method over a range of flight conditions.

The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of B-spline control points as design variables yet the resulting airfoil shape is fairly smooth, and (3) it allows the user to make a trade-off between the level of optimization and the amount of computing time consumed. The robust optimization method is demonstrated by solving a lift-constrained drag minimization problem for a two-dimensional airfoil in viscous flow with a large number of geometric design variables.

From time to time, existing aircraft need to be redesigned for new missions with modified operating conditions such as required lift or cruise speed. This research is motivated by the needs of conceptual and preliminary design teams for smooth airfoil shapes that are similar to the baseline design but have improved drag performance over a range of flight conditions. The proposed modified profile optimization method (MPOM) modifies a large number of design variables to search for nonintuitive performance improvements, while avoiding off-design performance degradation. Given a good initial design, the MPOM generates fairly smooth airfoils that are better than the baseline without making drastic shape changes. Moreover, the MPOM allows users to gain valuable information by exploring performance trades over various design conditions. Four simulation cases of airfoil optimization in transonic viscous ow are included to demonstrate the usefulness of the MPOM as a performance trades study tool. Simulation results are obtained by solving fully turbulent Navier-Stokes equations and the corresponding discrete adjoint equations using an unstructured grid computational fluid dynamics code FUN2D.

Free-form shape optimization of airfoils to minimize drag directly poses unexpected difficulties. Practical experience has indicated that a deterministic optimization for discrete operating conditions can result in dramatically inferior performance when the actual operating conditions are different from the (somewhat arbitrarily selected) design conditions used during the optimization. Extensions from single-point to multipoint optimization have proven unable to remedy this problem of localized optimization adequately near the sampled operating conditions. An intrinsically statistical approach is presented, and how the shortcomings of traditional optimization methods can be overcome is demonstrated. We discuss two algorithms. The first one is based on a numerical evaluation of the expectation integral during each optimization step. The second one is based on a closed-form second-order analytic approximation of the integration. The airfoil geometry optimization example (inviscid Euler flow) also reveals how the relative likelihood of each of the operating conditions is automatically taken into consideration during the optimization process. This is a key advantage over the use of multipoint methods.

An airfoil shape optimization method that reduces drag over a range of free stream Mach numbers is sought. We show that one acceptable choice is a weighted multipoint optimization method using more design points than there are free-design variables. Alternate methods that use far fewer design points are explored. A new method called profile optimization is developed and analyzed. This method has three main advantages: (a) it prevents severe degradation at the off-design points by using a smart descent direction, (b) there is no random airfoil shape distortion for any iterate it generates, and (c) it is not sensitive to the number of design points. For illustration purposes, we use the profile optimization method to solve a lift-constrained drag minimization problem for 2-D airfoil in Euler flow with 20 free-design variables. A comparison with other airfoil optimization methods is also included.

The NASA Morphing Project seeks to develop and assess advanced technologies and integrated component concepts to enable efficient, multi-point adaptability in air and space vehicles. In the context of the project, the world 'morphing' is defined as 'efficient, multi-point adaptability' and may include macro, micro, structural and/or fluidic approaches. The project includes research on smart materials, adaptive structures, micro flow control, biomimetic concepts, optimization and controls. This paper presents an updated overview of the content of the Morphing Project including highlights of recent research results.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The NASA Morphing Project seeks to develop and assess advanced technologies and integrated component concepts to enable efficient, multi-point adaptability in air and space vehicles. In the context of the project, the world 'morphing' is defined as 'efficient, multi-point adaptability' and may include macro, micro, structural and/or fluidic approaches. The project includes research on smart materials, adaptive structures, micro flow control, biomimetic concepts, optimization and controls. This paper presents an updated overview of the content of the Morphing Project including highlights of recent research results.

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.