Quantifying dependencies for sensitivity analysis with multivariate input sample data

We present a novel method for quantifying dependencies in multivariate datasets, based on estimating the R\'{e}nyi entropy by minimum spanning trees (MSTs). The length of the MSTs can be used to order pairs of variables from strongly to weakly dependent, making it a useful tool for sensitivity analysis with dependent input variables. It is well-suited for cases where the input distribution is unknown and only a sample of the inputs is available. We introduce an estimator to quantify dependency based on the MST length, and investigate its properties with several numerical examples. To reduce the computational cost of constructing the exact MST for large datasets, we explore methods to compute approximations to the exact MST, and find the multilevel approach introduced recently by Zhong et al. (2015) to be the most accurate. We apply our proposed method to an artificial testcase based on the Ishigami function, as well as to a real-world testcase involving sediment transport in the North Sea. The results are consistent with prior knowledge and heuristic understanding, as well as with variance-based analysis using Sobol indices in the case where these indices can be computed

Een natuurgebied inrichten op particulier terrein

Artikel over herinrichting van een gebied met een tweeledig doel: een bloemenweide maken die de bijen- en vlinderstand helpt verbeteren en aantrekkelijk is voor de mensen die in het huis verblijven en het onderhoud beperken tot een paar maal per jaar, omdat de eigenaren er zelf niet altijd zijn

Assessing uncertainties from physical parameters and modelling choices in an atmospheric large eddy simulation model

In this study, we investigate uncertainties in a large eddy simulation of the atmosphere, employing modern uncertainty quantification methods that have hardly been used yet in this context. When analysing the uncertainty of model results, one can distinguish between uncertainty related to physical parameters whose values are not exactly known, and uncertainty related to modelling choices such as the selection of numerical discretization methods, of the spatial domain size and resolution, and the use of different model formulations. While the former kind is commonly studied e.g. with forward uncertainty propagation, we explore the use of such techniques to also assess the latter kind. From a climate modelling perspective, uncertainties in the convective response and cloud formation are of particular interest, since these affect the cloud-climate feedback, one of the dominant sources of uncertainty in current climate models. Therefore we analyse the DALES model in the RICO case, a well-studied convection benchmark. We use the VECMA toolkit for uncertainty propagation, assessing uncertainties stemming from physical parameters as well as from modelling choices. We find substantial uncertainties due to small random initial state perturbations, and that the choice of advection scheme is the most influential of the modelling choices we assessed. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.Atmospheric Remote Sensin

Mitigation of Large Power Spills by a Storage Device

The unpredictable nature of wind energy makes its integration to the electric grid highly challenging. However, these challenges can be addressed by incorporating storage devices (batteries) in the system. We perform an overall assessment of a single domestic power system with a wind turbine supported by an energy storage device. The aim is to investigate the best operation mode of the storage device such that the occurrence of large power spills can be minimized. For estimating the small probability of large power spills, we use the splitting technique for rare-event simulations. An appropriate Importance Function for splitting is formulated such that it reduces the work-load of the probability estimator as compared to the conventional Crude Monte Carlo probability estimator. Simulation results show that the ramp constraints imposed on the charging/discharging rate of the storage device plays a pivotal role in mitigating large power spills. It is observed that by employing a new charging strategy for the storage device large power spills can be minimized further. There exists a trade-off between reducing the large power spills versus reducing the average power spills

Efficient estimator of probabilities of large power spills in an stand-alone system with wind generation and storage

The challenges of integrating unpredictable wind energy into a power system can be alleviated using energy storage devices. We assessed a single domestic energy system with a wind turbine and a battery. We investigated the best operation mode of the battery such that the occurrence of large power spills is minimized. To estimate the small probability of large power spills, we used the splitting technique for rare-event simulations and to do so, we formulated an appropriate Importance Function such that the workload of the probability estimator is reduced compared to the conventional Crude Monte Carlo estimator. We find that the ramp constraint imposed on the charging/discharging rate of the storage device plays a major role in minimizing large power spills. A new charging strategy for the battery is applied to reduce the large power spills further which results in a trade-off between reductions in large and average power spills, respectively

In vitro release studies on drugs suspended in non-polar media

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43362/1/11096_2005_Article_BF02273093.pd