A Verification and Validation Study Based on Resistance Submissions

"The junction flow between an hydrofoil and a plate is manifested by the generation of vortex structures as a result of the interaction between the boundary layer on the plate and the boundary layer on the profile. Two benchmark tests have been identified in the literature: one for the flat plate, and the other for the NACA 0012 profile. The Verification and Validation study for both hydrofoil and flat plate was performed by testing all turbulence model implemented in Ansys Fluent and results are compared with the experimental ones."

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A Verification and Validation Study of the Cyclops I PBX 9502 Experiment

ASME 2020 Verification and Validation Symposium ◽

10.1115/vvs2020-8841 ◽

2020 ◽

Author(s):

John W. Grove ◽

Adam C. Coleman ◽

Carl E. Johnson ◽

Ralph Menikoff

Keyword(s):

Validation Study ◽

High Explosive ◽

Verification And Validation ◽

Proton Radiography ◽

First Order ◽

Solution Convergence ◽

Convergence Study ◽

Photon Doppler Velocimetry ◽

Shock Fronts ◽

Better Than

Abstract A computational verification and validation study of the Cyclops I experiment [1–7] was conducted using the Los Alamos Eulerian Applications code xRage [8]. The purpose of this study was to validate the Scaled Unified Reactive Front (SURF) plus (SURFplus) model for insensitive high explosives [9–12]. Diagnostics from the experiment included photon doppler velocimetry measurements of the encasing shell for the device and proton radiography photographs of the explosions. This data was compared to the xRage computed data and a convergence study of burn front evolution was conducted. We conclude that the SURFplus high explosive model does an excellent job at predicting the high explosive burn front velocity and shape with results that converge to the experimental data at rates near to or better than first order in most cases. Some companion verification metrics for the solution convergence are also described. These metrics show that the xRage computed solution for the high explosive burn front converges to first order or better, as consistent with the treatment of shock fronts in a higher order Godunov hydrodynamic solver as used in xRage.

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An Extended Verification and Validation Study of CFD Simulations for Planing Hulls

Journal of Ship Research ◽

10.5957/josr.60.2.160010 ◽

2016 ◽

Vol 60 (2) ◽

pp. 101-118 ◽

Cited By ~ 29

Author(s):

Fabio De Luca ◽

Simone Mancini ◽

Salvatore Miranda ◽

Claudio Pensa

Keyword(s):

Validation Study ◽

Verification And Validation ◽

Cfd Simulations

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Technical Verification and Assessment of Independent Validation of Biomarker Models for Endometriosis

BioMed Research International ◽

10.1155/2019/3673060 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Dorien F. O ◽

Amelie Fassbender ◽

Rita Van Bree ◽

Annouschka Laenen ◽

Daniëlle P. Peterse ◽

...

Keyword(s):

Validation Study ◽

Prediction Models ◽

Annexin V ◽

Verification And Validation ◽

Ca 125 ◽

Plasma Samples ◽

Peripheral Blood Plasma ◽

Low Performance ◽

The Impact ◽

Collection Methods

There is a great need for a noninvasive diagnosis for endometriosis. Several biomarkers and biomarker panels have been proposed. Biomarker models consisting of CA-125, VEGF, Annexin V, and glycodelin/sICAM-1 were previously developed by our group. The objective of our current study was to assess the impact of technical and biological variability on the performance of those previously developed prediction models in a technical verification and a validation setting. The technical verification cohort consisted of peripheral blood plasma samples from a subset of the patients included in the original study of Vodolazkaia et al. (99 women with and 37 women without endometriosis). The validation study was done in plasma samples of an independent patient cohort (170 women with and 86 women without endometriosis). Single immunoassays were used for CA-125, VEGF-A, sICAM-1, Annexin V, and glycodelin. Statistical analyses were done using univariate and multivariate (logistic regression) approaches. The previously reported prediction models for endometriosis had a low performance in both the technical verification and validation setting. New prediction models were developed, which included CA-125, Annexin V, and sICAM-1, but CA-125 was the only marker that was retained in the models across the technical verification and validation study. Overall, successful validation of a biomarker model depends on several factors such as patient selection, collection methods, assay selection/handling, stability of the marker, and statistical analysis and interpretation. There is a need for standardized studies in large, well-defined patient cohorts with robust assay methodologies.

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A Verification and Validation Study With Respect to Simulating Undulating Propulsion

Volume 3: Computational Fluid Dynamics; Micro and Nano Fluid Dynamics ◽

10.1115/fedsm2020-20077 ◽

2020 ◽

Author(s):

George Loubimov ◽

Michael P. Kinzel

Keyword(s):

Validation Study ◽

Verification And Validation ◽

Vortex Wake ◽

Time Step ◽

Numerical Accuracy ◽

Fluid Physics ◽

Marine Propulsion ◽

Karman Vortex ◽

Computational Fluid Dynamics Cfd ◽

Input Uncertainties

Abstract In this present work, a detailed verification and validation study is presented for a computational fluid dynamics (CFD) framework that aims to predict fluid physics and forces associated with naturally inspired marine propulsion. This approach involves comparing experimental datasets to their simulated counterparts and evaluating the numerical accuracy of the CFD toolset. In this manner, the error and uncertainty of the predictions from the CFD toolset can be established, which both convey the accuracy of the model and gives additional confidence in the underlying physical character of undulation-based propulsion. Validation efforts include simulation of a D-tube shedding a von Kármán vortex wake, a heaving and pitching foil generating thrust, and a traversing flat plate which is abruptly heaved and pitch. For all cases, mesh and time-step refinements are employed to ascertain numerical uncertainty. Although not yet complete, we also aim to roll up uncertainty from experiments and input uncertainties.

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