scholarly journals A step-by-step data processing guideline for load model development based on field measurements

2015 ◽  
Author(s):  
Kazi N Hasan ◽  
Jovica V Milanovic ◽  
Paul Turner ◽  
Victoria Turnham
Keyword(s):  
Data Processing ◽  
Model Development ◽  
Field Measurements ◽  
Load Model

scholarly journals Can the heterogeneity in stream dissolved organic carbon be explained by contributing landscape elements?

2014 ◽  
Vol 11 (4) ◽  
pp. 1199-1213 ◽  
Author(s):  
A. M. Ågren ◽  
I. Buffam ◽  
D. M. Cooper ◽  
T. Tiwari ◽  
C. D. Evans ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
Model Development ◽  
Field Measurements ◽  
Mixing Model ◽  
Residual Analysis ◽  
Residence Times ◽  
Stream Network ◽  
Landscape Elements ◽  
Modelling Studies

Abstract. The controls on stream dissolved organic carbon (DOC) concentrations were investigated in a 68 km2 catchment by applying a landscape-mixing model to test if downstream concentrations could be predicted from contributing landscape elements. The landscape-mixing model reproduced the DOC concentration well throughout the stream network during times of high and intermediate discharge. The landscape-mixing model approach is conceptually simple and easy to apply, requiring relatively few field measurements and minimal parameterisation. Our interpretation is that the higher degree of hydrological connectivity during high flows, combined with shorter stream residence times, increased the predictive power of this whole watershed-based mixing model. The model was also useful for providing a baseline for residual analysis, which highlighted areas for further conceptual model development. The residual analysis indicated areas of the stream network that were not well represented by simple mixing of headwaters, as well as flow conditions during which simple mixing based on headwater watershed characteristics did not apply. Specifically, we found that during periods of baseflow the larger valley streams had much lower DOC concentrations than would be predicted by simple mixing. Longer stream residence times during baseflow and changing hydrological flow paths were suggested as potential reasons for this pattern. This study highlights how a simple landscape-mixing model can be used for predictions as well as providing a baseline for residual analysis, which suggest potential mechanisms to be further explored using more focused field and process-based modelling studies.

Statistical Methodologies for Reliability Assessment of Gas Turbine Measurements

2003 ◽  
Author(s):  
M. Pinelli ◽  
M. Venturini ◽  
M. Burgio
Keyword(s):  
Data Processing ◽  
Mathematical Models ◽  
Gas Turbine ◽  
Measurement Errors ◽  
Field Data ◽  
Reliability Assessment ◽  
Field Measurements ◽  
Measurement Reliability ◽  
Student Distribution ◽  
Analysis Models

All measurements, although taken as accurately as possible, are subjected to uncertainty. So the analysis of errors and uncertainty is crucial in all applications since such errors need to be estimated and, when possible, reduced. In particular, when gas turbine mathematical models based on the processing of field measurements (such as the Gas Path Analysis models) are used, the evaluation of measurement reliability is a key point. In fact, it has been demonstrated that these kinds of techniques are sensitive to measurement errors: thus, tools for field data processing to evaluate the presence of the so-called outliers are advisable. In this paper, some statistical methodologies for the assessment of the reliability of the measurements taken on a gas turbine are presented. The methodologies, taken from literature and used for historical measurements, are discussed. Moreover, a new methodology, based on a modified t-Student distribution, is proposed.

Load model development for next generation appliances

2011 ◽  
Author(s):  
Franklin L. Quilumba ◽  
Wei-Jen Lee ◽  
Heng Huang ◽  
David Yanshi Wang ◽  
Robert Louis Szabados
Keyword(s):  
Model Development ◽  
Next Generation ◽  
Load Model

scholarly journals Load Model Verification, Validation and Calibration Framework by Statistical Analysis on Field Data

2017 ◽  
Vol 18 (6) ◽  
Author(s):  
Xiangqing Jiao ◽  
Yuan Liao ◽  
Thai Nguyen
Keyword(s):  
Statistical Analysis ◽  
System Analysis ◽  
Research Work ◽  
Field Measurements ◽  
Model Verification ◽  
Verification And Validation ◽  
Load Model ◽  
Load Models ◽  
Utility System ◽  
Model Verification And Validation

AbstractAccurate load models are critical for power system analysis and operation. A large amount of research work has been done on load modeling. Most of the existing research focuses on developing load models, while little has been done on developing formal load model verification and validation (V&V) methodologies or procedures. Most of the existing load model validation is based on qualitative rather than quantitative analysis. In addition, not all aspects of model V&V problem have been addressed by the existing approaches. To complement the existing methods, this paper proposes a novel load model verification and validation framework that can systematically and more comprehensively examine load model’s effectiveness and accuracy. Statistical analysis, instead of visual check, quantifies the load model’s accuracy, and provides a confidence level of the developed load model for model users. The analysis results can also be used to calibrate load models. The proposed framework can be used as a guidance to systematically examine load models for utility engineers and researchers. The proposed method is demonstrated through analysis of field measurements collected from a utility system.

scholarly journals Evidence of the Nonstationarity of the Terrestrial Bow Shock from Multi-Spacecraft Observations: Methodology, Results and Quantitative Comparison with PIC Simulations

2020 ◽  
Author(s):  
Christian Mazelle ◽  
Bertrand Lembege
Keyword(s):  
Magnetic Field ◽  
Lower Bound ◽  
Data Processing ◽  
Shock Front ◽  
Upper Bound ◽  
Spatial Scales ◽  
Large Fraction ◽  
Field Measurements ◽  
Bow Shock ◽  
Time Variability

Abstract. The nonstationarity of the terrestrial bow shock is analyzed in detail from in situ magnetic field measurements issued from the FGM experiment on board of Cluster mission. Attention is focused on statistical analysis of quasiperpendicular supercritical shock crossings. The present analysis stresses for the first time the importance of a careful and accurate methodology in the data processing which can be a source of confusion/misunderstanding if not treated properly. The analysis performed using 96 shock front crossings shows evidence of a strong variability of the microstructures of the shock front (foot and ramp) which are analyzed in deep details. Main results are: (i) most statistics clearly evidence that the ramp thickness is very narrow and can be as low as a few c/ωpe (electron inertia length), (ii) the width is narrower when the angle θBn (between the shock normal and the upstream magnetic field) approaches 90°, (iii) the foot thickness strongly varies but its variation has an upper limit provided by theoretical estimates given in previous studies (e.g., Schwartz et al., 1983; Gosling and Thomsen, 1985; Gosling and Robson, 1985); (iv) the presence of foot and overshoot, as shown in all front profiles confirms the importance of dissipative effects. Present results indicate that these features can be signatures of the shock front self-reformation among a few mechanisms of nonstationarity identified from numerical simulation/theoretical works. A comparison 2D PIC simulation for a perpendicular supercritical shock (used as reference), has been performed and it shows that: (a) the ramp thickness varies only slightly in time over a large fraction of the reformation cycle and reaches a lower bound value of the order of a few electron inertial length, (ii) in contrast, the foot width strongly varies during a self-reformation cycle but always stays lower than an upper bound value in agreement with the value given by Woods (1971), and (iii) as a consequence, the time variability of the whole shock front is depending on both ramp and foot variations. Moreover, a detailed comparative analysis shows that much elements of analysis were missing in previous reported works concerning both (i) the important criteria used in the data selection and (ii) the different and careful steps of the methodology used in the data processing itself. This absence of these precise elements of analysis makes the comparison with present work difficult, worse, it makes some final results and conclusive statements quite questionable at present time. A least, looking for a precise estimate of the shock transition thickness presents nowadays a restricted interest, since recent results show that the terrestrial shock is rather nonstationary and one unique typical spatial scaling of the microstructures of the front (ramp, foot) must be replaced by some variation ranges (with lower bound/upper bound values) within which the spatial scales of the fine structures can extend.

Measured Wellhead Loads During Drilling Operations: Paper 1 — Data Processing and Preliminary Results

2015 ◽  
Author(s):  
Massimiliano Russo ◽  
Urszula Wolak ◽  
Erling Myhre ◽  
Guttorm Grytøyr
Keyword(s):  
Data Processing ◽  
Strain Gauge ◽  
Field Data ◽  
Field Measurements ◽  
Building Blocks ◽  
Measured Data ◽  
Analytical Models ◽  
Strain Gauges ◽  
Drilling Operations ◽  
Analytical Tools

The growing size of BOPs, longer drilling campaigns on wells, and operations in harsher environments has resulted in increased challenges in properly documenting wellhead fatigue during planned or executed drilling operations. The industry has started directing its efforts toward the calibration of analytical tools which are typically adopted for predicting wellhead fatigue. The ultimate goal for achieving this ambitious scope is to identify a benchmark set of analytical results that will predict field measurements. Early on Statoil identified a major obstacle: the absence of a good and comprehensive dataset of field measurements to serve as point of reference. Statoil and Aker Solutions cooperated on a pilot project with the intent of collecting a dataset of full scale measurements during drilling operations to be used to validate and calibrate the theoretical wellhead fatigue calculation methodologies. The main objective of the instrumentation campaign was to measure sectional forces as close as possible to typical wellhead hotspots by the use of three sets of strain gauges installed on the outside surface of the conductor and on the outside of the surface casing. With the objective of collecting an exhaustive dataset of measurements, accelerometers and inclinometers were installed on the BOP, the riser adapter, the riser below the upper flex joint and on the rig. An additional set of six strain gauges was installed on the riser to record riser tension variations. Environmental conditions were logged on board the rig and by the hindcast data provider. Operational events were carefully logged. This paper presents the following: • Data processing used for quality assurance and calibration of the measured data and the associated data challenges • Highlights of the instrumentation system capabilities to capture salient events of a typical drilling campaign and of ad-hoc performed rig operations to calibrate and validate the measured data • Effect of a controlled rig cross motion test, performed to evaluate quasi static loads on the well and calibrate strain gauge sensor orientations • A riser pull test, performed to validate strain gauge functioning • Several landing and disconnecting of the LMRP • Manipulation of the preload between the high pressure housing and the low pressure housing to investigate the effect of the preloading on the load sharing between the casings Since King and Soloman [2], the industry is still lacking quality field data to be used in order to validate the various analytical models used in the analyses of subsea conductor and wellheads. The results will confirm the quality of the measured data and will represent a first data point of comprehensive measured field data. This data will be used for future required work in calibrating the different building blocks pertaining to the analytical tools dedicated to well head fatigue predictions [3].

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