David A. Najera-Flores, Jonel Ortiz, Moheimin Khan, Robert Kuether, Paul Miles
The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. This paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.
{"title":"A Bayesian Multi-fidelity Neural Network to Predict Nonlinear Frequency Backbone Curves","authors":"David A. Najera-Flores, Jonel Ortiz, Moheimin Khan, Robert Kuether, Paul Miles","doi":"10.1115/1.4064776","DOIUrl":"https://doi.org/10.1115/1.4064776","url":null,"abstract":"\u0000 The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. This paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958967","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}
David A. Najera-Flores, Justin Jacobs, D. Quinn, Anthony Garland, Michael D. Todd
Structural nonlinearities are often spatially localized, such joints and interfaces, localized damage, or isolated connections, in an otherwise linearly behaving system. Quinn and Brink [12] modeled this localized nonlinearity as a deviatoric force component. In other previous work [13], the authors proposed a physics-informed machine learning framework to determine the deviatoric force from measurements obtained only at the boundary of the nonlinear region, assuming a noise-free environment. However, in real experimental applications, the data are expected to contain noise from a variety of sources. In the present work, we explore the sensitivity of the trained network by comparing the network responses when trained on deterministic (“noise-free”) model data and model data with additive noise (“noisy”). As the neural network does not yield a closed-form transformation from the input distribution to the response distribution, we leverage the use of conformal sets to build an illustration of sensitivity. Through the conformal set assumption of exchangeability, we may build a distribution-free prediction interval for both network responses of the clean and noisy training sets. This work will explore the application of conformal sets for uncertainty quantification of a deterministic structure-preserving neural network and its deployment in a structural health monitoring framework to detect deviations from a baseline state based on noisy measurements.
{"title":"Uncertainty Quantification of a Machine Learning Model for Identification of Isolated Nonlinearities with Conformal Prediction","authors":"David A. Najera-Flores, Justin Jacobs, D. Quinn, Anthony Garland, Michael D. Todd","doi":"10.1115/1.4064777","DOIUrl":"https://doi.org/10.1115/1.4064777","url":null,"abstract":"\u0000 Structural nonlinearities are often spatially localized, such joints and interfaces, localized damage, or isolated connections, in an otherwise linearly behaving system. Quinn and Brink [12] modeled this localized nonlinearity as a deviatoric force component. In other previous work [13], the authors proposed a physics-informed machine learning framework to determine the deviatoric force from measurements obtained only at the boundary of the nonlinear region, assuming a noise-free environment. However, in real experimental applications, the data are expected to contain noise from a variety of sources. In the present work, we explore the sensitivity of the trained network by comparing the network responses when trained on deterministic (“noise-free”) model data and model data with additive noise (“noisy”). As the neural network does not yield a closed-form transformation from the input distribution to the response distribution, we leverage the use of conformal sets to build an illustration of sensitivity. Through the conformal set assumption of exchangeability, we may build a distribution-free prediction interval for both network responses of the clean and noisy training sets. This work will explore the application of conformal sets for uncertainty quantification of a deterministic structure-preserving neural network and its deployment in a structural health monitoring framework to detect deviations from a baseline state based on noisy measurements.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451628","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}
William C. Tyson, Charles W. Jackson, Christopher J Roy
Over the past two decades, higher-order methods have gained much broader use in computational science and engineering as these schemes are often more efficient per degree-of-freedom at achieving a prescribed error tolerance than lower-order methods. During this time, higher-order variants of most discretization schemes, such as finite-difference methods, finite-volume methods, and finite-element methods, have emerged. The finite-volume method is arguably the most widely used discretization technique in production-level computational fluid dynamics solvers due to its robustness and conservation properties. However, most finite-volume solvers only employ a conventional second-order scheme. To leverage the benefits of higher-order methods, the higher-order finite-volume method seems the most natural for those seeking to extend their legacy solvers to higher-order. Nonetheless, ensuring higher-order accuracy is maintained is quite challenging as the implementation requirements for a higher-order scheme are much greater than that of a lower-order scheme. In this work, a methodology for verifying higher-order finite-volume codes is presented. The higher-order finite-volume method is outlined in detail. Order verification tests are proposed for all major components, including the treatment of curved boundaries and the higher-order solution reconstruction. System-level verification tests are performed using the weak form of the Method of Manufactured Solutions. Several canonical verification cases are also presented for the Euler and laminar Navier-Stokes equations.
{"title":"Development and Verification of a Higher-Order Computational Fluid Dynamics Solver","authors":"William C. Tyson, Charles W. Jackson, Christopher J Roy","doi":"10.1115/1.4064620","DOIUrl":"https://doi.org/10.1115/1.4064620","url":null,"abstract":"\u0000 Over the past two decades, higher-order methods have gained much broader use in computational science and engineering as these schemes are often more efficient per degree-of-freedom at achieving a prescribed error tolerance than lower-order methods. During this time, higher-order variants of most discretization schemes, such as finite-difference methods, finite-volume methods, and finite-element methods, have emerged. The finite-volume method is arguably the most widely used discretization technique in production-level computational fluid dynamics solvers due to its robustness and conservation properties. However, most finite-volume solvers only employ a conventional second-order scheme. To leverage the benefits of higher-order methods, the higher-order finite-volume method seems the most natural for those seeking to extend their legacy solvers to higher-order. Nonetheless, ensuring higher-order accuracy is maintained is quite challenging as the implementation requirements for a higher-order scheme are much greater than that of a lower-order scheme. In this work, a methodology for verifying higher-order finite-volume codes is presented. The higher-order finite-volume method is outlined in detail. Order verification tests are proposed for all major components, including the treatment of curved boundaries and the higher-order solution reconstruction. System-level verification tests are performed using the weak form of the Method of Manufactured Solutions. Several canonical verification cases are also presented for the Euler and laminar Navier-Stokes equations.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875781","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}
William C. Tyson, Charles W. Jackson, Christopher J Roy
Over the past two decades, higher-order methods have gained much broader use in computational science and engineering as these schemes are often more efficient per degree-of-freedom at achieving a prescribed error tolerance than lower-order methods. During this time, higher-order variants of most discretization schemes, such as finite-difference methods, finite-volume methods, and finite-element methods, have emerged. The finite-volume method is arguably the most widely used discretization technique in production-level computational fluid dynamics solvers due to its robustness and conservation properties. However, most finite-volume solvers only employ a conventional second-order scheme. To leverage the benefits of higher-order methods, the higher-order finite-volume method seems the most natural for those seeking to extend their legacy solvers to higher-order. Nonetheless, ensuring higher-order accuracy is maintained is quite challenging as the implementation requirements for a higher-order scheme are much greater than that of a lower-order scheme. In this work, a methodology for verifying higher-order finite-volume codes is presented. The higher-order finite-volume method is outlined in detail. Order verification tests are proposed for all major components, including the treatment of curved boundaries and the higher-order solution reconstruction. System-level verification tests are performed using the weak form of the Method of Manufactured Solutions. Several canonical verification cases are also presented for the Euler and laminar Navier-Stokes equations.
{"title":"Development and Verification of a Higher-Order Computational Fluid Dynamics Solver","authors":"William C. Tyson, Charles W. Jackson, Christopher J Roy","doi":"10.1115/1.4064620","DOIUrl":"https://doi.org/10.1115/1.4064620","url":null,"abstract":"\u0000 Over the past two decades, higher-order methods have gained much broader use in computational science and engineering as these schemes are often more efficient per degree-of-freedom at achieving a prescribed error tolerance than lower-order methods. During this time, higher-order variants of most discretization schemes, such as finite-difference methods, finite-volume methods, and finite-element methods, have emerged. The finite-volume method is arguably the most widely used discretization technique in production-level computational fluid dynamics solvers due to its robustness and conservation properties. However, most finite-volume solvers only employ a conventional second-order scheme. To leverage the benefits of higher-order methods, the higher-order finite-volume method seems the most natural for those seeking to extend their legacy solvers to higher-order. Nonetheless, ensuring higher-order accuracy is maintained is quite challenging as the implementation requirements for a higher-order scheme are much greater than that of a lower-order scheme. In this work, a methodology for verifying higher-order finite-volume codes is presented. The higher-order finite-volume method is outlined in detail. Order verification tests are proposed for all major components, including the treatment of curved boundaries and the higher-order solution reconstruction. System-level verification tests are performed using the weak form of the Method of Manufactured Solutions. Several canonical verification cases are also presented for the Euler and laminar Navier-Stokes equations.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139815793","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}
Hongyu Wang, Weicheng Xue, William Jordan, Christopher J. Roy
The focus of this work is on discretization error estimation for time-dependent simulations. Based on previous work on steady-state problems, the unsteady Error Transport Equations (ETE) are used to generate local discretization error estimates for a finite volume CFD code SENSEI. For steady-state problems, the ETE only need to be solved once after the solution has converged, whereas the unsteady ETE need to be co-advanced with the primal solve. All the test cases chosen in this work have known analytical solutions so that order of accuracy test can be performed and the accuracy of the error estimates can be unambiguously determined. The 2D convected vortex is used as the test case for inviscid flow. A Cross-Term Sinusoidal (CTS) manufactured solution for the laminar Navier-Stokes equations is used as the test case for viscous flow. Order of accuracy of the corrected solution is used to assess the quality of the error estimate. When iterative correction is not applied, higher-order convergence rate has been observed for the 2D convected vortex test case. For the 2D CTS manufactured solution higher-order convergence rate can also be observed but not for the finest grid levels. The current implementation of iterative correction is less stable than the primal solve but can improve the discretization error estimate in general. After iterative correction, the discretization error estimate of the unsteady ETE is higher-order for all grid levels for the 2D CTS manufactured solution.
{"title":"Discretization Error Estimation Using the Unsteady Error Transport Equations","authors":"Hongyu Wang, Weicheng Xue, William Jordan, Christopher J. Roy","doi":"10.1115/1.4064580","DOIUrl":"https://doi.org/10.1115/1.4064580","url":null,"abstract":"\u0000 The focus of this work is on discretization error estimation for time-dependent simulations. Based on previous work on steady-state problems, the unsteady Error Transport Equations (ETE) are used to generate local discretization error estimates for a finite volume CFD code SENSEI. For steady-state problems, the ETE only need to be solved once after the solution has converged, whereas the unsteady ETE need to be co-advanced with the primal solve. All the test cases chosen in this work have known analytical solutions so that order of accuracy test can be performed and the accuracy of the error estimates can be unambiguously determined. The 2D convected vortex is used as the test case for inviscid flow. A Cross-Term Sinusoidal (CTS) manufactured solution for the laminar Navier-Stokes equations is used as the test case for viscous flow. Order of accuracy of the corrected solution is used to assess the quality of the error estimate. When iterative correction is not applied, higher-order convergence rate has been observed for the 2D convected vortex test case. For the 2D CTS manufactured solution higher-order convergence rate can also be observed but not for the finest grid levels. The current implementation of iterative correction is less stable than the primal solve but can improve the discretization error estimate in general. After iterative correction, the discretization error estimate of the unsteady ETE is higher-order for all grid levels for the 2D CTS manufactured solution.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139592346","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}
Harrison Helmich, Charles J. Doherty, Donald Costello, Michael Kutzer
The United States Navy intends to increase the amount of uncrewed aircraft in a carrier air wing. To support this increase, carrier based uncrewed aircraft will be required to have some level of autonomy as there will be situations where a human cannot be in/on the loop. However, there is no existing and approved method to certify autonomy within Naval Aviation. In support of generating certification evidence for autonomy, the United States Naval Academy has created a training and evaluation system to provide quantifiable metrics for feedback performance in autonomous systems. The preliminary use-case for this work focuses on autonomous aerial refueling. Prior demonstrations of autonomous aerial refueling have leveraged a deep neural network (DNN) for processing visual feedback to approximate the relative position of an aerial refueling drogue. The training and evaluation system proposed in this work simulates the relative motion between the aerial refueling drogue and feedback camera system using industrial robotics. Ground truth measurements of the pose between camera and drogue is measured using a commercial motion capture system. Preliminary results demonstrate calibration methods providing ground truth measurements with millimeter precision. Leveraging this calibration, the proposed system is capable of providing large-scale data sets for DNN training and evaluation against a precise ground truth.
{"title":"Automatic Ground-Truth Image Labeling for Deep Neural Network Training and Evaluation Using Industrial Robotics and Motion Capture","authors":"Harrison Helmich, Charles J. Doherty, Donald Costello, Michael Kutzer","doi":"10.1115/1.4064311","DOIUrl":"https://doi.org/10.1115/1.4064311","url":null,"abstract":"The United States Navy intends to increase the amount of uncrewed aircraft in a carrier air wing. To support this increase, carrier based uncrewed aircraft will be required to have some level of autonomy as there will be situations where a human cannot be in/on the loop. However, there is no existing and approved method to certify autonomy within Naval Aviation. In support of generating certification evidence for autonomy, the United States Naval Academy has created a training and evaluation system to provide quantifiable metrics for feedback performance in autonomous systems. The preliminary use-case for this work focuses on autonomous aerial refueling. Prior demonstrations of autonomous aerial refueling have leveraged a deep neural network (DNN) for processing visual feedback to approximate the relative position of an aerial refueling drogue. The training and evaluation system proposed in this work simulates the relative motion between the aerial refueling drogue and feedback camera system using industrial robotics. Ground truth measurements of the pose between camera and drogue is measured using a commercial motion capture system. Preliminary results demonstrate calibration methods providing ground truth measurements with millimeter precision. Leveraging this calibration, the proposed system is capable of providing large-scale data sets for DNN training and evaluation against a precise ground truth.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139170985","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}
Abstract A verification study was conducted on an URANS (Unsteady Reynolds-Averaged Navier-Stoke) simulation of flow around a 5:1 rectangular cylinder at a Reynolds number of 56,700 (based on the cylinder depth) using the k-ω SST (Shear Stress Transport) turbulence model and the γ-Reθ transition model for three types of grids (a fully structured grid and two hybrid grids generated using Delaunay and advancing front techniques). The Grid Convergence Index (GCI) and Least Squares (LS) procedures were employed to estimate discretization error and associated uncertainties. The result indicates that the LS procedure provides the most reliable estimates of discretization error uncertainties for solution variables in the structure grid from the k-ω SST model. From the six solution variables, the highest relative uncertainty was typically observed in the rms of lift coefficient, followed by time-averaged reattachment length and peak of rms of pressure coefficient. The solution variable with the lowest uncertainty was Strouhal number, followed by time-averaged drag coefficient. It is also noted that the GCI and LS procedures produce noticeably different uncertainty estimates, primarily due to inconsistences in their estimated observed orders of accuracy and safety factors. To successfully apply the procedures to practical problems, further research is required to reliably estimate uncertainties in solutions with “noisy” grid convergence behaviors and observed orders of accuracy.
{"title":"A Solution Verification Study For Urans Simulations of Flow Over a 5:1 Rectangular Cylinder Using Grid Convergence Index And Least Squares Procedures","authors":"TarakN Nandi, DongHun Yeo","doi":"10.1115/1.4063818","DOIUrl":"https://doi.org/10.1115/1.4063818","url":null,"abstract":"Abstract A verification study was conducted on an URANS (Unsteady Reynolds-Averaged Navier-Stoke) simulation of flow around a 5:1 rectangular cylinder at a Reynolds number of 56,700 (based on the cylinder depth) using the k-ω SST (Shear Stress Transport) turbulence model and the γ-Reθ transition model for three types of grids (a fully structured grid and two hybrid grids generated using Delaunay and advancing front techniques). The Grid Convergence Index (GCI) and Least Squares (LS) procedures were employed to estimate discretization error and associated uncertainties. The result indicates that the LS procedure provides the most reliable estimates of discretization error uncertainties for solution variables in the structure grid from the k-ω SST model. From the six solution variables, the highest relative uncertainty was typically observed in the rms of lift coefficient, followed by time-averaged reattachment length and peak of rms of pressure coefficient. The solution variable with the lowest uncertainty was Strouhal number, followed by time-averaged drag coefficient. It is also noted that the GCI and LS procedures produce noticeably different uncertainty estimates, primarily due to inconsistences in their estimated observed orders of accuracy and safety factors. To successfully apply the procedures to practical problems, further research is required to reliably estimate uncertainties in solutions with “noisy” grid convergence behaviors and observed orders of accuracy.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824231","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}
Aldo Gargiulo, Julie E Duetsch-Patel, Aurelien Borgoltz, William Devenport, Christopher J Roy, K. Todd Lowe
Abstract The Benchmark Validation Experiment for RANS/LES Investigations (BeVERLI) aims to produce an experimental dataset of three-dimensional non-equilibrium turbulent boundary layers with various levels of separation that, for the first time, meets the most exacting requirements of computational fluid dynamics validation. The application of simulations and modeling in high-consequence engineering environments has become increasingly prominent in the past two decades, considerably raising the standards and demands of model validation and forcing a significant paradigm shift in the design of corresponding validation experiments. In this paper, based on the experiences of project BeVERLI, we present strategies for designing and executing validation experiments, hoping to ease the transition into this new era of fluid dynamics experimentation and help upcoming validation experiments succeed. We discuss the selection of a flow for validation, the synergistic use of simulations and experiments, cross-institutional collaborations, and tools, such as model scans, time-dependent measurements, and repeated and redundant measurements. The proposed strategies are shown to successfully mitigate risks and enable the methodical identification, measurement, uncertainty quantification, and characterization of critical flow features, boundary conditions, and corresponding sensitivities, promoting the highest levels of model validation experiment completeness per Oberkampf and Smith. Furthermore, the applicability of these strategies to estimating critical and difficult-to-obtain bias error uncertainties of different measurement systems, e.g., the underprediction of high-order statistical moments from particle image velocimetry velocity field data due to spatial filtering effects, and to systematically assessing the quality of uncertainty estimates is shown.
{"title":"Strategies for Computational Fluid Dynamics Validation Experiments","authors":"Aldo Gargiulo, Julie E Duetsch-Patel, Aurelien Borgoltz, William Devenport, Christopher J Roy, K. Todd Lowe","doi":"10.1115/1.4063639","DOIUrl":"https://doi.org/10.1115/1.4063639","url":null,"abstract":"Abstract The Benchmark Validation Experiment for RANS/LES Investigations (BeVERLI) aims to produce an experimental dataset of three-dimensional non-equilibrium turbulent boundary layers with various levels of separation that, for the first time, meets the most exacting requirements of computational fluid dynamics validation. The application of simulations and modeling in high-consequence engineering environments has become increasingly prominent in the past two decades, considerably raising the standards and demands of model validation and forcing a significant paradigm shift in the design of corresponding validation experiments. In this paper, based on the experiences of project BeVERLI, we present strategies for designing and executing validation experiments, hoping to ease the transition into this new era of fluid dynamics experimentation and help upcoming validation experiments succeed. We discuss the selection of a flow for validation, the synergistic use of simulations and experiments, cross-institutional collaborations, and tools, such as model scans, time-dependent measurements, and repeated and redundant measurements. The proposed strategies are shown to successfully mitigate risks and enable the methodical identification, measurement, uncertainty quantification, and characterization of critical flow features, boundary conditions, and corresponding sensitivities, promoting the highest levels of model validation experiment completeness per Oberkampf and Smith. Furthermore, the applicability of these strategies to estimating critical and difficult-to-obtain bias error uncertainties of different measurement systems, e.g., the underprediction of high-order statistical moments from particle image velocimetry velocity field data due to spatial filtering effects, and to systematically assessing the quality of uncertainty estimates is shown.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351524","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}
Here we offer an approach for being reasonably sure that finite element determinations of stress concentration factors are accurate enough to be included in engineering handbooks. The approach has two contributors. The first consists of analyzing a stress concentration on a sequence of systematically refined meshes until the error estimates of ASME have that sufficient accuracy has been achieved. The second consists of constructing a test problem with an exact and somewhat higher value of its stress concentration factor, then analyzing this test problem with the same sequence of meshes and showing that, in fact, sufficient accuracy has been achieved. In combination, these two means of verification are applied to a series of U-notches in a plate under tension. Together they show that it is reasonable to regard finite element values of stress concentration factors on the finest meshes as being accurate to three significant figures. Given this level of accuracy it is then also reasonable to use the approach to verify other existing stress concentration factors and resolve any discrepancies between them, as well as to verify new stress concentration factors.
{"title":"On the Verification of Finite Element Determinations of Stress Concentration Factors for Handbooks","authors":"A. Kardak, G. Sinclair","doi":"10.1115/1.4063064","DOIUrl":"https://doi.org/10.1115/1.4063064","url":null,"abstract":"\u0000 Here we offer an approach for being reasonably sure that finite element determinations of stress concentration factors are accurate enough to be included in engineering handbooks. The approach has two contributors. The first consists of analyzing a stress concentration on a sequence of systematically refined meshes until the error estimates of ASME have that sufficient accuracy has been achieved. The second consists of constructing a test problem with an exact and somewhat higher value of its stress concentration factor, then analyzing this test problem with the same sequence of meshes and showing that, in fact, sufficient accuracy has been achieved. In combination, these two means of verification are applied to a series of U-notches in a plate under tension. Together they show that it is reasonable to regard finite element values of stress concentration factors on the finest meshes as being accurate to three significant figures. Given this level of accuracy it is then also reasonable to use the approach to verify other existing stress concentration factors and resolve any discrepancies between them, as well as to verify new stress concentration factors.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47703623","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}
Roll decay of David Taylor Model Basin (DTMB) Model 5720, a 23rd scale free-running model of the research vessel (R/V) Melville, is evaluated with uncertainty estimates. Experimental roll-decay time series was accurately modeled as an exponentially decaying cosine function, which is the solution of a second-order ordinary differential equation for damping coefficient of less than one (N < 1). The curve-fit provides damping coefficient (N), period (T), and offset. Roll period in calm water was dependent on Froude number (Fr) and initial roll angle (a). Roll decay data are from 76 runs for three nominal Froude numbers, Fr = 0, 0.15, and 0.22. The initial roll angle variation was 30 to 250. The natural roll period was 2.139 10.041 s 11.9 %). The decay coefficient data were approximated by a plane in three dimensions with Fr and initial roll amplitudes (a) as the independent variables. Curve-fit results are compared to decay coefficient by log decrement and period from time between zero crossings. Examples demonstrate average values for a single roll decay event from log decrement are the same as values by the curve-fitting method within uncertainty estimates. The uncertainty estimate for the decay coefficient is significantly less by curve-fit method in comparison to log-decrement method. By log decrement, the relative uncertainty increases with decreasing roll amplitude peak; consequently, focus should be on the damping coefficient at the largest peaks, where the uncertainty is the smallest.
{"title":"Analysis of Roll Decay for Surface-ship Model Experiments with Uncertainty Estimates","authors":"J. Park","doi":"10.1115/1.4063010","DOIUrl":"https://doi.org/10.1115/1.4063010","url":null,"abstract":"\u0000 Roll decay of David Taylor Model Basin (DTMB) Model 5720, a 23rd scale free-running model of the research vessel (R/V) Melville, is evaluated with uncertainty estimates. Experimental roll-decay time series was accurately modeled as an exponentially decaying cosine function, which is the solution of a second-order ordinary differential equation for damping coefficient of less than one (N < 1). The curve-fit provides damping coefficient (N), period (T), and offset. Roll period in calm water was dependent on Froude number (Fr) and initial roll angle (a). Roll decay data are from 76 runs for three nominal Froude numbers, Fr = 0, 0.15, and 0.22. The initial roll angle variation was 30 to 250. The natural roll period was 2.139 10.041 s 11.9 %). The decay coefficient data were approximated by a plane in three dimensions with Fr and initial roll amplitudes (a) as the independent variables. Curve-fit results are compared to decay coefficient by log decrement and period from time between zero crossings. Examples demonstrate average values for a single roll decay event from log decrement are the same as values by the curve-fitting method within uncertainty estimates. The uncertainty estimate for the decay coefficient is significantly less by curve-fit method in comparison to log-decrement method. By log decrement, the relative uncertainty increases with decreasing roll amplitude peak; consequently, focus should be on the damping coefficient at the largest peaks, where the uncertainty is the smallest.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41477660","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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