Automated Design Optimization of a Three-Dimensional Subsonic Diffuser

2011 ◽  
Vol 27 (4) ◽  
pp. 838-846 ◽  
Author(s):  
Aniket Aranake ◽  
Jin Gyu Lee ◽  
Doyle Knight ◽  
Russell M. Cummings ◽  
John Cox ◽  
...  
Keyword(s):  
Design Optimization ◽  
Three Dimensional ◽  
Automated Design ◽  
Subsonic Diffuser

Automated Design of a Three-Dimensional Subsonic Diffuser

2000 ◽  
Vol 16 (6) ◽  
pp. 1132-1140 ◽  
Author(s):  
Wei-Li Zhang ◽  
Doyle D. Knight ◽  
Don Smith
Keyword(s):  
Three Dimensional ◽  
Automated Design ◽  
Subsonic Diffuser

Automated Design Optimization for Hypersonic Plasma-Aerodynamics

2005 ◽  
Author(s):  
Ramakanth Munipalli ◽  
Kamesh Subbarao ◽  
Shashi Aithal ◽  
Donald R. Wilson ◽  
Jennifer D. Goss
Keyword(s):  
Design Optimization ◽  
Automated Design ◽  
Plasma Aerodynamics

Robot Path Planning and Obstacle Avoidance: A Design Optimization Approach

1989 ◽  
Author(s):  
E. Sandgren ◽  
S. Venkataraman
Keyword(s):  
Path Planning ◽  
Design Optimization ◽  
Dynamic Programming Algorithm ◽  
Three Dimensional ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Robot Arm ◽  
Robot Path Planning ◽  
Real Time Control ◽  
Robot Path

Abstract A design optimization approach to robot path planning in a two dimensional workplace is presented. Obstacles are represented as a series of rectangular regions and collision detection is performed by an operation similar to clipping in computer graphics. The feasible design space is approximated by a discrete set of robot arm and gripper positions. Control is applied directly through the angular motion of each link. Feasible positions which are located between the initial and final robot link positions are grouped into stages. A dynamic programming algorithm is applied to locate the best state within each stage which minimizes the overall path length. An example is presented involving a three link planar manipulator. Extensions to three dimensional robot path planning and real time control in a dynamically changing workplace are discussed.

Design Optimization of Aircraft Engine-Mount Systems

1993 ◽  
Author(s):  
Hashem Ashrafiuon
Keyword(s):  
Design Optimization ◽  
Vibration Isolation ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Low Frequency ◽  
Aircraft Engine ◽  
Variable Metric ◽  
Design Variables ◽  
Engine Mount ◽  
Optimization Search

Abstract Design optimization of aircraft engine-mount systems for vibration isolation is presented. The engine is modeled as a rigid body connected to a flexible base representing the nacelle. The base is modeled with mass and stiffness matrices and structural damping using finite element modeling. The mounts are modeled as three-dimensional springs with hysteresis damping. The objective is to select the stiffness coefficients and orientation angles of the individual mounts to minimize the transmitted forces from the engine to the base. Meanwhile, the mounts have to be stiff enough not allowing engine deflection to exceed its limits under static and low frequency loadings. It is shown that with an optimal system the transmitted forces may be reduced significantly particularly when mount orientation angles are also treated as design variables. The optimization problems are solved using a Constraint Variable Metric approach. The closed form derivatives of the engine vibrational amplitudes with respect to design variables are derived in order to achieve a more effective optimization search technique.

scholarly journals Improved performance of stellarator coil design optimization

2020 ◽  
Vol 86 (2) ◽  
Author(s):  
Jim-Felix Lobsien ◽  
Michael Drevlak ◽  
Thomas Kruger ◽  
Samuel Lazerson ◽  
Caoxiang Zhu ◽  
...  
Keyword(s):  
Stochastic Optimization ◽  
Design Optimization ◽  
Three Dimensional ◽  
Local Maximum ◽  
Average Field ◽  
Test Case ◽  
Coil Design ◽  
Applied Optimization ◽  
Improved Performance ◽  
Coil Shape

Following up on earlier work which demonstrated an improved numerical stellarator coil design optimization performance by the use of stochastic optimization (Lobsien et al., Nucl. Fusion, vol. 58 (10), 2018, 106013), it is demonstrated here that significant further improvements can be made – lower field errors and improved robustness – for a Wendelstein 7-X test case. This is done by increasing the sample size and applying fully three-dimensional perturbations, but most importantly, by changing the design sequence in which the optimization targets are applied: optimization for field error is conducted first, with coil shape penalties only added to the objective function at a later step in the design process. A robust, feasible coil configuration with a local maximum field error of 3.66 % and an average field error of 0.95 % is achieved here, as compared to a maximum local field error of 6.08 % and average field error of 1.56 % found in our earlier work. These new results are compared to those found without stochastic optimization using the FOCUS and ONSET suites. The relationship between local minima in the optimization space and coil shape penalties is also discussed.

Aerodynamic Design Optimization of an Axial Flow Compressor Rotor

2002 ◽  
Author(s):  
Chan-Sol Ahn ◽  
Kwang-Yong Kim
Keyword(s):  
Design Optimization ◽  
Response Surface ◽  
Computing Time ◽  
Flow Analysis ◽  
Three Dimensional ◽  
Axial Flow ◽  
Navier Stokes ◽  
Transonic Compressor ◽  
Compressor Rotor ◽  
Design Variables

Design optimization of a transonic compressor rotor (NASA rotor 37) using the response surface method and three-dimensional Navier-Stokes analysis has been carried out in this work. The Baldwin-Lomax turbulence model was used in the flow analysis. Three design variables were selected to optimize the stacking line of the blade. Data points for response evaluations were selected by D-optimal design, and linear programming method was used for the optimization on the response surface. As a main result of the optimization, adiabatic efficiency was successfully improved. It was found that the optimization process provides reliable design of a turbomachinery blade with reasonable computing time.

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