scholarly journals Concentrations monitoring via software sensor for bioreactors under model parametric uncertainty: Application to cadmium removal in an anaerobic process

2016 ◽  
Vol 55 (2) ◽  
pp. 1893-1902 ◽  
Author(s):  
P.A. López Pérez ◽  
M.I. Neria González ◽  
M.R. Pérez García ◽  
R. Aguilar López
Keyword(s):  
Parametric Uncertainty ◽  
Software Sensor ◽  
Cadmium Removal ◽  
Anaerobic Process ◽  
Model Parametric Uncertainty

scholarly journals Reduction of predictive uncertainty in estimating irrigation water requirement through multi-model ensembles and ensemble averaging

2015 ◽  
Vol 8 (4) ◽  
pp. 1233-1244 ◽  
Author(s):  
S. Multsch ◽  
J.-F. Exbrayat ◽  
M. Kirby ◽  
N. R. Viney ◽  
H.-G. Frede ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Model Uncertainty ◽  
Parametric Uncertainty ◽  
Crop Coefficient ◽  
Water Requirement ◽  
Irrigation Water Requirement ◽  
Ensemble Averaging ◽  
Water Requirements ◽  
Crop Coefficients ◽  
Model Parametric Uncertainty

Abstract. Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural versus model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray–Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty among reference ET is far more important than model parametric uncertainty introduced by crop coefficients. These crop coefficients are used to estimate irrigation water requirement following the single crop coefficient approach. Using the reliability ensemble averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making.

scholarly journals Sensitivity analysis of large-eddy simulations to subgrid-scale-model parametric uncertainty using polynomial chaos

2007 ◽  
Vol 585 ◽  
pp. 255-279 ◽  
Author(s):  
DIDIER LUCOR ◽  
JOHAN MEYERS ◽  
PIERRE SAGAUT
Keyword(s):  
Polynomial Chaos ◽  
Parametric Uncertainty ◽  
Large Eddy Simulations ◽  
Scale Model ◽  
Subgrid Scale ◽  
Model Free ◽  
Numerical Predictions ◽  
Large Eddy ◽  
Subgrid Scale Model ◽  
Model Parametric Uncertainty

We address the sensitivity of large-eddy simulations (LES) to parametric uncertainty in the subgrid-scale model. More specifically, we investigate the sensitivity of the LES statistical moments of decaying homogeneous isotropic turbulence to the uncertainty in the Smagorinsky model free parameter Cs (i.e. the Smagorinsky constant). Our sensitivity methodology relies on the non-intrusive approach of the generalized Polynomial Chaos (gPC) method; the gPC is a spectral non-statistical numerical method well-suited to representing random processes not restricted to Gaussian fields. The analysis is carried out at Reλ, =, 100 and for different grid resolutions and Cs distributions. Numerical predictions are also compared to direct numerical simulation evidence. We have shown that the different turbulent scales of the LES solution respond differently to the variability in Cs. In particular, the study of the relative turbulent kinetic energy distributions for different Cs distributions indicates that small scales are mainly affected by changes in the subgrid-model parametric uncertainty.

scholarly journals Preliminary assessment of model parametric uncertainty in projections of Greenland Ice Sheet behavior

2011 ◽  
Vol 5 (6) ◽  
pp. 3175-3205 ◽  
Author(s):  
P. J. Applegate ◽  
N. Kirchner ◽  
E. J. Stone ◽  
K. Keller ◽  
R. Greve
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Parametric Uncertainty ◽  
Temperature And Precipitation ◽  
Input Parameters ◽  
Air Temperature Anomaly ◽  
Model Parametric Uncertainty

Abstract. Lack of knowledge about the values of ice sheet model input parameters introduces substantial uncertainty into projections of Greenland Ice Sheet contributions to future sea level rise. Computer models of ice sheet behavior provide one of several means of estimating future sea level rise due to mass loss from ice sheets. Such models have many input parameters whose values are not well known. Recent studies have investigated the effects of these parameters on model output, but the range of potential future sea level increases due to model parametric uncertainty has not been characterized. Here, we demonstrate that this range is large, using a 100-member perturbed-physics ensemble with the SICOPOLIS ice sheet model. Each model run is spun up over 125 000 yr using geological forcings, and subsequently driven into the future using an asymptotically increasing air temperature anomaly curve. All modeled ice sheets lose mass after 2005 AD. After culling the ensemble to include only members that give reasonable ice volumes in 2005 AD, the range of projected sea level rise values in 2100 AD is 30 % or more of the median. Data on past ice sheet behavior can help reduce this uncertainty, but none of our ensemble members produces a reasonable ice volume change during the mid-Holocene, relative to the present. This problem suggests that the model's exponential relation between temperature and precipitation does not hold during the Holocene, or that the central-Greenland temperature forcing curve used to drive the model is not representative of conditions around the ice margin at this time (among other possibilities). Our simulations also lack certain observed physical processes that may tend to enhance the real ice sheet's response. Regardless, this work has implications for other studies that use ice sheet models to project or hindcast the behavior of the Greenland ice sheet.

scholarly journals Reduction of predictive uncertainty in estimating irrigation water requirement through multi-model ensembles and ensemble averaging

2014 ◽  
Vol 7 (6) ◽  
pp. 7525-7558
Author(s):  
S. Multsch ◽  
J.-F. Exbrayat ◽  
M. Kirby ◽  
N. R. Viney ◽  
H.-G. Frede ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Model Uncertainty ◽  
Parametric Uncertainty ◽  
Water Requirement ◽  
Ensemble Average ◽  
Irrigation Water Requirement ◽  
Ensemble Averaging ◽  
Water Requirements ◽  
Probability Curve ◽  
Model Parametric Uncertainty

Abstract. Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural vs. model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray-Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty is far more important than model parametric uncertainty to estimate irrigation water requirement. Using the Reliability Ensemble Averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making.

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