Calibration Variable Selection and Natural Zero Determination for Semispan and Canard Balances

Independent calibration variables for the characterization of semispan and canard wind tunnel balances are discussed. It is shown that the variable selection for a semispan balance is determined by the location of the resultant normal and axial forces that act on the balance. These two forces are the first and second calibration variable. The pitching moment becomes the third calibration variable after the normal and axial forces are shifted to the pitch axis of the balance. Two geometric distances, i.e., the rolling and yawing moment arms, are the fourth and fifth calibration variable. They are traditionally substituted by corresponding moments to simplify the use of calibration data during a wind tunnel test. A canard balance is related to a semispan balance. It also only measures loads on one half of a lifting surface. However, the axial force and yawing moment are of no interest to users of a canard balance. Therefore, its calibration variable set is reduced to the normal force, pitching moment, and rolling moment. The combined load diagrams of the rolling and yawing moment for a semispan balance are discussed. They may be used to illustrate connections between the wind tunnel model geometry, the test section size, and the calibration load schedule. Then, methods are reviewed that may be used to obtain the natural zeros of a semispan or canard balance. In addition, characteristics of three semispan balance calibration rigs are discussed. Finally, basic requirements for a full characterization of a semispan balance are reviewed

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm

Development of a Non-Iterative Balance Load Prediction Algorithm for the NASA Ames Unitary Plan Wind Tunnel

A non-iterative load prediction algorithm for strain-gage balances was developed for the NASA Ames Unitary Plan Wind Tunnels that computes balance loads from the electrical outputs of the balance bridges and a set of state variables. A state variable could be, for example, a balance temperature difference or the bellows pressure of a flow-through balance. The algorithm directly uses regression models of the balance loads for the load prediction that were obtained by applying global regression analysis to balance calibration data. This choice greatly simplifies both implementation and use of the load prediction process for complex balance configurations as no load iteration needs to be performed. The regression model of a balance load is constructed by using terms from a total of nine term groups. Four term groups are derived from a Taylor Series expansion of the relationship between the load, gage outputs, and state variables. The remaining five term groups are defined by using absolute values of the gage outputs and state variables. Terms from these groups should only be included in the regression model if calibration data from a balance with known bi-directional outputs is analyzed. It is illustrated in detail how global regression analysis may be applied to obtain the coefficients of the chosen regression model of a load component assuming that no linear or massive near-linear dependencies between the regression model terms exist. Data from the machine calibration of a six-component force balance is used to illustrate both application and accuracy of the non-iterative load prediction process

A New Global Regression Analysis Method for the Prediction of Wind Tunnel Model Weight Corrections

A new global regression analysis method is discussed that predicts wind tunnel model weight corrections for strain-gage balance loads during a wind tunnel test. The method determines corrections by combining "wind-on" model attitude measurements with least squares estimates of the model weight and center of gravity coordinates that are obtained from "wind-off" data points. The method treats the least squares fit of the model weight separate from the fit of the center of gravity coordinates. Therefore, it performs two fits of "wind- off" data points and uses the least squares estimator of the model weight as an input for the fit of the center of gravity coordinates. Explicit equations for the least squares estimators of the weight and center of gravity coordinates are derived that simplify the implementation of the method in the data system software of a wind tunnel. In addition, recommendations for sets of "wind-off" data points are made that take typical model support system constraints into account. Explicit equations of the confidence intervals on the model weight and center of gravity coordinates and two different error analyses of the model weight prediction are also discussed in the appendices of the paper

Effects of Cyclooxygenase Inhibitors on Apoptotic Neuroretinal Cells

Glaucoma is characterized by a loss of retinal ganglion cells (RGC) which is associated with a decrease of visual function. Neuroprotective agents as a new therapeutic strategy could prevent the remaining neurons from apoptotic cell death. Previous studies have shown the involvement of the Cyclooxygenase (COX)-2 signalling in the apoptotic death of neurons. Herein we investigated the neuroprotective effect of COX-1/COX-2- and selective COX-2- inhibitors on apoptotic. R28, a neuroretinal cell line and determined the PGE 2 levels by ELISA. Furthermore we investigated differences in protein expression in the cells after exposure to elevated pressure compared to untreated cells by ProteinChip analysis. In addition, a protein profiling study of the cells after exposure to elevated pressure was performed. The protein expression profiles were measured by SELDI-TOF (Surface Enhanced Laser Desorption/Ionization-time of flight) Protein Chips. The protein identification was performed by mass spectrometry (MS). It could be shown that COX-2 inhibition significantly prevented the cells from apoptosis and reduced the PGE 2 concentrations. Selective COX-2 inhibitors were significant more potent than non-selective inhibitors or COX-1 inhibitors. We found differently expressed protein patterns in neuroretinal cells cultured at atmospheric pressure compared to those cells exposed to elevated pressure with or without celecoxib respectively. We identified three biomarkers, ubiquitin, HSP10 and NDKB, which were differently expressed in the groups. However, our data indicates a distinct neuroprotective effect of COX-2 inhibition. The local treatment with selective COX-2 inhibitors might provide an innovative strategy of therapeutic interventio