Wind Tunnel Strain-Gage Balance Calibration Data Analysis Using a Weighted Least Squares Approach

A new approach is presented that uses a weighted least squares fit to analyze wind tunnel strain-gage balance calibration data. The weighted least squares fit is specifically designed to increase the influence of single-component loadings during the regression analysis. The weighted least squares fit also reduces the impact of calibration load schedule asymmetries on the predicted primary sensitivities of the balance gages. A weighting factor between zero and one is assigned to each calibration data point that depends on a simple count of its intentionally loaded load components or gages. The greater the number of a data point's intentionally loaded load components or gages is, the smaller its weighting factor becomes. The proposed approach is applicable to both the Iterative and Non-Iterative Methods that are used for the analysis of strain-gage balance calibration data in the aerospace testing community. The Iterative Method uses a reasonable estimate of the tare corrected load set as input for the determination of the weighting factors. The Non-Iterative Method, on the other hand, uses gage output differences relative to the natural zeros as input for the determination of the weighting factors. Machine calibration data of a six-component force balance is used to illustrate benefits of the proposed weighted least squares fit. In addition, a detailed derivation of the PRESS residuals associated with a weighted least squares fit is given in the appendices of the paper as this information could not be found in the literature. These PRESS residuals may be needed to evaluate the predictive capabilities of the final regression models that result from a weighted least squares fit of the balance calibration data

Detection of Unexpected High Correlations between Balance Calibration Loads and Load Residuals

An algorithm was developed for the assessment of strain-gage balance calibration data that makes it possible to systematically investigate potential sources of unexpected high correlations between calibration load residuals and applied calibration loads. The algorithm investigates correlations on a load series by load series basis. The linear correlation coefficient is used to quantify the correlations. It is computed for all possible pairs of calibration load residuals and applied calibration loads that can be constructed for the given balance calibration data set. An unexpected high correlation between a load residual and a load is detected if three conditions are met: (i) the absolute value of the correlation coefficient of a residual/load pair exceeds 0.95; (ii) the maximum of the absolute values of the residuals of a load series exceeds 0.25 % of the load capacity; (iii) the load component of the load series is intentionally applied. Data from a baseline calibration of a six-component force balance is used to illustrate the application of the detection algorithm to a real-world data set. This analysis also showed that the detection algorithm can identify load alignment errors as long as repeat load series are contained in the balance calibration data set that do not suffer from load alignment problems

Regression Analysis and Calibration Recommendations for the Characterization of Balance Temperature Effects

Analysis and use of temperature-dependent wind tunnel strain-gage balance calibration data are discussed in the paper. First, three different methods are presented and compared that may be used to process temperature-dependent strain-gage balance data. The first method uses an extended set of independent variables in order to process the data and predict balance loads. The second method applies an extended load iteration equation during the analysis of balance calibration data. The third method uses temperature-dependent sensitivities for the data analysis. Physical interpretations of the most important temperature-dependent regression model terms are provided that relate temperature compensation imperfections and the temperature-dependent nature of the gage factor to sets of regression model terms. Finally, balance calibration recommendations are listed so that temperature-dependent calibration data can be obtained and successfully processed using the reviewed analysis methods

A New Load Residual Threshold Definition for the Evaluation of Wind Tunnel Strain-Gage Balance Data

A new definition of a threshold for the detection of load residual outliers of wind tunnel strain-gage balance data was developed. The new threshold is defined as the product between the inverse of the absolute value of the primary gage sensitivity and an empirical limit of the electrical outputs of a strain{gage. The empirical limit of the outputs is either 2.5 microV/V for balance calibration or check load residuals. A reduced limit of 0.5 microV/V is recommended for the evaluation of differences between repeat load points because, by design, the calculation of these differences removes errors in the residuals that are associated with the regression analysis of the data itself. The definition of the new threshold and different methods for the determination of the primary gage sensitivity are discussed. In addition, calibration data of a six-component force balance and a five-component semi-span balance are used to illustrate the application of the proposed new threshold definition to different types of strain{gage balances. During the discussion of the force balance example it is also explained how the estimated maximum expected output of a balance gage can be used to better understand results of the application of the new threshold definition

Independent policy learning: Contextual diffusion of active labour market policies

This chapter analyses in which ways diffusion based on interdependent policy learning explains the spread of active labour market policies (ALMP) in the OECD countries. By applying error correction models using multiplicative spatial Prais-Winsten regressions for analyzing the diffusion of ALMPs in 22 OECD countries from 1991–2013, we find evidence of governments adapting labour market policy strategies that have proven successful, that is, perform well in increasing labour market participation in other countries. However, interdependent learning is conditional on the institutional framework: policymakers rather learn from the experience of other countries in the same welfare regime. Even more importantly, the results bear witness to the importance of the European Employment Strategy (EES) as an international coordination framework facilitating policy learning

Diagnosing Autism Spectrum Disorders in Adults: the Use of Autism Diagnostic Observation Schedule (ADOS) Module 4

Autism Diagnostic Observation Schedule (ADOS) module 4 was investigated in an independent sample of high-functioning adult males with an autism spectrum disorder (ASD) compared to three specific diagnostic groups: schizophrenia, psychopathy, and typical development. ADOS module 4 proves to be a reliable instrument with good predictive value. It can adequately discriminate ASD from psychopathy and typical development, but is less specific with respect to schizophrenia due to behavioral overlap between autistic and negative symptoms. However, these groups differ on some core items and explorative analyses indicate that a revision of the algorithm in line with Gotham et al. (J Autism Dev Disord 37: 613–627, 2007) could be beneficial for discriminating ASD from schizophrenia

Systematizing Policy Learning: From Monolith to Dimensions

notes: The authors wish to express their gratitude to the Norwegian Political Science Association Annual Conference, 6 January 2010, University of Agder, Kristiansand, participants of the ‘Establishing Causality in Policy Learning’ panel at the American Political Science Association (APSA) annual meeting,2–5 September 2010,Washington DC, and the European Consortium of Political Research (ECPR) Joint Sessions, St Gallen, 12–17 April 2011, workshop 2. Dunlop and Radaelli gratefully acknowledge the support of the European Research Council, grant on Analysis of Learning in Regulatory Governance, ALREG, http://centres.exeter.ac.uk/ceg/research/ALREG/index.php.publication-status: AcceptedThe definitive version is available at www.blackwell-synergy.com and also from DOI: 10.1111/j.1467-9248.2012.00982.xThe field of policy learning is characterised by concept stretching and lack of systematic findings. To systematize them, we combine the classic Sartorian approach to classification with the more recent insights on explanatory typologies. At the outset, we classify per genus et differentiam – distinguishing between the genus and the different species within it. By drawing on the technique of explanatory typologies to introduce a basic model of policy learning, we identify four major genera in the literature. We then generate variation within each cell by using rigorous concepts drawn from adult education research. Specifically, we conceptualize learning as control over the contents and goals of knowledge. By looking at learning through the lenses of knowledge utilization, we show that the basic model can be expanded to reveal sixteen different species. These types are all conceptually possible, but are not all empirically established in the literature. Up until now the scope conditions and connections among types have not been clarified. Our reconstruction of the field sheds light on mechanisms and relations associated with alternatives operationalizations of learning and the role of actors in the process of knowledge construction and utilization. By providing a comprehensive typology, we mitigate concept stretching problems and aim to lay the foundations for the systematic comparison across and within cases of policy learning.European Research Council, grant no 230267 on Analysis of Learning in Regulatory Governance, ALREG