Uncertainty quantification for aerodynamic pressure probes, using adaptive quasi-Monte Carlo

2021 ◽  
Author(s):  
Alexandros Christos Chasoglou ◽  
Panagiotis Tsirikoglou ◽  
Anestis I Kalfas ◽  
Reza S Abhari
Keyword(s):  
Monte Carlo ◽  
Measurement Model ◽  
Fast Response ◽  
Two Stage ◽  
Detailed Measurement ◽  
Underlying Distribution ◽  
Factors Affecting ◽  
Quasi Monte Carlo ◽  
Measurement Models ◽  
Aerodynamic Pressure

Abstract In the present study, an adaptive randomized Quasi Monte Carlo methodology is presented, combining Stein’s two-stage adaptive scheme and Low Discrepancy Sobol sequences. The method is used for the propagation and calculation of uncertainties related to aerodynamic pneumatic probes and high frequency fast response aerodynamic probes (FRAP). The proposed methodology allows the fast and accurate, in a probabilistic sense, calculation of uncertainties, ensuring that the total number of Monte Carlo (MC) trials is kept low based on the desired numerical accuracy. Thus, this method is well-suited for aerodynamic pressure probes, where multiple points are evaluated in their calibration space. Complete and detailed measurement models are presented for both a pneumatic probe and FRAP. The models are segregated in sub-problems allowing the evaluation and inspection of intermediate steps of MC in a transparent manner, also enabling the calculation of the relative contributions of the elemental uncertainties on the measured quantities. Various, commonly used sampling techniques for MC simulation and different adaptive MC schemes are compared, using both theoretical toy distributions and actual examples from aerodynamic probes' measurement models. The robustness of Stein's two-stage scheme is demonstrated even in cases when signiffcant deviation from normality is observed in the underlying distribution of the output of the MC. With regards to FRAP, two issues related to piezo-resistive sensors are addressed, namely temperature dependent pressure hysteresis and temporal sensor drift, and their uncertainties are accounted for in the measurement model. These effects are the most dominant factors, affecting all flow quantities' uncertainties, with signiffcance that varies mainly with Mach and operating temperature. This work highlights the need to construct accurate and detailed measurement models for aerodynamic probes, that otherwise will result in signiffcant underestimation (in most cases in excess of 50%) of the final uncertainties.

On Turbulence Measurements and Analyses in a Two-Stage Two-Spool Turbine Rig

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Sabine Bauinger ◽  
Stephan Behre ◽  
Davide Lengani ◽  
Yavuz Guendogdu ◽  
Franz Heitmeir ◽  
...  
Keyword(s):  
Fast Response ◽  
Pressure Probe ◽  
Analysis Tool ◽  
Test Rig ◽  
Ensemble Averaging ◽  
Two Stage ◽  
Turbulence Measurements ◽  
Aerodynamic Pressure ◽  
Stochastic Fluctuations ◽  
Fourier Filtering

Since the experiment in turbulence research is of very high importance for evaluating turbulence hypothesis, turbulence measurements were carried out in a two-stage two-spool transonic turbine test rig at the Institute for Thermal Turbomachinery and Machine Dynamics in Graz in which the two rotors are counter-rotating with two different rotational speeds. For the current measurement campaign, triple hot-wire probes, which represent a very new measurement technique in this test rig, were used and their results validated with a fast response aerodynamic pressure probe (FRAPP). Based on the data measured with this device, turbulence intensities may be determined using a method called Fourier filtering. If the classical ensemble averaging procedure with only one trigger is applied, the periodic fluctuations of the other rotor will artificially increase the stochastic fluctuations. Therefore, the two trigger signals of the two rotors require a special analysis method, which was established at Graz University of Technology. The results from this method will be compared to the classical triple decomposition, which uses only one trigger signal. With this analysis tool, it is not only possible to evaluate unsteady signals triggered by one of the two rotors, but also the unsteady interactions of the rotors can be determined and investigated.

On Turbulence Measurements and Analyses in a Two-Stage Two-Spool Turbine Rig

2015 ◽  
Author(s):  
Sabine Bauinger ◽  
Stephan Behre ◽  
Davide Lengani ◽  
Yavuz Guendogdu ◽  
Franz Heitmeir ◽  
...  
Keyword(s):  
Fast Response ◽  
Pressure Probe ◽  
Analysis Tool ◽  
Test Rig ◽  
Ensemble Averaging ◽  
Two Stage ◽  
Turbulence Measurements ◽  
Aerodynamic Pressure ◽  
Stochastic Fluctuations ◽  
University Of Technology

Since the experiment in turbulence research is of very high importance for evaluating turbulence hypothesis, turbulence measurements were carried out in a two-stage two-spool transonic turbine test rig at the Institute for Thermal Turbomachinery and Machine Dynamics in Graz in which the two rotors are counter-rotating with two different rotational speeds. For the current measurement campaign, triple hot-wire probes, which represent a very new measurement technique in this test rig, were used and their results validated with a fast response aerodynamic pressure probe. Based on the data measured with this device, turbulence intensities may be determined using a method developed by Persico et al. [1]. If the classical ensemble averaging procedure with only one trigger is applied, the periodic fluctuations of the other rotor will artificially increase the stochastic fluctuations. Therefore, the two trigger signals of the two rotors require a special analysis method, which was established at Graz University of Technology by Lengani et al. The results from this method will be compared to the classical triple decomposition, which uses only one trigger signal. With this analysis tool, it is not only possible to evaluate unsteady signals triggered by one of the two rotors but also the unsteady interactions of the rotors can be determined and investigated.

A synergy measurement model to support the pre-deal decision making in mergers and acquisitions

2014 ◽  
Vol 52 (6) ◽  
pp. 1194-1216 ◽  
Author(s):  
Stefano Garzella ◽  
Raffaele Fiorentino
Keyword(s):  
Decision Making ◽  
Mergers And Acquisitions ◽  
Planning Process ◽  
Relevant Literature ◽  
Measurement Model ◽  
Content Type ◽  
Factors Affecting ◽  
Measurement Models ◽  
Strategic Factors ◽  
Measurement Practices

Purpose – The purpose of this paper is to develop an effective synergy measurement model to support the decision-making process in mergers and acquisitions (M&A). Design/methodology/approach – Relevant literature is reviewed and critically assessed. An interpretive methodology is used to analyse empirical data from a questionnaire survey and interviews of M&A experts. A framework is provided with the objective to support the process of synergy measurement and the success of pre-deal planning. Findings – The authors find several mismatches in synergy measurement practices. The strategic factors, which are considered very relevant to generating reliable forecasts, are surprisingly not adequately quantified. On the contrary, a synergy measurement model may integrate the assessment of these factors: the type of synergy, the size of synergy, the timing of synergy and the likelihood of achievement. Practical implications – The paper offers interesting implications for firms, advisors and consultants, pointing out that synergy measurement issues are related to the analysis of strategic factors affecting synergy. These findings suggest that the pre-planning process should integrate people and tools from different backgrounds, from strategy to accounting, to effectively measure the synergy value. The authors also suggest the development of new tools in response to the needs of practitioners for best practices in M&A. Originality/value – This paper highlights that the effective use of synergy measurement models are critical to improve the success of M&A due diligence.

scholarly journals Analysis of approximations of GUM supplement 2 based non-Gaussian PDFs of measurement models with Rosenblatt Gaussian transformation mappings

2020 ◽  
Vol 11 ◽  
pp. 2
Author(s):  
Vishal Ramnath
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Variables ◽  
Measurement Model ◽  
Linear Measurement ◽  
Statistical Simulation ◽  
Implicit Assumption ◽  
Measurement Models ◽  
Gaussian System ◽  
Non Gaussian

In scientific metrology practise the application of Monte Carlo simulations with the aid of the GUM Supplement 2 (GS2) technique for performing multivariate uncertainty analyses is now more prevalent, however a key remaining challenge for metrologists in many laboratories is the implicit assumption of Gaussian characteristics for summarizing and analysing measurement model results. Whilst non-Gaussian probability density functions (PDFs) may result from Monte Carlo simulations when the GS2 is applied for more complex non-linear measurement models, in practice results are typically only reported in terms of multivariate expected and covariance values. Due to this limitation the measurement model PDF summary is implicitly restricted to a multivariate Gaussian PDF in the absence of additional higher order statistics (HOS) information. In this paper an earlier classical theoretical result by Rosenblatt that allows for an arbitrary multivariate joint distribution function to be transformed into an equivalent system of Gaussian distributions with mapped variables is revisited. Numerical simulations are performed in order to analyse and compare the accuracy of the equivalent Gaussian system of mapped random variables for approximating a measurement model’s PDF with that of an exact non-Gaussian PDF that is obtained with a GS2 Monte Carlo statistical simulation. Results obtained from the investigation indicate that a Rosenblatt transformation offers a convenient mechanism to utilize just the joint PDF obtained from the GS2 data in order to both sample points from a non-Gaussian distribution, and also in addition which allows for a simple two-dimensional approach to estimate coupled uncertainties of random variables residing in higher dimensions using conditional densities without the need for determining parametric based copulas.

Structural Model of Service Quality Factors Affecting Loyalty Sustainability

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Eman Al-erqi ◽  
◽  
Mohd Lizam Mohd Diah ◽  
Najmaddin Abo Mosali ◽  
◽  
...  
Keyword(s):  
International Students ◽  
Service Quality ◽  
Goodness Of Fit ◽  
Structural Model ◽  
Quality Factors ◽  
Factors Affecting ◽  
Measurement Models ◽  
Confirmatory Factor ◽  
The University ◽  
The Impact

This study seeks to address the impact of service quality affecting international student's satisfaction towards loyalty tothe Universiti Tun Hussein Onn Malaysia(UTHM). The aim of thestudy is to develop relationship between service quality factor and loyalty to the university from the international students’ perspectives. The study adopted quantitative approach where data was collected through questionnaire survey and analysed statistically. A total of 246 responses were received and found to be valid. The model was developed and analysed using AMOS-SEM software. Confirmatory factor analysis (CFA) function of the software was to assessed the measurement models and found that all the models achieved goodness of fit. Then path analysis function was used to assessed structural model and found that service qualityfactors have a significant effect on the students’ satisfaction and thus affecting the loyaltyto the university. Hopefully the outcome form this study will benefit the university in providing services especially to the international students.

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