Method of analyzing and predicting both airplane and engine performance characteristics

• US 5,070,458 A
• Filed: 03/31/1989
• Issued: 12/03/1991
• Est. Priority Date: 03/31/1989
• Status: Expired due to Term

• Current Assignee: Honeywell Incorporated (Honeywell International Inc.)
• Original Assignee: Honeywell Incorporated (Honeywell International Inc.)
• Inventors: Clark, Dereck B., Gilmore, John P.
• Primary Examiner(s): Lall, Parshotam S.
• Assistant Examiner(s): PIPALA, EDWARD J
• Application Number:
  US07/332,648
• Time in Patent Office: 977 Days
• Field of Search: 364/431.01, 364/431.02, 364/424.06, 364/442, 364/550, 364/551.01, 364/148-152, 244/180-182, 244/194, 244/904
• US Class Current: 701/14
• CPC Class Codes: G05D 1/0005 with arrangements to save energy
• CPC Class Codes: G06F 30/15 Vehicle, aircraft or watercraft design
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

Aircraft/engine performance is determined by the method of the present invention, wherein the performance characteristics are defined by learned performance parameters. The method includes the steps of learning the performance parameters of the aircraft/engine combination, for a given flight, using previously learned performance parameters and current observations. Then, based on the performance parameters learned from the learning step, a prediction is made of the performance of the aircraft/engine combination for subsequent flights.

Claims

1. A method of determining aircraft/engine performance for a current flight, wherein predefined performance parameters define the performance characteristics of the aircraft/engine combination, said method comprising the steps of:

(a) based on the performance parameters learned from flights prior to the current flight, predicting the performance of the aircraft/engine combination for predetermined conditions;

(b) learning the performance parameters of the aircraft/engine combination during the current flight, using previously learned performance parameters, including climb, cruse, and descent phases of said current flight, said learning including;

(i) modeling the aircraft/engine to a mathematical model which includes predefined performance parameters;

(ii) filtering the output of the mathematical model such that the performance parameters mean and standard deviations provide a best curve fit for predetermined inputs to the mathematical model;

(iii) extracting the newly generated performance parameters obtained from the filtering step; and

(iv) curve fitting the performance parameters to altitude;

(c) saving the altitude curve fit performance parameters of step (b, iv) in a storage element; and

(d) repeating steps a-c, thereby iterating the method of determining aircraft/engine performance on a predetermined time period during the entire current flight.

2. A method of determining aircraft/engine performance according to claim 1, wherein the mathematical model comprises a drag/thrust model.

3. A method of determining aircraft/engine performance according to claim 1, wherein the step of filtering utilizes discrete square root filtering.

4. A method of determining aircraft/engine performance according to claim 1, wherein the step of extracting utilizes a Gaussian reduction/back substitution technique.

5. A method of aircraft/engine performance according to claim 1 , wherein the step of curve fitting utilizes a linear regression curve fitting technique.

6. A method of determining aircraft/engine performance according to claim 5, wherein the linear regression curve fitting technique is a least square curve fitting technique.

7. A method of determining aircraft/engine performance according to claim 1, wherein the storage element utilized to save the altitude curve fit performance parameters of step (b, iv) comprises a non-volatile memory thereby providing updated performance parameters for future flights, said performance parameters being learned parameters corresponding to the aircraft/engine combination.

8. A method of determining aircraft/engine performance according to claim 2, wherein the step of filtering utilizes discrete square root filtering.

9. A method of determining aircraft/engine performance according to claim 8, wherein the step of extracting utilizes a Gaussian reduction/back substitution technique.

10. A method of determining aircraft/engine performance according to claim 9, wherein the step of curve fitting utilizes a linear regression curve fitting technique.

11. A method of determining aircraft/engine performance according to claim 10, wherein the linear regression curve fitting technique is a least squares curve fitting technique.

13. A method of determining aircraft/engine performance according to claim 1, further comprising the steps of:

before repeating the steps of (a) through (c), performing a reasonableness test on predetermined parameters generated by the learning step.

14. A method of determining aircraft/engine performance according to claim 1, further including the step of:

if no previously learned parameters exist, initializing predetermined parameters in the storage element with predetermined values.

15. A method of determining aircraft/engine performance according to claim 1, further comprising the step of:

before the step of learning, determining if all predetermined criteria has been met before proceeding to the learning step.

12. A method of determining aircraft/engine performance according to claim 12, wherein the storage element utilized to save the altitude curve fit performance parameters of step (b, iv) comprises a non-volatile memory thereby providing updated performance parameters for future flights, said performance parameters being learned parameters corresponding to the aircraft/engine combination.

16. A method of determining aircraft/engine performance, wherein predefined performance parameters define the performance characteristics of the aircraft/engine combination, said method comprising the steps of:

a) if no previously calculated performance parameters are available, initializing predetermined parameters in a storage element with predetermined values;

b) utilizing the performance parameters from the storage element, predicting the performance of the aircraft/engine combination for the given flight;

c) determining if all predetermined criteria has been met;

d) learning the performance parameters of the aircraft/engine combination for a given flight plan, using performance parameters available in said storage element, said learning including;

i) modeling the aircraft/engine to a mathematical model which includes predefined performance parameters;

ii) filtering the output of the mathematical model such that the performance parameters mean and standard deviations provide a best curve fit for predetermined inputs to the mathematical model;

iii) extracting the newly generated performance parameters obtained from the filtering step;

iv) curve fitting the performance parameters to altitude;

v) performing a reasonableness test on the newly generated performance parameters; and

vi) saving the altitude curve fit performance parameters in a temporary storage;

e) repeating step (b) through (d) on a predetermined time interval during the entire given flight; and

f) at the conclusion of the given flight, updating the performance parameters in the storage element with the performance parameter in the temporary storage thereby including the parameters learned from the given flight.