; xyOh+'0HP
$TU Delft Repository search results0TU Delft Repository search results (max. 1000)TU Delft LibraryTU Delft Library@Tl_;@Tl_;՜.+,0HPX`hp
x
WorksheetFeuilles de calcul
B=%r8X"1Calibri1Calibri1Calibri1
Calibri 83ffff̙̙3f3fff3f3f33333f33333.ٍTU Delft Repositoryg Ouuidrepository linktitleauthorcontributorpublication yearabstract
subject topiclanguagepublication type publisherisbnissnpatent
patent statusbibliographic noteaccess restrictionembargo datefaculty
departmentresearch group programmeprojectcoordinates)uuid:a3b89c576b774d61aa3c91027510f202Dhttp://resolver.tudelft.nl/uuid:a3b89c576b774d61aa3c91027510f202SmoothnessIncreasing AccuracyConserving (SIAC) Filtering for Discontinuous Galerkin Solutions over Nonuniform Meshes: Superconvergence and Optimal AccuracyLi, Xiaozhou (University of Electronic Science and Technology of China); Ryan, J.K. (University of East Anglia; HeinrichHeineUniversitt); Kirby, Robert M. (University of Utah); Vuik, C. (TU Delft Numerical Analysis)Smoothnessincreasing accuracyconserving (SIAC) filtering is an area of increasing interest because it can extract the hidden accuracy in discontinuous Galerkin (DG) solutions. It has been shown that by applying a SIAC filter to a DG solution, the accuracy order of the DG solution improves from order k+ 1 to order 2 k+ 1 for linear hyperbolic equations over uniform meshes. However, applying a SIAC filter over nonuniform meshes is difficult, and the quality of filtered solutions is usually unsatisfactory applied to approximations defined on nonuniform meshes. The applicability to such approximations over nonuniform meshes is the biggest obstacle to the development of a SIAC filter. The purpose of this paper is twofold: to study the connection between the error of the filtered solution and the nonuniform mesh and to develop a filter scaling that approximates the optimal error reduction. First, through analyzing the error estimates for SIAC filtering, we computationally establish for the first time a relation between the filtered solutions and the unstructuredness of nonuniform meshes. Further, we demonstrate that there exists an optimal accuracy of the filtered solution for a given nonuniform mesh and that it is possible to obtain this optimal accuracy by the method we propose, an optimal filter scaling. By applying the newly designed filter scaling over nonuniform meshes, the filtered solution has demonstrated improvement in accuracy order as well as reducing the error compared to the original DG solution. Finally, we apply the proposed methods over a large number of nonuniform meshes and compare the performance with existing methods to demonstrate the superiority of our method.uDiscontinuous Galerkin method; Nonuniform meshes; Postprocessing; SIAC filtering; Superconvergence; Unstructurednessenjournal article)uuid:44a3f674f8e04414a92efe1dd7139f96Dhttp://resolver.tudelft.nl/uuid:44a3f674f8e04414a92efe1dd7139f96NDenoising controlledsource electromagnetic data using leastsquares inversion,Yang, Yang (Central South University China; Shandong University); Li, Diquan (Central South University China); Tong, Tiegang (Central South University China); Zhang, D. (TU Delft ImPhys/Acoustical Wavefield Imaging); Zhou, Yatong (Hebei University of Technology); Chen, Yangkang (Zhejiang University)kStrong noise is one of the toughest problems in the controlledsource electromagnetic (CSEM) method, which highly affects the quality of recorded data. The three main types of noise existing in CSEM data are periodic noise, Gaussian white noise, and nonperiodic noise, among which the nonperiodic noise is thought to be the most difficult to remove. We have developed a novel and effective method for removing such nonperiodic noise by formulating an inverse problem that is based on inverse discrete Fourier transform and several time windows in which only Gaussian white noise exists. These critical locations, which we call reconstruction locations, can be found by taking advantage of the continuous wavelet transform (CWT) and the temporal derivative of the scalogram generated by CWT. The coefficients of the nonperiodic noise are first estimated using the new leastsquares method, and then they are subtracted from the coefficients of the raw data to produce denoised data. Together wit< h the nonperiodic noise, we also remove Gaussian noise using the proposed method. We validate the methodology using realworld CSEM data.BElectromagnetics; Leastsquares; Noise; Signal processing; Wavelet#ImPhys/Acoustical Wavefield Imaging)uuid:feaedc8f708b4a18aeba7a9840f8e66aDhttp://resolver.tudelft.nl/uuid:feaedc8f708b4a18aeba7a9840f8e66a5Mechanical properties of bi and polycrystalline iceCao, Pinqiang (China University of Geosciences, Wuhan; Xiamen University); Wu, Jianyang (Xiamen University; Norwegian University of Science and Technology); Zhang, Zhisen (Xiamen University); Fang, Bin (China University of Geosciences, Wuhan); Peng, Li (China University of Geosciences, Wuhan); Li, Tianshu (The George Washington University); Vlugt, T.J.H. (TU Delft Engineering Thermodynamics); Ning, Fulong (China University of Geosciences, Wuhan)^A sound knowledge of fundamental mechanical properties of water ice is of crucial importance to address a wide range of applications in earth science, engineering, as well as ice sculpture and winter sports, such as ice skating, ice fishing, ice climbing, bobsleighs, and so on. Here, we report largescale molecular dynamics (MD) simulations of mechanical properties of bi and polycrystalline hexagonal ice (I<sub>h</sub>) under mechanical loads. Results show that bicrystals, upon tension, exhibit either brittle or ductile fracture, depending on the microstructure of grain boundaries (GBs), whereas they show ductile fracture by amorphization and crystallographic slips emitted from GBs under compression. Under shearing, the strength of bicrystals exhibits a characteristic plateau or sawtooth behavior drawn out the initial elastic strains. Nanograined polycrystals are destabilized by straininduced amorphization and collective GB sliding. Their mechanical responses depend on the grain size. Both tensile and compressive strengths decrease as grain size decreases, showing inverse HallPetch weakening behavior. Large fraction of amorphous water structure in polycrystals with small grain size is mainly responsible for the inverse HallPetch softening. Dislocation nucleation and propagation are also identified in nanograined ice, which is in good agreement with experimental measurements. Beyond the elastic strain, a combination of GB sliding, grain rotation, amorphization and recrystallization, phase transformation, and dislocation nucleation dominate the plastic deformation in both bicrystals and polycrystals.Polycrystalline material; Chemical processes; Ductility; Crystallization; Materials analysis; Geophysics; Molecular dynamics; Deformation; Phase transitions; Crystallographic defectsEngineering Thermodynamics)uuid:22805bfcaf7347d6abcd034108d95c4fDhttp://resolver.tudelft.nl/uuid:22805bfcaf7347d6abcd034108d95c4fESeismic noise attenuation using an online subspace tracking algorithmZhou, Yatong (Hebei University of Technology); Li, Shuhua (Hebei University of Technology); Zhang, D. (TU Delft ImPhys/Acoustical Wavefield Imaging); Chen, Yangkang (University of Texas at Austin)We propose a new lowrank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a lowrank space, the subspace tracking algorithm can be directly applied to the input lowrank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the stateoftheart algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVDbased singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several syn< thetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.6Image processing; Inverse theory; Timeseries analysis)uuid:2371e78990d842aa909d546406fae24aDhttp://resolver.tudelft.nl/uuid:2371e78990d842aa909d546406fae24a`Epidemic mitigation via awareness propagation in communication networks: The role of time scalesWang, H. (TU Delft Multimedia Computing); Chen, Chuyi (Extern); Qu, B. (TU Delft Multimedia Computing); Li, Daqing (Beihang University)]The participation of individuals in multilayer networks allows for feedback between network layers, opening new possibilities to mitigate epidemic spreading. For instance, the spread of a biological disease such as Ebola in a physical contact network may trigger the propagation of the information related to this disease in a communication network, e.g. an online social network. The information propagated in the communication network may increase the awareness of some individuals, resulting in them avoiding contact with their infected neighbors in the physical contact network, which might protect the population from the infection. In this work, we aim to understand how the time scale of the information propagation (speed that information is spread and forgotten) in the communication network relative to that of the epidemic spread (speed that an epidemic is spread and cured) in the physical contact network influences such mitigation using awareness information. We begin by proposing a model of the interaction between information propagation and epidemic spread, taking into account the relative time scale . We analytically derive the average fraction of infected nodes in the metastable state for this model (i) by developing an individualbased meanfield approximation (IBMFA) method and (ii) by extending the microscopic Markov chain approach (MMCA). We show that when the time scale of the information spread relative to the epidemic spread is large, our IBMFA approximation is better compared to MMCA near the epidemic threshold, whereas MMCA performs better when the prevalence of the epidemic is high. Furthermore, we find that an optimal mitigation exists that leads to a minimal fraction of infected nodes. The optimal mitigation is achieved at a nontrivial relative time scale , which depends on the rate at which an infected individual becomes aware. Contrary to our intuition, information spread too fast in the communication network could reduce the mitigation effect. Finally, our finding has been validated in the realworld twolayer network obtained from the locationbased social network Brightkite.`epidemic mitigation; epidemic spreading; interacting processes; multilayer networks; time scaleMultimedia Computing)uuid:9f05eba06f66438a9abc109dae23842aDhttp://resolver.tudelft.nl/uuid:9f05eba06f66438a9abc109dae23842aSmoothnessIncreasing AccuracyConserving Filters for Discontinuous Galerkin Methods: Challenging the Assumptions of Symmetry and UniformityLi, X.Vuik, C. (promotor)!In this dissertation, we focus on exploiting superconvergence for discontinuous Galerkin methods and constructing a superconvergence extraction technique, in particular, SmoothnessIncreasing AccuracyConserving (SIAC) filtering. The SIAC filtering technique is based on the superconvergence property of discontinuous Galerkin methods and aims to achieve a solution with higher accuracy order, reduced errors and improved smoothness. The main contributions described in this dissertation are: 1) an efficient onesided SIAC filter for both uniform and nonuniform meshes; 2) onesided derivative SIAC filters for nonuniform meshes; 3) the theoretical and computational foundation for using SIAC filters for nonuniform meshes; and 4) the application of SIAC filters for streamline integration. Onesided SIAC filtering is a technique that enhances the accuracy and smoothness of the DG solution near boundary regions< . Previously introduced onesided filters are not directly useful for most applications since they are limited to uniform meshes, linear equations, and the use of multiprecision packages in the computation. Also, the theoretical proofs relied on a periodic boundary assumption. We aim to overcome these deficiencies and develop a new fast onesided filter for both uniform and nonuniform meshes. By studying Bsplines and the negative order norm analysis, we generalized the structure of SIAC filters from a combination of central Bsplines to using more general Bsplines. Then, a "boundary shape" Bspline (using multiple knots at the boundary) was used to construct a new onesided filter. We also presented the first theoretical proof of convergence for SIAC filtering over nonuniform meshes (smoothlyvarying meshes). One purpose of SIAC filtering is to improve the smoothness of DG solutions. Because of the increased smoothness, we can obtain a better approximation for the derivatives of DG solutions. Derivative filtering over the interior region of uniform meshes was previously studied. However, nonuniform meshes and boundary regions remain a significant challenge. We extended the onesided filter to a onesided derivative filter. To deal with nonuniform meshes, we investigated the negative order norm over arbitrary meshes and proposed to scale the onesided derivative filter with scaling h. For arbitrary nonuniform rectangular meshes, we proved that the onesided derivative filter can enhance the order of convergence for the ?th derivative of the DG solution from k + 1  ? to (2k + 2), where ? 2/3. The most challenging part of this project is recovering the superconvergence of the DG solution over nonuniform meshes through SIAC filtering. Typically, most theoretical proofs for SIAC filters are limited to uniform meshes (or translation invariant meshes). The only theoretical investigations for nonuniform meshes were included in our onesided and derivative filtering studies. Although our earlier research for nonuniform meshes provides good engineering accuracy, we want to do better mathematically. This is not an easy task since unstructured meshes give DG solutions irregular performance under the negative order norm. In our work, we introduced a parameter to measure the unstructuredness of a given nonuniform mesh. Then, by adjusting the scaling of the SIAC filter based on this unstructuredness parameter, we can obtain the optimal filtered approximation (best accuracy) over a given nonuniform mesh. SIAC filtering for streamline integration is an attempt to use SIAC filters in a realistic engineering application. By using the onesided filter and onesided derivative filter, we designed an efficient algorithm: filtering the velocity field along the streamline and then use a backward differentiation formula for integration. Compared to the traditional method of filtering the entire field (multidimensional algorithm), the computational cost drops dramatically since its complexity corresponds to a onedimensional algorithm. We finally note that most of the work presented originates from published and submitted papers for the past four years of this PhD research.oDiscontinuous Galerkin method; postprocessing; superconvergence; nonuniform meshes; SIAC filtering; boundariesdoctoral thesis8Electrical Engineering, Mathematics and Computer ScienceApplied mathematics)uuid:910fbe01d6f64a5887a9b2420b76fe2bDhttp://resolver.tudelft.nl/uuid:910fbe01d6f64a5887a9b2420b76fe2b_Development of a framework for information acquisition and processing in cyberphysical systems5Li, Y.; Song, Y.; Horvath, I.; Opiyo, E.Z.; Zhang, G.In the designing and modeling of CPSs, the information acquisition and processing processes are often application dependent and process oriented. Those information management frameworks are simple and effective for small scale systems. However, many functions developed are not reusable or cannot be directly reused, when a large number of details and relations need to be added. Aiming at designing a flexible and scalable <x system with plugandplay components, a preliminary information acquisition and processing framework for CPSs is proposed in this paper based on the object oriented design (OOD) method. The concept of informational hierarchy within CPSs is identified first. Then it is further elaborated as instantaneous information, dynamic information and context information. Using these three types of information, together with the physical properties of a component in CPSs, the concept of hybrid object is proposed as the basic component of the proposed framework. By defining the inherent and update operation of hybrid objects, the proposed information acquisition and processing framework is formed with hierarchical hybrid objects. To verify the effectiveness and the efficiency of the proposed framework, a case study on designing and modeling a gas metal arc welding (GMAW) based rapid manufacturing system is presented. Limitations of the proposed framework and future research directions are discussed as well.~Information acquisition and processing framework; object oriented design; hybrid object; plugandplay; cyberphysical systemsconference paperTMCEIndustrial Design EngineeringDesign Engineering
*+&ffffff?'ffffff?(?)?"dXX333333?333333?U}}}}}}}}}} }
}}}
}}}}}}}}}}}}
@
!
"
#@
$
%
&
'
(
)
*@
+
,

.
/
0
1@
2
3
&
4
5
6
7@
8
9
:
;
<
=
>
?@
@
A
B
C
D
E
F
G
Hx@
I
J
K
L
M
N
!"#$%&'()*+,./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:a3b89c576b774d61aa3c91027510f202yKyKhttp://resolver.tudelft.nl/uuid:44a3f674f8e04414a92efe1dd7139f96yKyKhttp://resolver.tudelft.nl/uuid:feaedc8f708b4a18aeba7a9840f8e66ayKyKhttp://resolver.tudelft.nl/uuid:22805bfcaf7347d6abcd034108d95c4fyKyKhttp://resolver.tudelft.nl/uuid:2371e78990d842aa909d546406fae24ayKyKhttp://resolver.tudelft.nl/uuid:9f05eba06f66438a9abc109dae23842ayKyKhttp://resolver.tudelft.nl/uuid:910fbe01d6f64a5887a9b2420b76fe2bgg
Root Entry FTl_;Tl_;@SummaryInformation( F<Workbook F;DocumentSummaryInformation8 F
!"#$%&'()*+,./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvw