This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. The methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.
{"title":"Processing Aleatory and Epistemic Uncertainties in Experimental Data From Sparse Replicate Tests of Stochastic Systems for Real-Space Model Validation","authors":"V. Romero, A. Black","doi":"10.1115/1.4051069","DOIUrl":"https://doi.org/10.1115/1.4051069","url":null,"abstract":"\u0000 This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. The methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45313509","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}
F. Pereira, Fernando Grinstein, Daniel Israel, L. Eça
This work investigates the importance of verification and validation (V&V) to achieve predictive scale-resolving simulations (SRS) of turbulence, i.e., computations capable of resolving a fraction of the turbulent flow scales. Toward this end, we propose a novel but simple V&V strategy based on grid and physical resolution refinement studies that can be used even when the exact initial flow conditions are unknown, or reference data are unavailable. This is particularly relevant for transient and transitional flow problems, as well as for the improvement of turbulence models. We start by presenting a literature survey of results obtained with distinct SRS models for flows past circular cylinders. It confirms the importance of V&V by illustrating a large variability of results, which is independent of the selected mathematical model and Reynolds number. The proposed V&V strategy is then used on three representative problems of practical interest. The results illustrate that it is possible to conduct reliable verification and validation exercises with SRS models, and evidence the importance of V&V to predictive SRS of turbulence. Most notably, the data also confirm the advantages and potential of the proposed V&V strategy: separate assessment of numerical and modeling errors, enhanced flow physics analysis, identification of key flow phenomena, and ability to operate when the exact flow conditions are unknown or reference data are unavailable.
{"title":"Verification and Validation: the Path to Predictive Scale-Resolving Simulations of Turbulence","authors":"F. Pereira, Fernando Grinstein, Daniel Israel, L. Eça","doi":"10.1115/1.4053884","DOIUrl":"https://doi.org/10.1115/1.4053884","url":null,"abstract":"\u0000 This work investigates the importance of verification and validation (V&V) to achieve predictive scale-resolving simulations (SRS) of turbulence, i.e., computations capable of resolving a fraction of the turbulent flow scales. Toward this end, we propose a novel but simple V&V strategy based on grid and physical resolution refinement studies that can be used even when the exact initial flow conditions are unknown, or reference data are unavailable. This is particularly relevant for transient and transitional flow problems, as well as for the improvement of turbulence models. We start by presenting a literature survey of results obtained with distinct SRS models for flows past circular cylinders. It confirms the importance of V&V by illustrating a large variability of results, which is independent of the selected mathematical model and Reynolds number. The proposed V&V strategy is then used on three representative problems of practical interest. The results illustrate that it is possible to conduct reliable verification and validation exercises with SRS models, and evidence the importance of V&V to predictive SRS of turbulence. Most notably, the data also confirm the advantages and potential of the proposed V&V strategy: separate assessment of numerical and modeling errors, enhanced flow physics analysis, identification of key flow phenomena, and ability to operate when the exact flow conditions are unknown or reference data are unavailable.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45375874","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}
Computational modeling and simulation is a central tool in science and engineering, directed at solving partial differential equations for which analytical solutions are unavailable. The continuous equations are generally discretized in time, space, energy, etc., to obtain approximate solutions using a numerical method. The aspiration is for the numerical solutions to asymptotically converge to the exact-but-unknown solution as the discretization size approaches zero. A generally applicable procedure to assure convergence is unavailable. The Richardson extrapolation is the main method for dealing with this challenge, but its assumptions introduce uncertainty to the resulting approximation. We use info-gap decision theory to model and manage its main uncertainty, namely, in the rate of convergence of numerical solutions. The theory is illustrated with a numerical application to Hertz contact in solid mechanics.
{"title":"Richardson Extrapolation: An Info-Gap Analysis of Numerical Uncertainty","authors":"Y. Ben-Haim, F. Hemez","doi":"10.1115/1.4048004","DOIUrl":"https://doi.org/10.1115/1.4048004","url":null,"abstract":"\u0000 Computational modeling and simulation is a central tool in science and engineering, directed at solving partial differential equations for which analytical solutions are unavailable. The continuous equations are generally discretized in time, space, energy, etc., to obtain approximate solutions using a numerical method. The aspiration is for the numerical solutions to asymptotically converge to the exact-but-unknown solution as the discretization size approaches zero. A generally applicable procedure to assure convergence is unavailable. The Richardson extrapolation is the main method for dealing with this challenge, but its assumptions introduce uncertainty to the resulting approximation. We use info-gap decision theory to model and manage its main uncertainty, namely, in the rate of convergence of numerical solutions. The theory is illustrated with a numerical application to Hertz contact in solid mechanics.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42901275","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}
Engineers and computational scientists often study the behavior of their simulations by repeated solutions with variations in their parameters, which can be, for instance, boundary values or initial conditions. Through such simulation ensembles, uncertainty in a solution is studied as a function of various input parameters. Solutions of numerical simulations are often temporal functions, spatial maps, or spatio-temporal outputs. The usual way to deal with such complex outputs is to limit the analysis to several probes in the temporal/spatial domain. This leads to smaller and more tractable ensembles of functional outputs (curves) with their associated input parameters: augmented ensembles of curves. This article describes a system for the interactive exploration and analysis of such augmented ensembles. Descriptive statistics on the functional outputs are performed by principal component analysis (PCA) projection, kernel density estimation, and the computation of high density regions. This makes possible the calculation of functional quantiles and outliers. Brushing and linking the elements of the system allows in-depth analysis of the ensemble. The system allows for functional descriptive statistics, cluster detection, and finally, for the realization of a visual sensitivity analysis via cobweb plots. We present two synthetic examples and then validate our approach in an industrial use-case concerning a marine current study using a hydraulic solver.
{"title":"A Visual Sensitivity Analysis for Parameter-Augmented Ensembles of Curves","authors":"A. Ribés, Joachim Pouderoux, B. Iooss","doi":"10.1115/1.4046020","DOIUrl":"https://doi.org/10.1115/1.4046020","url":null,"abstract":"Engineers and computational scientists often study the behavior of their simulations by repeated solutions with variations in their parameters, which can be, for instance, boundary values or initial conditions. Through such simulation ensembles, uncertainty in a solution is studied as a function of various input parameters. Solutions of numerical simulations are often temporal functions, spatial maps, or spatio-temporal outputs. The usual way to deal with such complex outputs is to limit the analysis to several probes in the temporal/spatial domain. This leads to smaller and more tractable ensembles of functional outputs (curves) with their associated input parameters: augmented ensembles of curves. This article describes a system for the interactive exploration and analysis of such augmented ensembles. Descriptive statistics on the functional outputs are performed by principal component analysis (PCA) projection, kernel density estimation, and the computation of high density regions. This makes possible the calculation of functional quantiles and outliers. Brushing and linking the elements of the system allows in-depth analysis of the ensemble. The system allows for functional descriptive statistics, cluster detection, and finally, for the realization of a visual sensitivity analysis via cobweb plots. We present two synthetic examples and then validate our approach in an industrial use-case concerning a marine current study using a hydraulic solver.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47240511","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}
M. Horner, Stephen M. Luke, K. Genc, T. Pietila, R. Cotton, Benjamin Ache, Z. Levine, Kevin Townsend
Patient-specific computational modeling is increasingly used to assist with visualization, planning, and execution of medical treatments. This trend is placing more reliance on medical imaging to provide accurate representations of anatomical structures. Digital image analysis is used to extract anatomical data for use in clinical assessment/planning. However, the presence of image artifacts, whether due to interactions between the physical object and the scanning modality or the scanning process, can degrade image accuracy. The process of extracting anatomical structures from the medical images introduces additional sources of variability, e.g., when thresholding or when eroding along apparent edges of biological structures. An estimate of the uncertainty associated with extracting anatomical data from medical images would therefore assist with assessing the reliability of patient-specific treatment plans. To this end, two image datasets were developed and analyzed using standard image analysis procedures. The first dataset was developed by performing a "virtual voxelization" of a CAD model of a sphere, representing the idealized scenario of no error in the image acquisition and reconstruction algorithms (i.e., a perfect scan). The second dataset was acquired by scanning three spherical balls using a laboratory-grade CT scanner. For the idealized sphere, the error in sphere diameter was less than or equal to 2% if 5 or more voxels were present across the diameter. The measurement error degraded to approximately 4% for a similar degree of voxelization of the physical phantom. The adaptation of established thresholding procedures to improve segmentation accuracy was also investigated.
{"title":"Towards Estimating the Uncertainty Associated with Three-Dimensional Geometry Reconstructed from Medical Image Data.","authors":"M. Horner, Stephen M. Luke, K. Genc, T. Pietila, R. Cotton, Benjamin Ache, Z. Levine, Kevin Townsend","doi":"10.1115/1.4045487","DOIUrl":"https://doi.org/10.1115/1.4045487","url":null,"abstract":"Patient-specific computational modeling is increasingly used to assist with visualization, planning, and execution of medical treatments. This trend is placing more reliance on medical imaging to provide accurate representations of anatomical structures. Digital image analysis is used to extract anatomical data for use in clinical assessment/planning. However, the presence of image artifacts, whether due to interactions between the physical object and the scanning modality or the scanning process, can degrade image accuracy. The process of extracting anatomical structures from the medical images introduces additional sources of variability, e.g., when thresholding or when eroding along apparent edges of biological structures. An estimate of the uncertainty associated with extracting anatomical data from medical images would therefore assist with assessing the reliability of patient-specific treatment plans. To this end, two image datasets were developed and analyzed using standard image analysis procedures. The first dataset was developed by performing a \"virtual voxelization\" of a CAD model of a sphere, representing the idealized scenario of no error in the image acquisition and reconstruction algorithms (i.e., a perfect scan). The second dataset was acquired by scanning three spherical balls using a laboratory-grade CT scanner. For the idealized sphere, the error in sphere diameter was less than or equal to 2% if 5 or more voxels were present across the diameter. The measurement error degraded to approximately 4% for a similar degree of voxelization of the physical phantom. The adaptation of established thresholding procedures to improve segmentation accuracy was also investigated.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77614129","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}
Soil drilling operation has become one of the most important interests to researchers due to its many applications in engineering systems. Auger drilling is one of the ideal methods in many applications such as pile foundation engineering, sampling test for geological, and space sciences. However, the dominant factor in determination of drilling parameters drilling operations experience. Therefore, soil-drilling process using auger drilling is studied to obtain the controlling parameters and to optimize these parameters to improve drilling performance which enables proper selection of machine for a required job. One of the main challenges that faces researchers during using of modeling techniques to define the soil drilling problem is the complex nonlinear behavior of the drilled medium itself due to its discontinuity and heterogeneous formation. This article presents two models that can be used to predict the total resistive forces which affect the auger during soil drilling operations. The first proposed model discusses the problem analytically in a way that depends on empirical data that can be collected from previous experience. The second model discusses the problem numerically with less depending on empirical experienced data. The analytical model is developed using matlab® interface, while the numerical model is developed using discrete element method (DEM) using edem software. A simplified auger drilling machine is built in the soil–tool interaction laboratory, Military Technical College to obtain experimental results that can be used to verify the presented models. Data acquisition measuring system is established to obtain experimental results using a labview® software which enables displaying and recording the measured data collected mainly from transducers planted in the test rig. Both analytical and numerical model results are compared to experimental values to aid in developing the presented parametric study that can be used to define the working parameters during drilling operations in different types of soils. Uncertainty calculations have been applied to ensure the reliability of the models. The combined calculated uncertainty leads to the level of confidence of about 95%.
{"title":"Analytical and Numerical Modeling of Soil Cutting and Transportation During Auger Drilling Operation","authors":"M. Abdeldayem, M. Mabrouk, M. Abo-Elnor","doi":"10.1115/imece2019-10311","DOIUrl":"https://doi.org/10.1115/imece2019-10311","url":null,"abstract":"\u0000 Soil drilling operation has become one of the most important interests to researchers due to its many applications in engineering systems. Auger drilling is one of the ideal methods in many applications such as pile foundation engineering, sampling test for geological, and space sciences. However, the dominant factor in determination of drilling parameters drilling operations experience. Therefore, soil-drilling process using auger drilling is studied to obtain the controlling parameters and to optimize these parameters to improve drilling performance which enables proper selection of machine for a required job. One of the main challenges that faces researchers during using of modeling techniques to define the soil drilling problem is the complex nonlinear behavior of the drilled medium itself due to its discontinuity and heterogeneous formation. This article presents two models that can be used to predict the total resistive forces which affect the auger during soil drilling operations. The first proposed model discusses the problem analytically in a way that depends on empirical data that can be collected from previous experience. The second model discusses the problem numerically with less depending on empirical experienced data. The analytical model is developed using matlab® interface, while the numerical model is developed using discrete element method (DEM) using edem software. A simplified auger drilling machine is built in the soil–tool interaction laboratory, Military Technical College to obtain experimental results that can be used to verify the presented models. Data acquisition measuring system is established to obtain experimental results using a labview® software which enables displaying and recording the measured data collected mainly from transducers planted in the test rig. Both analytical and numerical model results are compared to experimental values to aid in developing the presented parametric study that can be used to define the working parameters during drilling operations in different types of soils. Uncertainty calculations have been applied to ensure the reliability of the models. The combined calculated uncertainty leads to the level of confidence of about 95%.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48003820","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 modeling and simulations of deteriorated turbulent heat transfer (DTHT) for a wall-heated fluid flows, which can be observed in gas-cooled nuclear power reactors during pressurized conduction cooldown (PCC) event due to loss of force circulation flow. The DTHT regime is defined as the deterioration of normal turbulent heat transport due to increase of acceleration and buoyancy forces. The computational fluid dynamics (CFD) tools such as Nek5000 and STAR-CCM+ can help to analyze the DTHT phenomena in reactors for efficient thermal-fluid designs. Three-dimensional (3D) CFD nonisothermal modeling and simulations were performed in a wall-heated circular tube. The simulation results were validated with two different CFD tools, Nek5000 and STAR-CCM+, and validated with an experimental data. The predicted bulk temperatures were identical in both CFD tools, as expected. Good agreement between simulated results and measured data were obtained for wall temperatures along the tube axis using Nek5000. In STAR-CCM+, the under-predicted wall temperatures were mainly due to higher turbulence in the wall region. In STAR-CCM+, the predicted DTHT was over 48% at outlet when compared to inlet heat transfer values.
{"title":"Modeling and Simulations of Deteriorated Turbulent Heat Transfer in Wall-Heated Cylindrical Tube","authors":"P. Vegendla, R. Hu","doi":"10.1115/1.4045522","DOIUrl":"https://doi.org/10.1115/1.4045522","url":null,"abstract":"\u0000 This paper discusses the modeling and simulations of deteriorated turbulent heat transfer (DTHT) for a wall-heated fluid flows, which can be observed in gas-cooled nuclear power reactors during pressurized conduction cooldown (PCC) event due to loss of force circulation flow. The DTHT regime is defined as the deterioration of normal turbulent heat transport due to increase of acceleration and buoyancy forces. The computational fluid dynamics (CFD) tools such as Nek5000 and STAR-CCM+ can help to analyze the DTHT phenomena in reactors for efficient thermal-fluid designs. Three-dimensional (3D) CFD nonisothermal modeling and simulations were performed in a wall-heated circular tube. The simulation results were validated with two different CFD tools, Nek5000 and STAR-CCM+, and validated with an experimental data. The predicted bulk temperatures were identical in both CFD tools, as expected. Good agreement between simulated results and measured data were obtained for wall temperatures along the tube axis using Nek5000. In STAR-CCM+, the under-predicted wall temperatures were mainly due to higher turbulence in the wall region. In STAR-CCM+, the predicted DTHT was over 48% at outlet when compared to inlet heat transfer values.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42035878","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}
Present research inspects the performance of rotor–bearing–coupling system in the presence of active magnetic bearings (AMBs). A methodology is suggested to quantify various fault characteristics along with AMB characteristic parameters of a coupled turbine generator system. A simplest possible turbogenerator system is modeled to analyze coupling misalignment. Conventional methodology to estimate dynamic system parameters based on forced response information is not enough for AMB-integrated rotor system because it requires current information along with displacement information. The controlling current of AMB is tuned and controlled with a controller of proportional–integral–derivative (PID) type. A numerical technique (Lagrange's equation) is applied to get equations of motion (EOM). Runge–Kutta technique is used to obtain EOM to acquire the time domain responses. The fast Fourier transformation (FFT) is applied on obtained responses to acquire responses in the frequency domain, and full spectrum technique is applied to propose the methodology. A methodology that depends on the least squares regression approach is proposed to evaluate the multifault parameters of AMB-integrated rotor system. The robustness of the algorithm is checked against various levels of noise and modeling error and observed efficient. An appreciable reduction in misalignment forces and moments is observed by using AMBs.
{"title":"Characteristic Parameters Estimation of Active Magnetic Bearings in a Coupled Rotor System","authors":"Sampath Kumar Kuppa, M. Lal","doi":"10.1115/1.4045295","DOIUrl":"https://doi.org/10.1115/1.4045295","url":null,"abstract":"\u0000 Present research inspects the performance of rotor–bearing–coupling system in the presence of active magnetic bearings (AMBs). A methodology is suggested to quantify various fault characteristics along with AMB characteristic parameters of a coupled turbine generator system. A simplest possible turbogenerator system is modeled to analyze coupling misalignment. Conventional methodology to estimate dynamic system parameters based on forced response information is not enough for AMB-integrated rotor system because it requires current information along with displacement information. The controlling current of AMB is tuned and controlled with a controller of proportional–integral–derivative (PID) type. A numerical technique (Lagrange's equation) is applied to get equations of motion (EOM). Runge–Kutta technique is used to obtain EOM to acquire the time domain responses. The fast Fourier transformation (FFT) is applied on obtained responses to acquire responses in the frequency domain, and full spectrum technique is applied to propose the methodology. A methodology that depends on the least squares regression approach is proposed to evaluate the multifault parameters of AMB-integrated rotor system. The robustness of the algorithm is checked against various levels of noise and modeling error and observed efficient. An appreciable reduction in misalignment forces and moments is observed by using AMBs.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44928609","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 presents a statistical methodology for a quantified validation of the OCARINa simulation tool, which models the unprotected transient overpower (UTOP) accidents. This validation on CABRI experiments is based on a best-estimate plus uncertainties (BEPU) approach. To achieve this, a general methodology based on recent statistical techniques is developed. In particular, a method for the quantification of multivariate data is applied for the visualization of simulator outputs and their comparison with experiments. Still for validation purposes, a probabilistic indicator is proposed to quantify the degree of agreement between the simulator OCARINa and the experiments, taking into account both experimental uncertainties and those on OCARINa inputs. Going beyond a qualitative validation, this work is of great interest for the verification, validation and uncertainty quantification or evaluation model development and assessment process approaches, which leads to the qualification of scientific calculation tools. Finally, for an in-depth analysis of the influence of uncertain parameters, a sensitivity analysis based on recent dependence measures is also performed. The usefulness of the statistical methodology is demonstrated on CABRI-E7 and CABRI-E12 tests. For each case, the BEPU propagation study is carried out performing 1000 Monte Carlo simulations with the OCARINa tool, with nine uncertain input parameters. The validation indicators provide a quantitative conclusion on the validation of the OCARINa tool on both transients and highlight future efforts to strengthen the demonstration of validation of safety tools. The sensitivity analysis improves the understanding of the OCARINa tool and the underlying UTOP scenario.
{"title":"Statistical Methodology for a Quantified Validation of Sodium Fast Reactor Simulation Tools","authors":"N. Marie, A. Marrel, K. Herbreteau","doi":"10.1115/1.4045233","DOIUrl":"https://doi.org/10.1115/1.4045233","url":null,"abstract":"\u0000 This paper presents a statistical methodology for a quantified validation of the OCARINa simulation tool, which models the unprotected transient overpower (UTOP) accidents. This validation on CABRI experiments is based on a best-estimate plus uncertainties (BEPU) approach. To achieve this, a general methodology based on recent statistical techniques is developed. In particular, a method for the quantification of multivariate data is applied for the visualization of simulator outputs and their comparison with experiments. Still for validation purposes, a probabilistic indicator is proposed to quantify the degree of agreement between the simulator OCARINa and the experiments, taking into account both experimental uncertainties and those on OCARINa inputs. Going beyond a qualitative validation, this work is of great interest for the verification, validation and uncertainty quantification or evaluation model development and assessment process approaches, which leads to the qualification of scientific calculation tools. Finally, for an in-depth analysis of the influence of uncertain parameters, a sensitivity analysis based on recent dependence measures is also performed. The usefulness of the statistical methodology is demonstrated on CABRI-E7 and CABRI-E12 tests. For each case, the BEPU propagation study is carried out performing 1000 Monte Carlo simulations with the OCARINa tool, with nine uncertain input parameters. The validation indicators provide a quantitative conclusion on the validation of the OCARINa tool on both transients and highlight future efforts to strengthen the demonstration of validation of safety tools. The sensitivity analysis improves the understanding of the OCARINa tool and the underlying UTOP scenario.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41957003","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}
Submodeling enables finite element engineers to focus analysis on the subregion containing the stress concentrator of interest with consequent computational savings. Such benefits are only really gained if the boundary conditions on the edges of the subregion that are drawn from an initial global finite element analysis (FEA) are verified to have been captured sufficiently accurately. Here, we offer a two-pronged approach aimed at realizing such solution verification. The first element of this approach is an improved means of assessing the error induced by submodel boundary conditions. The second element is a systematic sizing of the submodel region so that boundary-condition errors become acceptable. The resulting submodel procedure is demonstrated on a series of two-dimensional (2D) configurations with significant stress concentrations: four test problems and one application. For the test problems, the assessment means are uniformly successful in determining when submodel boundary conditions are accurate and when they are not. When, at first, they are not, the sizing approach is also consistently successful in enlarging submodel regions until submodel boundary conditions do become sufficiently accurate.
{"title":"Verification of Submodeling for the Finite Element Analysis of Stress Concentrations","authors":"A. Kardak, G. Sinclair","doi":"10.1115/1.4045232","DOIUrl":"https://doi.org/10.1115/1.4045232","url":null,"abstract":"\u0000 Submodeling enables finite element engineers to focus analysis on the subregion containing the stress concentrator of interest with consequent computational savings. Such benefits are only really gained if the boundary conditions on the edges of the subregion that are drawn from an initial global finite element analysis (FEA) are verified to have been captured sufficiently accurately. Here, we offer a two-pronged approach aimed at realizing such solution verification. The first element of this approach is an improved means of assessing the error induced by submodel boundary conditions. The second element is a systematic sizing of the submodel region so that boundary-condition errors become acceptable. The resulting submodel procedure is demonstrated on a series of two-dimensional (2D) configurations with significant stress concentrations: four test problems and one application. For the test problems, the assessment means are uniformly successful in determining when submodel boundary conditions are accurate and when they are not. When, at first, they are not, the sizing approach is also consistently successful in enlarging submodel regions until submodel boundary conditions do become sufficiently accurate.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41626747","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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