; ]^Oh+'0HP|
$TU Delft Repository search results0TU Delft Repository search results (max. 1000)TU Delft LibraryTU Delft Library@>2@>2՜.+,0HPX`hp
x
WorksheetFeuilles de calcul
B=%r8X"1Calibri1Calibri1Calibri1
Calibri 83ffff̙̙3f3fff3f3f33333f33333.ZTU Delft Repositoryg ;uuidrepository linktitleauthorcontributorpublication yearabstract
subject topiclanguagepublication type publisherisbnissnpatent
patent statusbibliographic noteaccess restrictionembargo datefaculty
departmentresearch group programmeprojectcoordinates)uuid:0a29e4da-649a-4919-9c13-00da4aa061efDhttp://resolver.tudelft.nl/uuid:0a29e4da-649a-4919-9c13-00da4aa061ef<Modelling epidemic spreading phenomena processes on networksUHamad, Abdul Aziz (TU Delft Electrical Engineering, Mathematics and Computer Science)Van Mieghem, Piet (mentor); Liu, Qiang (mentor); Ma, Long (mentor); Delft University of Technology (degree granting institution)
Spreading processes are ubiquitous in nature and societies, e.g. spreading of diseases and computer virus, propagation of messages, and activation of neurons. Computer viruses cause an enormous economic loss. Moreover, many illnesses/diseases still causing a serious threat to public health. For example, the outbreaks of circulating influenza strains cause millions of illness and deaths worldwide every year. Pronounced outbreaks of flu usually occur during winter. This recognized timing allows public health agencies to organize their flu-related mitigation and response activities to prepare for the winter flu season. Although the general wintertime peak of influenza incidence in temperate regions can be easily forecast, the specific intensity, duration, and time of individual local outbreaks are quite changeable. Even after an outbreak has begun, it is still difficult to predict the future characteristics of the epidemic curve. If the diseases/viruses outbreak characteristics could be reliably predicted, the public health response will be better coordinated.The goal is to develop a fast and accurate epidemic model to estimate, fit and forecast the spreading of an epidemic on a defined network. The aim is to conduct a study over viruses spreading phenomena both theoretically and numerically, then create a general model/algorithm that can be easily applied to different diseases and computer viruses. In this master thesis, we propose a new approach which can be used on real illness/viruses data (such as influenza) to estimate and forecast the epidemic more accurately. The approach is to use a model-inference system combining the network science, susceptible-infected-recovered-susceptible (SIRS) model, statistical filtering techniques and gradient descent. We are able to fit and estimate with a relatively low error compared to other algorithms. Moreover, we forecast the out-breaker with a high accuracy, four weeks before the true out-breaker on synthetic epidemic data. The model is evaluated on a regular graph, Erds-Rnyi graph, Watts-Strogatz small-world graph, & Barabsi-Albert graph. Furthermore, the model is carried out on real-world epidemic data (influenza data) for four countries (the Netherlands, Germany, Belgium and the United Kingdom), from the years 2012 to 2017.cEpidemic; SIRS; Ensemble Kalman filter; Gradient descent; Real-world epidemic data (influenza data)en
master thesis<Electrical Engineer | Telecommunications and sensing systems)uuid:20f6a380-efc2-419e-a284-bd9ecdf4bcb6Dhttp://resolver.tudelft.nl/uuid:20f6a380-efc2-419e-a284-bd9ecdf4bcb6XReducing Water Level Prediction Uncertainty in a Delfland Polder using Data AssimilationVIzeboud, Petra (TU Delft Civil Engineering and Geosciences; TU Delft Water Management)Schoups, Gerrit (mentor); van de Giesen, Nick (mentor); Verlaan, Martin (mentor); Delft University of Technology (degree granting institution)Polder areas as typical in the Netherlands require real time control on the water levels. Efficient pumping is a subject with increasing interest due to a required use of 20% green energy by 2020 set by the government. In 2010, the energy requirements already exceeded 175 GWh per year. For optimal control, water level predictions are important. However, they are uncertain due to errors in model structure, parameter estimation, initial states and observed forcing data. Especially the rainfall was found to< contribute to water level prediction uncertainty. The uncorrected radar rainfall forcing data available in real time underestimate the corrected radar rainfall measurement over 100%, worsening with increasing rainfall intensities. <br/><br/> The aim of the research was to asses whether data assimilation can reduce the uncertainty on the water level predictions in a Delfland polder. The polder is modelled with a conceptual rainfall-runoff model in Sobek RR, while the data assimilation is implemented as an ensemble Kalman filter in OpenDA. <br/> In order to manipulate the states in Sobek RR for data assimilation, the model was extended by a black-box wrapper that was newly created in Java for this research. <br/> <br/> To asses the theoretical applicability of data assimilation in a polder area, four twin experiments were carried out, which differed only in the model components to which noise was applied. The four set-ups included the use of a rainfall multiplier, additive noise on the model states, additive noise on the groundwater state and a single multiplier for all states. The results were compared visually and objectively through the probabilistic Nash Sutcliffe, a model efficiency coefficient.<br/> <br/> All set ups for the twin experiment generated good results for the assimilated water level, mimicking the observed water levels closely with probabilistic Nash Sutcliffe values ranging between 0.92 to 0.97. However, only the set up with the rainfall multiplier exhibited good score measures for the hidden states, with an average of 0.6, showing a significant improvement over the case without assimilation. The other twin experiments failed to represent the hidden states, with average score measures below zero. <br/> <br/> The data assimilation set-up with the rainfall multiplier was then applied using water level observations from Tedingerbroek polder. The pump pattern and magnitude of water levels of the modelled and observed water level differed significantly. The influence of the different model components on the modelled water level was assessed, from which was concluded that these could not account for the difference in modelled and observed values. Possible explanations are due to a non-representative measurement point in the polder or additional unknown water fluxes coming into the polder. <br/> <br/> Recommended further research should focus on creating a larger observation network; implementing multiple water level observation locations, collecting pump discharges and taking measurements of the storage in greenhouse basins. The last could give a method to validate the assimilation of the hidden states, whereas the pump discharges should be used as additional observation in the data assimilation. <br/> <brkData assimilation; Operational water management; Ensemble Kalman filter; water management; polder; Delfland)uuid:7441b61a-50ce-4b2b-90a5-eb5ff8fdeba4Dhttp://resolver.tudelft.nl/uuid:7441b61a-50ce-4b2b-90a5-eb5ff8fdeba4NStochastic Nodal Analysis: EnKF and PF applied to petroleum production systemsbAmmiwala, Huzefa (TU Delft Civil Engineering and Geosciences; TU Delft Geoscience and Engineering)WJansen, Jan Dirk (mentor); Delft University of Technology (degree granting institution)A petroleum production system is generally modelled based on the concept of nodal analysis, where the entire system is broken down into discrete elements such as near-well bore, tubing, surface choke and flow line. Operating flow rates and pressures can be estimated with a nodal analysis procedure by calculation of the intersection of performance curves. Input parameters in nodal analysis of production systems are considered deterministic, however, some of these parameters are better represented as distributions. In this report, the ensemble-based data assimilation methods ensemble Kalman filter (EnKF) and particle filter (PF) are applied to steady-state models of a production system for tuning of uncertain model parameters during the test separator phase. The performance of the EnKF and the PF is tested with the use of twin experiments. T<he calibrated model parameters of the choke, tubing and the near-well bore elements with EnKF and PF can be used to create an ensemble of performance curves leading to an ensemble of operating flow rates and pressures. The foreseen next step is to use the posterior distributions of model parameters as inputs for soft sensing of flow rates during semi-steady-state production for a single phase oil reservoir, where the oil rate and reservoir pressure are considered as unknown parameters. In the twin experiments as used in this thesis, a total number of three steady-state pressure drop measurements was used to estimate a total of six independent parameters which constitutes an ill-posed problem, resulting in non-unique parameter estimates. It is recommended to alleviate this issue by either reducing the number of parameters or by using multiple separator tests at different flow rates.Data assimilation; Production systems; Petroleum; Ensemble Kalman filter; EnKF; particle filter; Nodal analysis; Tuning of model parametersPetroleum Engineering)uuid:392abee2-abb3-4402-80a2-c1939d1e5a66Dhttp://resolver.tudelft.nl/uuid:392abee2-abb3-4402-80a2-c1939d1e5a66Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands
De Koning, D.Steele Dunne, S.C. (mentor); Van de Giesen, N.C. (mentor); Alderlieste, M.A.A. (mentor); Spijker, M. (mentor); Dong, J. (mentor); Yeh, J.F. (mentor)This thesis discusses the applicability of assimilation of artificial SMAP data into a quasi steady state hydrological model to improve soil moisture estimates. The model used for this research was SIMGRO and since no real SMAP data were available at the time of the research, artificial SMAP data were used. The ensemble Kalman filter was used to assimilate the data. To test the method a case study was performed in the North-East of the Netherlands. It was found that this is a feasible method for improving soil moisture estimates. Even if a quasi steady state model is used. However, for practical application more research is necessary and it is very important to use a correctly validated and calibrated model. The systematic errors of the model should be as small as possible. A fully dynamic model could improve the results. Furthermore it was found that due to the difference in scale between the model (1 x 1 km) and the SMAP data (3 x 3 km) the effect of the Kalman filter is not as large on the finer grid as on the coarser observation grid. This effect might increase when a even finer grid is used. Due to the generic nature of the method it can be applied to more locations in the Netherlands, where it can potentially help improving soil moisture estimates in real time forecasting systems.>data assimilation; Ensemble Kalman filter; SMAP; soil moisture!Civil Engineering and GeosciencesWater ManagementWater Resources
*+&ffffff?'ffffff?(?)?"dXX333333?333333?U}}}}}}}}}} }
}}}
}}}}}}}}}}}}
@
!
"
#
$
%
&@
'
(
)
*
+
,
-@
.
/
0
1
2
3
4
5|@
6
7
8
9
:
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~>@ddyKyKhttp://resolver.tudelft.nl/uuid:0a29e4da-649a-4919-9c13-00da4aa061efyKyKhttp://resolver.tudelft.nl/uuid:20f6a380-efc2-419e-a284-bd9ecdf4bcb6yKyKhttp://resolver.tudelft.nl/uuid:7441b61a-50ce-4b2b-90a5-eb5ff8fdeba4yKyKhttp://resolver.tudelft.nl/uuid:392abee2-abb3-4402-80a2-c1939d1e5a66gg
Root Entry F>2>2@SummaryInformation( F<Workbook FDocumentSummaryInformation8 F
!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\