Efficient analytical and computational tools for simultaneous optimal design of the structural and control components of aeroservoelastic systems are presented. The optimization objective is to achieve aircraft perfor...
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Efficient analytical and computational tools for simultaneous optimal design of the structural and control components of aeroservoelastic systems are presented. The optimization objective is to achieve aircraft performance requirements and sufficient flutter and control stability margins with a minimal weight penalty and without violating the design constraints. Analytical sensitivity derivatives facilitate an efficient optimization process that allows a relatively large number of design variables. Standard finite element and unsteady aerodynamic routines are used to construct a modal data base. Minimum-state aerodynamic approximations and dynamic residualization methods are used to construct a high-accuracy, low-order aeroservoelastic model. Sensitivity derivatives of flutter dynamic pressure, control stability margins, and control effectiveness with respect to structural and control design variables are presented. A gradient-based constrained optimization algorithm is used to minimize an overall cost function. A realistic numerical example of a composite wing with four controls is used to demonstrate the modeling technique, the optimization process, and their accuracy and efficiency.
This paper presents the generalized compound scaling algorithm and its application to optimum weight design of plate structures. The optimum designs are reached by simply scaling the design variables to an optimum int...
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This paper presents the generalized compound scaling algorithm and its application to optimum weight design of plate structures. The optimum designs are reached by simply scaling the design variables to an optimum intersection of multiple constraints. A four-noded isoparametric plate element is used for modeling the structure. Sensitivity computations and the optimization algorithm are discussed. The optimization cost involved (excluding the finite element analyses) is very small using this algorithm. The procedure is demonstrated on three example problems using stress and displacement constraints with side bounds on the design variables.
optimization procedures allow one to design a spur gear reduction for maximum life and other end-use criteria. A modified feasible directions search algorithm permits a wide variety of inequality constraints and exact...
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optimization procedures allow one to design a spur gear reduction for maximum life and other end-use criteria. A modified feasible directions search algorithm permits a wide variety of inequality constraints and exact design requirements to be met with low sensitivity to initial guess values. The optimization algorithm is described and the models for gear life and performance are presented. The algorithm is compact and has been programmed for execution on a desktop computer. In the program, the designer is given the opportunity to change the mathematical optimum to a more practical design for comparative evaluation. Two examples are presented to illustrate the method and its application.
The application of composite materials to aircraft construction has provided the designer with increased flexibility. The purpose of this Note is to mathematically define manufacturing constraints needed to control pl...
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The application of composite materials to aircraft construction has provided the designer with increased flexibility. The purpose of this Note is to mathematically define manufacturing constraints needed to control ply orientation percentage, thickness variation, and interleaving of plies from two adjacent zones. The constraints were implemented in the ASTROS optimization code and applied to the design of the simple wing structure described in the ASTROS applications manual.
The problem of performance robustness, especially in the face of significant parametric uncertainty, has been increasingly recognized as a predominant issue of engineering significance in many design applications. Qua...
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The problem of performance robustness, especially in the face of significant parametric uncertainty, has been increasingly recognized as a predominant issue of engineering significance in many design applications. Quantitative feedback theory is very effective for dealing with this class of problems even when there exist hard constraints on closed-loop response. In this paper, single-input/single-output quantitative feedback theory is viewed formally as a sensitivity constrained multiobjective optimization problem whose solution cannot be obtained analytically but (when feasible) can be obtained graphically. In contrast to the more recent robust control methods where phase uncertainty information is often neglected, the direct use of parametric uncertainty and phase information in quantitative feedback theory results in a significant reduction in the cost of feedback. An example involving a standard quantitative feedback theory problem is included for completeness.
In this paper, the dynamic behavior of the optimized blade design obtained in an earlier study is investigated in detail. The paper investigates the vibratory loads on the optimized rotor over a wide range of operatin...
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In this paper, the dynamic behavior of the optimized blade design obtained in an earlier study is investigated in detail. The paper investigates the vibratory loads on the optimized rotor over a wide range of operating conditions and for a large number of rotor characteristics than those considered in the design process, in order to assess the design. This is accomplished by: studying the dynamic performance criterion of the rotor blade, optimized at a prescribed flight condition, at off-design flight conditions;and investigating the behavior of the optimized blade with respect to the dynamic performance criteria that were not included in the optimization formulation.
We propose an algorithm for minimizing a function f on R(n) in the presence of m equality constraints c that locally is a reduced secant method. The local method is globalized using a nondifferentiable augmented Lagra...
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We propose an algorithm for minimizing a function f on R(n) in the presence of m equality constraints c that locally is a reduced secant method. The local method is globalized using a nondifferentiable augmented Lagrangian whose decrease is obtained by both a longitudinal search that decreases mainly f and a transversal search that decreases mainly parallel-to c. Our main objective is to show that the longitudinal path can be designed to maintain the positive definiteness of the reduced matrices by means of the positivity of gamma-k(T)delta-k, where gamma-k is the change in the reduced gradient and delta-k is the reduced longitudinal displacement.
There is almost no practical reliability optimization technique for modern large systems because of the complexity and tremendous computation associated with these systems. This paper presents a model and an algorithm...
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There is almost no practical reliability optimization technique for modern large systems because of the complexity and tremendous computation associated with these systems. This paper presents a model and an algorithm for reliability optimization in generalized stochastic-flow networks. This algorithm uses the relationship of the k-weak-link sets and sets of failure events to the parameters of the generalized stochastic flow networks. This facilitates a fast location of the optimal capacity expansion for a system from the information obtained by the latest iteration and alleviates the dimension calamity on computation. Consequently, the reliability optimization method is powerful for large systems. As an example, the IEEE Reliability Test System with 24 nodes and 70 components has been tested, and the components and their capacity values which should be enhanced are revealed. The computation results show that the algorithm can be applied to practical problems.
A new approach to integrated structure/control law design based on multilevel optimization is presented. This new approach is applicable to aircraft and spacecraft and allows for the independent design of the structur...
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A new approach to integrated structure/control law design based on multilevel optimization is presented. This new approach is applicable to aircraft and spacecraft and allows for the independent design of the structure and control law. Integration of the designs is achieved through use of an upper level coordination problem formulation within the multilevel optimization framework. The method requires the use of structure and control law design sensitivity information. A general multilevel structure/control law design problem formulation is given, and the use of linear quadratic Gaussian control law design and design sensitivity methods within the formulation is illustrated. Results of three simple integrated structure/control law design examples are presented. These results show the capability of structure and control law design tradeoffs to improve controlled system performance within the multilevel approach.
The design of a trajectory for an aerospace vehicle involves choosing a set of variables to optimally shape the path of the vehicle. Typically the trajectory is simulated by numerically solving the differential equati...
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The design of a trajectory for an aerospace vehicle involves choosing a set of variables to optimally shape the path of the vehicle. Typically the trajectory is simulated by numerically solving the differential equations describing the dynamics of the vehicle. The optimal trajectory is usually determined by using a nonlinear programming (parameter optimization) algorithm to select the variables. Problems that require choosing control functions are usually reduced to choosing a finite set of parameters. The computational expense of a trajectory optimization is dominated by two factors: the cost of simulating a trajectory and the cost of computing gradient information for the optimization algorithm. This paper presents a technique for using a parallel processor to reduce the cost of these calculations. The trajectory is broken into phases, which can be simulated in parallel, thereby reducing the cost of an individual trajectory. This multiple shooting technique has been suggested by a number of authors. The nonlinear optimization problem that results from this formulation produces a Jacobian matrix that is sparse. The Jacobian is computed using sparse finite differencing, which is also performed in parallel, thereby reducing the cost of obtaining gradient information for the optimization algorithm. This paper describes the application of sparse finite differencing to a multiple shooting formulation of the two-point boundary-value problem, in a manner suitable for implementation on a parallel processor. Computational experience with the algorithm as implemented on the BBN GP1000 (Butterfly) parallel processing computer is described.
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