Computational physicists are commonly faced with the task of resolving discrepancies between the predictions of a complex, integrated multiphysics numerical simulation, and corresponding experimental datasets. Such efforts commonly require a slow iterative procedure. However, a different approach is available in casesx where the multiphysics system of interest admits closed-form analytic solutions. In this situation, the ambiguity is conveniently broken into separate consideration of theory–simulation comparisons (issues of verification) and theory–data comparisons (issues of validation). We demonstrate this methodology via application to the specific example of a fluid-instability-based ejecta source model under development at Los Alamos National Laboratory and implemented in flag, a Los Alamos continuum mechanics code. The formalism is conducted in the forward sense (i.e., from source to measurement) and enables us to compute, purely analytically, time-dependent piezoelectric ejecta mass measurements for a specific class of explosively driven metal coupon experiments. We incorporate published measurement uncertainties on relevant experimental parameters to estimate a time-dependent uncertainty on these analytic predictions. This motivates the introduction of a “compatibility score” metric, our primary tool for quantitative analysis of the RMI + SSVD model. Finally, we derive a modification to the model, based on boundary condition considerations, that substantially improves its predictions.
{"title":"Analytic Solutions as a Tool for Verification and Validation of a Multiphysics Model","authors":"I. Tregillis","doi":"10.2172/1542799","DOIUrl":"https://doi.org/10.2172/1542799","url":null,"abstract":"\u0000 Computational physicists are commonly faced with the task of resolving discrepancies between the predictions of a complex, integrated multiphysics numerical simulation, and corresponding experimental datasets. Such efforts commonly require a slow iterative procedure. However, a different approach is available in casesx where the multiphysics system of interest admits closed-form analytic solutions. In this situation, the ambiguity is conveniently broken into separate consideration of theory–simulation comparisons (issues of verification) and theory–data comparisons (issues of validation). We demonstrate this methodology via application to the specific example of a fluid-instability-based ejecta source model under development at Los Alamos National Laboratory and implemented in flag, a Los Alamos continuum mechanics code. The formalism is conducted in the forward sense (i.e., from source to measurement) and enables us to compute, purely analytically, time-dependent piezoelectric ejecta mass measurements for a specific class of explosively driven metal coupon experiments. We incorporate published measurement uncertainties on relevant experimental parameters to estimate a time-dependent uncertainty on these analytic predictions. This motivates the introduction of a “compatibility score” metric, our primary tool for quantitative analysis of the RMI + SSVD model. Finally, we derive a modification to the model, based on boundary condition considerations, that substantially improves its predictions.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41625636","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 Reynolds-averaged simulations have found success in the evaluation of many canonical shear flows and moderately separated flows, their application to highly separated flows have shown notable deficiencies. This study aimed to investigate these deficiencies in the eddy-viscosity formulation of four commonly used turbulence models under separated flow in an attempt to aid in the improved formulation of such models. Analyses are performed on the flow field around a wall-mounted cube (WMC) at a Reynolds number of 40,000 based on the cube height, h, and freestream velocity, U0. While a common occurrence in industrial applications, this type of flow constitutes a complex structure exhibiting a large separated wake region, high anisotropy, and multiple vortex structures. As well, interactions between vortices developed off of different faces of the cube significantly alter the overall flow characteristics, posing a significant challenge for the commonly used industrial turbulence models. Comparison of mean flow characteristics show remarkable agreement between experimental values and turbulence models which are capable of predicting transitional flow. Evaluation of turbulence parameters show the general underestimation of Reynolds stress for transitional models, while fully turbulent models show this value to be overestimated, resulting in completely disparate representations of mean flow structures between the two classes of models (transitional and fully turbulent).
{"title":"High-Resolution RANS Simulations of Flow Past a Surface-Mounted Cube Using Eddy-Viscosity Closure Models","authors":"M. Goldbach, M. Uddin","doi":"10.1115/1.4044695","DOIUrl":"https://doi.org/10.1115/1.4044695","url":null,"abstract":"\u0000 While Reynolds-averaged simulations have found success in the evaluation of many canonical shear flows and moderately separated flows, their application to highly separated flows have shown notable deficiencies. This study aimed to investigate these deficiencies in the eddy-viscosity formulation of four commonly used turbulence models under separated flow in an attempt to aid in the improved formulation of such models. Analyses are performed on the flow field around a wall-mounted cube (WMC) at a Reynolds number of 40,000 based on the cube height, h, and freestream velocity, U0. While a common occurrence in industrial applications, this type of flow constitutes a complex structure exhibiting a large separated wake region, high anisotropy, and multiple vortex structures. As well, interactions between vortices developed off of different faces of the cube significantly alter the overall flow characteristics, posing a significant challenge for the commonly used industrial turbulence models. Comparison of mean flow characteristics show remarkable agreement between experimental values and turbulence models which are capable of predicting transitional flow. Evaluation of turbulence parameters show the general underestimation of Reynolds stress for transitional models, while fully turbulent models show this value to be overestimated, resulting in completely disparate representations of mean flow structures between the two classes of models (transitional and fully turbulent).","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46587355","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}
An end-to-end example of the application of the procedures of verification, validation, and uncertainty quantification (VVUQ) is presented with reference to mathematical models formulated for the prediction of fatigue failure in the high cycle range. A validation metric based on the log likelihood function is defined. It is shown that the functional forms of the notch sensitivity factors proposed by Neuber and Peterson cannot be validated but a revised form can be. Calibration and validation are based on published records of fatigue tests performed on notch-free and notched test coupons fabricated from aluminum alloy and alloy steel sheets.
{"title":"Validation of Notch Sensitivity Factors","authors":"B. Szabó, R. Actis, D. Rusk","doi":"10.1115/1.4044236","DOIUrl":"https://doi.org/10.1115/1.4044236","url":null,"abstract":"An end-to-end example of the application of the procedures of verification, validation, and uncertainty quantification (VVUQ) is presented with reference to mathematical models formulated for the prediction of fatigue failure in the high cycle range. A validation metric based on the log likelihood function is defined. It is shown that the functional forms of the notch sensitivity factors proposed by Neuber and Peterson cannot be validated but a revised form can be. Calibration and validation are based on published records of fatigue tests performed on notch-free and notched test coupons fabricated from aluminum alloy and alloy steel sheets.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46046539","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 fluid dynamic (CFD) techniques have played a significant role in improving the efficiency of the hydraulic turbines. To achieve safe and reliable design, numerical results should be trustworthy and free from any suspicion. Proper verification and validation (V&V) are vital to obtain credible results. In this work, first we present verification of a numerical model, Francis turbine, using different approaches to ensure minimum discretization errors and proper convergence. Then, we present detailed validation of the numerical model. Two operating conditions, best efficiency point (BEP) (100% load) and part load (67.2% load), are selected for the study. Turbine head, power, efficiency, and local pressure are used for validation. The pressure data are validated in time- and frequency-domains at sensitive locations in the turbine. We also investigated the different boundary conditions, turbulence intensity, and time-steps. The results showed that, while assessing the convergence history, convergence of local pressure/velocity in the turbine is important in addition to the mass and momentum parameters. Furthermore, error in hydraulic efficiency can be misleading, and effort should make to determine the errors in torque, head, and flow rate separately. The total error is 9.82% at critical locations in the turbine. The paper describes a customized V&V approach for the turbines that will help users to determine total error and to establish credibility of numerical models within hydraulic turbines.
{"title":"A Systematic Validation of a Francis Turbine Under Design and Off-Design Loads","authors":"C. Trivedi","doi":"10.1115/1.4043965","DOIUrl":"https://doi.org/10.1115/1.4043965","url":null,"abstract":"Computational fluid dynamic (CFD) techniques have played a significant role in improving the efficiency of the hydraulic turbines. To achieve safe and reliable design, numerical results should be trustworthy and free from any suspicion. Proper verification and validation (V&V) are vital to obtain credible results. In this work, first we present verification of a numerical model, Francis turbine, using different approaches to ensure minimum discretization errors and proper convergence. Then, we present detailed validation of the numerical model. Two operating conditions, best efficiency point (BEP) (100% load) and part load (67.2% load), are selected for the study. Turbine head, power, efficiency, and local pressure are used for validation. The pressure data are validated in time- and frequency-domains at sensitive locations in the turbine. We also investigated the different boundary conditions, turbulence intensity, and time-steps. The results showed that, while assessing the convergence history, convergence of local pressure/velocity in the turbine is important in addition to the mass and momentum parameters. Furthermore, error in hydraulic efficiency can be misleading, and effort should make to determine the errors in torque, head, and flow rate separately. The total error is 9.82% at critical locations in the turbine. The paper describes a customized V&V approach for the turbines that will help users to determine total error and to establish credibility of numerical models within hydraulic turbines.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4043965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43001066","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}
Björn Heling, Thomas Oberleiter, B. Schleich, K. Willner, S. Wartzack
Although mass production parts look the same at first sight, every manufactured part is unique, at least on a closer inspection. The reason for this is that every manufactured part is inevitable subjected to different scattering influencing factors and variation in the manufacturing process, such as varying temperatures or tool wear. Products, which are built from these deviation-afflicted parts, consequently show deviations from their ideal properties. To ensure that every single product nevertheless meets its technical requirements, it is necessary to specify the permitted deviations. Furthermore, it is crucial to estimate the consequences of the permitted deviations, which is done via tolerance analysis. During this process, the imperfect parts are assembled virtually and the effects of the geometric deviations can be calculated. Since the tolerance analysis enables engineers to identify weak points in an early design stage, it is important to know which contribution every single tolerance has on a certain quality-relevant characteristic to restrict or increase the correct tolerances. In this paper, four different methods to calculate the sensitivity are introduced and compared. Based on the comparison, guidelines are derived which are intended to facilitate a selection of these different methods. In particular, a newly developed approach, which is based on fuzzy arithmetic, is compared to the established high–low–median method, a variance-based method, and a density-based approach. Since all these methods are based on different assumptions, their advantages and disadvantages are critically discussed based on two case studies.
{"title":"On the Selection of Sensitivity Analysis Methods in the Context of Tolerance Management","authors":"Björn Heling, Thomas Oberleiter, B. Schleich, K. Willner, S. Wartzack","doi":"10.1115/1.4043912","DOIUrl":"https://doi.org/10.1115/1.4043912","url":null,"abstract":"Although mass production parts look the same at first sight, every manufactured part is unique, at least on a closer inspection. The reason for this is that every manufactured part is inevitable subjected to different scattering influencing factors and variation in the manufacturing process, such as varying temperatures or tool wear. Products, which are built from these deviation-afflicted parts, consequently show deviations from their ideal properties. To ensure that every single product nevertheless meets its technical requirements, it is necessary to specify the permitted deviations. Furthermore, it is crucial to estimate the consequences of the permitted deviations, which is done via tolerance analysis. During this process, the imperfect parts are assembled virtually and the effects of the geometric deviations can be calculated. Since the tolerance analysis enables engineers to identify weak points in an early design stage, it is important to know which contribution every single tolerance has on a certain quality-relevant characteristic to restrict or increase the correct tolerances. In this paper, four different methods to calculate the sensitivity are introduced and compared. Based on the comparison, guidelines are derived which are intended to facilitate a selection of these different methods. In particular, a newly developed approach, which is based on fuzzy arithmetic, is compared to the established high–low–median method, a variance-based method, and a density-based approach. Since all these methods are based on different assumptions, their advantages and disadvantages are critically discussed based on two case studies.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49001589","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 reports a verification study for a method that fits functions to sets of data from several experiments simultaneously. The method finds a maximum a posteriori probability estimate of a function subject to constraints (e.g., convexity in the study), uncertainty about the estimate, and a quantitative characterization of how data from each experiment constrains that uncertainty. While this work focuses on a model of the equation of state (EOS) of gasses produced by detonating a high explosive, the method can be applied to a wide range of physics processes with either parametric or semiparametric models. As a verification exercise, a reference EOS is used and artificial experimental data sets are created using numerical integration of ordinary differential equations and pseudo-random noise. The method yields an estimate of the EOS that is close to the reference and identifies how each experiment most constrains the result.
{"title":"Estimating Physics Models and Quantifying Their Uncertainty Using Optimization With a Bayesian Objective Function","authors":"Stephen A. Andrews, A. Fraser","doi":"10.1115/1.4043807","DOIUrl":"https://doi.org/10.1115/1.4043807","url":null,"abstract":"This paper reports a verification study for a method that fits functions to sets of data from several experiments simultaneously. The method finds a maximum a posteriori probability estimate of a function subject to constraints (e.g., convexity in the study), uncertainty about the estimate, and a quantitative characterization of how data from each experiment constrains that uncertainty. While this work focuses on a model of the equation of state (EOS) of gasses produced by detonating a high explosive, the method can be applied to a wide range of physics processes with either parametric or semiparametric models. As a verification exercise, a reference EOS is used and artificial experimental data sets are created using numerical integration of ordinary differential equations and pseudo-random noise. The method yields an estimate of the EOS that is close to the reference and identifies how each experiment most constrains the result.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49586187","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}
Marc Horner, Stephen M Luke, Kerim O Genc, Todd M Pietila, Ross T Cotton, Benjamin A Ache, Zachary H Levine, Kevin C 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":"Marc Horner, Stephen M Luke, Kerim O Genc, Todd M Pietila, Ross T Cotton, Benjamin A Ache, Zachary H Levine, Kevin C Townsend","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448268/pdf/nihms-1572949.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38318163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Experiments of a shock hitting a curtain of particles were conducted at the multiphase shock tube facility at Sandia National Laboratories. These are studied in this paper for quantifying the epistemic uncertainty in the experimental measurements due to processing via measurement models. Schlieren and X-ray imaging techniques were used to obtain the measurements that characterize the particle curtain with particle volume fraction and curtain edge locations. The epistemic uncertainties in the experimental setup and image processing methods were identified and measured. The effects of these uncertainties on the uncertainty in the extracted experimental measurements were quantified. The influence of the epistemic uncertainty was significantly higher than the experimental variability that has been previously considered as the most important uncertainty of experiments.
{"title":"Epistemic Uncertainty Stemming From Measurement Processing—A Case Study of Multiphase Shock Tube Experiments","authors":"Chanyoung Park, J. Matthew, N. Kim, R. Haftka","doi":"10.1115/1.4042814","DOIUrl":"https://doi.org/10.1115/1.4042814","url":null,"abstract":"Experiments of a shock hitting a curtain of particles were conducted at the multiphase shock tube facility at Sandia National Laboratories. These are studied in this paper for quantifying the epistemic uncertainty in the experimental measurements due to processing via measurement models. Schlieren and X-ray imaging techniques were used to obtain the measurements that characterize the particle curtain with particle volume fraction and curtain edge locations. The epistemic uncertainties in the experimental setup and image processing methods were identified and measured. The effects of these uncertainties on the uncertainty in the extracted experimental measurements were quantified. The influence of the epistemic uncertainty was significantly higher than the experimental variability that has been previously considered as the most important uncertainty of experiments.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4042814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46609727","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}
Lucas Konnigk, B. Torner, Sebastian Hallier, M. Witte, F. Wurm
Adverse events due to flow-induced blood damage remain a serious problem for blood pumps as cardiac support systems. The numerical prediction of blood damage via computational fluid dynamics (CFD) is a helpful tool for the design and optimization of reliable pumps. Blood damage prediction models primarily are based on the acting shear stresses, which are calculated by solving the Navier–Stokes equations on computational grids. The purpose of this paper is to analyze the influence of the spatial discretization and the associated discretization error on the shear stress calculation in a blood pump in comparison to other important flow quantities like the pressure head of the pump. Therefore, CFD analysis using seven unsteady Reynolds-averaged Navier–Stokes (URANS) simulations was performed. Two simple stress calculation indicators were applied to estimate the influence of the discretization on the results using an approach to calculate numerical uncertainties, which indicates discretization errors. For the finest grid with 19 × 106 elements, numerical uncertainties up to 20% for shear stresses were determined, while the pressure heads show smaller uncertainties with a maximum of 4.8%. No grid-independent solution for velocity gradient-dependent variables could be obtained on a grid size that is comparable to mesh sizes in state-of-the-art blood pump studies. It can be concluded that the grid size has a major influence on the shear stress calculation, and therefore, the potential blood damage prediction, and that the quantification of this error should always be taken into account.
{"title":"Grid-Induced Numerical Errors for Shear Stresses and Essential Flow Variables in a Ventricular Assist Device: Crucial for Blood Damage Prediction?","authors":"Lucas Konnigk, B. Torner, Sebastian Hallier, M. Witte, F. Wurm","doi":"10.1115/1.4042989","DOIUrl":"https://doi.org/10.1115/1.4042989","url":null,"abstract":"Adverse events due to flow-induced blood damage remain a serious problem for blood pumps as cardiac support systems. The numerical prediction of blood damage via computational fluid dynamics (CFD) is a helpful tool for the design and optimization of reliable pumps. Blood damage prediction models primarily are based on the acting shear stresses, which are calculated by solving the Navier–Stokes equations on computational grids. The purpose of this paper is to analyze the influence of the spatial discretization and the associated discretization error on the shear stress calculation in a blood pump in comparison to other important flow quantities like the pressure head of the pump. Therefore, CFD analysis using seven unsteady Reynolds-averaged Navier–Stokes (URANS) simulations was performed. Two simple stress calculation indicators were applied to estimate the influence of the discretization on the results using an approach to calculate numerical uncertainties, which indicates discretization errors. For the finest grid with 19 × 106 elements, numerical uncertainties up to 20% for shear stresses were determined, while the pressure heads show smaller uncertainties with a maximum of 4.8%. No grid-independent solution for velocity gradient-dependent variables could be obtained on a grid size that is comparable to mesh sizes in state-of-the-art blood pump studies. It can be concluded that the grid size has a major influence on the shear stress calculation, and therefore, the potential blood damage prediction, and that the quantification of this error should always be taken into account.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4042989","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43099660","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}
K. Hughes, S. Balachandar, N. Kim, Chanyoung Park, R. Haftka, A. Diggs, D. Littrell, Jason Darr
Six explosive experiments were performed in October 2014 and February of 2015 at the Munitions Directorate of the Air Force Research Laboratory with the goal of providing validation-quality data for particle drag models in the extreme regime of detonation. Three repeated single particle experiments and three particle array experiments were conducted. The time-varying position of the particles was captured within the explosive products by X-ray imaging. The contact front and shock locations were captured by high-speed photography to provide information on the early time gas behavior. Since these experiments were performed in the past and could not be repeated, we faced an interesting challenge of quantifying and reducing uncertainty through a detailed investigation of the experimental setup and operating conditions. This paper presents the results from these unique experiments, which can serve as benchmark for future modeling, and also our effort to reduce uncertainty, which we dub forensic uncertainty quantification (FUQ).
{"title":"Forensic Uncertainty Quantification for Experiments on the Explosively Driven Motion of Particles","authors":"K. Hughes, S. Balachandar, N. Kim, Chanyoung Park, R. Haftka, A. Diggs, D. Littrell, Jason Darr","doi":"10.1115/1.4043478","DOIUrl":"https://doi.org/10.1115/1.4043478","url":null,"abstract":"Six explosive experiments were performed in October 2014 and February of 2015 at the Munitions Directorate of the Air Force Research Laboratory with the goal of providing validation-quality data for particle drag models in the extreme regime of detonation. Three repeated single particle experiments and three particle array experiments were conducted. The time-varying position of the particles was captured within the explosive products by X-ray imaging. The contact front and shock locations were captured by high-speed photography to provide information on the early time gas behavior. Since these experiments were performed in the past and could not be repeated, we faced an interesting challenge of quantifying and reducing uncertainty through a detailed investigation of the experimental setup and operating conditions. This paper presents the results from these unique experiments, which can serve as benchmark for future modeling, and also our effort to reduce uncertainty, which we dub forensic uncertainty quantification (FUQ).","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4043478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46948980","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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