scholarly journals FIRST DEGREE POLYNOMIAL FITTING FOR FORCES AND MOMENTS ON THE AERODYNAMIC TEST MODEL

2017 ◽  
Vol 14 (27) ◽  
pp. 97-104
Author(s):  
Itamar Magno BARBOSA ◽  
Bogos Nubar SISMANOGLU ◽  
Pedro Ivo Pinto OLIVEIRA
Keyword(s):  
Systematic Errors ◽  
Standard Uncertainty ◽  
Test Model ◽  
Aerodynamic Forces ◽  
Degree Polynomial ◽  
Multivariate Polynomial ◽  
Error Sources ◽  
Iso 17025 ◽  
Polynomial Modeling ◽  
Aerodynamic Test

We analyzed a multivariate polynomial model related to Calibration Curve of an External Balance of Aerodynamic Forces. The ISO 17025 explicit that it is not always possible to calculate rigorously the measurement uncertainty in Test Laboratories. The test nature circumscribes error sources control, time and costs. This fact implies that choosing a Mathematical Modeling is also an affair of productivity management, not only a computational matter. For a First Degree Polynomial Modeling we evaluated the Statistical Performance Index, as to Measurement Bias, Standard Uncertainty, X – Square and Uncertainties and Co – Variances Matrix. We evaluated the Measurement System Repeatability through successive resembling Calibrations. We showed Aerodynamic Forces that may be considered individual ones trough uncertainties and systematic errors. In the same way, bias was verified through systematic error analysis, i. e., in what conditions the polynomial intersects at the origin or not.

Error sources and models of the laser tracker without a beam steering mirror

2021 ◽  
Author(s):  
Radoslav Choleva ◽  
Alojz Kopáčik
Keyword(s):  
Measurement Accuracy ◽  
Beam Steering ◽  
Systematic Errors ◽  
Error Modeling ◽  
Laser Tracker ◽  
High Demand ◽  
Error Sources ◽  
Error Models ◽  
Measurement Results

AbstractThe laser tracker is a widely used instrument in many industrial and metrological applications with high demand measurement accuracy. Imperfections in construction and misalignment of individual parts deliver systematic errors in the measurement results. All error sources need to be identified and reduced to the minimum to achieve the best possible accuracy. The paper summarizes error sources of the laser tracker without beam steering mirror with emphasis on error modeling. Descriptions of error models are provided for the static and kinematic type of measurement.

Development of Steam Chest With Valve for Steam Extraction in Cogeneration

2014 ◽  
Author(s):  
Zhenzhen Hao ◽  
Puning Jiang ◽  
Xingzhu Ye ◽  
Gang Chen ◽  
Yifeng Hu ◽  
...  
Keyword(s):  
Flow Field ◽  
Pressure Distribution ◽  
Fourier Transforms ◽  
Operating Conditions ◽  
Mode Analysis ◽  
Technical Solution ◽  
Test Model ◽  
Aerodynamic Forces ◽  
Valve Disc ◽  
The Impact

Cogeneration has been identified as a key technical solution to improve environment, by reducing the impact of global climate change and reducing local emissions, such as particulates, sulphur and nitrogen oxides. In cogeneration, a certain pressure of steam has to be extracted from steam turbine. A mechanical device shall be used to maintain the pressure of the extracted steam. In this paper a new steam chest with valve used for cogeneration which is installed in the steam flow is introduced. Different amount of steam extractions need different valve openings. In order to obtain these several valve openings in typical operating conditions, CFD-program is used to simulate the flow path in the steam chest. The pressure distribution on the surface of valve disc can be calculated through CFD method, and corresponding steady aerodynamic forces and torques can be calculated by integral. Pulsatile flow will change the forces and moments acting on the valve discs with time constantly. Frequency spectrograms of the aerodynamic forces are obtained by using the fast Fourier transforms and compared to the characteristic frequencies of the valve disc obtained by mode analysis. For the purpose of validating the accuracy of CFD model, a test with test model scale of 1:5 has been designed. In the test, the pressure distribution on the valve disc surface and the flow field in the steam chest are acquired respectively by the method PSP (Pressure-Sensitive Paint) and PIV (Particle Image Velocimetry). CFD calculations and experimental results have been compared and it is shown that CFD calculations using K-ε turbulence model has satisfactory precision to calculate the pressure distribution, flow field and the torques.

scholarly journals Trends of solar ultraviolet irradiance at Barrow, Alaska, and the effect of measurement uncertainties on trend detection

2011 ◽  
Vol 11 (9) ◽  
pp. 26617-26655 ◽  
Author(s):  
G. Bernhard
Keyword(s):  
Time Series ◽  
Systematic Errors ◽  
Correction Method ◽  
Natural Variability ◽  
Trend Detection ◽  
Transfer Model ◽  
Error Sources ◽  
Model Calculations ◽  
Uv Index ◽  
Measurement Uncertainties

Abstract. Spectral ultraviolet (UV) irradiance has been observed near Barrow, Alaska (71° N, 157° W) between 1991 and 2011 with an SUV-100 spectroradiometer. The instrument was historically part of the US. National Science Foundation's UV Monitoring Network and is now a component of NSF's Arctic Observing Network. From these measurements, trends in monthly average irradiance and their uncertainties were calculated. The analysis focuses on two quantities, the UV Index (which is affected by atmospheric ozone concentrations) and irradiance at 345 nm (which is virtually insensitive to ozone). Uncertainties of trend estimates depend on variations in the data due to (1) natural variability, (2) systematic and random errors of the measurements, and (3) uncertainties caused by gaps in the time series. Using radiative transfer model calculations, systematic errors of the measurements were detected and corrected. Different correction schemes were tested to quantify the sensitivity of the trend estimates on the treatment of systematic errors. Depending on the correction method, estimates of decadal trends changed between 1.5% and 2.9%. Uncertainties in the trend estimates caused by error sources (2) and (3) were set into relation with the overall uncertainty of the trend determinations. Results show that these error sources are only relevant for February, March, and April when natural variability is low due to high surface albedo. This method of addressing measurement uncertainties in time series analysis is also applicable to other geophysical parameters. Trend estimates varied between −14% and +5% per decade and were significant (95.45% confidence level) only for the month of October. Depending on the correction method, October trends varied between −11.4% and −13.7% for irradiance at 345 nm and between −11.7% and −14.1% for the UV Index. These large trends are consistent with trends in short-wave (0.3–3.0 μm) solar irradiance measured with pyranometers at NOAA's Barrow Observatory and can be explained by a change in snow cover over the observation period: analysis of pyranometer data indicates that the first day of fall when albedo becomes larger than 0.6 after snow fall, and remains above 0.6 for the rest of the winter, has advanced with a statistically significant trend of 13.6 ± 9.7 days per decade.

scholarly journals Error sources in the force mode of the “PB2” Planck-Balance

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 53
Author(s):  
N. Rogge ◽  
C. Rothleitner ◽  
S. Lin ◽  
S. Vasilyan ◽  
T. Fröhlich ◽  
...  
Keyword(s):  
Systematic Errors ◽  
Error Sources ◽  
Force Mode ◽  
Class E

The PB2 Planck-Balance is a table-top Kibble balance, that is designed for the calibration of class E<sub>2</sub> weights in a range of 1 mg up to 100 g. This work presents typical systematic errors which have to be considered during the calibration and will show results for measurements with small masses.

scholarly journals Trends of solar ultraviolet irradiance at Barrow, Alaska, and the effect of measurement uncertainties on trend detection

2011 ◽  
Vol 11 (24) ◽  
pp. 13029-13045 ◽  
Author(s):  
G. Bernhard
Keyword(s):  
Time Series ◽  
Systematic Errors ◽  
Correction Method ◽  
Natural Variability ◽  
Trend Detection ◽  
Transfer Model ◽  
Error Sources ◽  
Model Calculations ◽  
Uv Index ◽  
Measurement Uncertainties

Abstract. Spectral ultraviolet (UV) irradiance has been observed near Barrow, Alaska (71° N, 157° W) between 1991 and 2011 with an SUV-100 spectroradiometer. The instrument was historically part of the US National Science Foundation's UV Monitoring Network and is now a component of NSF's Arctic Observing Network. From these measurements, trends in monthly average irradiance and their uncertainties were calculated. The analysis focuses on two quantities, the UV Index (which is affected by atmospheric ozone concentrations) and irradiance at 345 nm (which is virtually insensitive to ozone). Uncertainties of trend estimates depend on variations in the data due to (1) natural variability, (2) systematic and random errors of the measurements, and (3) uncertainties caused by gaps in the time series. Using radiative transfer model calculations, systematic errors of the measurements were detected and corrected. Different correction schemes were tested to quantify the sensitivity of the trend estimates on the treatment of systematic errors. Depending on the correction method, estimates of decadal trends changed between 1.5% and 2.9%. Uncertainties in the trend estimates caused by error sources (2) and (3) were set into relation with the overall uncertainty of the trend determinations. Results show that these error sources are only relevant for February, March, and April when natural variability is low due to high surface albedo. This method of addressing measurement uncertainties in time series analysis is also applicable to other geophysical parameters. Trend estimates varied between −14% and +5% per decade and were significant (95.45% confidence level) only for the month of October. Depending on the correction method, October trends varied between −11.4% and −13.7% for irradiance at 345 nm and between −11.7% and −14.1% for the UV Index. These large trends are consistent with trends in short-wave (0.3–3.0 μm) solar irradiance measured with pyranometers at NOAA's Barrow Observatory and can be explained by a change in snow cover over the observation period: analysis of pyranometer data indicates that the first day of fall when albedo becomes larger than 0.6 after snow fall, and remains above 0.6 for the rest of the winter, has advanced with a statistically significant trend of 13.6 ± 9.7 days per decade.

scholarly journals Impact of uncertainties in discharge determination on the parameter estimation and performance of a hydrological model

2012 ◽  
Vol 44 (3) ◽  
pp. 454-466 ◽  
Author(s):  
Sander P. M. van den Tillaart ◽  
Martijn J. Booij ◽  
Maarten S. Krol
Keyword(s):  
Impact Assessment ◽  
Water Level ◽  
Measurement Errors ◽  
Hydrological Model ◽  
Model Performance ◽  
Systematic Errors ◽  
Error Sources ◽  
Random Measurement ◽  
Discharge Water ◽  
And Performance

Uncertainties in discharge determination may have serious consequences for hydrological modelling and resulting discharge predictions used for flood forecasting, climate change impact assessment and reservoir operation. The aim of this study is to quantify the effect of discharge errors on parameters and performance of a conceptual hydrological model for discharge prediction applied to two catchments. Six error sources in discharge determination are considered: random measurement errors without autocorrelation; random measurement errors with autocorrelation; systematic relative measurement errors; systematic absolute measurement errors; hysteresis in the discharge–water level relation and effects of an outdated discharge–water level relation. Assuming realistic magnitudes for each error source, results show that systematic errors and an outdated discharge–water level relation have a considerable influence on model performance, while other error sources have a small to negligible effect. The effects of errors on parameters are large if the effects on model performance are large as well and vice versa. Parameters controlling the water balance are influenced by systematic errors and parameters related to the shape of the hydrograph are influenced by random errors. Large effects of discharge errors on model performance and parameters should be taken into account when using discharge predictions for flood forecasting and impact assessment.

scholarly journals Carbon source/sink information provided by column CO<sub>2</sub> measurements from the Orbiting Carbon Observatory

2010 ◽  
Vol 10 (9) ◽  
pp. 4145-4165 ◽  
Author(s):  
D. F. Baker ◽  
H. Bösch ◽  
S. C. Doney ◽  
D. O'Brien ◽  
D. S. Schimel
Keyword(s):  
Measurement Errors ◽  
Simulated Data ◽  
Systematic Errors ◽  
Surface Type ◽  
Error Sources ◽  
Time Space ◽  
Space Error ◽  
Measurement Biases ◽  
Source Sink ◽  
The Impact

Abstract. We quantify how well column-integrated CO2 measurements from the Orbiting Carbon Observatory (OCO) should be able to constrain surface CO2 fluxes, given the presence of various error sources. We use variational data assimilation to optimize weekly fluxes at a 2°×5° resolution (lat/lon) using simulated data averaged across each model grid box overflight (typically every ~33 s). Grid-scale simulations of this sort have been carried out before for OCO using simplified assumptions for the measurement error. Here, we more accurately describe the OCO measurements in two ways. First, we use new estimates of the single-sounding retrieval uncertainty and averaging kernel, both computed as a function of surface type, solar zenith angle, aerosol optical depth, and pointing mode (nadir vs. glint). Second, we collapse the information content of all valid retrievals from each grid box crossing into an equivalent multi-sounding measurement uncertainty, factoring in both time/space error correlations and data rejection due to clouds and thick aerosols. Finally, we examine the impact of three types of systematic errors: measurement biases due to aerosols, transport errors, and mistuning errors caused by assuming incorrect statistics. When only random measurement errors are considered, both nadir- and glint-mode data give error reductions over the land of ~45% for the weekly fluxes, and ~65% for seasonal fluxes. Systematic errors reduce both the magnitude and spatial extent of these improvements by about a factor of two, however. Improvements nearly as large are achieved over the ocean using glint-mode data, but are degraded even more by the systematic errors. Our ability to identify and remove systematic errors in both the column retrievals and atmospheric assimilations will thus be critical for maximizing the usefulness of the OCO data.

scholarly journals Assessment of lidar depolarization uncertainty by means of a polarimetric lidar simulator

2016 ◽  
Vol 9 (10) ◽  
pp. 4935-4953 ◽  
Author(s):  
Juan Antonio Bravo-Aranda ◽  
Livio Belegante ◽  
Volker Freudenthaler ◽  
Lucas Alados-Arboledas ◽  
Doina Nicolae ◽  
...  
Keyword(s):  
Particle Shape ◽  
Polarization State ◽  
Systematic Errors ◽  
Shape Information ◽  
Error Sources ◽  
Microphysical Properties ◽  
Error Space ◽  
Linear Depolarization Ratio ◽  
Relative Errors

Abstract. Lidar depolarization measurements distinguish between spherical and non-spherical aerosol particles based on the change of the polarization state between the emitted and received signal. The particle shape information in combination with other aerosol optical properties allows the characterization of different aerosol types and the retrieval of aerosol particle microphysical properties. Regarding the microphysical inversions, the lidar depolarization technique is becoming a key method since particle shape information can be used by algorithms based on spheres and spheroids, optimizing the retrieval procedure. Thus, the identification of the depolarization error sources and the quantification of their effects are crucial. This work presents a new tool to assess the systematic error of the volume linear depolarization ratio (δ), combining the Stokes–Müller formalism and the complete sampling of the error space using the lidar model presented in Freudenthaler (2016a). This tool is applied to a synthetic lidar system and to several EARLINET lidars with depolarization capabilities at 355 or 532 nm. The lidar systems show relative errors of δ larger than 100 % for δ values around molecular linear depolarization ratios (∼ 0.004 and up to ∼  10 % for δ = 0.45). However, one system shows only relative errors of 25 and 0.22 % for δ = 0.004 and δ = 0.45, respectively, and gives an example of how a proper identification and reduction of the main error sources can drastically reduce the systematic errors of δ. In this regard, we provide some indications of how to reduce the systematic errors.

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