1,303 research outputs found
Assessment of uncertainties in hot-wire anemometry and oil-film interferometry measurements for wall-bounded turbulent flows
In this study, the sources of uncertainty of hot-wire anemometry (HWA) and
oil-film interferometry (OFI) measurements are assessed. Both statistical and
classical methods are used for the forward and inverse problems, so that the
contributions to the overall uncertainty of the measured quantities can be
evaluated. The correlations between the parameters are taken into account
through the Bayesian inference with error-in-variable (EiV) model. In the
forward problem, very small differences were found when using Monte Carlo (MC),
Polynomial Chaos Expansion (PCE) and linear perturbation methods. In flow
velocity measurements with HWA, the results indicate that the estimated
uncertainty is lower when the correlations among parameters are considered,
than when they are not taken into account. Moreover, global sensitivity
analyses with Sobol indices showed that the HWA measurements are most sensitive
to the wire voltage, and in the case of OFI the most sensitive factor is the
calculation of fringe velocity. The relative errors in wall-shear stress,
friction velocity and viscous length are 0.44%, 0.23% and 0.22%, respectively.
Note that these values are lower than the ones reported in other wall-bounded
turbulence studies. Note that in most studies of wall-bounded turbulence the
correlations among parameters are not considered, and the uncertainties from
the various parameters are directly added when determining the overall
uncertainty of the measured quantity. In the present analysis we account for
these correlations, which may lead to a lower overall uncertainty estimate due
to error cancellation. Furthermore, our results also indicate that the crucial
aspect when obtaining accurate inner-scaled velocity measurements is the
wind-tunnel flow quality, which is more critical than the accuracy in
wall-shear stress measurements
Generalized Hoeffding-Sobol Decomposition for Dependent Variables -Application to Sensitivity Analysis
In this paper, we consider a regression model built on dependent variables.
This regression modelizes an input output relationship. Under boundedness
assumptions on the joint distribution function of the input variables, we show
that a generalized Hoeffding-Sobol decomposition is available. This leads to
new indices measuring the sensitivity of the output with respect to the input
variables. We also study and discuss the estimation of these new indices
Characterization of process-oriented hydrologic model behavior with temporal sensitivity analysis for flash floods in Mediterranean catchments
This paper presents a detailed analysis of 10 flash flood events in the Mediterranean region using the distributed hydrological model MARINE. Characterizing catchment response during flash flood events may provide new and valuable insight into the dynamics involved for extreme catchment response and their dependency on physiographic properties and flood severity. The main objective of this study is to analyze flash-flood-dedicated hydrologic model sensitivity with a new approach in hydrology, allowing model outputs variance decomposition for temporal patterns of parameter sensitivity analysis. Such approaches enable ranking of uncertainty sources for nonlinear and nonmonotonic mappings with a low computational cost. Hydrologic model and sensitivity analysis are used as learning tools on a large flash flood dataset. With Nash performances above 0.73 on average for this extended set of 10 validation events, the five sensitive parameters of MARINE process-oriented distributed model are analyzed. This contribution shows that soil depth explains more than 80% of model output variance when most hydrographs are peaking. Moreover, the lateral subsurface transfer is responsible for 80% of model variance for some catchment-flood events’ hydrographs during slow-declining limbs. The unexplained variance of model output representing interactions between parameters reveals to be very low during modeled flood peaks and informs that model parsimonious parameterization is appropriate to tackle the problem of flash floods. Interactions observed after model initialization or rainfall intensity peaks incite to improve water partition representation between flow components and initialization itself. This paper gives a practical framework for application of this method to other models, landscapes and climatic conditions, potentially helping to improve processes understanding and representation
- …