S. Lemaire, G. Vaz, Menno Deij ‐ van Rijswijk, S. Turnock
The overset method and associated interpolation schemes are usually thoroughly verified only on synthetic or academic test cases for which conclusions might not directly translate to real engineering problems. In the present work, an overset grid method is used to simulate a rudder-propeller flow, for which a comprehensive verification and validation study is performed. Three overset interpolation schemes (from first to third order) are tested to quantify and qualify numerical errors on integral quantities, mass imbalance, flow features and rudder pressure distributions. The performance overhead is also measured to help make accuracy-performance balance decisions. Rigorous solution verification is performed to estimate time and space discretisation, iterative and statistical uncertainties. Validation of the rudder flow against experimental data is also done. The results show that, while the choice of interpolation scheme has minimal impact on time-averaged integral quantities (like forces), they do influence the smoothness of the time signals, with the first order scheme resulting in large intensity high-frequency temporal oscillations. Lower order interpolation methods also produce more interpolation artefacts in fringe cells, which are then convected downstream. Mass imbalance is also affected by the interpolation scheme, with higher order schemes (third order) resulting in an order of magnitude lower flux errors. The limitations of first order schemes do not, however, result in significant lower computational overhead, with the second order being even cheaper than the first order one in the tested implementation. Lastly, validation shows promising results with rudder forces within 10% of the experiments.
{"title":"Influence of Interpolation Scheme On the Accuracy of Overset Method for Computing Rudder-propeller Interaction","authors":"S. Lemaire, G. Vaz, Menno Deij ‐ van Rijswijk, S. Turnock","doi":"10.1115/1.4056681","DOIUrl":"https://doi.org/10.1115/1.4056681","url":null,"abstract":"\u0000 The overset method and associated interpolation schemes are usually thoroughly verified only on synthetic or academic test cases for which conclusions might not directly translate to real engineering problems. In the present work, an overset grid method is used to simulate a rudder-propeller flow, for which a comprehensive verification and validation study is performed. Three overset interpolation schemes (from first to third order) are tested to quantify and qualify numerical errors on integral quantities, mass imbalance, flow features and rudder pressure distributions. The performance overhead is also measured to help make accuracy-performance balance decisions. Rigorous solution verification is performed to estimate time and space discretisation, iterative and statistical uncertainties. Validation of the rudder flow against experimental data is also done.\u0000 The results show that, while the choice of interpolation scheme has minimal impact on time-averaged integral quantities (like forces), they do influence the smoothness of the time signals, with the first order scheme resulting in large intensity high-frequency temporal oscillations. Lower order interpolation methods also produce more interpolation artefacts in fringe cells, which are then convected downstream. Mass imbalance is also affected by the interpolation scheme, with higher order schemes (third order) resulting in an order of magnitude lower flux errors. The limitations of first order schemes do not, however, result in significant lower computational overhead, with the second order being even cheaper than the first order one in the tested implementation. Lastly, validation shows promising results with rudder forces within 10% of the experiments.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42858651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joshua J. E. Blauer, R. Gray, D. Swenson, P. Pathmanathan
Survival rates for sudden cardiac death treated with external defibrillation are estimated to be up to five times greater compared to cardio-pulmonary resuscitation alone. Computational modeling can be used to investigate the relationship between patch location and defibrillation efficacy. However, credibility of model predictions is unclear. The aims of this paper are to: (1) assess credibility of a commonly used computational approach for predicting impact of patch relocation on defibrillation efficacy; and (2) provide a concrete biomedical example of a model validation study with supporting applicability analysis, to systematically assess the relevance of the validation study for a proposed model context of use (COU). Using an electrostatic heart and torso computational model, simulations were compared against experimental recordings from a swine subject with external patches and multiple body surface and intracardiac recording electrodes. Applicability of this swine validation study to the human COU was assessed using an applicability analysis framework. Knowledge gaps identified by the applicability analysis were addressed using sensitivity analysis. In the swine validation study, quantitative agreement (R2=0.85) was observed between predicted and observed potentials at both surface and intracardiac electrodes using a left-right patch placement. Applicability analysis identified uncertainty in tissue conductivities as one of the main potential sources of unreliability; however, a sensitivity analysis demonstrated that uncertainty in conductivity parameters had relatively little impact on model predictions (less than 10% relative change for two-fold conductivity changes). We believe the results support pursuing human simulations further to evaluate impact of patch relocation.
{"title":"Validation and Applicability Analysis of a Computational Model of External Defibrillation","authors":"Joshua J. E. Blauer, R. Gray, D. Swenson, P. Pathmanathan","doi":"10.1115/1.4056596","DOIUrl":"https://doi.org/10.1115/1.4056596","url":null,"abstract":"\u0000 Survival rates for sudden cardiac death treated with external defibrillation are estimated to be up to five times greater compared to cardio-pulmonary resuscitation alone. Computational modeling can be used to investigate the relationship between patch location and defibrillation efficacy. However, credibility of model predictions is unclear. The aims of this paper are to: (1) assess credibility of a commonly used computational approach for predicting impact of patch relocation on defibrillation efficacy; and (2) provide a concrete biomedical example of a model validation study with supporting applicability analysis, to systematically assess the relevance of the validation study for a proposed model context of use (COU). Using an electrostatic heart and torso computational model, simulations were compared against experimental recordings from a swine subject with external patches and multiple body surface and intracardiac recording electrodes. Applicability of this swine validation study to the human COU was assessed using an applicability analysis framework. Knowledge gaps identified by the applicability analysis were addressed using sensitivity analysis. In the swine validation study, quantitative agreement (R2=0.85) was observed between predicted and observed potentials at both surface and intracardiac electrodes using a left-right patch placement. Applicability analysis identified uncertainty in tissue conductivities as one of the main potential sources of unreliability; however, a sensitivity analysis demonstrated that uncertainty in conductivity parameters had relatively little impact on model predictions (less than 10% relative change for two-fold conductivity changes). We believe the results support pursuing human simulations further to evaluate impact of patch relocation.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41777465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juan Diego Ceballos Payares, Maika Karen Gambús Ordaz, Samuel Fernando Muñoz Navarro
Surfactant flooding comes up as a potential enhanced oil recovery method for heavy oil exploitation as a solution to energy losses in thermal processes. This research aims to use an inverse problem to determine the most suitable application scenario of this process at the laboratory scale by the use of reservoir simulation. First of all, a numerical laboratory model was built, representing an alkali-surfactant flooding test of crude oil with a viscosity of 1800 MPa*s; secondly, an optimization process was carried out, where operational parameters, such as, alkali and surfactant concentration, size of the main chemical slug and injection rate, were evaluated. Results showed that the most suitable scenario consisted on the injection of 0.5 PV from the combined mixture 0.2% Na2CO3 + 0.2% NaOH + 100 ppm Surfactant at an injection rate of 0.1 cm3/min, getting a final chemical-oil relation about 0.0010 cm3 of chemical per cm3 of oil.
{"title":"Technical Evaluation of a Surfactant Injection Process for Heavy Oil Recovery by Laboratory-Scale Numerical Simulation","authors":"Juan Diego Ceballos Payares, Maika Karen Gambús Ordaz, Samuel Fernando Muñoz Navarro","doi":"10.1115/1.4056550","DOIUrl":"https://doi.org/10.1115/1.4056550","url":null,"abstract":"\u0000 Surfactant flooding comes up as a potential enhanced oil recovery method for heavy oil exploitation as a solution to energy losses in thermal processes. This research aims to use an inverse problem to determine the most suitable application scenario of this process at the laboratory scale by the use of reservoir simulation. First of all, a numerical laboratory model was built, representing an alkali-surfactant flooding test of crude oil with a viscosity of 1800 MPa*s; secondly, an optimization process was carried out, where operational parameters, such as, alkali and surfactant concentration, size of the main chemical slug and injection rate, were evaluated. Results showed that the most suitable scenario consisted on the injection of 0.5 PV from the combined mixture 0.2% Na2CO3 + 0.2% NaOH + 100 ppm Surfactant at an injection rate of 0.1 cm3/min, getting a final chemical-oil relation about 0.0010 cm3 of chemical per cm3 of oil.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42658314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew White, S. Mahadevan, Jason Schmucker, Alexander Karl
Model validation for real-world systems involves multiple sources of uncertainty, multivariate model outputs, and often a limited number of measurement samples. These factors preclude the use of many existing validation metrics, or at least limit the ability of the practitioner to derive insights from computed metrics. This paper seeks to extend the area metric (univariate only) and the model reliability metric (univariate and multivariate) to account for these issues. The model reliability metric was found to be more extendable to multivariate outputs, whereas the area metric presented some difficulties. Metrics of different types (area and model reliability), dimensionality (univariate and multivariate), and objective (bias effects, shape effects, or both) are used together in a ‘multi-metric’ approach that provides a more informative validation assessment. The univariate metrics can be used for output-by-output model diagnosis and the multivariate metrics contributes an overall model assessment that includes correlation among the outputs. The extensions to the validation metrics in this paper address limited measurement sample size, improve the interpretability of the metric results by separating the effects of distribution bias and shape, and enhance the model reliability metric's tolerance parameter. The proposed validation approach is demonstrated with a bivariate numerical example and then applied to a gas turbine engine heat transfer model.
{"title":"Multi-Metric Validation Under Uncertainty for Multivariate Model Outputs and Limited Measurements","authors":"Andrew White, S. Mahadevan, Jason Schmucker, Alexander Karl","doi":"10.1115/1.4056548","DOIUrl":"https://doi.org/10.1115/1.4056548","url":null,"abstract":"\u0000 Model validation for real-world systems involves multiple sources of uncertainty, multivariate model outputs, and often a limited number of measurement samples. These factors preclude the use of many existing validation metrics, or at least limit the ability of the practitioner to derive insights from computed metrics. This paper seeks to extend the area metric (univariate only) and the model reliability metric (univariate and multivariate) to account for these issues. The model reliability metric was found to be more extendable to multivariate outputs, whereas the area metric presented some difficulties. Metrics of different types (area and model reliability), dimensionality (univariate and multivariate), and objective (bias effects, shape effects, or both) are used together in a ‘multi-metric’ approach that provides a more informative validation assessment. The univariate metrics can be used for output-by-output model diagnosis and the multivariate metrics contributes an overall model assessment that includes correlation among the outputs. The extensions to the validation metrics in this paper address limited measurement sample size, improve the interpretability of the metric results by separating the effects of distribution bias and shape, and enhance the model reliability metric's tolerance parameter. The proposed validation approach is demonstrated with a bivariate numerical example and then applied to a gas turbine engine heat transfer model.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48798425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper discusses the application of the Area Metric to the quantification of modeling errors. The focus of the discussion is the effect of the shape of the two distributions on the result produced by the Area Metric. Two different examples that assume negligible experimental and numerical errors are presented: the first case has experimental and simulated quantities of interest defined by normal distributions that require the definition of a mean value and a standard deviation; the second example is taken from the V&V10.1 ASME Standard. The first example, shows that relatively small differences between the mean values are sufficient for the area metric to be insensitive to the standard deviation. Furthermore, the example of the V&V10.1 ASME Standard produces an Area Metric equal to the difference between the mean values of experiments and simulations. Therefore, the error quantification is reduced to a single number that is obtained from a simple difference of two mean values. This means that the Area Metric fails to reflect a dependence for the difference in the shape of the distributions representing variability. The paper also presents an alternative version of the Area Metric that does not filter the effect of the shape of the distributions by utilizing a reference simulation that has the same mean value of the experiments. This means that the quantification of the modeling error will have contributions from the difference in mean values and from the shape of the distributions.
{"title":"On the Failure of the Area Metric for Validation Exercises of Stochastic Simulations","authors":"L. Eça, K. Dowding, P. Roache","doi":"10.1115/1.4056492","DOIUrl":"https://doi.org/10.1115/1.4056492","url":null,"abstract":"\u0000 This paper discusses the application of the Area Metric to the quantification of modeling errors. The focus of the discussion is the effect of the shape of the two distributions on the result produced by the Area Metric. Two different examples that assume negligible experimental and numerical errors are presented: the first case has experimental and simulated quantities of interest defined by normal distributions that require the definition of a mean value and a standard deviation; the second example is taken from the V&V10.1 ASME Standard.\u0000 The first example, shows that relatively small differences between the mean values are sufficient for the area metric to be insensitive to the standard deviation. Furthermore, the example of the V&V10.1 ASME Standard produces an Area Metric equal to the difference between the mean values of experiments and simulations. Therefore, the error quantification is reduced to a single number that is obtained from a simple difference of two mean values. This means that the Area Metric fails to reflect a dependence for the difference in the shape of the distributions representing variability.\u0000 The paper also presents an alternative version of the Area Metric that does not filter the effect of the shape of the distributions by utilizing a reference simulation that has the same mean value of the experiments. This means that the quantification of the modeling error will have contributions from the difference in mean values and from the shape of the distributions.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47449066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pasquale Cascarano, Andrea Sebastiani, Giorgia Franchini, F. Porta
Deep learning methods have state-of-the-art performances in many image restoration tasks. Their effectiveness is mostly related to the size of the dataset used for the training. Deep Image Prior (DIP) is an energy function framework which eliminates the dependency on the training set, by considering the structure of a neural network as an handcrafted prior offering high impedance to noise and low impedance to signal. In this paper, we analyze and compare the use of different optimization schemes inside the DIP framework for the denoising task.
{"title":"On the First Order Optimization Methods in Deep Image Prior","authors":"Pasquale Cascarano, Andrea Sebastiani, Giorgia Franchini, F. Porta","doi":"10.1115/1.4056470","DOIUrl":"https://doi.org/10.1115/1.4056470","url":null,"abstract":"\u0000 Deep learning methods have state-of-the-art performances in many image restoration tasks. Their effectiveness is mostly related to the size of the dataset used for the training. Deep Image Prior (DIP) is an energy function framework which eliminates the dependency on the training set, by considering the structure of a neural network as an handcrafted prior offering high impedance to noise and low impedance to signal. In this paper, we analyze and compare the use of different optimization schemes inside the DIP framework for the denoising task.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42029360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While current computational capability has led to finite element analysis becoming the predominant means of assessing three-dimensional stress concentrations, there are nonetheless some three-dimensional configurations where the desired level of accuracy of stresses is not realized on the finest mesh used. Here we offer some simple means of improving the accuracy of finite element stresses for such configurations, and doing so with modest increases in computational effort. These improved stresses are obtained by using an adaptation of Richardson extrapolation on original mesh results, and also on mesh results with a reduced mesh refinement factor. Verification of the improvements is undertaken using the convergence checks and error estimates reported earlier. The approach is applied to nine three-dimensional test problems. Finite element analysis of these test problems leads to eleven stresses on the finest meshes used that could benefit from being improved. The extrapolation procedure in conjunction with the reduced refinement factor improved all eleven stresses. Error estimates confirmed these improvements for all eleven.
{"title":"Verifiable Improvements of Finite Element Stresses At Three-dimensional Stress Concentrations","authors":"Jeffrey R. Beisheim, G. Sinclair","doi":"10.1115/1.4056395","DOIUrl":"https://doi.org/10.1115/1.4056395","url":null,"abstract":"\u0000 While current computational capability has led to finite element analysis becoming the predominant means of assessing three-dimensional stress concentrations, there are nonetheless some three-dimensional configurations where the desired level of accuracy of stresses is not realized on the finest mesh used. Here we offer some simple means of improving the accuracy of finite element stresses for such configurations, and doing so with modest increases in computational effort. These improved stresses are obtained by using an adaptation of Richardson extrapolation on original mesh results, and also on mesh results with a reduced mesh refinement factor. Verification of the improvements is undertaken using the convergence checks and error estimates reported earlier. The approach is applied to nine three-dimensional test problems. Finite element analysis of these test problems leads to eleven stresses on the finest meshes used that could benefit from being improved. The extrapolation procedure in conjunction with the reduced refinement factor improved all eleven stresses. Error estimates confirmed these improvements for all eleven.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43363503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A simple procedure for estimating the uncertainty of estimates of true solutions to problems of deflection, stress concentrations, and force resultants in solid and structural mechanics is introduced. Richardson extrapolation is carried out on a dataset of samples from a sequence of four grids. Simple median-based statistical analysis is used to establish 95% confidence intervals. The procedure leads to simple calculations that deliver reasonably tight estimates of the true solution and confidence intervals.
{"title":"Confidence Intervals for Richardson Extrapolation in Solid Mechanics","authors":"P. Krysl","doi":"10.1115/1.4055728","DOIUrl":"https://doi.org/10.1115/1.4055728","url":null,"abstract":"\u0000 A simple procedure for estimating the uncertainty of estimates of true solutions to problems of deflection, stress concentrations, and force resultants in solid and structural mechanics is introduced. Richardson extrapolation is carried out on a dataset of samples from a sequence of four grids. Simple median-based statistical analysis is used to establish 95% confidence intervals. The procedure leads to simple calculations that deliver reasonably tight estimates of the true solution and confidence intervals.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42005852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantifying the fractional change in a predicted quantity of interest with successive mesh refinement is an attractive and widely used but limited approach to assessing numerical error and uncertainty in physics-based computational modeling. Herein, we introduce the concept of a scalar multiplier αGCI to clarify the connection between fractional change and a more rigorous and accepted estimate of numerical uncertainty, the grid convergence index (GCI). Specifically, we generate lookup tables for αGCI as a function of observed order of accuracy and mesh refinement factor. We then illustrate the limitations of relying on fractional change alone as an acceptance criterion for mesh refinement using a case study involving the radial compression of a Nitinol stent. Results illustrate that numerical uncertainty is often many times larger than the observed fractional change in a mesh pair, especially in the presence of small mesh refinement factors or low orders of accuracy. We strongly caution against relying on fractional change alone as an acceptance criterion for mesh refinement studies, particularly in any high-risk applications requiring absolute prediction of quantities of interest. When computational resources make the systematic refinement required for calculating GCI impractical, submodeling approaches as demonstrated herein can be used to rigorously quantify discretization error at comparatively minimal computational cost. To facilitate future quantitative mesh refinement studies, αGCI lookup tables herein provide a useful tool for guiding the selection of mesh refinement factor and element order.
{"title":"Two Calculation Verification Metrics Used in the Medical Device Industry: Revisiting the Limitations of Fractional Change","authors":"Ismail Guler, K. Aycock, N. Rebelo","doi":"10.1115/1.4055506","DOIUrl":"https://doi.org/10.1115/1.4055506","url":null,"abstract":"\u0000 Quantifying the fractional change in a predicted quantity of interest with successive mesh refinement is an attractive and widely used but limited approach to assessing numerical error and uncertainty in physics-based computational modeling. Herein, we introduce the concept of a scalar multiplier αGCI to clarify the connection between fractional change and a more rigorous and accepted estimate of numerical uncertainty, the grid convergence index (GCI). Specifically, we generate lookup tables for αGCI as a function of observed order of accuracy and mesh refinement factor. We then illustrate the limitations of relying on fractional change alone as an acceptance criterion for mesh refinement using a case study involving the radial compression of a Nitinol stent. Results illustrate that numerical uncertainty is often many times larger than the observed fractional change in a mesh pair, especially in the presence of small mesh refinement factors or low orders of accuracy. We strongly caution against relying on fractional change alone as an acceptance criterion for mesh refinement studies, particularly in any high-risk applications requiring absolute prediction of quantities of interest. When computational resources make the systematic refinement required for calculating GCI impractical, submodeling approaches as demonstrated herein can be used to rigorously quantify discretization error at comparatively minimal computational cost. To facilitate future quantitative mesh refinement studies, αGCI lookup tables herein provide a useful tool for guiding the selection of mesh refinement factor and element order.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46119426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niloofar Rashidi, R. Burgos, C. Roy, D. Boroyevich
This paper presents the numerical approaches for sensitivity analysis and its application in the modeling effort of a modular multilevel converter (MMC). A review of the state-of-the-art techniques in the sensitivity analysis is provided, with a special focus on the numerical approaches, followed by the sensitivity analysis of an MMC. To further reduce the computational cost per model evaluation in parametric uncertainty quantification (P-UQ) of the MMC, this paper also proposes a simplified model with a minimum number of power modules for P-UQ analysis without introducing any further uncertainties in the modeling and simulation.
{"title":"Sensitivity Analysis and Parametric Uncertainty Quantification of a Modular Multilevel Converter","authors":"Niloofar Rashidi, R. Burgos, C. Roy, D. Boroyevich","doi":"10.1115/1.4055139","DOIUrl":"https://doi.org/10.1115/1.4055139","url":null,"abstract":"\u0000 This paper presents the numerical approaches for sensitivity analysis and its application in the modeling effort of a modular multilevel converter (MMC). A review of the state-of-the-art techniques in the sensitivity analysis is provided, with a special focus on the numerical approaches, followed by the sensitivity analysis of an MMC. To further reduce the computational cost per model evaluation in parametric uncertainty quantification (P-UQ) of the MMC, this paper also proposes a simplified model with a minimum number of power modules for P-UQ analysis without introducing any further uncertainties in the modeling and simulation.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42774412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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