Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering

We perform an ensemble of $N$-body simulations with $2048^3$ particles for 101 flat $w$CDM cosmological models sampled based on a maximin-distance Sliced Latin Hypercube Design. By using the halo catalogs extracted at multiple redshifts in the range of $z=[0,1.48]$, we develop Dark Emulator, which enables fast and accurate computations of the halo mass function, halo-matter cross-correlation, and halo auto-correlation as a function of halo masses, redshift, separations and cosmological models, based on the Principal Component Analysis and the Gaussian Process Regression for the large-dimensional input and output data vector. We assess the performance of the emulator using a validation set of $N$-body simulations that are not used in training the emulator. We show that, for typical halos hosting CMASS galaxies in the Sloan Digital Sky Survey, the emulator predicts the halo-matter cross correlation, relevant for galaxy-galaxy weak lensing, with an accuracy better than $2\%$ and the halo auto-correlation, relevant for galaxy clustering correlation, with an accuracy better than $4\%$. We give several demonstrations of the emulator. It can be used to study properties of halo mass density profiles such as the mass-concentration relation and splashback radius for different cosmologies. The emulator outputs can be combined with an analytical prescription of halo-galaxy connection such as the halo occupation distribution at the equation level, instead of using the mock catalogs, to make accurate predictions of galaxy clustering statistics such as the galaxy-galaxy weak lensing and the projected correlation function for any model within the $w$CDM cosmologies, in a few CPU seconds.Comment: 46 pages, 47 figures; version accepted for publication in Ap

Modeling and Simulation of Random Processes and Fields in Civil Engineering and Engineering Mechanics

This thesis covers several topics within computational modeling and simulation of problems arising in Civil Engineering and Applied Mechanics. There are two distinct parts. Part 1 covers work in modeling and analyzing heterogeneous materials using the eXtended Finite Element Method (XFEM) with arbitrarily shaped inclusions. A novel enrichment function, which can model arbitrarily shaped inclusions within the framework of XFEM, is proposed. The internal boundary of an arbitrarily shaped inclusion is first discretized, and a numerical enrichment function is constructed "on the fly" using spline interpolation. This thesis considers a piecewise cubic spline which is constructed from seven localized discrete boundary points. The enrichment function is then determined by solving numerically a nonlinear equation which determines the distance from any point to the spline curve. Parametric convergence studies are carried out to show the accuracy of this approach, compared to pointwise and linear segmentation of points, for the construction of the enrichment function in the case of simple inclusions and arbitrarily shaped inclusions in linear elasticity. Moreover, the viability of this approach is illustrated on a Neo-Hookean hyperelastic material with a hole undergoing large deformation. In this case, the enrichment is able to adapt to the deformation and effectively capture the correct response without remeshing. Part 2 then moves on to research work in simulation of random processes and fields. Novel algorithms for simulating random processes and fields such as earthquakes, wind fields, and properties of functionally graded materials are discussed. Specifically, a methodology is presented to determine the Evolutionary Spectrum (ES) for non-stationary processes from a prescribed or measured non-stationary Auto-Correlation Function (ACF). Previously, the existence of such an inversion was unknown, let alone possible to compute or estimate. The classic integral expression suggested by Priestley, providing the ACF from the ES, is not invertible in a unique way so that the ES could be determined from a given ACF. However, the benefits of an efficient inversion from ACF to ES are vast. Consider for example various problems involving simulation of non-stationary processes or non-homogeneous fields, including non-stationary seismic ground motions as well as non-homogeneous material properties such as those of functionally graded materials. In such cases, it is sometimes more convenient to estimate the ACF from measured data, rather than the ES. However, efficient simulation depends on knowing the ES. Even more important, simulation of non-Gaussian and non-stationary processes depends on this inversion, when following a spectral representation based approach. This work first examines the existence and uniqueness of such an inversion from the ACF to the ES under a set of special conditions and assumptions (since such an inversion is clearly not unique in the most general form). It then moves on to efficient methodologies of computing the inverse, including some established optimization techniques, as well as proposing a novel methodology. Its application within the framework of translation models for simulation of non-Gaussian, non-stationary processes is developed and discussed. Numerical examples are provided demonstrating the capabilities of the methodology. Additionally in Part 2, a methodology is presented for efficient and accurate simulation of wind velocities along long span structures at a virtually infinite number of points. Currently, the standard approach is to model wind velocities as a multivariate stochastic process, characterized by a Cross-Spectral Density Matrix (CSDM). In other words, the wind velocities are modeled as discrete components of a vector process. To simulate sample functions of the vector process, the Spectral Representation Method (SRM) is used. The SRM involves a Cholesky decomposition of the CSDM. However, it is a well known issue that as the length of the structure, and consequently the size of the vector process, increases, this Cholesky decomposition breaks down (from the numerical point of view). To avoid this issue, current research efforts in the literature center around approximate techniques to simplify the decomposition. Alternatively, this thesis proposes the use of the frequency-wavenumber (F-K) spectrum to model the wind velocities as a stochastic "wave," continuous in both space and time. This allows the wind velocities to be modeled at a virtually infinite number of points along the length of the structure. In this work, the relationship between the CSDM and the F-K spectrum is first examined, as well as simulation techniques for both. The F-K spectrum for wind velocities is then derived. Numerical examples are then carried out demonstrating that the simulated wave samples exhibit the desired spectral and coherence characteristics. The efficiency of this method, specifically through the use of the Fast Fourier Transform, is demonstrated

Reconstructing the Initial Density Field of the Local Universe: Method and Test with Mock Catalogs

Our research objective in this paper is to reconstruct an initial linear density field, which follows the multivariate Gaussian distribution with variances given by the linear power spectrum of the current CDM model and evolves through gravitational instability to the present-day density field in the local Universe. For this purpose, we develop a Hamiltonian Markov Chain Monte Carlo method to obtain the linear density field from a posterior probability function that consists of two components: a prior of a Gaussian density field with a given linear spectrum, and a likelihood term that is given by the current density field. The present-day density field can be reconstructed from galaxy groups using the method developed in Wang et al. (2009a). Using a realistic mock SDSS DR7, obtained by populating dark matter haloes in the Millennium simulation with galaxies, we show that our method can effectively and accurately recover both the amplitudes and phases of the initial, linear density field. To examine the accuracy of our method, we use $N$-body simulations to evolve these reconstructed initial conditions to the present day. The resimulated density field thus obtained accurately matches the original density field of the Millennium simulation in the density range 0.3 <= rho/rho_mean <= 20 without any significant bias. Especially, the Fourier phases of the resimulated density fields are tightly correlated with those of the original simulation down to a scale corresponding to a wavenumber of ~ 1 h/Mpc, much smaller than the translinear scale, which corresponds to a wavenumber of ~ 0.15 h\Mpc.Comment: 43 pages, 15 figures, accepted for publication in Ap

Bayesian non-linear large scale structure inference of the Sloan Digital Sky Survey data release 7

In this work we present the first non-linear, non-Gaussian full Bayesian large scale structure analysis of the cosmic density field conducted so far. The density inference is based on the Sloan Digital Sky Survey data release 7, which covers the northern galactic cap. We employ a novel Bayesian sampling algorithm, which enables us to explore the extremely high dimensional non-Gaussian, non-linear log-normal Poissonian posterior of the three dimensional density field conditional on the data. These techniques are efficiently implemented in the HADES computer algorithm and permit the precise recovery of poorly sampled objects and non-linear density fields. The non-linear density inference is performed on a 750 Mpc cube with roughly 3 Mpc grid-resolution, while accounting for systematic effects, introduced by survey geometry and selection function of the SDSS, and the correct treatment of a Poissonian shot noise contribution. Our high resolution results represent remarkably well the cosmic web structure of the cosmic density field. Filaments, voids and clusters are clearly visible. Further, we also conduct a dynamical web classification, and estimated the web type posterior distribution conditional on the SDSS data.Comment: 18 pages, 11 figure

Development of an Analytic Nodal Diffusion Solver in Multigroups for 3D Reactor Cores with Rectangular or Hexagonal Assemblies.

More accurate modelling of physical phenomena involved in present and future nuclear reactors requires a multi-scale and multi-physics approach. This challenge can be accomplished by the coupling of best-estimate core-physics, thermal-hydraulics and multi-physics solvers. In order to make viable that coupling, the current trends in reactor simulations are along the development of a new generation of tools based on user-friendly, modular, easily linkable, faster and more accurate codes to be integrated in common platforms. These premises are in the origin of the NURESIM Integrated Project within the 6th European Framework Program, which is envisaged to provide the initial step towards a Common European Standard Software Platform for nuclear reactors simulations. In the frame of this project and to reach the above-mentioned goals, a 3-D multigroup nodal solver for neutron diffusion calculations called ANDES (Analytic Nodal Diffusion Equation Solver) has been developed and tested in-depth in this Thesis. ANDES solves the steady-state and time-dependent neutron diffusion equation in threedimensions and any number of energy groups, utilizing the Analytic Coarse-Mesh Finite-Difference (ACMFD) scheme to yield the nodal coupling equations. It can be applied to both Cartesian and triangular-Z geometries, so that simulations of LWR as well as VVER, HTR and fast reactors can be performed. The solver has been implemented in a fully encapsulated way, enabling it as a module to be readily integrated in other codes and platforms. In fact, it can be used either as a stand-alone nodal code or as a solver to accelerate the convergence of whole core pin-by-pin code systems. Verification of performance has shown that ANDES is a code with high order definition for whole core realistic nodal simulations. In this paper, the methodology developed and involved in ANDES is presented

Spatiotemporal nonlinearity in resting-state fMRI of the human brain

In this work, the spatiotemporal nonlinearity in resting-state fMRI data of the human brain was detected by nonlinear dynamics methods. Nine human subjects during resting state were imaged using single-shot gradient echo planar imaging on a 1.5T scanner. Eigenvalue spectra for the covariance matrix, correlation dimensions and Spatiotemporal Lyapunov Exponents were calculated to detect the spatiotemporal nonlinearity in resting-state fMRI data. By simulating, adjusting, and comparing the eigenvalue spectra of pure correlated noise with the corresponding real fMRI data, the intrinsic dimensionality was estimated. The intrinsic dimensionality was used to extract the first few principal components from the real fMRI data using Principal Component Analysis, which will preserve the correct phase dynamics, while reducing both computational load and noise level of the data. Then the phase-space was reconstructed using the time-delay embedding method for their principal components and the correlation dimension was estimated by the Grassberger-Procaccia algorithm of multiple variable series. The Spatiotemporal Lyapunov Exponents were calculated by using the method based on coupled map lattices. Through nonlinearity testing, there are significant differences of correlation dimensions and Spatiotemporal Lyapunov Exponents between fMRI data and their surrogate data. The fractal dimension and the positive Spatiotemporal Lyapunov Exponents characterize the spatiotemporal nonlinear dynamics property of resting-state fMRI data. Therefore, the results suggest that fluctuations presented in resting state may be an inherent model of basal neural activation of human brain, cannot be fully attributed to noise

Application of Higher-Order Statistics and Subspace-Based Techniques to the Analysis and Diagnosis of Electrocardiogram Signals

The first and main contribution of this research work is the higher-order statistics (HOS)-based non-linear analysis and subsequent diagnosis of abnormal electrocardiogram (ECG) signals, particularly myocardial ischaemia. In the time domain; the second-, third-, and the fourth-order cumulants have been used in the analysis. In the frequency domain; up to the tenth-order polyspectra have been exploited. This HOS-based analysis of normal and ischaemic electrocardiogram signals has led to the identification of certain key discriminant features for the two physiological states of the heart. These features are then fed to different backpropagation-based multiple layer perceptrons for classification. The second contribution is a proposed new methodology to discriminate patients with angina pectoris or with old myocardial infarction (MI) during the first 60 seconds of stress test (or in some cases using rest ECG). It is based on the pseudo-spectral Multiple Signal Classification (MUSIC) and has the potential of being highly sensitive diagnostic signal processing tool. The third contribution is the development of a novel higher-order statistics, high-resolution estimator for quadratically coupled frequencies based on subspace spectral estimation. Extensive studies of cumulants, bispectra and bicoherence-squared of normal and ischaemic ECG signals collected from MIT and ST-T European databases has enabled us to see key discriminant features in both the third- and fourth-order cumulant domains. In the frequency domain, the polyspectral study has been extended to the lOth-order poly spectra. By calculating one-dimensional polyspectrum slices using an algorithm developed by Zhou and Giannakis (1995) a considerable reduction in the CPU time has been achieved. Furthermore, Zhou’s algorithm has been further extended to estimate the polycoherency slices which are used to characterise non-linearities in normal and ischaemic ECG signals. An important finding in this thesis is the decrease of the order of non-linearity representing the electrocardiogram signals of ischaemic patients. This thesis also includes the results of a pilot study involving eighteen healthy subjects (MIT database) and confirmed that the ECG signal is non-Gaussian, cyclostationary and quasi periodic. Combined spectral and bispectral analysis of the signal revealed that there are unique harmonic characteristics for the P-wave, QRS complex and T-wave and other frequencies due to harmonic interactions. In this work three linear and one non-linear adaptive filtering/predictions techniques have been applied to noisy ECG signals and their respective performances appraised. It is shown that the Kalman filter gives the best mean-square error MSE error but its comparatively long execution time and problems arising from ill-conditioning of the state-error covariance matrix render it of limited use in ECG applications. It is also shown that the LMS-based quadratic and cubic Volterra filters are the most superior for the ECG signal prediction. For ECG classifications; three multi-layer perceptrons employing back-propagation and modified back-propagation algorithms, and using two sets from the higher-order most discriminant features as their inputs, have yielded fairly high classification rates

Framework for Seismic Vulnerability Assessment of RC High-rise Wall Buildings

With population growth and urbanization, the number of high-rise buildings is rapidly growing worldwide resulting in increased exposure to multiple-scenario earthquakes and associated risks. The wide range in the frequency content of expected ground motions impacts the seismic response and vulnerability of this class of structures. While the seismic vulnerability of some high-rise building classes has been evaluated, the vulnerability of these structures under multiple earthquake scenarios is not fully understood, highlighting the pressing need for the development of a framework to address this complex issue. This study aims to establish a refined framework to assess the seismic vulnerability of RC high-rise wall buildings in multiple-scenario earthquake-prone regions. A deeper understanding of the responsive nature of these structures under different seismic scenarios is developed as a tool to build the framework. The framework is concluded with analytically-driven sets of Seismic Scenario-Structure-Based (SSSB) fragility relations. Different nonlinear modelling approaches, software, and key parameters contributing to the nonlinear analytical models of RC high-rise wall structures are investigated and verified against full-scale shake table tests through a multi-level nonlinear modelling verification scheme. The study reveals the superior performance of 4-noded fibre-based wall/shell element modelling approach in accounting for the 3D effects and deformation compatibility. A fundamental mode damping value in the range of 0.5% is found sufficient to capture the inelastic response when initial stiffness-based damping matrix is employed. A 30-storey reference wall building located in the multiple-scenario earthquake-prone city of Dubai (UAE) is fully designed and numerically modelled as a case study to illustrate the proposed framework. A total of 40 real earthquake records, representing severe distant and moderate near-field seismic scenarios, are used in the Multi-Record Incremental Dynamic Analyses (MRIDAs) along with a new scalar intensity measure. A methodology is proposed to obtain reliable SSSB definitions of limit state criteria for RC high-rise wall buildings. The local response of the reference building is mapped using Net Inter-Storey Drift (NISD) as a global damage measure. The study reveals that for this class of structures, higher modes shift the shear wall response from flexure-controlled under severe distant earthquakes to shear-controlled under moderate near-field events. A numerical parametric study employing seven RC high-rise wall buildings with varying height is conducted to investigate the effect of total height on the local damage-drift relation. The study reveals that, for buildings with varying heights and similar structural system, NISD is better linked to the building response and well correlated to structural member damage, which indicates that only one set of SSSB limit state criteria is necessary for a range of buildings. The study concludes with finalising the layout of the proposed refined framework to assess the seismic vulnerability of RC high-rise wall buildings under multiple earthquake scenarios. A methodology to develop refined fragility relations is presented where the derived fragility curves are analysed, compared, and correlated to varying states of damage. Finally, a methodology to develop Cheaper (simplified) Fragility Curves (CFC) using the defined limit state criteria with a lower number of records is proposed along with a new record selection criterion and fragility curve acceptance procedure. It is concluded that fairly reliable CFCs can be achieved with 5 to 6 earthquake records only