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Results for “objective function”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Adjoint methods for aerodynamic wing design

A model inverse design problem is used to investigate the effect of flow discontinuities on the optimization process. The optimization involves finding the cross-sectional area distribution of a duct that produces velocities that closely match a targeted velocity distribution. Quasi-one-dimensional flow theory is used, and the target is chosen to have a shock wave in its distribution. The objective function which quantifies the difference between the targeted and calculated velocity distributions may become non-smooth due to the interaction between the shock and the discretization of the flowfield. This paper offers two techniques to resolve the resulting problems for the optimization algorithms. The first, shock-fitting, involves careful integration of the objective function through the shock wave. The second, coordinate straining with shock penalty, uses a coordinate transformation to align the calculated shock with the target and then adds a penalty proportional to the square of the distance between the shocks. The techniques are tested using several popular sensitivity and optimization methods, including finite-differences, and direct and adjoint discrete sensitivity methods. Two optimization strategies, Gauss-Newton and sequential quadratic programming (SQP), are used to drive the objective function to a minimum.

Grossman, Bernard↗
NASA NTRS2004

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

Ortiz, Francisco↗
NASA NTRS2020

Multiple Shaker Placement for Ground Vibration Test of X-59 Aircraft Using Topology Optimization

A multiple shaker placement methodology is developed and tested using a topology optimization technique. Current multiple shaker placement methodology requires optimum accelerometer placement and optimum single-shaker placement techniques. The proposed methodology is tested using a finite element model of the X-59 Low Boom Flight Demonstrator aircraft. The effective independence and the driving point acceleration transfer function (DPATF) methods are used for the accelerometer placement study. In this study, four shakers are used to excite each mode more effectively during the ground vibration test; all the modes of interest thus are separated into four groups. Each shaker takes care of a separate group of modes. Grouping the modes of interest is performed utilizing topology optimization. The number of modes for each group therefore will be automatically decided during grouping. For each group of modes, perform the following two steps to determine optimal location of four shakers: 1) At each accelerometer location, compare the magnitude of DPATF values at natural frequencies, select the minimum value, and make a vector with these minimum values of the DPATF magnitudes for each group; and 2) Select the degrees of freedom corresponding to the maximum value of this vector. The objective function value is the maximum value of the vector with minimum value of the magnitude of the superposed acceleration transfer function. This objective function value is maximized by changing the modes for each group. Forty accelerometers are enough to have good correlation between mode shapes obtained from the reduced order model and the simulated ground vibration test.

Optimization Analysis↗
NASA NTRS1994

Application of two procedures for dual-point design of transonic airfoils

Two dual-point design procedures were developed to reduce the objective function of a baseline airfoil at two design points. The first procedure to develop a redesigned airfoil used a weighted average of the shapes of two intermediate airfoils redesigned at each of the two design points. The second procedure used a weighted average of two pressure distributions obtained from an intermediate airfoil redesigned at each of the two design points. Each procedure was used to design a new airfoil with reduced wave drag at the cruise condition without increasing the wave drag or pitching moment at the climb condition. Two cycles of the airfoil shape-averaging procedure successfully designed a new airfoil that reduced the objective function and satisfied the constraints. One cycle of the target (desired) pressure-averaging procedure was used to design two new airfoils that reduced the objective function and came close to satisfying the constraints.

Mineck, Raymond E.↗
NASA NTRS1998

A Numerical Optimization Approach for Tuning Fuzzy Logic Controllers

This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science instrument line-of-sight pointing control is used to demonstrate results.

Woodard, Stanley E.↗
NASA NTRS1975

DSN functions and facilities

The objectives, functions, and organization of the Deep Space Network were summarized. Deep space station, ground communication, and network operations control capabilities are described.

N. A. Renzetti↗
NASA NTRS1976

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1976

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1976

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1976

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1977

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1977

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1977

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1977

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1978

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described. The network is designed for two-way communications with unmanned spacecraft traveling approximately 16,000 km (10,000 miles) from earth to the farthest planets and to the edge of our solar system.

Renzetti, N. A.↗
NASA NTRS1978

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1978

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗
NASA NTRS1978

Network functions and facilities

The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.

Renzetti, N. A.↗