Adjoint-based design variable adaptation

• US 20060058985A1
• Filed: 08/31/2004
• Published: 03/16/2006
• Est. Priority Date: 08/31/2004
• Status: Abandoned Application

• Current Assignee: Supersonic Aerospace International LLC
• Original Assignee: Supersonic Aerospace International LLC
• Inventors: Arslan, Alan E., Morgenstern, John M., Jordan, Josiah David
• Application Number:
  US10/930,375
• Publication Number:
  US 20060058985A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 2111/04 Constraint-based CAD
• CPC Class Codes: G06F 2111/06 Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
• CPC Class Codes: G06F 30/15 Vehicle, aircraft or watercraft design

Abstract

A method for determining an optimum design includes determining an initial field solution for a design configuration based on an initial set of design variables; determining an adjoint solution to the field solution; determining the regions of interest on the design configuration based on magnitudes and/or gradients of the adjoint solution; and establishing design variables at the regions of interest.

Claims

1. A method for determining an optimum design comprising:

determining a initial solution for a design configuration based on an initial set of design variables;

determining an adjoint solution to the initial solution;

determining a region of interest on the design configuration based on at least one the gradients and the magnitudes of the adjoint solution; and

establishing design variables at the region of interest.

2. The method according to claim 1 wherein establishing design variables includes creating new design variables.

3. The method according to claim 1 wherein establishing design variables includes moving existing design variables to the region of interest.

4. The method according to claim 1 further comprising:

re-establishing the relationship between grids representing the design configuration and the design variables.

5. The method according to claim 1 further comprising:

determining whether an objective function has converged for the design variables.

6. The method according to claim 1 further comprising:

increasing the resolution of a Cartesian grid describing the design variables to increase the number of design variables outside the region of interest.

7. The method according to claim 1 further comprising:

decreasing the resolution of a Cartesian grid describing the design variables to decrease the number of design variables outside the region of interest.

8. The method according to claim 1 further comprising:

concentrating the design variables at the region of interest using a swarming technique based on at least one of the magnitudes and the gradients of the adjoint above or below a specified threshold.

9. The method according to claim I further comprising:

concentrating the design variables at the region of interest using a genetic algorithm to randomly search for an optimum solution based on at least one of the magnitudes and the gradients above a specified threshold.

10. A design system comprising:

logic instructions operable to;

determine a field solution for a design configuration based on an initial set of design variables;

determine an adjoint solution to the field solution;

determine a region of interest on the design configuration; and

establish design variables at the regions of interest.

11. The design system according to claim 10 wherein new design variables are created at the region of interest.

12. The design system according to claim 10 wherein existing design variables are moved to the region of interest.

13. The design system according to claim 10 wherein the logic instructions are further operable to:

re-establish the relationship between surface grids and the design variables.

14. The design system according to claim 10 wherein the logic instructions are further operable to:

determine whether the field solution has converged.

15. The design system according to claim 10 wherein the logic instructions are further operable to:

use an Octree adaptive grid of design variable locations to increase the number of design variables in the region of interest.

16. The design system according to claim 10 wherein the logic instructions are further operable to use an Octree adaptive grid of design variable locations to decrease the number of design variables outside the region of interest.

17. The design system according to claim 10 wherein the logic instructions are further operable to:

concentrate the design variables at the region of interest using a swarming technique based at least one of gradients and magnitudes above or below a specified threshold.

18. The design system according to claim 10 wherein the logic instructions are further operable to:

concentrate the design variables at the region of interest using a genetic algorithm to randomly search for an optimum location of design variables based on at least one of gradients and magnitudes of the adjoint field above or below a specified threshold.

19. The design system according to claim 10 wherein the logic instructions are further operable to:

concentrate the design variables at the region of interest using a Neural Network algorithm to search for an optimum design variable distribution based on at least one of gradients and magnitudes of the adjoint field above or below a specified threshold.

20. The design system according to claim 10, further comprising a processor.

21. The design system according to claim 10, wherein the logic instructions are distributed on a computer readable medium.

22. A system comprising:

means for determining a field solution for a design configuration based on an initial set of design variables;

means for determining an adjoint solution to the field solution;

means for determining a region of interest on the design configuration;

means for establishing new design variables at the regions of interest; and

means for moving existing design variables to the region of interest.