Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis
This research presents a new method to improve analytical model fidelity for non-linear systems. The approach investigates several mechanisms to assist the analyst in updating an analytical model based on experimental data and statistical analysis of parameter effects. The first is a new approach at...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2001-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2001/581978 |
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| _version_ | 1849411930389741568 |
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| author | John F. Schultze François M. Hemez Scott W. Doebling Hoon Sohn |
| author_facet | John F. Schultze François M. Hemez Scott W. Doebling Hoon Sohn |
| author_sort | John F. Schultze |
| collection | DOAJ |
| description | This research presents a new method to improve analytical model fidelity for non-linear systems. The approach investigates several mechanisms to assist the analyst in updating an analytical model based on experimental data and statistical analysis of parameter effects. The first is a new approach at data reduction called feature extraction. This approach is an expansion of the `classic' update metrics to include specific phenomena or character of the response that is critical to model application. This is an extension of the familiar linear updating paradigm of utilizing the eigen-parameters or frequency response functions (FRFs) to include such devices as peak acceleration, time of arrival or standard deviation of model error. The next expansion of the updating process is the inclusion of statistical based parameter analysis to quantify the effects of uncertain or significant effect parameters in the construction of a meta-model. This provides indicators of the statistical variation associated with parameters as well as confidence intervals on the coefficients of the resulting meta-model. Also included in this method is the investigation of linear parameter effect screening using a partial factorial variable array for simulation. This is intended to aid the analyst in eliminating from the investigation the parameters that do not have a significant variation effect on the feature metric. Finally, an investigation of the model to replicate the measured response variation is examined. |
| format | Article |
| id | doaj-art-6540ffb30d844eb583b9d7a3edc123ea |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2001-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6540ffb30d844eb583b9d7a3edc123ea2025-08-20T03:34:36ZengWileyShock and Vibration1070-96221875-92032001-01-018632533710.1155/2001/581978Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects AnalysisJohn F. Schultze0François M. Hemez1Scott W. Doebling2Hoon Sohn3Weapon Response Group (ESA-WR), M/S P946, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USAWeapon Response Group (ESA-WR), M/S P946, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USAWeapon Response Group (ESA-WR), M/S P946, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USAWeapon Response Group (ESA-WR), M/S P946, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USAThis research presents a new method to improve analytical model fidelity for non-linear systems. The approach investigates several mechanisms to assist the analyst in updating an analytical model based on experimental data and statistical analysis of parameter effects. The first is a new approach at data reduction called feature extraction. This approach is an expansion of the `classic' update metrics to include specific phenomena or character of the response that is critical to model application. This is an extension of the familiar linear updating paradigm of utilizing the eigen-parameters or frequency response functions (FRFs) to include such devices as peak acceleration, time of arrival or standard deviation of model error. The next expansion of the updating process is the inclusion of statistical based parameter analysis to quantify the effects of uncertain or significant effect parameters in the construction of a meta-model. This provides indicators of the statistical variation associated with parameters as well as confidence intervals on the coefficients of the resulting meta-model. Also included in this method is the investigation of linear parameter effect screening using a partial factorial variable array for simulation. This is intended to aid the analyst in eliminating from the investigation the parameters that do not have a significant variation effect on the feature metric. Finally, an investigation of the model to replicate the measured response variation is examined.http://dx.doi.org/10.1155/2001/581978 |
| spellingShingle | John F. Schultze François M. Hemez Scott W. Doebling Hoon Sohn Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis Shock and Vibration |
| title | Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis |
| title_full | Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis |
| title_fullStr | Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis |
| title_full_unstemmed | Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis |
| title_short | Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis |
| title_sort | application of non linear system model updating using feature extraction and parameter effects analysis |
| url | http://dx.doi.org/10.1155/2001/581978 |
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