VARIABLE AREA FAN NOZZLE FAN FLUTTER MANAGEMENT SYSTEM

• US 20090226303A1
• Filed: 03/05/2008
• Published: 09/10/2009
• Est. Priority Date: 03/05/2008
• Status: Abandoned Application

• Current Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Original Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Morris, Robert J., Grabowski, Zbigniew M.
• Application Number:
  US12/042,361
• Publication Number:
  US 20090226303A1
• Time in Patent Office: Days
• US Class Current: 415/151
• CPC Class Codes: F02C 9/16 Control of working fluid flow F02C9/48 takes precedence; control of air-intake flow F02C7/057
• CPC Class Codes: F02K 1/16 conjointly with another control
• CPC Class Codes: F02K 3/06 with front fan
• CPC Class Codes: F04D 29/563 specially adapted for elastic fluid pumps
• CPC Class Codes: F04D 29/667 by influencing the flow pattern, e.g. suppression of turbulence
• CPC Class Codes: F05D 2270/709 with neural networks

Abstract

A system and method of controlling a fan blade flutter characteristic of a gas turbine engine includes adjusting a variable area fan nozzle in response to a neural network.

Claims

1. A gas turbine engine comprising:

a core engine defined about an axis;

a fan driven by said core engine about said axis;

a core nacelle defined at least partially about said core engine;

a fan nacelle defined around said fan and at least partially around said core nacelle, and

a variable area fan nozzle (VAFN) to define a fan exit area downstream of said fan between said fan nacelle and said core nacelle; and

a controller trimmed in response to a neural network to control a fan blade flutter characteristic through control of said VAFN.

2. The engine as recited in claim 1, wherein said neural network is a trainable neural network.

3. The engine as recited in claim 1, wherein said controller is a FADEC.

4. The engine as recited in claim 3, wherein said FADEC is in communication with a VAFN controller which controls said VAFN.

5. The engine as recited in claim 3, wherein said FADEC is operable to control a fan speed of said fan through control of fuel to a combustor.

6. The engine as recited in claim 5, wherein said fan is driven by said core engine through a gear system.

7. A method of controlling a gas turbine engine comprising the steps of:

adjusting a variable area fan nozzle in response to a neural network to control a fan blade flutter characteristic.

8. A method as recited in claim 7, further comprising:

training the neural network in response to a prior flight event.

9. A method as recited in claim 7, further comprising:

training the neural network during a flight event.

10. A method as recited in claim 7, further comprising:

training the neural network through fan blade companion testing.

11. A method as recited in claim 7, further comprising:

training the neural network through aircraft mission data.

12. A method as recited in claim 7, further comprising:

training the neural network in response to fleet data.

13. A method as recited in claim 7, further comprising:

adjusting a fan speed in response to the neural network.