Quantification Of Errors Research Articles

Assessing Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals over urban versus non-urban regions

Abstract. The Measurements of Pollution in the Troposphere (MOPITT) retrievals over urban regions have not been validated systematically, even though MOPITT observations are widely used to study CO over urban regions. Here we compare MOPITT products over urban and non-urban regions with aircraft measurements from the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ – 2011–2014), Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS – 2013), Air Chemistry Research In Asia (ARIAs – 2016), A-FORCE (2009, 2013), and Korea United States Air Quality (KORUS-AQ – 2016) campaigns. In general, MOPITT agrees reasonably well with the in situ profiles, over both urban and non-urban regions. Version 8 multispectral product (V8J) biases vary from −0.7 % to 0.0 % and version 8 thermal-infrared product (TIR) biases vary from 2.0 % to 3.5 %. The evaluation statistics of MOPITT V8J and V8T over non-urban regions are better than those over urban regions with smaller biases and higher correlation coefficients. We find that the agreement of MOPITT V8J and V8T with aircraft measurements at high CO concentrations is not as good as that at low CO concentrations, although CO variability may tend to exaggerate retrieval biases in heavily polluted scenes. We test the sensitivities of the agreements between MOPITT and in situ profiles to assumptions and data filters applied during the comparisons of MOPITT retrievals and in situ profiles. The results at the surface layer are insensitive to the model-based profile extension (required due to aircraft altitude limitations), whereas the results at levels with limited aircraft observations (e.g., the 600 hPa layer) are more sensitive to the model-based profile extension. The results are insensitive to the maximum allowed time difference criterion for co-location (12, 6, 3, and 1 h) and are generally insensitive to the radius for co-location, except for the case where the radius is small (25 km), and hence few MOPITT retrievals are included in the comparison. Daytime MOPITT products have smaller overall biases than nighttime MOPITT products when comparing both MOPITT daytime and nighttime retrievals to the daytime aircraft observations. However, it would be premature to draw conclusions on the performance of MOPITT nighttime retrievals without nighttime aircraft observations. Applying signal-to-noise ratio (SNR) filters does not necessarily improve the overall agreement between MOPITT retrievals and in situ profiles, likely due to the reduced number of MOPITT retrievals for comparison. Comparisons of MOPITT retrievals and in situ profiles over complex urban or polluted regimes are inherently challenging due to spatial and temporal variabilities of CO within MOPITT retrieval pixels (i.e., footprints). We demonstrate that some of the errors are due to CO representativeness with these sensitivity tests, but further quantification of representativeness errors due to CO variability within the MOPITT footprint will require future work.

Preface

The Summer Workshop on Interval Methods (SWIM) is an annual meeting initiated in 2008 by the French MEA working group on Set Computation and Interval Techniques of the French research group on Automatic Control. A special focus of the MEA group is on promoting interval analysis techniques and applications to a broader community of researchers, facilitated by such multidisciplinary workshops. Since 2008, SWIM has become a keystone event for researchers dealing with various aspects of interval and set-based methods.
 In 2018, the 11th edition in this workshop series was held at the University of Rostock, Germany, with a focus on research topics in the fields of engineering, computer science, and mathematics. A total of 31 talks were given during this workshop, covering the following areas:
 
 verified solution of initial value problems for ordinary differential equations, differential-algebraic system models, and partial differential equations,
 scientific computing with guaranteed error bounds,
 design of robust and fault-tolerant control systems,
 modeling and quantification of errors in engineering tasks,
 implementation of software libraries, and
 usage of the aforementioned approaches for system models in control engineering, data analysis, signal and image processing.
 
 After a peer-review process, 15 high-quality articles were selected for publication in this special issue. They are roughly divided into two thematic groups: Uncertainty Modeling, Software, Verified Computing and Optimization as well as Interval Methods in Control and Robotics.
 The first part, Uncertainty Modeling, Software, Verified Computing and Optimization, contains methodological aspects concerning reliable modeling of dynamic systems as well as visualization and quantification of uncertainty in the fields of measurement and simulation. Moreover, existence proofs for solutions of partial differential equations and their reliable optimal control synthesis are considered. A paper making use of quantifier elimination for robust linear output feedback control by means of eigenvalue placement concludes this section.
 The second part of this special issue, Interval Methods in Control and Robotics, is focused on the design as well as numerical and experimental validation of robust state observation and control procedures along with reliable parameter and state estimation approaches in the fields of control for thermal systems, robotics, localization of drones and global positioning systems.

Are geometric morphometric analyses replicable? Evaluating landmark measurement error and its impact on extant and fossil Microtus classification.

Geometric morphometric analyses are frequently employed to quantify biological shape and shape variation. Despite the popularity of this technique, quantification of measurement error in geometric morphometric datasets and its impact on statistical results is seldom assessed in the literature. Here, we evaluate error on 2D landmark coordinate configurations of the lower first molar of five North American Microtus (vole) species. We acquired data from the same specimens several times to quantify error from four data acquisition sources: specimen presentation, imaging devices, interobserver variation, and intraobserver variation. We then evaluated the impact of those errors on linear discriminant analysis‐based classifications of the five species using recent specimens of known species affinity and fossil specimens of unknown species affinity. Results indicate that data acquisition error can be substantial, sometimes explaining >30% of the total variation among datasets. Comparisons of datasets digitized by different individuals exhibit the greatest discrepancies in landmark precision, and comparison of datasets photographed from different presentation angles yields the greatest discrepancies in species classification results. All error sources impact statistical classification to some extent. For example, no two landmark dataset replicates exhibit the same predicted group memberships of recent or fossil specimens. Our findings emphasize the need to mitigate error as much as possible during geometric morphometric data collection. Though the impact of measurement error on statistical fidelity is likely analysis‐specific, we recommend that all geometric morphometric studies standardize specimen imaging equipment, specimen presentations (if analyses are 2D), and landmark digitizers to reduce error and subsequent analytical misinterpretations.

Relevance of the small frequency separation for asteroseismic stellar age, mass, and radius

Aims. We performed a theoretical analysis aimed at quantifying the relevance of the small frequency separation δν in determining stellar ages, masses, and radii. We aimed to establish a minimum uncertainty on these quantities for low-mass stars across different evolutionary stages of the main sequence and to evaluate the biases that come from some systematic differences between the stellar model grid adopted for the recovery and the observed stars. Methods. We adopted the Stellar CharactEristics Pisa Estimation gRid (SCEPtER) pipeline for low-mass stars, [0.7, 1.05] M⊙, from the zero-age main sequence (ZAMS) to the central hydrogen depletion. For each model in the grid, we computed oscillation frequencies. Synthetic stars were generated and reconstructed based on different assumptions about the relative precision in the δν parameter (namely 5% and 2%). The quantification of the systematic errors arising from a possible mismatch between synthetic stars and the recovery grid was performed by generating stars from synthetic grids of stellar models with different initial helium abundance and microscopic diffusion efficiency. The results obtained without δν as an observable are included for comparison. Results. The investigation highlighted and confirmed the improvement in the age estimates when δν is available, which has already been reported in the literature. While the biases were negligible, the statistical error affecting age estimates was strongly dependent on the stellar evolutionary phase. The error is at its maximum at ZAMS and it decreases to about 11% and 6% (δν known at 5% and 2% level, respectively) when stars reach the 30% of their evolutionary MS lifetime. The usefulness of small frequency separation in improving age estimates vanishes in the last 20% of the MS. The availability of δν in the fit for mass and radius estimates provided an effect that was nearly identical to its effect on age, assuming an observational uncertainty of 5%. As a departure, with respect to age estimates, no benefit was detected for mass and radius determinations from a reduction of the observational error in δν to 2%. The age variability attributed to differences in the initial helium abundance resulted in negligible results owing to compensation effects that have already been discussed in previous works. On the other hand, the current uncertainty in the initial helium abundance leads to a greater bias (2% and 1% level) in mass and radius estimates whenever δν is in the observational pool. This result, together with the presence of further unexplored uncertainty sources, suggest that precision in the derived stellar quantities below these thresholds may possibly be overoptimistic. The impact of microscopic diffusion was investigated by adopting a grid of models for the recovery which totally neglected the process. The availability of the small frequency separation resulted in biases lower than 5% and 2% for observational errors of 5% and 2%, respectively. The estimates of mass and radius showed again a greater distortion when δν is included among the observables. These biases are at the level of 1%, confirming that threshold as a minimum realistic uncertainty on the derived stellar quantities. Finally, we compared the estimates by the SCEPtER pipeline for 13 Kepler asteroseismic LEGACY sample stars with those given by six different pipelines from literature. This procedure demonstrated a fair agreement for the results. The comparison suggests that a realistic approach to the determination of the error on the estimated parameters consists of approximately doubling the error in the recovered stellar characteristics from a single pipeline. Overall, on the LEGACY sample data, we obtained a multi-pipeline precision of about 4.4%, 1.7%, and 11% on the estimated masses, radii, and ages, respectively.

How large are temporal representativeness errors in paleoclimatology?

Abstract. Ongoing work in paleoclimate reconstruction prioritizes understanding the origins and magnitudes of errors that arise when comparing models and data. One class of such errors arises from assumptions of proxy temporal representativeness (TR), i.e., how accurately proxy measurements represent climate variables at particular times and time intervals. Here we consider effects arising when (1) the time interval over which the data average and the climate interval of interest have different durations, (2) those intervals are offset from one another in time (including when those offsets are unknown due to chronological uncertainty), and (3) the paleoclimate archive has been smoothed in time prior to sampling. Because all proxy measurements are time averages of one sort or another and it is challenging to tailor proxy measurements to precise time intervals, such errors are expected to be common in model–data and data–data comparisons, but how large and prevalent they are is unclear. This work provides a 1st-order quantification of temporal representativity errors and studies the interacting effects of sampling procedures, archive smoothing, chronological offsets and errors (e.g., arising from radiocarbon dating), and the spectral character of the climate process being sampled. Experiments with paleoclimate observations and synthetic time series reveal that TR errors can be large relative to paleoclimate signals of interest, particularly when the time duration sampled by observations is very large or small relative to the target time duration. Archive smoothing can reduce sampling errors by acting as an anti-aliasing filter but destroys high-frequency climate information. The contribution from stochastic chronological errors is qualitatively similar to that when an observation has a fixed time offset from the target. An extension of the approach to paleoclimate time series, which are sequences of time-average values, shows that measurement intervals shorter than the spacing between samples lead to errors, absent compensating effects from archive smoothing. Nonstationarity in time series, sampling procedures, and archive smoothing can lead to changes in TR errors in time. Including these sources of uncertainty will improve accuracy in model–data comparisons and data comparisons and syntheses. Moreover, because sampling procedures emerge as important parameters in uncertainty quantification, reporting salient information about how records are processed and assessments of archive smoothing and chronological uncertainties alongside published data is important to be able to use records to their maximum potential in paleoclimate reconstruction and data assimilation.

A novel simulation-based framework for sensor error impact analysis in smart building systems: A case study for a demand-controlled ventilation system

Sensors are one of the fundamental components for sensor-rich controls in buildings but are prone to different errors. Existing studies show that sensor errors hold a place among top-priority faults in building systems. Before we take countermeasures to mitigate the sensor errors, it is vital to prioritize key sensors and quantify the collective impacts of concurrent sensor errors. In response to this, a simulation-based methodology is introduced to conduct a comprehensive sensor error impact analysis in building systems, which adds a stochastic sensor prioritization through a sensitivity analysis on top of a commonly used deterministic sensor error quantification. The synergies of these two parts help better interpret the sensor error impacts on building energy consumption, ventilation performance, thermal comfort, etc. A sensor-rich CO2-based Demand-Controlled Ventilation system is used as a case study to demonstrate the viability of the methodology as a proof-of-the-concept. The results show that the energy savings potential and ventilation performance are mostly influenced by the accuracy of the AHU outdoor airflow sensors. The accuracy of zone level airflow sensors has a negligible impact on both energy savings and ventilation performance. The accuracy of zone CO2 sensors has more influence on the ventilation performance compared with the accuracy of zone airflow sensors. Compared with the baseline case with zero errors, the largest deviation percentages could reach 16.90% and 94.32%, respectively, in terms of the Heating, Ventilation, and Air-Conditioning (HVAC) annual energy consumption and the Outdoor Air Ratio (OAR) when multiple key sensors suffer from normal error intensities simultaneously.

Validation of an automated shape-matching algorithm for biplane radiographic spine osteokinematics and radiostereometric analysis error quantification.

Biplane radiography and associated shape-matching provides non-invasive, dynamic, 3D osteo- and arthrokinematic analysis. Due to the complexity of data acquisition, each system should be validated for the anatomy of interest. The purpose of this study was to assess our system’s acquisition methods and validate a custom, automated 2D/3D shape-matching algorithm relative to radiostereometric analysis (RSA) for the cervical and lumbar spine. Additionally, two sources of RSA error were examined via a Monte Carlo simulation: 1) static bead centroid identification and 2) dynamic bead tracking error. Tantalum beads were implanted into a cadaver for RSA and cervical and lumbar spine flexion and lateral bending were passively simulated. A bead centroid identification reliability analysis was performed and a vertebral validation block was used to determine bead tracking accuracy. Our system’s overall root mean square error (RMSE) for the cervical spine ranged between 0.21–0.49mm and 0.42–1.80° and the lumbar spine ranged between 0.35–1.17mm and 0.49–1.06°. The RMSE associated with RSA ranged between 0.14–0.69mm and 0.96–2.33° for bead centroid identification and 0.25–1.19mm and 1.69–4.06° for dynamic bead tracking. The results of this study demonstrate our system’s ability to accurately quantify segmental spine motion. Additionally, RSA errors should be considered when interpreting biplane validation results.

Improved Estimation and Forecasting Through Residual-Based Model Error Quantification

Summary Owing to the complex nature of hydrocarbon reservoirs, the numerical model constructed by geoscientists is always a simplified version of reality: for example, it might lack resolution from discretization and lack accuracy in modeling some physical processes. This flaw in the model that causes mismatch between actual observations and simulated data when “perfect” model parameters are used as model inputs is known as “model error”. Even in a situation when the model is a perfect representation of reality, the inputs to the model are never completely known. During a typical model calibration procedure, only a subset of model inputs is adjusted to improve the agreement between model responses and historical data. The remaining model inputs that are not calibrated and are likely fixed at incorrect values result in model error in a similar manner as the imperfect model scenario. Assimilation of data without accounting for model error can result in the incorrect adjustment to model parameters, the underestimation of prediction uncertainties, and bias in forecasts. In this paper, we investigate the benefit of recognizing and accounting for model error when an iterative ensemble smoother is used to assimilate production data. The correlated “total error” (a combination of model error and observation error) is estimated from the data residual after a standard history-matching using the Levenberg-Marquardt form of iterative ensemble smoother (LM-EnRML). This total error is then used in further data assimilations to improve the estimation of model parameters and quantification of prediction uncertainty. We first illustrate the method using a synthetic 2D five-spot example, where some model errors are deliberately introduced, and the results are closely examined against the known “true” model. Then, the Norne field case is used to further evaluate the method. The Norne model has previously been history-matched using the LM-EnRML (Chen and Oliver 2014), where cell-by-cell properties (permeability, porosity, net-to-gross, vertical transmissibility) and parameters related to fault transmissibility, depths of water/oil contacts, and relative permeability function are adjusted to honor historical data. In this previous study, the authors highlighted the importance of including large amounts of model parameters, the proper use of localization, and heuristic adjustment of data noise to account for modeling error. In this paper, we improve the last aspect by quantitatively estimating model error using residual analysis.

Comparative study of measurement and verification (M&V) baseline models for quantifying energy savings in building renovations

Measurement and verification (M&V) is the process of quantifying energy savings originated by one or several energy conservation measures (ECM) in an existing building. The estimation of the savings consist of comparing actual energy consumption to the adjusted baseline model. This paper focuses on comparing three approaches for creating baseline models: linear, symbolic regression (SR) and extreme gradient boost (XGBoost); and discusses the advantages and drawbacks on each of them from a practitioner’s perspective. In this paper, these approaches are assessed qualitatively and quantitatively. The qualitative assessment compares the type of model output, interpretability and calibration time. Linear model excels in all three criteria whereas the XGBoost is the worst option for model output and interpretability. SR model is the worst performing in terms of calibration time, but intermediate model for output and interpretability. Quantitative assessment is done through the quantification of prediction errors in 367 buildings after being calibrated with hourly data for a 12-month period. The XGBoost model has the highest prediction accuracy in terms of CVRMSE. The linear model performs significantly well in terms of NMBE. SR preforms well in terms of CVRMSE and has the best median NMBE overall remaining as the most accurate interpretable option. The results show different benefits and drawbacks of each approach and the implementation of SR model for this application is the main innovation of this paper.

Approximation of Bayesian models for time-to-event data

Random measures are the key ingredient for effective nonparametric Bayesian modeling of time-to-event data. This paper focuses on priors for the hazard rate function, a popular choice being the kernel mixture with respect to a gamma random measure. Sampling schemes are usually based on approximations of the underlying random measure, both a priori and conditionally on the data. Our main goal is the quantification of approximation errors through the Wasserstein distance. Though easy to simulate, the Wasserstein distance is generally difficult to evaluate, making tractable and informative bounds essential. Here we accomplish this task on the wider class of completely random measures, yielding a measure of discrepancy between many noteworthy random measures, including the gamma, generalized gamma and beta families. By specializing these results to gamma kernel mixtures, we achieve upper and lower bounds for the Wasserstein distance between hazard rates, cumulative hazard rates and survival functions.

A systematic stochastic design strategy achieving an optimal tradeoff between peak BGL and probability of hypoglycaemic events for individuals having type 1 diabetes mellitus

This paper has two key contributions. The first contribution is a systematic procedure for fitting an envelope of models which captures a range of possible blood glucose level (BGL) responses for a particular individual having Type 1 diabetes. An important aspect of the procedure is that it requires minimal testing on the individual. Moreover, the testing can be carried out by the individual at home. The developed envelope of models, termed ‘Metabolic Digital Twin Envelope’ (MDTE) takes into account the quantification of possible errors including those arising from utilising a simplified model (commonly called “bias” errors) and those arising from unmodelled disturbances and noise (commonly called “variance” errors). The second, and most important, contribution is a methodology that allows convex optimisation to be used to develop an insulin injection policy which minimises mean square peak BGL whilst ensuring that there is a strict lower bound on the probability of hyperglycaemic events. The optimisation methodology is posed as a stochastic design strategy based on using the probabilistic models for each individual afforded by the MDTE.

Simulation-Based Quantification of Alkali-Metal Evaporation Rate and Systematic Errors From Current–Voltage Characteristics of Langmuir–Taylor Detectors

This article presents an analysis for deducing the total emission rate of cesium from Langmuir–Taylor detectors embedded on the cesium ovens for a large negative-ion (NI) source. The angular distribution of the Cs emission is simulated to obtain the flux intercepted by the detector and its intensity along its length. The space-charge-limited emission of surface-ionized Cs positive ions is also studied with particle-in-cell methods, to include the geometric features of the Cs emission nozzles. Combining the two methods reproduces with good agreement the full current–voltage characteristics measured by the detector. The proposed approach can be effectively applied to obtain the total emission rate, as it was verified in the absence of impurities. The measurement uncertainties in terms of shape and alignment of the detector filament are investigated and their influence is quantified. Measuring and controlling the Cs emission rate is essential for the success and repeatability of cesium operations in the prototype NI beam source for the ITER neutral beam.

Integrated pipeline corrosion growth modeling and reliability analysis using dynamic Bayesian network and parameter learning technique

The present study integrates the corrosion growth modeling, reliability analysis and quantification of measurement errors of in-line inspection (ILI) tools in a single dynamic Bayesian network (DBN) model for the reliability-based corrosion management of oil and gas pipelines. The Expectation-Maximization algorithm in the context of the parameter learning technique is employed to learn the parameters of the DBN model. The application of the model on simulated and real-world corrosion data demonstrates the effectiveness of the parameter learning and accuracy of the corrosion growth predicted by the DBN model. In comparison with existing growth models, the integrating and graphical features of the developed model make the process of corrosion management more intuitive and transparent to users. The employment of the parameter learning technique provides an objective and convenient approach to elicit the probabilistic information from ILI and field measurement data. The above advantages make the model more amenable to the corrosion management practice in the pipeline industry.

Reliability and measurement error of multi-segment trunk kinematics and kinetics during cerebral palsy gait

Children with cerebral palsy (CP) have been shown to have altered trunk movements during gait resulting in increased loads at the lower lumbar spine. Detailed assessment is possible using 3D gait analysis. However, reliability and quantification of measurement error have not been established. The aim of this study was to evaluate test-retest reliability of thorax and lumbar segment kinematics and L5/S1 kinetics during gait in children with CP. Eight children with CP participated in this study with repeat assessments conducted within 1 week. Reliability was assessed using the one-way random ICC, standard error of measurement and an examination of extrinsic-to-intrinsic variability. Thorax kinematics demonstrated mixed level of reliability with SEM values ranging from 5.94o to 1.15o. Lumbar kinematics demonstrated poor-to-good reliability with the largest SEM values for peak lumbar flexion at 4.14o. L5/S1 moment values demonstrated only poor to good test-retest reliability while L5/S1 reactive forces demonstrated poor to excellent test-retest reliability.This study provides estimates of reliability and change needed to exceed measurement error. While reliability was mixed and some measures for thorax movement were above 5o, stated as a measure of acceptable error, the results of this study support the use of these measures in children with CP.

Understanding acquisition and processing error in microseismic data: An example from Pouce Coupe Field, Canada

ABSTRACTA challenge in microseismic monitoring is quantification of survey acquisition and processing errors, and how these errors jointly affect estimated locations. Quantifying acquisition and processing errors and uncertainty has multiple benefits, such as more accurate and precise estimation of locations, anisotropy, moment tensor inversion and, potentially, allowing for detection of 4D reservoir changes. Here, we quantify uncertainty due to acquisition, receiver orientation error, and hodogram analysis. Additionally, we illustrate the effects of signal to noise ratio variances upon event detection. We apply processing steps to a downhole microseismic dataset from Pouce Coupe, Alberta, Canada. We use a probabilistic location approach to identify the optimal bottom well location based upon known source locations. Probability density functions are utilized to quantify uncertainty and propagate it through processing, including in source location inversion to describe the three‐dimensional event location likelihood. Event locations are calculated and an amplitude stacking approach is used to reduce the error associated with first break picking and the minimization with modelled travel times. Changes in the early processing steps have allowed for understanding of location uncertainty of the mapped microseismic events.

A publicly available GIS-based web platform for reservoir inundation mapping in the lower Mekong region

Dam construction in mainland Southeast Asia has increased substantially in recent years. Most dams have the potential to generate value, however, potential and existing impacts include alterations in water regimes, loss and degradation of natural forests and bio-diversity. For mapping impacts, we have developed the Reservoir Mapping Tool for the greater Mekong region designed in ArcGIS Desktop 10.5 ModelBuilder, with dam point location and digital elevation model SRTM-30 m on a publicly available web interface which provides inundated area and dam volume based on user inputs. We validate our results and find excellent agreement with ground data from the Lanh Ra dam located in Vietnam. Further validation and error quantification are done comparing results of three different DEMS's and compared with reported values. We also illustrate various application areas using this information in combination with other geospatial layers, which could provide key inputs towards assessing overall social impacts of dams.

Quantification and Statistical Analysis of Errors Related to the Approximate Description of Active Site Models in Metal‐Exchanged Zeolites

AbstractMetal‐exchanged zeolites are complex catalytic systems with great potential for applications in hydrocarbon conversion processes. The numerous combinations of zeotypes and metal‐based Lewis acid motifs create a vast material space that can only be effectively navigated with robust structure‐function relationships. Exhaustive modeling of extraframework metal sites in all crystallographically distinct framework positions is not practical; thus, we quantified the uncertainties arising from the use of simplified active site representations using the C−H bond activation in methane as a probe reaction. Density functional theory calculations and statistical analysis of associated error ensembles suggest that the Lewis acid identity primarily determines the reactivity of the zeolite towards methane activation, with other factors such as zeolite model characteristics deemed secondary. The error ensemble distributions were used to predict the relative probability of creating active C−H bond scission fragments in metal‐exchanged zeolites, and thus can be used to efficiently screen various metal‐exchange candidates in their efficacy towards hydrocarbon conversion.

Statistical Characterization of PMU Error for Robust WAMS Based Analytics

Synchronized phasor measurement unit (PMU) data contain rich information about power systems, hence the quality of estimates is of utmost concern. The existing methodologies for estimation rely on certain assumptions regarding the error in the measurement data. This paper revisits a key assumption specifically the Gaussian character of the error. A quantification of the PMU error yields its nature and statistical properties including its dependence on various sections of the PMU instrumentation channel (supposedly the major source of error in the PMU data). The non Gaussian nature of the error is asserted using various null hypotheses tests and a novel Gaussian mixture model based clustering technique is proposed to characterize and relate the errors present in PMU measurement data to the saturation in current transformer, cable length and the PMU burden. The proposed approach is tested using both real and synthetic PMU datasets. The ultimate goal of the paper is towards creating a PMU error emulator for testing and research of data analytic algorithms focused on crucial WAMS based applications.

Analysis of human-induced initiating events in a multi-unit loss of offsite power scenario

ABSTRACTHuman-induced initiators (category-B actions) are the initiators that are caused by human errors and are rarely explicitly identified and modeled in probabilistic safety assessments (PSAs). The current concern over the safety of multi-unit nuclear power sites is also a motivation for this research. This study proposes a novel process for identifying and quantifying category-B actions and ultimately, how to derive a human-induced initiating event frequency in a multi-unit scenario. Hence, this study fundamentally applies a scenario–system–action search scheme using maintenance and testing procedures, quantifies the human error probability by using the cause-based decision tree and technique for human error rate prediction method, models category-B human actions in the developed fault trees, and derives the human-induced initiating event frequency. The procedure, which is used in this approach, essentially involves system analysis, fault tree development, human error identification, screening, and quantification. The multi-unit loss of offsite power is used as an example accident situation which illustrates the application of the suggested method. Hence, the human-induced initiating event frequency for the loss of off-site power scenario for two units is derived. The application of this method would advance the efforts concerning multi-unit nuclear power plant (NPP) site risk analysis.

Characterization and quantification of numerical errors in threshold displacement energy calculated by molecular dynamics in bcc-Fe

The accumulation of radiation defects in nuclear fusion/fission materials often causes adverse changes in material properties. The threshold displacement energy (TDE) is one of the fundamental quantities used to evaluate the number of formed radiation defects. In the present study, to understand the error sources in TDE calculation using the molecular dynamics method and to find an optimal strategy for accurate and precise calculation, we analyze how the calculated TDE is affected by the calculation settings, including the system shape and size, the recoil energy increment in searching the threshold value, the sampling numbers of recoil directions and recoil event time samples, and the potential model. Bcc-Fe is used as a test material, and three potential models are employed for comparison. As a result, the calculation errors caused by these settings are successfully classified into accuracy error and precision error and then quantified. The type and trend of the error caused by each setting are not qualitatively different between the tested potential models, although they are quantitatively different. This potential model dependence can often be reasonably explained from the recoil direction dependence of the TDE and the defect formation mechanisms. Finally, using the equations derived to quantify the accuracy and precision of the calculated TDE and the calculation cost, we suggest some strategies to minimize the error with a given limited computational resource.