Calibration Data

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Norbert Ulbrich - One of the best experts on this subject based on the ideXlab platform.

  • Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data
    31st AIAA Aerodynamic Measurement Technology and Ground Testing Conference, 2015
    Co-Authors: Norbert Ulbrich
    Abstract:

    An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of Calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance Calibration Data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary Calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent Data obtained from Triumph Aerospace's 2013 Calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.

  • Hidden Connections between Regression Models of Strain-Gage Balance Calibration Data
    51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 2013
    Co-Authors: Norbert Ulbrich
    Abstract:

    Hidden connections between regression models of wind tunnel strain–gage balance Calibration Data are investigated. These connections become visible whenever balance Calibration Data is supplied in its design format and both the Iterative and Non–Iterative Method are used to process the Data. First, it is shown how the regression coefficients of the fitted balance loads of a force balance can be approximated by using the corresponding regression coefficients of the fitted strain–gage outputs. Then, Data from the manual Calibration of the Ames MK40 six–component force balance is chosen to illustrate how estimates of the regression coefficients of the fitted balance loads can be obtained from the regression coefficients of the fitted strain–gage outputs. The study illustrates that load predictions obtained by applying the Iterative or the Non–Iterative Method originate from two related regression solutions of the balance Calibration Data as long as balance loads are given in the design format of the balance, gage outputs behave highly linear, strict statistical quality metrics are used to assess regression models of the Data, and regression model term combinations of the fitted loads and gage outputs can be obtained by a simple variable exchange.

  • Detection of Bi-Directionality in Strain-Gage Balance Calibration Data
    28th Aerodynamic Measurement Technology Ground Testing and Flight Testing Conference, 2012
    Co-Authors: Norbert Ulbrich
    Abstract:

    An indicator variable was developed for both visualization and detection of bi-directionality in wind tunnel strain-gage balance Calibration Data. First, the calculation of the indicator variable is explained in detail. Then, a criterion is discussed that may be used to decide which gage outputs of a balance have bi- directional behavior. The result of this analysis could be used, for example, to justify the selection of certain absolute value or other even function terms in the regression model of gage outputs whenever the Iterative Method is chosen for the balance Calibration Data analysis. Calibration Data of NASA s MK40 Task balance is analyzed to illustrate both the calculation of the indicator variable and the application of the proposed criterion. Finally, bi directionality characteristics of typical multi piece, hybrid, single piece, and semispan balances are determined and discussed.

  • Evaluation of Regression Models of Balance Calibration Data Using an Empirical Criterion
    28th Aerodynamic Measurement Technology Ground Testing and Flight Testing Conference, 2012
    Co-Authors: Norbert Ulbrich, Thomas Volden
    Abstract:

    An empirical criterion for assessing the significance of individual terms of regression models of wind tunnel strain–gage balance outputs is evaluated. The criterion is based on the percent contribution of a regression model term. It considers a term to be significant if its percent contribution exceeds the empirical threshold of 0.05 %. The criterion has the advantage that it can easily be computed using the regression coefficients of the gage outputs and the load capacities of the balance. First, a definition of the empirical criterion is provided. Then, it is compared with an alternate statistical criterion that is widely used in regression analysis. Finally, Calibration Data sets from a variety of balances are used to illustrate the connection between the empirical and the statistical criterion. A review of these results indicated that the empirical criterion seems to be suitable for a crude assessment of the significance of a regression model term as the boundary between a significant and an insignificant term cannot be defined very well. Therefore, regression model term reduction should only be performed by using the more universally applicable statistical criterion.

  • Iterative Strain-Gage Balance Calibration Data Analysis for Extended Independent Variable Sets
    49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 2011
    Co-Authors: Norbert Ulbrich
    Abstract:

    A new method was developed that makes it possible to use an extended set of independent Calibration variables for an iterative analysis of wind tunnel strain–gage balance Calibration Data. The new method permits the application of the iterative analysis method whenever the total number of balance loads and other independent Calibration variables is greater than the total number of measured strain–gage outputs. Iteration equations used by the iterative analysis method have the limitation that the number of independent and dependent variables must match. The new method circumvents this limitation. It simply adds a missing dependent variable to the original Data set by using an additional independent variable also as an additional dependent variable. Then, the desired solution of the regression analysis problem can be obtained that fits each gage output as a function of both the original and additional independent Calibration variables. The final regression coefficients can be converted to Data reduction matrix coefficients because the missing dependent variables were added to the Data set without changing the regression analysis result for each gage output. Therefore, the new method still supports the application of the two load iteration equation choices that the iterative method traditionally uses for the prediction of balance loads during a wind tunnel test. An example is discussed in the paper that illustrates the application of the new method to a realistic simulation of temperature dependent Calibration Data set of a six–component balance.

Soroosh Sorooshian - One of the best experts on this subject based on the ideXlab platform.

  • automatic Calibration of conceptual rainfall runoff models sensitivity to Calibration Data
    Journal of Hydrology, 1996
    Co-Authors: Patrice Ogou Yapo, Hoshin Vijai Gupta, Soroosh Sorooshian
    Abstract:

    Abstract The identification of hydrologic models requires that appropriate Data be selected for model Calibration. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the NWSRFS-SMA conceptual rainfall-runoff flood forecasting model of the US National Weather Service, using a 40-year record of historical Data. Based on 344 Calibration runs using different lengths of Data from different sections of the historical record, we conclude that approximately 8 years of Data are required to obtain Calibrations that are relatively insensitive to the period selected. Further, the reduction in parameter uncertainty is maximal when the wettest Data periods on record are used. A residual analysis is used to compare the performance of the daily root mean square (DRMS) and heteroscedastic maximum likelihood error (HMLE) objective functions. The results suggest that the factor currently limiting model performance is the unavailability of strategies that explicitly account for model error during Calibration.

D. R. Keyser - One of the best experts on this subject based on the ideXlab platform.

Patrice Ogou Yapo - One of the best experts on this subject based on the ideXlab platform.

  • automatic Calibration of conceptual rainfall runoff models sensitivity to Calibration Data
    Journal of Hydrology, 1996
    Co-Authors: Patrice Ogou Yapo, Hoshin Vijai Gupta, Soroosh Sorooshian
    Abstract:

    Abstract The identification of hydrologic models requires that appropriate Data be selected for model Calibration. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the NWSRFS-SMA conceptual rainfall-runoff flood forecasting model of the US National Weather Service, using a 40-year record of historical Data. Based on 344 Calibration runs using different lengths of Data from different sections of the historical record, we conclude that approximately 8 years of Data are required to obtain Calibrations that are relatively insensitive to the period selected. Further, the reduction in parameter uncertainty is maximal when the wettest Data periods on record are used. A residual analysis is used to compare the performance of the daily root mean square (DRMS) and heteroscedastic maximum likelihood error (HMLE) objective functions. The results suggest that the factor currently limiting model performance is the unavailability of strategies that explicitly account for model error during Calibration.

J. W. Murdock - One of the best experts on this subject based on the ideXlab platform.

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