Uncertainty Analysis of Centrifugal Compressor Aero-Performance Test Data: Effects of Correlated Systematic Error

2006 ◽  
Author(s):  
Jose´ L. Gilarranz R.
Keyword(s):  
Uncertainty Analysis ◽  
Test Data ◽  
Centrifugal Compressor ◽  
Performance Test ◽  
Equations Of State ◽  
Uncertainty Propagation ◽  
Acceptance Test ◽  
Performance Measurements ◽  
Monte Carlo Simulation Technique ◽  
Real Gas Equations

This paper presents the continuation of the work performed during the development of an uncertainty analysis method for estimating error levels in data gathered during factory aero-performance acceptance tests of centrifugal compressors. The previous work incorporated the effects of the variation and uncertainty levels associated with every parameter used in the calculation of centrifugal compressor aero-thermal performance. The work discussed herein focuses on the effects of the variation and uncertainty levels associated with the key measured variables, which are the parameters identified as having the greatest effect on the uncertainty of the performance measurements. Also included in this work is an evaluation of the effects of the correlated bias uncertainty components associated with said key variables, as well as comments on how these effects can be harnessed to reduce the uncertainty of the test data. The evaluation is performed via parametric studies, which present the test uncertainty levels achievable as a function of different correlation levels between the systematic uncertainty components of the measured data. Two different methods are used for the analysis of data measured for several machines. The first method is based on the direct use of the Monte Carlo simulation technique combined with real gas equations of state. The second method employs uncertainty propagation equations and the methodology included in the ASME PTC-19.1(1998) test code. Both approaches use the polytropic compression model and equations for performance evaluation included in the ASME PTC 10 (1997) Power Test Code. Data gathered during an on-site acceptance test of a centrifugal gas compression package are used to illustrate the effects of the uncertainty in the knowledge of the gas composition handled by the compressor over the uncertainty levels that can be obtained with this type of tests.

Uncertainty Analysis of a Polytropic Compression Process and Application to Centrifugal Compressor Performance Testing

2005 ◽  
Author(s):  
Jose´ L. Gilarranz
Keyword(s):  
Uncertainty Analysis ◽  
Centrifugal Compressor ◽  
Equations Of State ◽  
Pressure Ratio ◽  
Ideal Gas ◽  
Isentropic Compression ◽  
Real Gas ◽  
Compression Model ◽  
Test Loop ◽  
Real Gas Equations

In recent years, several papers have been written concerning the application of uncertainty analyses for isentropic compression processes under the assumption of ideal gas behavior. However, for high-pressure ratio machines, the ideal gas model fails to capture the physics of the process. Still, the estimation of test uncertainty for polytropic processes is hindered by the complexity of the equations used to calculate the performance parameters and by the incorporation of real gas equations into the models. This paper presents an uncertainty analysis developed to estimate the error levels in data gathered during factory aero-performance tests of single- or multi-stage centrifugal compressors. The analysis incorporates the effects of the variation and uncertainty levels of every parameter used to calculate centrifugal compressor aero-thermal performance. Included are the variables used to define the thermodynamic states of the fluid inside the compressor, as well as geometric and operational parameters associated with the machine and test loop. Two different methods have been utilized and the results compared to evaluate the advantages and drawbacks of each. The first method is based on the direct use of the Monte Carlo simulation technique combined with real gas equations of state. The second method employs uncertainty propagation equations and the methodology included in the ASME PTC-19.1 (1998) Test Code. Both approaches utilize the polytropic compression model and equations for performance evaluation that are included in the ASME PTC 10 (1997) Power Test Code for compressors and exhausters. The methods and results from this work may be easily extended to the isentropic compression model as well. The use of real gas equations of state make the methods applicable to virtually any gas composition. Although the analysis was intended to be applied to ASME PTC 10 Type 2 tests, the method can be extended to evaluate Type 1 and/or on-site field tests, as long as certain considerations are addressed. The uncertainty analysis presented is then used to evaluate data from several machines, ranging from a low-pressure ratio gas pipeline compressor to an eight-stage machine used for natural gas processing. Comments are offered concerning the effects of machine pressure ratio on the levels of uncertainty, as well as the importance of proper selection of instrumentation to minimize the error level of the test data. Special emphasis is placed on the benefits of using this analysis during the planning phase of the test program, to determine the optimal combination of instruments, to guarantee acceptable levels of uncertainty.

Application of Data Reconciliation Method to Increase CCPP Performance Test Result Accuracy

2015 ◽  
Author(s):  
Zengqian Wang ◽  
Jingjin Ji ◽  
Xinghao Wang ◽  
Bo Sun ◽  
Lei He ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Power Output ◽  
Performance Test ◽  
Heat Rate ◽  
Performance Data ◽  
Data Reconciliation ◽  
Acceptance Test ◽  
Analysis Method ◽  
Reconciliation Method ◽  
Test Result

Performance acceptance test for gas-steam Combined Cycle Power Plant (CCPP) is of great significance for both equipment manufacturer and customer. The influence of measurement error on the calculation of guaranteed performance data as power output and heat rate can lead to unnecessary loss for either party. Commonly used uncertainty analysis method based on ASME PTC 19.1 would require all measuring instrumentation working at designed accuracy range. Meanwhile, due to the complexity of CCPP system and large number of measuring items, and as well the propagation of measurement and data reduction error, the uncertainty of corrected performance data could be significant. In this paper, process data reconciliation method based on VDI 2048 is introduced. With access to complete performance test data from a CCPP project, data reconciliation calculation is performed with an appropriate thermodynamic model. Several measurement values with gross error are identified and verified in heat balance calculation. Moreover, after recalculating with the reconciled data instead of raw data for the corrected power output and heat rate, comparison with the common uncertainty analysis method is also carried out. It is shown that with this reconciliation method, it is not only possible to find out gross errors such as instrumentation drift, but also able to dramatically increase the test result accuracy, which is of great value for both manufacturer and customer.

Investigation on Effect of Curvilinear Element Blades on Centrifugal Impeller Performance

2017 ◽  
Author(s):  
Kiyotaka Hiradate ◽  
Hiromi Kobayashi ◽  
Takahiro Nishioka
Keyword(s):  
Centrifugal Compressor ◽  
Performance Test ◽  
Design Guidelines ◽  
Secondary Flows ◽  
Flow Coefficient ◽  
Numerical Calculations ◽  
Centrifugal Impeller ◽  
Compressor Stage ◽  
Vaneless Diffuser ◽  
Compressor Impeller

This study experimentally and numerically investigates the effect of application of curvilinear element blades to fully-shrouded centrifugal compressor impeller on the performance of centrifugal compressor stage. Design suction flow coefficient of compressor stage investigated in this study is 0.125. The design guidelines for the curvilinear element blades which had been previously developed was applied to line element blades of a reference conventional impeller and a new centrifugal compressor impeller with curvilinear element blades was designed. Numerical calculations and performance tests of two centrifugal compressor stages with the conventional impeller and the new one were conducted to investigate the effectiveness of application of the curvilinear element blades and compare the inner flowfield in details. Despite 0.5% deterioration of the impeller efficiency, it was confirmed from the performance test results that the compressor stage with the new impeller achieved 1.7% higher stage efficiency at the design point than that with the conventional one. Moreover, it was confirmed that the compressor stage with the new impeller achieved almost the same off-design performance as that of the conventional stage. From results of the numerical calculations and the experiments, it is considered that this efficiency improvement of the new stage was achieved by suppression of the secondary flows in the impeller due to application of negative tangential lean. The suppression of the secondary flows in the impeller achieved uniformalized flow distribution at the impeller outlet and increased the static pressure recovery coefficient in the vaneless diffuser. As a result, it is thought that the total pressure loss was reduced downstream of the vaneless diffuser outlet in the new stage.

A Digital Computer Program for Processing Turbine-Generator Acceptance Test Data

1962 ◽  
Vol 84 (3) ◽  
pp. 295-304 ◽  
Author(s):  
G. A. Maneatis ◽  
W. H. Barr
Keyword(s):  
Thermodynamic Properties ◽  
Computer Program ◽  
Steam Turbine ◽  
Test Data ◽  
Digital Computer ◽  
Heat Rate ◽  
Acceptance Test ◽  
Turbine Generator

This paper describes a digital computer program which processes rapidly all of the data taken during a steam turbine-generator acceptance test. Specifically, it determines all thermodynamic properties of steam and water, computes corrected test heat rate, and finally develops a contract heat rate for purposes of comparison with manufacturer’s guarantees. The application of this program on two 330-megawatt units is discussed. The thinking leading to certain key decisions involving the ultimate approach taken is presented for the benefit of those contemplating a similar effort.

Uncertainty Evaluation on Multi-Hole Aerodynamic Pressure Probes

2020 ◽  
Author(s):  
Andrea Notaristefano ◽  
Paolo Gaetani ◽  
Vincenzo Dossena ◽  
Alberto Fusetti
Keyword(s):  
Uncertainty Analysis ◽  
Evaluation Method ◽  
Experimental Testing ◽  
Uncertainty Propagation ◽  
Calibration Procedure ◽  
Fast Response ◽  
Uncertainty Evaluation ◽  
Sensor Calibration ◽  
Pressure Probe ◽  
Aerodynamic Pressure

Abstract In the frame of a continuous improvement of the performance and accuracy in the experimental testing of turbomachines, the uncertainty analysis on measurements instrumentation and techniques is of paramount importance. For this reason, since the beginning of the experimental activities at the Laboratory of Fluid Machines (LFM) located at Politecnico di Milano (Italy), this issue has been addressed and different methodologies have been applied. This paper proposes a comparison of the results collected applying two methods for the measurement uncertainty quantification to two different aerodynamic pressure probes: sensor calibration, aerodynamic calibration and probe application are considered. The first uncertainty evaluation method is the so called “Uncertainty Propagation” method (UPM); the second is based on the “Monte Carlo” method (MCM). Two miniaturized pressure probes have been selected for this investigation: a pneumatic 5-hole probe and a spherical fast response aerodynamic pressure probe (sFRAPP), the latter applied as a virtual 4-hole probe. Since the sFRAPP is equipped with two miniaturized pressure transducers installed inside the probe head, a specific calibration procedure and a dedicated uncertainty analysis are required.

Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems

2021 ◽  
Vol 21 (4) ◽  
Author(s):  
Seyede Fatemeh Ghoreishi ◽  
Mahdi Imani
Keyword(s):  
Uncertainty Analysis ◽  
Closed Form ◽  
Uncertainty Propagation ◽  
Uncertainty Reduction ◽  
System Level ◽  
Engineering Systems ◽  
Sample Point ◽  
Design Variables ◽  
Multidisciplinary Systems ◽  
Stationary Behavior

Abstract Engineering systems are often composed of many subsystems that interact with each other. These subsystems, referred to as disciplines, contain many types of uncertainty and in many cases are feedback-coupled with each other. In designing these complex systems, one needs to assess the stationary behavior of these systems for the sake of stability and reliability. This requires the system level uncertainty analysis of the multidisciplinary systems, which is often computationally intractable. To overcome this issue, techniques have been developed for capturing the stationary behavior of the coupled multidisciplinary systems through available data of individual disciplines. The accuracy and convergence of the existing techniques depend on a large amount of data from all disciplines, which are not available in many practical problems. Toward this, we have developed an adaptive methodology that adds the minimum possible number of samples from individual disciplines to achieve an accurate and reliable uncertainty propagation in coupled multidisciplinary systems. The proposed method models each discipline function via Gaussian process (GP) regression to derive a closed-form policy. This policy sequentially selects a new sample point that results in the highest uncertainty reduction over the distribution of the coupling design variables. The effectiveness of the proposed method is demonstrated in the uncertainty analysis of an aerostructural system and a coupled numerical example.

scholarly journals Uncertainty Analysis for Topographic Correction of Hyperspectral Remote Sensing Images

2020 ◽  
Vol 12 (4) ◽  
pp. 705 ◽  
Author(s):  
Zhaoning Ma ◽  
Guorui Jia ◽  
Michael E. Schaepman ◽  
Huijie Zhao
Keyword(s):  
Uncertainty Analysis ◽  
Uncertainty Propagation ◽  
Spectral Feature ◽  
Metrological Traceability ◽  
Soil Surface ◽  
Topographic Correction ◽  
Uncertainty In Measurement ◽  
Hyperion Data ◽  
Set Up ◽  
Hyperspectral Remote Sensing Images

Quantitative uncertainty analysis is generally taken as an indispensable step in the calibration of a remote sensor. A full uncertainty propagation chain has not been established to set up the metrological traceability for surface reflectance inversed from remotely sensed images. As a step toward this goal, we proposed an uncertainty analysis method for the two typical semi-empirical topographic correction models, i.e., C and Minnaert, according to the ‘Guide to the Expression of Uncertainty in Measurement (GUM)’. We studied the data link and analyzed the uncertainty propagation chain from the digital elevation model (DEM) and at-sensor radiance data to the topographic corrected radiance. We obtained spectral uncertainty characteristics of the topographic corrected radiance as well as its uncertainty components associated with all of the input quantities by using a set of Earth Observation-1 (EO-1) Hyperion data acquired over a rugged soil surface partly covered with snow. Firstly, the relative uncertainty of cover types with lower radiance values was larger for both C and Minnaert corrections. Secondly, the trend of at-sensor radiance contributed to a spectral feature, where the uncertainty of the topographic corrected radiance was poor in bands below 1400 nm. Thirdly, the uncertainty components associated with at-sensor radiance, slope, and aspect dominated the total combined uncertainty of corrected radiance. It was meaningful to reduce the uncertainties of at-sensor radiance, slope, and aspect for reducing the uncertainty of corrected radiance and improving the data quality. We also gave some suggestions to reduce the uncertainty of slope and aspect data.

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