System and method for modeling the flow performance features of an object
First Claim
1. A method for generating a model of one or more flow performance features of an object in a flowing medium having a plurality of geometric configurations, the method comprising the steps of:
- (a) forming a neural network comprising;
a plurality of input nodes associated with the geometric configurations of an object,a plurality of hidden nodes,one or more output nodes associated with the flow performance features of the object, anda weight matrix for defining connections between the input nodes and the hidden nodes, connections between the hidden nodes and the output nodes, and weight values associated with each connection,(b) forming a test database, coupled to the neural network, comprising a plurality of test input signals and corresponding test output signals, the test input signals associated with the geometric configurations of the object and the test output signals associated with the flow performance features of the object, the test input signals associated with the input nodes and the test output signals associated with the output nodes, said weight values being modified as a function of said test input signals;
(c) solving for a search direction to obtain one of said flow performance features comprising the steps of;
(i) initializing a parameter vector representative of a weight value of said one of said flow performance features;
(ii) selecting a random predictor value for said one of said flow performance features and having an error cost function with a gradient; and
(iii) iterating the parameter vector and random predictor value to determine said search direction, said determination being satisfied when qualifying one of the conditions selected from the group comprising;
(1) the gradient of the error cost function is near zero, (2) the error cost function is below a predetermined value, and (3) a predetermined number of iterating of the parameter vector and random predictor value has been accomplished.
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Abstract
The method and apparatus includes a neural network for generating a model of an object in a wind tunnel from performance data on the object. The network is trained from test input signals (e.g., leading edge flap position, trailing edge flap position, angle of attack, and other geometric configurations, and power settings) and test output signals (e.g., lift, drag, pitching moment, or other performance features). In one embodiment, the neural network training method employs a modified Levenberg-Marquardt optimization technique. The model can be generated "real time" as wind tunnel testing proceeds. Once trained, the model is used to estimate performance features associated with the aircraft given geometric configuration and/or power setting input. The invention can also be applied in other similar static flow modeling applications in aerodynamics, hydrodynamics, fluid dynamics, and other such disciplines. For example, the static testing of cars, sails, and foils, propellers, keels, rudders, turbines, fins, and the like, in a wind tunnel, water trough, or other flowing medium.
52 Citations
15 Claims
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1. A method for generating a model of one or more flow performance features of an object in a flowing medium having a plurality of geometric configurations, the method comprising the steps of:
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(a) forming a neural network comprising; a plurality of input nodes associated with the geometric configurations of an object, a plurality of hidden nodes, one or more output nodes associated with the flow performance features of the object, and a weight matrix for defining connections between the input nodes and the hidden nodes, connections between the hidden nodes and the output nodes, and weight values associated with each connection, (b) forming a test database, coupled to the neural network, comprising a plurality of test input signals and corresponding test output signals, the test input signals associated with the geometric configurations of the object and the test output signals associated with the flow performance features of the object, the test input signals associated with the input nodes and the test output signals associated with the output nodes, said weight values being modified as a function of said test input signals; (c) solving for a search direction to obtain one of said flow performance features comprising the steps of; (i) initializing a parameter vector representative of a weight value of said one of said flow performance features; (ii) selecting a random predictor value for said one of said flow performance features and having an error cost function with a gradient; and (iii) iterating the parameter vector and random predictor value to determine said search direction, said determination being satisfied when qualifying one of the conditions selected from the group comprising;
(1) the gradient of the error cost function is near zero, (2) the error cost function is below a predetermined value, and (3) a predetermined number of iterating of the parameter vector and random predictor value has been accomplished. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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Specification