Research Papers

J. Verif. Valid. Uncert. 2019;4(1):011001-011001-10. doi:10.1115/1.4043912.

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.

Commentary by Dr. Valentin Fuster
J. Verif. Valid. Uncert. 2019;4(1):011002-011002-15. doi:10.1115/1.4043807.

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.

Commentary by Dr. Valentin Fuster
J. Verif. Valid. Uncert. 2019;4(1):011003-011003-16. doi:10.1115/1.4043965.

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.

Commentary by Dr. Valentin Fuster
J. Verif. Valid. Uncert. 2019;4(1):011004-011004-8. doi:10.1115/1.4044236.

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.

Commentary by Dr. Valentin Fuster

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