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Application of a Temperature-Dependent Load Prediction Method to a RUAG Six-Component Block-Type BalanceTemperature-dependent data of a RUAG six-component block-type balance was analyzed to assess the accuracy of two load prediction methods for temperature-dependent balance data. The supplied data was prepared for the analysis by splitting it into calibration and check load data subsets. The first calibration data subset was obtained at a temperature of 294 Kelvin. The second calibration data subset was obtained at a temperature of 315 Kelvin. A subset of 38 points was extracted from the second data set and used as check loads so that the accuracy of the two load prediction methods could be tested. First, the Iterative Method in combination with an extended independent and dependent variable set was used for the balance load prediction. This approach fits electrical outputs as a function of loads and the temperature and, afterwards, constructs a load iteration scheme from the regression coefficients so that loads can be predicted from outputs and the temperature during a wind tunnel test. The Non-Iterative Method was also used for the load prediction. This alternate method can more easily be implemented in a data system as loads are directly fitted as a function of electrical outputs and the temperature. Analysis results for the axial force are only discussed in the paper as similar results were obtained for the other five load components. Results for both methods clearly show that the cross-product term constructed from either a primary gage load or a primary gage output and the temperature explains the majority of the temperature-dependent part of the predicted balance load. This term models the temperature dependent nature of the gage sensitivity. Therefore, it is recommended to apply primary gage loadings at different temperatures during a balance calibration whenever temperature effects need to be described. These loadings will contain information about the temperature-dependent nature of the gage sensitivities that can be quantified by related cross-product terms in regression models of the data.
Document ID
20190000039
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Ulbrich, N.
(Jacobs Technology, Inc. Moffett Field, CA, United States)
Zimmermann, C.
(RUAG Aerospace Emmen, Switzerland)
Date Acquired
January 10, 2019
Publication Date
January 7, 2019
Subject Category
Aeronautics (General)
Report/Patent Number
ARC-E-DAA-TN62271
Meeting Information
Meeting: SciTech Forum
Location: San Diego, CA
Country: United States
Start Date: January 7, 2019
End Date: January 11, 2019
Sponsors: American Inst. of Aeronautics and Astronautics
Funding Number(s)
CONTRACT_GRANT: NNA16BD26C
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Keywords
strain-gage balance
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