Optimal methodology for allocation and flowdown
AssigneeRaytheon Company
InventorsJerry L. Alderman, George Runger
Life of this patent
Estimating the term…
Abstract
A system and method of implementing a top-down flow down of at least one system requirement to allocate variability to system components such that overall system response meets the at least one system requirement, includes specifying a target value for the system response, and using a non-linear model to relate response variable values to targeted variable values and targeted mean values of the response variable values.
Claims29
A device for implementing a top-down flow down of at least one system requirement to allocate variability to a plurality of system components, comprising:
- a processor circuit having a processor and a memory, said processor circuit configured to
- receive data corresponding to a non-linear model of the at least one system;
- receive data corresponding to each of the plurality of system components;
- receive a user selectable real number (m-value);
- receive the at least one system requirement;
- use the non-linear model data, the data corresponding to each of the plurality of system components, and the m-value to allocate variance to each of the plurality of system components such that overall system response satisfies the at least one system requirement, wherein the variance of the non-linear model is expressed by at least one of
- σ
- R
- 2
- =
- ∑
- i
-
- {
- FS
- i
- 2
-
- ζ
- i
- 2
- }
- ,
-
- ζ
- i
- =
- σ
- R
- ∑
- j
-
- FS
- j
- 2
-
- p
- p
- -
- 2
-
- FS
- i
- 2
- p
- -
- 2
- ,
-
- ζ
- i
- =
- σ
- R
- ∑
- j
-
- (
- μ
- j
-
- FS
- j
- )
- 2
-
- p
- p
- -
- 2
-
- μ
- i
- p
- p
- -
- 2
-
- FS
- i
- 2
- p
- -
- 2
- ,
- or
- σ
- R
- 2
- =
- ∑
- i
-
- {
- S
- i
- 2
-
- σ
- i
- 2
- +
- S
- i
-
- T
- i
-
- σ
- i
- 3
-
- SK
- i
- +
- [
- T
- i
-
- σ
- i
- 2
- 2
- ]
- 2
-
- (
- K
- i
- -
- 1
- )
- }
- ,
- where σR is the targeted variance of the response variable R, FSi is the generalized sensitivity coefficient, ζi is the generalized variance coefficient, μi is the mean value of, p is a real number based on the m-value Si and Ti are first and second derivatives of the response, SKi is the Skewness and Ti is the Kurtosis;
- and
- output the variance for each of the plurality of system components.
A computer implemented method for creating a top-down flow down of at least one system requirement to allocate variability to a plurality of system components, comprising:
- receiving data corresponding to a non-linear model of the at least one system;
- receiving data corresponding to each of the plurality of system components;
- receiving a user selectable real number (m-value);
- receiving the at least one system requirement;
- using the non-linear model data, the data corresponding to each of the plurality of system components, and the m-value to allocate, with computer assistance, variance to each of the plurality of system components such that overall system response satisfies the at least one system requirement, wherein the variance of the non-linear model is expressed by at least one of
- σ
- R
- 2
- =
- ∑
- i
-
- {
- FS
- i
- 2
-
- ζ
- i
- 2
- }
- ,
-
- ζ
- i
- =
- σ
- R
- ∑
- j
-
- FS
- j
- 2
-
- p
- p
- -
- 2
-
- FS
- i
- 2
- p
- -
- 2
- ,
-
- ζ
- i
- =
- σ
- R
- ∑
- j
-
- (
- μ
- j
-
- FS
- j
- )
- 2
-
- p
- p
- -
- 2
-
- μ
- i
- p
- p
- -
- 2
-
- FS
- i
- 2
- p
- -
- 2
- ,
- or
- σ
- R
- 2
- =
- ∑
- i
-
- {
- S
- i
- 2
-
- σ
- i
- 2
- +
- S
- i
-
- T
- i
-
- σ
- i
- 3
-
- SK
- i
- +
- [
- T
- i
-
- σ
- i
- 2
- 2
- ]
- 2
-
- (
- K
- i
- -
- 1
- )
- }
- ,
- where σR is the targeted variance of the response variable R, FSi is the generalized sensitivity coefficient, ζi is the generalized variance coefficient, μi is the mean value of, p is a real number based on the m-value, Si and Ti are first and second derivatives of the response, SKi is the Skewness and Ti is the Kurtosis;
- and
- outputting the variance for each of the plurality of system components.
Dependencies are inferred from the claim text.