Autonomous experimental design optimization

• US 7,831,418 B1
• Filed: 06/05/2003
• Issued: 11/09/2010
• Est. Priority Date: 06/21/2002
• Status: Expired due to Fees

• Current Assignee: Honda R&D Europe (Deutschland) GmbH (Honda Motor Company), Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: Richter, Andreas, Körner, Edgar, Sendhoff, Bernhard A.
• Primary Examiner(s): Silver; David
• Application Number:
  US10/456,179
• Publication Number:
  US 20100305925A1
• Time in Patent Office: 2,714 Days
• Field of Search: 703/6, 703/8, 700/29, 700/97
• US Class Current: 703/6
• 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 2111/10 Numerical modelling
• CPC Class Codes: G06F 30/15 Vehicle, aircraft or watercraft design
• CPC Class Codes: Y02T 90/00 Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

Iterative (nondeterministic) optimization of aerodynamic and hydrodynamic surface structures can be accomplished with a computer software program and a system using a combination of a variable encoding length optimization algorithm based on an evolution strategy and an experimental hardware set-up that allows to automatically change the surface properties of the applied material, starting with the overall shape and proceeding via more detailed modifications in local surface areas. The optimization of surface structures may be done with a computing device for calculating optimized parameters of at least one (virtual) surface structure, an experimental hardware set-up for measuring dynamic properties of a specific surface structure, and an interface for feeding calculated parameters from the computing device to the experimental set-up and for feeding measured results back to the computing device as quality values for the next cycle of the optimizing step.

Claims

1. A method for iteratively optimizing a shape of an object, the method comprising:

(a) calculating on a computer a set of parameter values by a first optimization algorithm or a second optimization algorithm depending on quality values from a set of initial parameters, the set of initial parameters representing a shape of an object having at least one continuous surface structure;

(b) automatically adjusting hardware setup comprising a plurality of actuators coupled to the computer through an interface, the plurality of actuators controlled according to the calculated set of parameter values in order to alter a shape of the object with respect to the set of calculated parameter values;

(c) measuring dynamic properties of the adjusted hardware setup;

(d) retrieving measured dynamic properties by the computer;

(e) evaluating the measured dynamic properties by the computer to obtain updated quality values;

(f) repeating (a) through (e) until a set of optimized parameter values with respect to the measured dynamic properties are obtained based on the updated quality values; and

(g) storing the set of the optimized parameter values in a computer memory.

2. The method of claim 1, wherein the step of calculating the set of parameter values comprises the step of changing the number of said parameter values.

3. The method of claim 1, wherein the first optimization algorithm comprises an Evolutionary Algorithm (EA).

4. The method of claim 1, wherein the second optimization algorithm comprises at least one of a Simulated Annealing (SA) and a Tabu Search (TS) algorithm.

5. The method of claim 1, wherein the surface structure to be optimized is superimposed to a second predefined surface structure.

6. The method of claim 1, further comprising evaluating additional quality values by a Computational Fluid Dynamics (CFD) solver responsive to measuring the dynamic properties.

7. The method of claim 1, further comprising evaluating additional quality values by an Artificial Neuronal Network (ANN) or a Response Surface Model (RSM) responsive to measuring the dynamic properties.

8. A computer program stored in a computer readable storage medium structured to store instructions executable by a processor for iteratively optimizing dynamic properties of a shape of an object, the instructions, when executed cause the processor to:

(a) calculate a set of parameter values by a first optimization algorithm or a second optimization algorithm depending on quality values from a set of initial parameters, the set of initial parameters representing a shape of an object having at least one continuous surface structure;

(b) automatically adjust hardware setup comprising a plurality of actuators coupled to a computer through an interface, the plurality of actuators controlled according to the calculated set of parameter values in order to alter the shape of the object with respect to the set of calculated parameter values;

(c) measure dynamic properties of the adjusted hardware setup;

(d) retrieve measured dynamic properties;

(e) evaluate the measured dynamic properties to obtain updated quality values;

(f) repeat (a) through (e) until a set of optimized parameter values with respect to the measured dynamic properties are obtained based on the updated quality values; and

(g) store the set of the optimized parameter values in a computer memory.

9. A method for iteratively optimizing a continuous surface of a road vehicle, comprising:

(a) calculating on a computer a set of parameter values by a first optimization algorithm or a second optimization algorithm depending on quality values from a set of initial parameters, the set of initial parameters representing a shape of a road vehicle including at least one continuous surface;

(b) automatically adjusting hardware setup comprising a plurality of actuators coupled to the computer through an interface, the plurality of actuators controlled according to the calculated set of parameter values;

(c) measuring dynamic properties of the adjusted hardware setup;

(d) retrieving measured dynamic properties by the computer;

(e) evaluating the measured dynamic properties by the computer to obtain updated quality values;

(f) repeating (a) through (e) until a set of optimized parameter values with respect to the measured dynamic properties are obtained based on the updated quality values; and

(g) storing the set of the optimized parameter values in a computer memory.

10. A closed-loop control system for iteratively optimizing a shape of an object, the system comprising:

a computing device for calculating a set of parameter values by a first optimization algorithm or a second optimization algorithm depending on quality values from a set of initial parameters, the set of initial parameters representing a shape of an object having at least one continuous surface structure;

an interface for automatically adjusting hardware setup comprising a plurality of actuators by controlling the plurality of actuators according to the calculated set of parameter values in order to alter a shape of the object with respect to the set of calculated parameter values; and

a measurement control unit for measuring properties of the adjusted hardware setup, the measured properties evaluated by the computing device to obtain updated quality values;

wherein the computing device, the hardware setup, and the measurement control unit repeat calculating the set of parameter values, adjusting the experimental hardware setup, and obtaining the updated quality values with respect to the measured dynamic properties until optimized parameter values are obtained based on the updated quality values.

11. The system of claim 10, wherein the first optimization algorithm comprises an Evolutionary Algorithm (EA).

12. The system of claim 10, wherein the second optimization algorithm comprises at least one of a Tabu Search (TS) algorithm and Simulated Annealing (SA) algorithm.

13. The system of claim 10, wherein the surface structure to be optimized is superimposed to a second predefined surface structure.

14. The system of claim 10, further comprising a second computing device for evaluating the quality values by a Computational Fluid Dynamics (CFD) solver.

15. The system of claim 14, wherein the second computing device also evaluates the quality values by an Artificial Neuronal Network (ANN) or a Response Surface Model (RSM).