Method of airplane performance estimation and prediction

• US 5,606,505 A
• Filed: 05/26/1995
• Issued: 02/25/1997
• Est. Priority Date: 09/17/1993
• Status: Expired due to Term

• Current Assignee: Honeywell Incorporated (Honeywell International Inc.)
• Original Assignee: Honeywell Incorporated (Honeywell International Inc.)
• Inventors: Smith, Joseph A., Tarbutton, Victor F., Ringnes, Erik A.
• Primary Examiner(s): NOT, DEFINED
• Assistant Examiner(s): Walder, Jr., Stephen J.
• Application Number:
  US08/451,780
• Time in Patent Office: 641 Days
• Field of Search: 364/431.01, 364/424.06, 364/551.01, 364/149, 364/151, 364/427, 364/428, 364/424.01, 244/180, 244/194
• US Class Current: 701/99
• CPC Class Codes: G05D 1/0005 with arrangements to save energy

Abstract

A method of predicting performance characteristics of an aircraft including modeling thrust-minus-drag of an aircraft with at least one mathematical model using aircraft specific data and input parameters which define the performance characteristics. Coefficients of the at least one mathematical model are learned over at least one flight. The performance characteristics are predicted as a function of the learned coefficients representative of a combined and non-independent thrust-minus-drag relationship.

Claims

1. An method for controlling the flight profile of an aircraft, said method comprising the steps of:

determining aircraft input parameters;

retrieving aircraft specific data from non-volatile memory;

retrieving learned coefficients from non-volatile memory;

modeling a thrust-minus-drag relationship of the aircraft with a first series expansion using said aircraft specific data, said input parameters, and said learned coefficients which define the performance characteristics;

learning coefficients of said at least one series expansion over at least one flight so that said learned coefficients can be utilized for predicting performance characteristics of subsequent flights;

finding at least one point of interest on curves representative of functions derived from said thrust-minus-drag relationship;

controlling said aircraft responsive to said at least one point of interest; and

,

storing said learned coefficients into non-volatile memory for use on subsequent flights.

2. A method according to claim 1, wherein said learning step includes the step of utilizing at least one filter to estimate said coefficients of said at least one series expansion.

3. A method according to claim 1, wherein said first series expansion is a Taylor series expansion.

4. A method according to claim 1, wherein said first series expansion includes first terms representative of thrust and second terms representative of drag, said second terms including a compressible drag portion.

5. A method according to claim 1, wherein said input parameters include altitude.

6. A method according to claim 1, wherein said modeling step further includes the step of modeling fuelflow with a second series expansion.

7. A method according to claim 6, wherein said learning step includes the step of utilizing a first filter for estimating coefficients of said first series expansion and a second filter for estimating coefficients of said second series expansion.

8. A method according to claim 7, wherein said first filter is an extended Kalman filter and said second filter is a Kalman filter.

9. A method according to claim 6, wherein said first and second series expansions are Taylor series expansions.

10. A method according to claim 1, wherein said learning step includes the steps of estimating an accuracy of said coefficients of said at least one series expansion to provide information as to an accuracy of performance characteristics predicted in subsequent flights.

11. A method according to claim 1, wherein said first series expansion includes first terms representative of thrust and second terms representative of drag, said second terms including a compressible drag portion which is a function of Mach number and Coefficient of lift.

12. A method according to claim 1, wherein said first series expansion includes first terms representative of thrust and second terms representative of drag, said second terms including a compressible drag portion defined substantially as:

space="preserve" listing-type="equation">Γ

=0 when M≦

M.sub.cr

space="preserve" listing-type="equation">Γ

=15(M-M.sub.cr).sup.2 tan[(M-M.sub.cr).sup.2 (C.sub.L.sup.3 +0.1)λ

] when M>

M.sub.cr.

13. A method of predicting performance characteristics of an aircraft and controlling the flight said aircraft, said method comprising the steps of:

determining at least one input parameter;

retrieving aircraft specific data from non-volatile memory;

retrieving coefficients from non-volatile memory;

modeling a thrust-minus-drag relationship of the aircraft with at least one mathematical model using said aircraft specific data, said at least one input parameter, and said coefficients which define the performance characteristics;

learning said coefficients of said mathematical model over at least one flight;

predicting certain performance characteristics utilizing at least a first derivative taken with respect to at least one of said input parameters given certain predetermined conditions and said learned coefficients; and

,

controlling said aircraft according to said performance characteristics.

14. The method according to claim 13 further comprising the step of predicting certain performance characteristics utilizing at least a first derivative of at least one function derived from said at least one mathematical model taken with respect to at least one of said input parameters given certain predetermined conditions and said learned coefficients.

15. A method according to claim 14, wherein said modeling step includes the step of modeling said thrust-minus-drag relationship with a series expansion.

16. A method according to claim 15, wherein said modeling step further includes modeling fuelflow with an additional series expansion.

17. A method according to claim 16, wherein said learning step includes estimating said coefficients of said series expansion and said additional series expansion over at least one flight.

18. A method according to claim 17, wherein said predicting step further includes predicting climb performance characteristics of the aircraft as a function of said series expansion and/or said additional series expansion.

19. A method according to claim 18, wherein said predicting step further includes predicting descent performance characteristics of the aircraft as a function of said series expansion and/or said additional series expansion.

20. A method according to claim 18, wherein said predicting steps include performing step integrations over said series expansion and/or said additional series expansion.

21. A method according to claim 13, wherein said modeling step includes the steps of modeling a thrust-minus-drag relationship with a first mathematical model and modeling fuelflow with a second mathematical model.

22. A method according to claim 21, wherein said learning step includes the steps of utilizing a first filter to estimate coefficients of said first mathematical model and utilizing a second filter to estimate coefficients of said second mathematical model.

23. A method according to claim 22, wherein said modeling step includes the steps of modeling the thrust-minus-drag relationship with a first Taylor series expansion and modeling fuelflow with a second Taylor series expansion, and further wherein said learning step includes the steps of estimating coefficients of said first Taylor series expansion utilizing an extended Kalman filter and estimating coefficients of said second Taylor series expansion utilizing a Kalman filter.

24. A method according to claim 13, wherein said certain performance characteristics include cruise performance characteristics.

25. A method according to claim 24, wherein said predicting step includes the step of predicting long range cruise speed.

26. A method according to claim 25, wherein said predicting step includes the step of predicting optimum altitude for said predicted long range cruise speed.

27. A method according to claim 24, wherein said predicting step includes the step of predicting maximum speed.

28. A method according to claim 27, wherein said predicting step includes the step of predicting optimum altitude for said predicted maximum speed.

29. A method according to claim 24, wherein said predicting step includes the step of predicting optimum altitude at a manual speed.

30. A method according to claim 24, wherein said predicting step includes the step of predicting maximum endurance speed.

31. A method according to claim 13, wherein said learning step includes the step of utilizing previously learned coefficients from a separate flight of other like aircraft for predicting performance characteristics of a current flight and given flight plan.

32. A method according to claim 13, wherein said learning step includes the step of utilizing altitude as an input parameter in defining the performance characteristics.

33. A method according to claim 13, further including the step of predicting the accuracy of predictions of said certain performance characteristics.

34. A method according to claim 13, wherein said learning step includes the step of utilizing previously learned data from a separate flight of other like aircraft to initialize coefficients of said mathematical model.

35. A method of predicting performance characteristics of an aircraft and controlling said aircraft according to said characteristics, said method comprising the steps of:

determining at least airspeed, altitude and pitch of said aircraft;

retrieving aircraft specific data from non-volatile memory;

retrieving coefficients from non-volatile memory;

modeling thrust minus drag of said aircraft with at least one mathematical model using aircraft specific data and said airspeed, altitude and pitch of said aircraft;

predicting the performance characteristics as a function of said coefficients representative of a combined and non-independent thrust-minus-drag relationship; and

,

controlling said aircraft responsive to said predicted performance characteristics.

36. A method according to claim 35, wherein said method further includes estimating an accuracy of the performance characteristics predicted.

37. A method according to claim 35, wherein said predicting step includes the step of predicting certain performance characteristics utilizing at least a first derivative of a function derived from said mathematical model with respect to at least one input parameter given certain predetermined conditions.

38. A method according to claim 37 further comprising a step of learning coefficients of said at least one mathematical model.

39. A method according to claim 35, wherein said modeling step further includes the step of estimating modeling fuelflow with an additional mathematical model, said predicting step further includes the step of predicting performance characteristics as a function of said mathematical model and/or said additional mathematical model.

40. A method according to claim 39, wherein said predicting step further includes predicting performance characteristics of the aircraft for a given flight plan utilizing a closed-form integration technique.

41. A method according to claim 40, wherein said predicting step includes predicting time enroute, distance and/or total fuel burned for said given flight plan.