1.
Crepier, P.
Ship Resistance Prediction: Verification And Validation Exercise On Unstructured Grids Conference
VII International Conference on Computational Methods in Marine Engineering (MARINE2017), 2017.
@conference{Crepier2017,
title = {Ship Resistance Prediction: Verification And Validation Exercise On Unstructured Grids},
author = {Crepier, P.},
url = {http://www.marin.nl/web/Publications/Publication-items/Ship-Resistance-Prediction-Verification-And-Validation-Exercise-On-Unstructured-Grids.htm},
year = {2017},
date = {2017-05-01},
booktitle = {VII International Conference on Computational Methods in Marine Engineering (MARINE2017)},
abstract = {The prediction of the resistance of a ship is, together with the propeller performance prediction, part of the key aspects during the design process of a ship, as it partly ensures the quality of the power-prediction. Body fitted structured grids for ship simulations can be rather challenging and time consuming to build, especially when dealing with appended ship geometries. For this reason, unstructured hexahedral trimmed grids are more and more used. Such grids can be build by various CFD package such as CD-Adapcos Star CCM+, NUMECAs Hexpress grid generator or OpenFOAMSs SnappyHexMesh. Although their use is increasing or even already adopted, the numerical uncertainty of these simulations seems to be a well-kept secret.
In the study presented, an attempt at quantifying the numerical uncertainty of the resistance for the combination of the RANS Solver ReFRESCO [1] with grids generated using the commercial package Hexpress is made. The studied case is the flow around the bare-hull KVLCC2 at model scale Reynolds number. Extensive verification and validation on the same test case has already been published for the combination of ReFRESCO and structured grids by Pereira et al. [2].
The method to generate grids as geometrically similar as possible is presented, and the uncertainty analysis by L. Ec¸a and M. Hoekstra [3] is performed on the integral results obtained.
The simulations are performed using the k − ω SST, k − ω TNT and the k −√kL turbulence models. The velocity fields calculated in the propeller plane are compared to the measured ones and to the results obtained by Pereira et al. [2] on structured grids.
The results show that the differences with the experimental results are in the same range as the differences obtained with structured grids. The numerical uncertainties are, however, higher. They are also strongly dependent on the turbulence model used, like for structured grids, and are spread between 1.3% and 12%.
Concerning the wake flow details, not all features present in the experimental results are obtained and, compared to structured grids, the flow features are smoothed. The wake flow is also influenced by the turbulence modelling and needs to be adressed in more detail.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The prediction of the resistance of a ship is, together with the propeller performance prediction, part of the key aspects during the design process of a ship, as it partly ensures the quality of the power-prediction. Body fitted structured grids for ship simulations can be rather challenging and time consuming to build, especially when dealing with appended ship geometries. For this reason, unstructured hexahedral trimmed grids are more and more used. Such grids can be build by various CFD package such as CD-Adapcos Star CCM+, NUMECAs Hexpress grid generator or OpenFOAMSs SnappyHexMesh. Although their use is increasing or even already adopted, the numerical uncertainty of these simulations seems to be a well-kept secret.
In the study presented, an attempt at quantifying the numerical uncertainty of the resistance for the combination of the RANS Solver ReFRESCO [1] with grids generated using the commercial package Hexpress is made. The studied case is the flow around the bare-hull KVLCC2 at model scale Reynolds number. Extensive verification and validation on the same test case has already been published for the combination of ReFRESCO and structured grids by Pereira et al. [2].
The method to generate grids as geometrically similar as possible is presented, and the uncertainty analysis by L. Ec¸a and M. Hoekstra [3] is performed on the integral results obtained.
The simulations are performed using the k − ω SST, k − ω TNT and the k −√kL turbulence models. The velocity fields calculated in the propeller plane are compared to the measured ones and to the results obtained by Pereira et al. [2] on structured grids.
The results show that the differences with the experimental results are in the same range as the differences obtained with structured grids. The numerical uncertainties are, however, higher. They are also strongly dependent on the turbulence model used, like for structured grids, and are spread between 1.3% and 12%.
Concerning the wake flow details, not all features present in the experimental results are obtained and, compared to structured grids, the flow features are smoothed. The wake flow is also influenced by the turbulence modelling and needs to be adressed in more detail.
In the study presented, an attempt at quantifying the numerical uncertainty of the resistance for the combination of the RANS Solver ReFRESCO [1] with grids generated using the commercial package Hexpress is made. The studied case is the flow around the bare-hull KVLCC2 at model scale Reynolds number. Extensive verification and validation on the same test case has already been published for the combination of ReFRESCO and structured grids by Pereira et al. [2].
The method to generate grids as geometrically similar as possible is presented, and the uncertainty analysis by L. Ec¸a and M. Hoekstra [3] is performed on the integral results obtained.
The simulations are performed using the k − ω SST, k − ω TNT and the k −√kL turbulence models. The velocity fields calculated in the propeller plane are compared to the measured ones and to the results obtained by Pereira et al. [2] on structured grids.
The results show that the differences with the experimental results are in the same range as the differences obtained with structured grids. The numerical uncertainties are, however, higher. They are also strongly dependent on the turbulence model used, like for structured grids, and are spread between 1.3% and 12%.
Concerning the wake flow details, not all features present in the experimental results are obtained and, compared to structured grids, the flow features are smoothed. The wake flow is also influenced by the turbulence modelling and needs to be adressed in more detail.
2.
L. Eça, Vaz; Abreu, H.
Validation: What, Why And How Conference
OMAE ASME 35th International Conference on Ocean, Offshore and Arctic Engineering, Busan, South Korea, 2016.
@conference{Eça2016,
title = {Validation: What, Why And How},
author = {Eça, L., Vaz, G., Koop, A., Pereira, F. and Abreu, H.},
url = {http://www.marin.nl/web/Publications/Papers/Validation-What-Why-And-How.htm},
year = {2016},
date = {2016-06-19},
booktitle = {OMAE ASME 35th International Conference on Ocean, Offshore and Arctic Engineering, Busan, South Korea},
pages = {OMAE2016-54005},
abstract = {Offshore and Naval engineering have relied on physical models, i.e. experimental fluid dynamics (EFD), for several decades. Although the role of experiments in engineering is still unquestionable, some of the limitations of physical models, as for example domain size (blockage and scale effects), can be addressed using mathematical models, i.e. computational fluid dynamics (CFD). However, to gain confidence in the use of CFD it is fundamental to determine the modelling accuracy, i.e. to determine the difference between the “physical reality” and the selected mathematical model. The quantification of the modelling error is the goal of Validation. It must be emphasized that Validation applies to the mathematical model (and not the code) and is performed for selected flow quantities (the so-called quantities of interest).
Ideally, Validation would be performed comparing an exact measurement of the “physical reality” with the exact solution of the selected mathematical model. However, exact measurements do not exist and mathematical models for turbulent flows do not have analytical solutions. Therefore, procedures must be developed to take into account experimental and numerical uncertainties. Furthermore, the exact values of the flow parameters as for example Reynolds number, fluid viscosity or inlet turbulence quantities are often unknown, which leads to the so-called parameter uncertainty that also has to be dealt within the assessment of the modelling error.
The main goal of this paper is to demonstrate that the very popular designation of ”code X is validated” is meaningless without saying what is the mathematical model embedded in the code, what are the quantities of interest for the specific application and what is the Validation uncertainty imposed by the experimental, numerical and parameter uncertainties. Furthermore, we also illustrate that Validation is not a pass or fail exercise. A modelling error of 10% may be acceptable for a given application, whereas 1% may not be enough for a different one.
To this end, we present the application of the ASME V&V 20 Validation procedure for local set points and the metric for multiple set points to several practical test cases: prediction of transition from laminar to turbulent regime for the flow over a flat plate; flow around a circular cylinder; flow around the KVLCC2 tanker and current loads in shallow water for a LNG carrier. In most of these exercises, parameter uncertainty is assumed to be zero, which is an assumption often required for the so-called practical calculations due to the computational effort required to address it. Nonetheless, as an illustration of its application, the flow over the flat plate includes parameter uncertainty for the specification of the inlet turbulence quantities.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Offshore and Naval engineering have relied on physical models, i.e. experimental fluid dynamics (EFD), for several decades. Although the role of experiments in engineering is still unquestionable, some of the limitations of physical models, as for example domain size (blockage and scale effects), can be addressed using mathematical models, i.e. computational fluid dynamics (CFD). However, to gain confidence in the use of CFD it is fundamental to determine the modelling accuracy, i.e. to determine the difference between the “physical reality” and the selected mathematical model. The quantification of the modelling error is the goal of Validation. It must be emphasized that Validation applies to the mathematical model (and not the code) and is performed for selected flow quantities (the so-called quantities of interest).
Ideally, Validation would be performed comparing an exact measurement of the “physical reality” with the exact solution of the selected mathematical model. However, exact measurements do not exist and mathematical models for turbulent flows do not have analytical solutions. Therefore, procedures must be developed to take into account experimental and numerical uncertainties. Furthermore, the exact values of the flow parameters as for example Reynolds number, fluid viscosity or inlet turbulence quantities are often unknown, which leads to the so-called parameter uncertainty that also has to be dealt within the assessment of the modelling error.
The main goal of this paper is to demonstrate that the very popular designation of ”code X is validated” is meaningless without saying what is the mathematical model embedded in the code, what are the quantities of interest for the specific application and what is the Validation uncertainty imposed by the experimental, numerical and parameter uncertainties. Furthermore, we also illustrate that Validation is not a pass or fail exercise. A modelling error of 10% may be acceptable for a given application, whereas 1% may not be enough for a different one.
To this end, we present the application of the ASME V&V 20 Validation procedure for local set points and the metric for multiple set points to several practical test cases: prediction of transition from laminar to turbulent regime for the flow over a flat plate; flow around a circular cylinder; flow around the KVLCC2 tanker and current loads in shallow water for a LNG carrier. In most of these exercises, parameter uncertainty is assumed to be zero, which is an assumption often required for the so-called practical calculations due to the computational effort required to address it. Nonetheless, as an illustration of its application, the flow over the flat plate includes parameter uncertainty for the specification of the inlet turbulence quantities.
Ideally, Validation would be performed comparing an exact measurement of the “physical reality” with the exact solution of the selected mathematical model. However, exact measurements do not exist and mathematical models for turbulent flows do not have analytical solutions. Therefore, procedures must be developed to take into account experimental and numerical uncertainties. Furthermore, the exact values of the flow parameters as for example Reynolds number, fluid viscosity or inlet turbulence quantities are often unknown, which leads to the so-called parameter uncertainty that also has to be dealt within the assessment of the modelling error.
The main goal of this paper is to demonstrate that the very popular designation of ”code X is validated” is meaningless without saying what is the mathematical model embedded in the code, what are the quantities of interest for the specific application and what is the Validation uncertainty imposed by the experimental, numerical and parameter uncertainties. Furthermore, we also illustrate that Validation is not a pass or fail exercise. A modelling error of 10% may be acceptable for a given application, whereas 1% may not be enough for a different one.
To this end, we present the application of the ASME V&V 20 Validation procedure for local set points and the metric for multiple set points to several practical test cases: prediction of transition from laminar to turbulent regime for the flow over a flat plate; flow around a circular cylinder; flow around the KVLCC2 tanker and current loads in shallow water for a LNG carrier. In most of these exercises, parameter uncertainty is assumed to be zero, which is an assumption often required for the so-called practical calculations due to the computational effort required to address it. Nonetheless, as an illustration of its application, the flow over the flat plate includes parameter uncertainty for the specification of the inlet turbulence quantities.
2017
Crepier, P.
Ship Resistance Prediction: Verification And Validation Exercise On Unstructured Grids Conference
VII International Conference on Computational Methods in Marine Engineering (MARINE2017), 2017.
Abstract | Links | BibTeX | Tags: CFD, Double-body, KVLCC2, Unstructured grid, Validation, verification
@conference{Crepier2017,
title = {Ship Resistance Prediction: Verification And Validation Exercise On Unstructured Grids},
author = {Crepier, P.},
url = {http://www.marin.nl/web/Publications/Publication-items/Ship-Resistance-Prediction-Verification-And-Validation-Exercise-On-Unstructured-Grids.htm},
year = {2017},
date = {2017-05-01},
booktitle = {VII International Conference on Computational Methods in Marine Engineering (MARINE2017)},
abstract = {The prediction of the resistance of a ship is, together with the propeller performance prediction, part of the key aspects during the design process of a ship, as it partly ensures the quality of the power-prediction. Body fitted structured grids for ship simulations can be rather challenging and time consuming to build, especially when dealing with appended ship geometries. For this reason, unstructured hexahedral trimmed grids are more and more used. Such grids can be build by various CFD package such as CD-Adapcos Star CCM+, NUMECAs Hexpress grid generator or OpenFOAMSs SnappyHexMesh. Although their use is increasing or even already adopted, the numerical uncertainty of these simulations seems to be a well-kept secret.
In the study presented, an attempt at quantifying the numerical uncertainty of the resistance for the combination of the RANS Solver ReFRESCO [1] with grids generated using the commercial package Hexpress is made. The studied case is the flow around the bare-hull KVLCC2 at model scale Reynolds number. Extensive verification and validation on the same test case has already been published for the combination of ReFRESCO and structured grids by Pereira et al. [2].
The method to generate grids as geometrically similar as possible is presented, and the uncertainty analysis by L. Ec¸a and M. Hoekstra [3] is performed on the integral results obtained.
The simulations are performed using the k − ω SST, k − ω TNT and the k −√kL turbulence models. The velocity fields calculated in the propeller plane are compared to the measured ones and to the results obtained by Pereira et al. [2] on structured grids.
The results show that the differences with the experimental results are in the same range as the differences obtained with structured grids. The numerical uncertainties are, however, higher. They are also strongly dependent on the turbulence model used, like for structured grids, and are spread between 1.3% and 12%.
Concerning the wake flow details, not all features present in the experimental results are obtained and, compared to structured grids, the flow features are smoothed. The wake flow is also influenced by the turbulence modelling and needs to be adressed in more detail.},
keywords = {CFD, Double-body, KVLCC2, Unstructured grid, Validation, verification},
pubstate = {published},
tppubtype = {conference}
}
The prediction of the resistance of a ship is, together with the propeller performance prediction, part of the key aspects during the design process of a ship, as it partly ensures the quality of the power-prediction. Body fitted structured grids for ship simulations can be rather challenging and time consuming to build, especially when dealing with appended ship geometries. For this reason, unstructured hexahedral trimmed grids are more and more used. Such grids can be build by various CFD package such as CD-Adapcos Star CCM+, NUMECAs Hexpress grid generator or OpenFOAMSs SnappyHexMesh. Although their use is increasing or even already adopted, the numerical uncertainty of these simulations seems to be a well-kept secret.
In the study presented, an attempt at quantifying the numerical uncertainty of the resistance for the combination of the RANS Solver ReFRESCO [1] with grids generated using the commercial package Hexpress is made. The studied case is the flow around the bare-hull KVLCC2 at model scale Reynolds number. Extensive verification and validation on the same test case has already been published for the combination of ReFRESCO and structured grids by Pereira et al. [2].
The method to generate grids as geometrically similar as possible is presented, and the uncertainty analysis by L. Ec¸a and M. Hoekstra [3] is performed on the integral results obtained.
The simulations are performed using the k − ω SST, k − ω TNT and the k −√kL turbulence models. The velocity fields calculated in the propeller plane are compared to the measured ones and to the results obtained by Pereira et al. [2] on structured grids.
The results show that the differences with the experimental results are in the same range as the differences obtained with structured grids. The numerical uncertainties are, however, higher. They are also strongly dependent on the turbulence model used, like for structured grids, and are spread between 1.3% and 12%.
Concerning the wake flow details, not all features present in the experimental results are obtained and, compared to structured grids, the flow features are smoothed. The wake flow is also influenced by the turbulence modelling and needs to be adressed in more detail.
In the study presented, an attempt at quantifying the numerical uncertainty of the resistance for the combination of the RANS Solver ReFRESCO [1] with grids generated using the commercial package Hexpress is made. The studied case is the flow around the bare-hull KVLCC2 at model scale Reynolds number. Extensive verification and validation on the same test case has already been published for the combination of ReFRESCO and structured grids by Pereira et al. [2].
The method to generate grids as geometrically similar as possible is presented, and the uncertainty analysis by L. Ec¸a and M. Hoekstra [3] is performed on the integral results obtained.
The simulations are performed using the k − ω SST, k − ω TNT and the k −√kL turbulence models. The velocity fields calculated in the propeller plane are compared to the measured ones and to the results obtained by Pereira et al. [2] on structured grids.
The results show that the differences with the experimental results are in the same range as the differences obtained with structured grids. The numerical uncertainties are, however, higher. They are also strongly dependent on the turbulence model used, like for structured grids, and are spread between 1.3% and 12%.
Concerning the wake flow details, not all features present in the experimental results are obtained and, compared to structured grids, the flow features are smoothed. The wake flow is also influenced by the turbulence modelling and needs to be adressed in more detail.
2016
L. Eça, Vaz; Abreu, H.
Validation: What, Why And How Conference
OMAE ASME 35th International Conference on Ocean, Offshore and Arctic Engineering, Busan, South Korea, 2016.
Abstract | Links | BibTeX | Tags: a circular cylinder, a flat plate, CFD, EFD, KVLCC2, LNG carrier, Validation
@conference{Eça2016,
title = {Validation: What, Why And How},
author = {Eça, L., Vaz, G., Koop, A., Pereira, F. and Abreu, H.},
url = {http://www.marin.nl/web/Publications/Papers/Validation-What-Why-And-How.htm},
year = {2016},
date = {2016-06-19},
booktitle = {OMAE ASME 35th International Conference on Ocean, Offshore and Arctic Engineering, Busan, South Korea},
pages = {OMAE2016-54005},
abstract = {Offshore and Naval engineering have relied on physical models, i.e. experimental fluid dynamics (EFD), for several decades. Although the role of experiments in engineering is still unquestionable, some of the limitations of physical models, as for example domain size (blockage and scale effects), can be addressed using mathematical models, i.e. computational fluid dynamics (CFD). However, to gain confidence in the use of CFD it is fundamental to determine the modelling accuracy, i.e. to determine the difference between the “physical reality” and the selected mathematical model. The quantification of the modelling error is the goal of Validation. It must be emphasized that Validation applies to the mathematical model (and not the code) and is performed for selected flow quantities (the so-called quantities of interest).
Ideally, Validation would be performed comparing an exact measurement of the “physical reality” with the exact solution of the selected mathematical model. However, exact measurements do not exist and mathematical models for turbulent flows do not have analytical solutions. Therefore, procedures must be developed to take into account experimental and numerical uncertainties. Furthermore, the exact values of the flow parameters as for example Reynolds number, fluid viscosity or inlet turbulence quantities are often unknown, which leads to the so-called parameter uncertainty that also has to be dealt within the assessment of the modelling error.
The main goal of this paper is to demonstrate that the very popular designation of ”code X is validated” is meaningless without saying what is the mathematical model embedded in the code, what are the quantities of interest for the specific application and what is the Validation uncertainty imposed by the experimental, numerical and parameter uncertainties. Furthermore, we also illustrate that Validation is not a pass or fail exercise. A modelling error of 10% may be acceptable for a given application, whereas 1% may not be enough for a different one.
To this end, we present the application of the ASME V&V 20 Validation procedure for local set points and the metric for multiple set points to several practical test cases: prediction of transition from laminar to turbulent regime for the flow over a flat plate; flow around a circular cylinder; flow around the KVLCC2 tanker and current loads in shallow water for a LNG carrier. In most of these exercises, parameter uncertainty is assumed to be zero, which is an assumption often required for the so-called practical calculations due to the computational effort required to address it. Nonetheless, as an illustration of its application, the flow over the flat plate includes parameter uncertainty for the specification of the inlet turbulence quantities.},
keywords = {a circular cylinder, a flat plate, CFD, EFD, KVLCC2, LNG carrier, Validation},
pubstate = {published},
tppubtype = {conference}
}
Offshore and Naval engineering have relied on physical models, i.e. experimental fluid dynamics (EFD), for several decades. Although the role of experiments in engineering is still unquestionable, some of the limitations of physical models, as for example domain size (blockage and scale effects), can be addressed using mathematical models, i.e. computational fluid dynamics (CFD). However, to gain confidence in the use of CFD it is fundamental to determine the modelling accuracy, i.e. to determine the difference between the “physical reality” and the selected mathematical model. The quantification of the modelling error is the goal of Validation. It must be emphasized that Validation applies to the mathematical model (and not the code) and is performed for selected flow quantities (the so-called quantities of interest).
Ideally, Validation would be performed comparing an exact measurement of the “physical reality” with the exact solution of the selected mathematical model. However, exact measurements do not exist and mathematical models for turbulent flows do not have analytical solutions. Therefore, procedures must be developed to take into account experimental and numerical uncertainties. Furthermore, the exact values of the flow parameters as for example Reynolds number, fluid viscosity or inlet turbulence quantities are often unknown, which leads to the so-called parameter uncertainty that also has to be dealt within the assessment of the modelling error.
The main goal of this paper is to demonstrate that the very popular designation of ”code X is validated” is meaningless without saying what is the mathematical model embedded in the code, what are the quantities of interest for the specific application and what is the Validation uncertainty imposed by the experimental, numerical and parameter uncertainties. Furthermore, we also illustrate that Validation is not a pass or fail exercise. A modelling error of 10% may be acceptable for a given application, whereas 1% may not be enough for a different one.
To this end, we present the application of the ASME V&V 20 Validation procedure for local set points and the metric for multiple set points to several practical test cases: prediction of transition from laminar to turbulent regime for the flow over a flat plate; flow around a circular cylinder; flow around the KVLCC2 tanker and current loads in shallow water for a LNG carrier. In most of these exercises, parameter uncertainty is assumed to be zero, which is an assumption often required for the so-called practical calculations due to the computational effort required to address it. Nonetheless, as an illustration of its application, the flow over the flat plate includes parameter uncertainty for the specification of the inlet turbulence quantities.
Ideally, Validation would be performed comparing an exact measurement of the “physical reality” with the exact solution of the selected mathematical model. However, exact measurements do not exist and mathematical models for turbulent flows do not have analytical solutions. Therefore, procedures must be developed to take into account experimental and numerical uncertainties. Furthermore, the exact values of the flow parameters as for example Reynolds number, fluid viscosity or inlet turbulence quantities are often unknown, which leads to the so-called parameter uncertainty that also has to be dealt within the assessment of the modelling error.
The main goal of this paper is to demonstrate that the very popular designation of ”code X is validated” is meaningless without saying what is the mathematical model embedded in the code, what are the quantities of interest for the specific application and what is the Validation uncertainty imposed by the experimental, numerical and parameter uncertainties. Furthermore, we also illustrate that Validation is not a pass or fail exercise. A modelling error of 10% may be acceptable for a given application, whereas 1% may not be enough for a different one.
To this end, we present the application of the ASME V&V 20 Validation procedure for local set points and the metric for multiple set points to several practical test cases: prediction of transition from laminar to turbulent regime for the flow over a flat plate; flow around a circular cylinder; flow around the KVLCC2 tanker and current loads in shallow water for a LNG carrier. In most of these exercises, parameter uncertainty is assumed to be zero, which is an assumption often required for the so-called practical calculations due to the computational effort required to address it. Nonetheless, as an illustration of its application, the flow over the flat plate includes parameter uncertainty for the specification of the inlet turbulence quantities.