Model Error Assessment of Burst Capacity Models for Corroded Pipes

2012 ◽  
Author(s):  
W. Zhou ◽  
G. (Terry) Huang ◽  
S. Zhang
Keyword(s):  
Probability Distribution ◽  
Test Data ◽  
Prediction Models ◽  
Model Error ◽  
Burst Pressure ◽  
Error Assessment ◽  
Model Errors ◽  
Coefficients Of Variation ◽  
Corrosion Defects ◽  
The Impact

The model errors associated with five representative burst pressure prediction models, namely B31G, B31G Modified, DNV, PCORRC and RSTRENG, for corroded pipelines are evaluated based on a relatively large number full-scale burst tests on corroded pipes reported in the literature. All the test specimens in the database contain single isolated real corrosion defects. The means, coefficients of variation (COVs) and probability distribution of the model errors for the considered burst capacity models are derived based on the test-to-predicted burst pressure ratios for the collected test data. A numerical example is used to illustrate the impact of the model error on the probability of burst of the corroding pipeline.

scholarly journals Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

2020 ◽  
Vol 27 (2) ◽  
pp. 187-207
Author(s):  
Michiel Van Ginderachter ◽  
Daan Degrauwe ◽  
Stéphane Vannitsem ◽  
Piet Termonia
Keyword(s):  
Reference Model ◽  
Deep Convection ◽  
Model Error ◽  
Statistical Properties ◽  
Forecast Skill ◽  
Small Scale ◽  
Model Errors ◽  
Target Model ◽  
Convection Parameterization ◽  
The Impact

Abstract. Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled and tuned to maximize the skill of the ensemble forecast. In this paper a general methodology is developed to diagnose the model error, linked to a specific physical process, based on a comparison between a target and a reference model. Here, the reference model is a configuration of the ALADIN (Aire Limitée Adaptation Dynamique Développement International) model with a parameterization of deep convection. This configuration is also run with the deep-convection parameterization scheme switched off, degrading the forecast skill. The model error is then defined as the difference of the energy and mass fluxes between the reference model with scale-aware deep-convection parameterization and the target model without deep-convection parameterization. In the second part of the paper, the diagnosed model-error characteristics are used to stochastically perturb the fluxes of the target model by sampling the model errors from a training period in such a way that the distribution and the vertical and multivariate correlation within a grid column are preserved. By perturbing the fluxes it is guaranteed that the total mass, heat and momentum are conserved. The tests, performed over the period 11–20 April 2009, show that the ensemble system with the stochastic flux perturbations combined with the initial condition perturbations not only outperforms the target ensemble, where deep convection is not parameterized, but for many variables it even performs better than the reference ensemble (with scale-aware deep-convection scheme). The introduction of the stochastic flux perturbations reduces the small-scale erroneous spread while increasing the overall spread, leading to a more skillful ensemble. The impact is largest in the upper troposphere with substantial improvements compared to other state-of-the-art stochastic perturbation schemes. At lower levels the improvements are smaller or neutral, except for temperature where the forecast skill is degraded.

Model Error Assessment of Burst Capacity Models for Defect-Free Pipes

2012 ◽  
Author(s):  
W. Zhou ◽  
G. (Terry) Huang
Keyword(s):  
Structural Reliability ◽  
Prediction Models ◽  
Yield Criterion ◽  
Limit State ◽  
Model Error ◽  
Pressure Vessels ◽  
Strain Hardening Exponent ◽  
Model Errors ◽  
Reliability Based Design ◽  
Von Mises

The model errors associated with 19 burst pressure prediction models for defect-free thin-walled pipes are evaluated using a total of 76 full-scale burst test data of perfect pipes and pressure vessels collected from the literature. The considered models are based on the Tresca yield criterion, the von Mises yield criterion, or the average shear stress yield criterion. The probabilistic characteristics of the model error, i.e. the mean, coefficient of variation and best-fit probability distribution, are obtained based on the test-to-predicted ratios. The applicability of an empirical equation for estimating the strain hardening exponent in the burst capacity models is also evaluated. The model errors obtained in this study can be used in the structural reliability analysis of energy pipelines with respect to the limit state of burst of defect-free pipes and will facilitate the reliability-based design and assessment of pipelines.

scholarly journals Systematic Model Error: The Impact of Increased Horizontal Resolution versus Improved Stochastic and Deterministic Parameterizations

2012 ◽  
Vol 25 (14) ◽  
pp. 4946-4962 ◽  
Author(s):  
J. Berner ◽  
T. Jung ◽  
T. N. Palmer
Keyword(s):  
Climate Models ◽  
Zonal Flow ◽  
Model Error ◽  
Horizontal Resolution ◽  
Systematic Bias ◽  
Model Errors ◽  
Tropical Variability ◽  
The Tropics ◽  
Ecmwf Model ◽  
The Impact

Abstract Long-standing systematic model errors in both tropics and extratropics of the ECMWF model run at a horizontal resolution typical for climate models are investigated. Based on the hypothesis that the misrepresentation of unresolved scales contributes to the systematic model error, three model refinements aimed at their representation—fluctuating or deterministically—are investigated. Increasing horizontal resolution to explicitly simulate smaller-scale features, representing subgrid-scale fluctuations by a stochastic parameterization, and improving the deterministic physics parameterizations all lead to a decrease in the systematic bias of the Northern Hemispheric circulation. These refinements reduce the overly zonal flow and improve the model’s ability to capture the frequency of blocking. However, the model refinements differ greatly in their impact in the tropics. While improving the deterministic and introducing stochastic parameterizations reduces the systematic precipitation bias and improves the characteristics of convectively coupled waves and tropical variability in general, increasing horizontal resolution has little impact. The fact that different model refinements can lead to reductions in systematic model error is consistent with the hypothesis that unresolved scales play an important role. At the same time, this degeneracy of the response to different forcings can lead to compensating model errors. Hence, if one takes the view that stochastic parameterization should be an important element of next-generation climate models, if only to provide reliable estimates of model uncertainty, then a fundamental conclusion of this study is that stochasticity should be incorporated within the design of physical process parameterizations and improvements of the dynamical core and not added a posteriori.

scholarly journals Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

2019 ◽  
Author(s):  
Michiel Van Ginderachter ◽  
Daan Degrauwe ◽  
Stéphane Vannitsem ◽  
Piet Termonia
Keyword(s):  
Reference Model ◽  
Deep Convection ◽  
Model Error ◽  
Statistical Properties ◽  
Forecast Skill ◽  
Small Scale ◽  
Model Errors ◽  
Target Model ◽  
Convection Parameterization ◽  
The Impact

Abstract. Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled and tuned to maximize the skill of the ensemble forecast. In this paper a general methodology is developed to diagnose the model error, linked to a specific physical process, based on a comparison between a target and a reference model. Here, the reference model is a configuration of the ALADIN (Aire Limitée Adaptation Dynamique Développement International) model with a parameterization of deep convection. This configuration is also run with the deep convection parameterization scheme switched off, degrading the forecast skill. The model error is then defined as the difference of the energy and mass fluxes between the reference model with scale-aware deep convection parameterization and the target model without deep convection parameterization. In the second part of the paper, the diagnosed model-error characteristics are used to stochastically perturb the fluxes of the target model by sampling the model errors from a training period in such a way that the distribution and the vertical and multivariate correlation within a grid column are preserved. By perturbing the fluxes it is guaranteed that that the total mass, heat and momentum remain conserved. The tests, performed over the period 11–20 April 2009, show that the ensemble system with the stochastic flux perturbations combined with the initial condition perturbations, not only outperforms the target ensemble, where deep convection is not parameterized, but for many variables it even performs better than the reference ensemble (with scale-aware deep convection scheme). The introduction of the stochastic flux perturbations reduces the small-scale erroneous spread while increasing the overall spread leading to a more skillful ensemble. The impact is largest in the upper troposphere with substantial improvements compared to other state-of-the-art stochastic perturbation schemes. At lower levels the improvements are smaller or neutral, except for temperature where the forecast skill is degraded.

A Limit State Function for Pipelines Containing Long Corrosion Defects

2010 ◽  
Author(s):  
Mohamed R. Chebaro ◽  
Wenxing Zhou
Keyword(s):  
Limit State ◽  
Model Error ◽  
Burst Pressure ◽  
State Function ◽  
General Corrosion ◽  
Limit State Function ◽  
Burst Test ◽  
Error Factor ◽  
Corrosion Defects ◽  
The University

Currently, there exist various models that predict the burst capacity of a pipeline containing corrosion defects. Recent studies have indicated that these models tend to be overly conservative for long corrosion defects. This paper, based on a PRCI-sponsored study, aims at minimizing this conservatism through a series of steps. First, different definitions for long corrosion defects prevalent in the literature were examined and compared, and the most suitable criterion was implemented. Next, three existing burst pressure models for general corrosion defects were identified and evaluated: ASME B31G-modified, a model developed at C-FER and a model developed at the University of Waterloo. The suitability of these models for long corrosion defects was assessed using a database of 50 full-scale burst test specimens containing natural long corrosion defects. Finally, based on this evaluation, the most apposite burst pressure prediction model for long corrosion defects was selected and a corresponding model error factor was derived.

scholarly journals Learning from mistakes - Assessing the performance and uncertainty in process-based models

2021 ◽  
Author(s):  
Benjamin Roesky ◽  
Moritz Feigl ◽  
Mathew Herrnegger ◽  
Karsten Schulz ◽  
Masaki Hayashi
Keyword(s):  
Stream Water ◽  
Statistical Tests ◽  
Longwave Radiation ◽  
Model Error ◽  
Model Errors ◽  
Systematic Observation ◽  
Physically Based ◽  
Error Generation ◽  
The Impact ◽  
Process Based Models

<p>Typical applications of process- or physically-based models aim to gain a better process understanding of certain natural phenomena or to estimate the impact of changes in the examined system caused by anthropogenic influences, such as land-use or climate change. To adequately represent the physical system, it is necessary to include all (essential) processes in the applied model and to observe relevant inputs in the field. However, model errors, i.e. deviations between observed and simulated values, can still occur. Other than large systematic observation errors, simplified, misrepresented or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analyzing errors of process-based models as a basis for relating them to process representations.</p><p>The evaluated approach consists of three steps: (1) prediction of model errors with a machine learning (ML) model using data that might be associated with model errors (e.g., model input data), (2) derivation of variable importance (i.e. contribution of each input variable to prediction) for each predicted model error using SHapley Additive exPlanations (SHAP), (3) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analyzing these groups of different error/variable association, hypotheses on error generation and corresponding processes can be formulated. This analysis framework can ultimately lead to improving hydrologic understanding and prediction.</p><p>The framework is applied to the physically-based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. Initial statistical tests show a significant association of model errors with available meteorological and hydrological variables. By using these variables as input features, the applied ML model is able to predict model residuals. Clustering of SHAP values results in four distinct error groups that can be related to tree shading, sensible and latent heat flux and longwave radiation emitted by trees.</p><p>Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.</p>

scholarly journals GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2050
Author(s):  
Beatriz Castro Dias Cuyabano ◽  
Gabriel Rovere ◽  
Dajeong Lim ◽  
Tae Hun Kim ◽  
Hak Kyo Lee ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Phenotypic Expression ◽  
Genetic Evaluation ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Korean Cattle ◽  
Evaluation Programs ◽  
The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

scholarly journals Largest-Crown-Width Prediction Models for 53 Species in the Western United States

2004 ◽  
Vol 19 (4) ◽  
pp. 245-251 ◽  
Author(s):  
William A. Bechtold
Keyword(s):  
United States ◽  
Prediction Models ◽  
Basal Area ◽  
Stem Diameter ◽  
Western United States ◽  
Quadratic Term ◽  
Crown Width ◽  
Coefficients Of Variation ◽  
Bioclimatic Index ◽  
Minor Improvement

Abstract The mean crown diameters of stand-grown trees 5.0-in. dbh and larger were modeled as a function of stem diameter, live-crown ratio, stand-level basal area, latitude, longitude, elevation, and Hopkins bioclimatic index for 53 tree species in the western United States. Stem diameter was statistically significant in all models, and a quadratic term for stem diameter was required for some species. Crown ratio and/or Hopkins index also improved the models for most species. A term for stand-level basal area was not generally needed but did yield some minor improvement for a few species. Coefficients of variation from the regression solutions ranged from 17 to 33%, and model R2 ranged from 0.15 to 0.85. Simpler models, based solely on stem diameter, are also presented. West. J. Appl. For. 19(4):245–251.

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