Modeling Of Large-scale Structures Research Articles

Constraints on UHECR Sources and Extragalactic Magnetic Fields from Directional Anisotropies

A dipole anisotropy in ultra–high-energy cosmic ray (UHECR) arrival directions, of extragalactic origin, is now firmly established at energies E > 8 EeV. Furthermore, the UHECR angular power spectrum shows no power at smaller angular scales than the dipole, apart from hints of possible individual hot or warm spots for energy thresholds ≳40 EeV. Here we exploit the magnitude of the dipole and the limits on smaller-scale anisotropies to place constraints on two quantities: the extragalactic magnetic field (EGMF) and the number density of UHECR sources or the volumetric event rate if UHECR sources are transient. We also vary the bias between the extragalactic matter and the UHECR source densities, reflecting whether UHECR sources are preferentially found in over- or underdense regions, and find that little or no bias is favored. We follow Ding et al. (2021) in using the CosmicFlows-2 density distribution of the local universe as our baseline distribution of UHECR sources, but we improve and extend that work by employing an accurate and self-consistent treatment of interactions and energy losses during propagation. Deflections in the Galactic magnetic field are treated using either the full JF12 magnetic field model, with both random and coherent components, or just the coherent part, to bracket the impact of the GMF on the dipole anisotropy. This large-scale structure model gives good agreement with both the direction and magnitude of the measured dipole anisotropy and forms the basis for simulations of discrete sources and the inclusion of EGMF effects.

Research on the automatic modeling and performance prediction of plain-woven composites based on beam elements

This work presents an approach for the automatic modeling and high-efficiency simulation of the mechanical properties of plain-woven composites. For the beam element finite element model, a technical scheme for the automatic modeling of plain-woven fabrics is presented. Considering the constraint relationship between the matrix and the fibers, a method for creating matrix beam elements is proposed. Then, a beam element solver was compiled to simulate the tensile properties of the plain-woven composites. In addition, a method of rendering a three-dimensional model of yarn and the simulation results were presented, and a comparison between real fabric images and modeled fabric structure confirmed that the method proposed in this work could effectively model plain-weave fabric structures. Experimental results showed that the deviations of the tensile modulus and strength were 0.41% and −7.59% respectively, which verified the validity of the beam element model used in this study. Moreover, testing verification of the method used in this study could realize the automatic modeling and simulation of representative cells of plain-woven composites in a few seconds. The above results show that this work offers the potential to handle the efficient calculations of large-scale woven structure models.

How to Shape Local Public Acceptance of Not-in-My-Backyard Infrastructures? A Social Cognitive Theory Perspective

Acceptance by the local public is the key determinant for the successful implementation of NIMBY (Not-In-My-Backyard) infrastructures and may be shaped in different ways among different infrastructure types. Based on social cognitive theory (SCT), this study clarifies the specific mechanism shaping local public acceptance of NIMBY facilities with two types of hazardous effects (i.e., pollution and psychological exclusion) using a large-scale questionnaire survey and structural equation model. The results reveal that, firstly, SCT provides a solid theoretical basis for exploring the mechanism under the joint action of environmental and personal factors. Secondly, it is verified that self-efficacy indirectly predicts local public acceptance by influencing perceived risk. The effect of the positive affect tag is mediated by perceived risk in shaping acceptance of polluting facilities but not of psychologically excluded facilities. In general, people tend to have a lower perceived risk, higher perceived benefit, stronger sense of self-efficacy, and more positive attitude when faced with the siting of psychologically excluded NIMBY facilities over polluting ones. These findings are helpful for planning and decision-making of NIMBY facilities with different types of hazardous impacts, reducing NIMBY conflicts and promoting the construction of NIMBY infrastructures. Furthermore, it contributes to the achievement of Sustainable Development Goal (SDG) 16 (promoting peaceful and inclusive societies for sustainable development) and (SDG) 11 (building inclusive, safe, resilient and sustainable cities and human settlements).

Analysis of unified galaxy power spectrum multipole measurements

ABSTRACTWe present a series of full-shape analyses of galaxy power spectrum multipole measurements from the 6dFGS, BOSS, and eBOSS galaxy surveys. We use an emulated effective field theory of large-scale structure (EFTofLSS) model to conduct these analyses. We exploit the accelerated prediction speed of the neural-network-based emulator to explore various analysis setups for our cosmological inference pipeline. Via a set of mock full-shape analyses of synthetic power spectrum multipoles, designed to approximate measurements from the surveys above, we demonstrate that the use of alternative priors on nuisance parameters and restricted model complexity reduces many of the biases previously observed in marginalized cosmological constraints coming from EFTofLSS analyses. The alternative priors take the form of a Jeffreys prior; a non-informative prior that can mitigate against biases induced by marginalizing over poorly constrained nuisance parameters. When performing a joint analysis of all synthetic multipoles, we see an improvement in the level of agreement between the marginalized ln (1010As) constraints and the truth; from ∼2.0σ to ∼0.42σ. Using our pipeline to analyse the measured multipoles, we find an improvement in the level of agreement with cosmic microwave background (CMB) results; from ∼2.4σ to ∼0.5σ. Therefore, we conclude that the spectroscopic galaxy survey data sets listed above are consistent with constraints obtained from the CMB.

Model Updating of Nonproportionally Damped Structural Systems Using an Adapted Complex Sum-of-Squares Optimization Algorithm

Various model updating approaches relying on solving nonconvex optimization problems have been developed; however, few of them can reach the global optimum. To obtain the global optimum, the real sum-of-squares optimization algorithm (SOSOA) was proposed to minimize the modal dynamic residuals of proportionally damped structural systems by recasting the nonconvex optimization into a convex optimization. However, this algorithm becomes inefficient for model updating of nonproportionally damped systems with complex-valued eigenvalues and eigvenvectors. This study proposes a model updating approach that exploits and adapts a complex SOSOA to minimize the modal dynamic residuals of nonproportionally damped systems. Numerical considerations unique to using the adapted complex SOSOA to update medium- or large-scale structural models are discussed. Finite-element models of three nonproportionally damped structures—including a base-isolated shear building, a frame building implemented with a tuned mass damper, and a frame building implemented with buckling-restrained braces—are updated to demonstrate that the adapted complex SOSOA finds the global optimum while being orders of magnitude more computationally efficient and far less demanding of computer memory than the real SOSOA.

Improving initialization and evolution accuracy of cosmological neutrino simulations

Neutrino mass constraints are a primary focus of current and future large-scale structure (LSS) surveys. Non-linear LSS models rely heavily on cosmological simulations — the impact of massive neutrinos should therefore be included in these simulations in a realistic, computationally tractable, and controlled manner. A recent proposal to reduce the related computational cost employs a symmetric neutrino momentum sampling strategy in the initial conditions. We implement a modified version of this strategy into the Hardware/Hybrid Accelerated Cosmology Code (HACC) and perform convergence tests on its internal parameters. We illustrate that this method can impart \U0001d4aa(1%) numerical artifacts on the total matter field on small scales, similar to previous findings, and present a method to remove these artifacts using Fourier-space filtering of the neutrino density field. Moreover, we show that the converged neutrino power spectrum does not follow linear theory predictions on relatively large scales at early times at the 15% level, prompting a more careful study of systematics in particle-based neutrino simulations. We also present an improved method for backscaling linear transfer functions for initial conditions in massive neutrino cosmologies that is based on achieving the same relative neutrino growth as computed with Boltzmann solvers. Our self-consistent backscaling method yields sub-percent accuracy in the total matter growth function. Comparisons for the non-linear power spectrum with the Mira-Titan emulator at a neutrino mass of mν = 0.15 eV are in very good agreement with the expected level of errors in the emulator and in the direct N-body simulation.

Outskirts of dark matter haloes

Halo models of large scale structure provide powerful and indispensable tools for phenomenological understanding of the clustering of matter in the Universe. While the halo model builds structures out of the superposition of haloes, defining halo profiles in their outskirts---beyond their virial radii---becomes increasingly ambiguous, as one cannot assign matter to individual haloes in a clear way. In this paper, we address this issue by finding a systematic definition of mean halo profile that can be extended to large distances---beyond the virial radius of the halo---and matched to simulation results. These halo profiles are compensated and are the key ingredients for the computation of cosmological correlation functions in an amended halo model. The latter, introduced in our earlier work [A. Y. Chen and N. Afshordi, Phys. Rev. D 101, 103522 (2020)], provides a more physically accurate phenomenological description of nonlinear structure formation, which respects conservation laws on large scales. Here, we show that this model can be extended from the matter auto-power spectrum to the halo-matter cross-power spectra by using data from $N$-body simulations. Furthermore, we find that this (dimensionless) definition of the compensated halo profile, ${r}^{3}\ifmmode\times\else\texttimes\fi{}\ensuremath{\rho}(r)/{M}_{200c}$, has a near-universal maximum in the small range of 0.03--0.04 around the virial radius, $r\ensuremath{\simeq}{r}_{200\mathrm{c}}$, nearly independent of the halo mass. The profiles cross zero into negative values in the halo outskirts---beyond $2--3\ifmmode\times\else\texttimes\fi{}{r}_{200\mathrm{c}}$---consistent with our previous results. We provide a preliminary fitting function for the compensated halo profiles (extensions of Navarro-Frenk-White profiles), which can be used to compute more physical observables in large scale structure.

Computationally Efficient Learning of Gaussian Linear Structural Equation Models with Equal Error Variances

This study develops a computationally efficient and statistically consistent learning algorithm for large-scale Gaussian linear structural equation models with equal error variances. The novelty of the proposed algorithm is that it applies a hypothesis test rather than a score when recovering the directions of the edges. Specifically, the proposed algorithm estimates the topological layers using a Z-test. Subsequently, it estimates the parents of each node using a consistent conditional independence test. This study proves that the proposed topological layer estimation approach is asymptotically consistent. It further shows that the proposed method is computationally more efficient than existing score-based algorithms for Gaussian linear SEMs by comparing the computational complexities. It is confirmed through synthetic and real-world data that the proposed algorithm is consistent and computationally much faster than the state-of-the-art large-scale Gaussian linear SEM learning algorithms, such as the USF, USB, TD, and HGSM algorithms. Supplementary materials for this article are available online.

Co-rotational Reduced Order Kalman Filter Finite Element Method for the Real-time Implementation of Large-scale Structural Models

The real-time implementation of large-scale structural models without sacrificing their physical realism is challenging. This paper introduces a conceptual framework named the Co-rotational Reduced Order Kalman Filter Finite Element Method (CROKF-FEM) for the realistic linear elastic analysis of large-scale structures in real-time. The standard linear finite element method (FEM) is employed for high-precision deformation modeling, and element matrices are pre-computed in the space domain. To avoid the inflated elements of standard linear FEM due to large rotation, a co-rotational formulation is devised. Then, the system is discretized in the time domain using the Wilson- θ implicit integration scheme and a further state-space model is developed. Subsequently, a reduced-order model is constructed using the balanced truncation method. Finally, a Kalman filter algorithm is created on the reduced model for online estimation. This approach preserves all the benefits of traditional linear FEM with a similar deformation accuracy while achieving significant real-time performance.

The influence of sedimentary heterogeneity on the diffusion of radionuclides in the sandy facies of Opalinus Clay at the field scale

Abstract. Radionuclide migration in clay-rich formations is typically dominated by diffusion considering the low permeability of these formations. An accurate estimation of radionuclide migration in host rocks using numerical tools plays a key role in the safety assessment of disposal concepts for nuclear waste. In the sandy facies of the Opalinus Clay (SF-OPA), the spatial variability of the pore space network and compositional heterogeneity at the pore scale (nm to µm) cause heterogeneous diffusion at the core scale (cm to dm). Such heterogeneous diffusion patterns affect the migration of radionuclides in various sedimentary layers even above the core scale (m). In this work, we study the heterogeneous diffusion of cations based on a two-dimensional (2D) structural model at the m-scale. As key parameters for the diffusive transport calculation, the effective diffusion coefficients in different sedimentary layers are quantified based on our previous developed up-scaling workflow from pore- to core-scale simulation combined with the multi-scale digital rock models. The heterogeneous effective diffusivities are then implemented into the large-scale structural model for diffusive transport simulation using the FEM-based OpenGeoSys-6 simulator. The sensitivity analysis focuses on the effects of the SF-OPA bedding angle and the effect of different layer-succession layout with different canister emplacement on the spatio-temporal evolution of radionuclide diffusion front line. Results show that the moving distance of the diffusion front is farther away from the canister center, along the direction with the neighboring layer having lower diffusion coefficient within the total simulation time of 2000 years. When the bedding angle increases, the diffusion front moves farther in in vertical upward direction direction, which has less retardation effect for the radionuclide from the ground surface point. For different layer-succession layout with different canister emplacement, the smallest migration distance of the diffusion front line is 1.65 m. Within 2000 years, for the conceptual model 2B that the canister is emplaced in the layer with the highest diffusivity coefficient, the diffusion front can migrate 0.19 m farther along vertical downward direction due to the influence of the neighboring layer. The numerical results provide insight into the effects of rocks heterogeneity on diffusion of radionuclides, contributing to enhanced long-term predictability of radionuclide migration in SF-OPA as potential host rock for a deep geological repository.

Phase field modeling of brittle fracture in large-deformation solid shells with the efficient quasi-Newton solution and global–local approach

To efficiently predict the crack propagation in thin-walled structures, a global–local approach for phase field modeling using large-deformation solid shell finite elements considering the enhanced assumed strain (EAS) and the assumed natural strain (ANS) methods for the alleviation of locking effects is developed in this work. Aiming at tackling the poor convergence performance of standard Newton schemes, a quasi-Newton (QN) scheme is proposed for the solution of coupled governing equations stemming from the enhanced assumed strain shell formulation in a monolithic manner. The excellent convergence performance of this QN monolithic scheme for the multi-field shell formulation is demonstrated through several paradigmatic boundary value problems, including single edge notched tension and shear, fracture of cylindrical structure under mixed loading and fatigue induced crack growth. Compared with the popular alternating minimization (AM) or staggered solution scheme, it is also found that the QN monolithic solution scheme for the phase field modeling using enhanced strain shell formulation is very efficient without the loss of robustness, and significant computational gains are observed in all the numerical examples. In addition, to further reduce the computational cost in fracture modeling of large-scale thin-walled structures, a specific global–local phase field approach for solid shell elements in the 3D setting is proposed, in which the full displacement-phase field problem is considered at the local level, while addressing only the elastic problem at the global level. Its capability is demonstrated by the modeling of a cylindrical structure subjected to both static and fatigue cyclic loading conditions, which can be appealing to industrial applications.

Translational surface coupling along a line with non-conforming meshes

In the modelling of large-scale metal structures, comprising plated components intersecting along weld lines, the meshing efficiency and flexibility are limited by the requirements of nodal alignment and compliance of element size, shape and edge orientation throughout the domain. Such limitations necessitate the use of complex transitional meshes in intersection regions and result in highly complex global mesh configurations. This paper presents an original and systematic methodology for surface coupling along an arbitrary 1D interface, which is applicable to any type of 2D and 3D FEs, and which provides a systematic framework for: (i) geometric modelling of weld lines; (ii) coupling of regions with different levels of discretisation detail or element types within a system; and (iii) domain partitioning problems involving computationally heterogeneous partitions. The strategy is based upon a novel coupling element formulation, which uses the fundamental principles of the mortar method and an augmented Lagrangian Multiplier optimisation approach. Particular consideration is given to an element formulation that enforces rigid translational coupling, which has been implemented for employment with co-rotational Reissner-Mindlin shell elements. Various numerical examples are presented to demonstrate the accuracy, versatility and substantial computational benefits of the developed methodology for modelling large-scale metal structural systems.

Characterization of hydrodynamic properties from free vibration tests of a large-scale bridge model

To accurately predict the dynamic response of a structure subjected to fluid induced loading, a thorough understanding of the dynamic properties (mass, stiffness, and damping) and associated interactions is required. Limited data are available to characterize dynamic fluid–structure interactions. Data are particularly limited for large-scale and flexible structural models. This article reports the results of the first free vibration tests of a dynamic large-scale laboratory highway bridge superstructure model. The dynamic response characteristics of the model were extracted and analyzed from free vibration tests under varying levels of water submersion and for different horizontal substructure flexibilities. The nature of the damping response was identified based on the empirically measured logarithmic decrements of the model’s free vibration displacement amplitudes, and a suitable equation of motion (EOM) was subsequently developed. Using the classical fourth-order Runge–Kutta method, the EOM was solved for the different test trials and the dynamic properties of the model were obtained through an optimization approach. The concept of added mass was introduced to explain the observed decrease in natural frequency with increasing levels of water submersion. Finally, added mass factor was computed for the case where the water level was even with the top of the bridge superstructure model. This study provides a suitable EOM needed for numerical simulations of this and similar models that study fluid–structure interaction and also provides a methodology for establishing the structural dynamic properties of generalized hydrodynamic analytical models.

3D Modeling of Large-Scale Geological Structures by Linear Combinations of Implicit Functions: Application to a Large Banded Iron Formation

3D Modeling of Large-Scale Geological Structures by Linear Combinations of Implicit Functions: Application to a Large Banded Iron Formation

A model of large-scale thematic structure

The coherent organisation of thematic material into large-scale structures within a composition is an important concept in both traditional and cognitive theories of music. However, empirical evidence supporting their perception is scarce. Providing a more nuanced approach, this paper introduces a computational model of hypothesised cognitive mechanisms underlying perception of large-scale thematic structure. Repetition detection based on statistical learning forms the model's foundation, hypothesising that predictability arising from repetition creates perceived thematic coherence. Measures are produced that characterise structural properties of a corpus of 623 monophonic compositions. Exploratory analysis reveals the extent to which these measures vary systematically and independently.

A redefinition of the halo boundary leads to a simple yet accurate halo model of large-scale structure

ABSTRACT We present a model for the halo–mass correlation function that explicitly incorporates halo exclusion and allows for a redefinition of the halo boundary in a flexible way. We assume that haloes trace mass in a way that can be described using a single scale-independent bias parameter. However, our model exhibits scale-dependent biasing due to the impact of halo-exclusion, the use of a ‘soft’ (i.e. not infinitely sharp) halo boundary, and differences in the one halo term contributions to ξhm and ξmm. These features naturally lead us to a redefinition of the halo boundary that lies at the ‘by eye’ transition radius from the one-halo to the two-halo term in the halo–mass correlation function. When adopting our proposed definition, our model succeeds in describing the halo–mass correlation function with $\approx 2{{\ \rm per\ cent}}$ residuals over the radial range 0.1 h−1 Mpc < r < 80 h−1 Mpc, and for halo masses in the range 1013 h−1 M⊙ < M < 1015 h−1 M⊙. Our proposed halo boundary is related to the splashback radius by a roughly constant multiplicative factor. Taking the 87 percentile as reference we find rt/Rsp ≈ 1.3. Surprisingly, our proposed definition results in halo abundances that are well described by the Press–Schechter mass function with δsc = 1.449 ± 0.004. The clustering bias parameter is offset from the standard background-split prediction by $\approx 10{{\ \rm per\ cent}}\!-\!15{{\ \rm per\ cent}}$. This level of agreement is comparable to that achieved with more standard halo definitions.

Mapping tree diversity in the tropical forest region of Chocó-Colombia

Understanding spatial patterns of diversity in tropical forests is indispensable for their sustainable use and conservation. Recent studies have reported relationships between forest structure and α-diversity. While tree α-diversity is difficult to map via remote sensing, large-scale forest structure models are becoming more common, which would facilitate mapping the relationship between tree α-diversity and forest structure, contributing to our understanding of biogeographic patterns in the tropics. We developed a methodology to map tree α-diversity in tropical forest regions at 50 m spatial resolution using α-diversity estimates from forest inventories as response variables and forest structural metrics and environmental variables as predictors. To include forest structural metrics in our modelling, we first developed a method to map seven of these metrics integrating discrete light detection and ranging (LiDAR), multispectral, and synthetic aperture radar imagery (SAR). We evaluated this methodology in the Chocó region of Colombia, a tropical forest with high tree diversity and complex forest structure. The relative errors (REs) of the random forest models used to map the seven forest structural variables ranged from low (6%) to moderate (35%). The α-diversity maps had moderate RE; the maps of Simpson and Shannon diversity indices had the lowest RE (9% and 13%), followed by richness (17%), while Shannon and Simpson effective number of species indices had the highest RE, 27% and 47%, respectively. The highest concentrations of tree α-diversity are located along the Pacific Coast from the centre to the northwest of the Chocó Region and in non-flooded forest along the boundary between the Chocó region and the Andes. Our results reveal strong relationships between canopy structure and tree α-diversity, providing support for ecological theories that link structure to diversity via niche partitioning and environmental conditions. With modification, our methods could be applied to assess tree α-diversity of any tropical forest where tree α-diversity field observations coincident with LiDAR data.

An n-th order Lagrangian forward model for large-scale structure

A forward model of matter and biased tracers at arbitrary order in Lagrangian perturbation theory (LPT) is presented. The forward model contains the complete LPT displacement field at any given order in perturbations, as well as all relevant bias operators at that order and leading order in derivatives. The construction is done for any expansion history and does not rely on the Einstein-de Sitter approximation. A large subset of higher-derivative bias operators is also included. As validation test, we compare the nLPT-predicted matter density field and that from N-body simulations using the same initial conditions. For simulations using a cutoff in the initial conditions, we find subpercent agreement up to scales of k ∼ 0.2 h -1 Mpc. We also find subpercent agreement with full simulations without cutoff, both for the power spectrum and nonlinear σ8-inference, when allowing for the effective sound speed. The application to biased tracers (halos) has already been presented in a recent paper [1].

Sensitivity estimation of first excursion probabilities of linear structures subject to stochastic Gaussian loading

This contribution focuses on evaluating the sensitivity associated with first excursion probabilities of linear structural systems subject to stochastic Gaussian loading. The sensitivity measure considered is the partial derivative of the probability with respect to parameters that affect the structural response, such as dimensions of structural elements. The actual calculation of the sensitivity demands solving high dimensional integrals over hypersurfaces, which can be challenging from a numerical viewpoint. Hence, sensitivity evaluation is cast within the context of a reliability analysis that is conducted with Directional Importance Sampling. In this way, the sought sensitivity is obtained as a byproduct of the calculation of the failure probability, where the post-processing step demands performing a sensitivity analysis of the unit impulse response functions of the structure. Thus, the sensitivity is calculated using sampling by means of an estimator, whose precision can be quantified in terms of its standard deviation. Numerical examples involving both small- and large-scale structural models illustrate the procedure for probability sensitivity estimation.

Nonlinear 3D cosmic web simulation with heavy-tailed generative adversarial networks

Fast and accurate simulations of the non-linear evolution of the cosmic density field are a major component of many cosmological analyses, but the computational time and storage required to run them can be exceedingly large. For this reason, we use generative adversarial networks (GANs) to learn a compressed representation of the 3D matter density field that is fast and easy to sample, and for the first time show that GANs are capable of generating samples at the level of accuracy of other conventional methods. Using sub-volumes from a suite of GADGET-2 N-body simulations, we demonstrate that a deep-convolutional GAN can generate samples that capture both large- and small-scale features of the matter density field, as validated through a variety of n-point statistics. The use of a data scaling that preserves high-density features and a heavy-tailed latent space prior allow us to obtain state of the art results for fast 3D cosmic web generation. In particular, the mean power spectra from generated samples agree to within 5% up to k=3 and within 10% for k<5 when compared with N-body simulations, and similar accuracy is obtained for a variety of bispectra. By modeling the latent space with a heavy-tailed prior rather than a standard Gaussian, we better capture sample variance in the high-density voxel PDF and reduce errors in power spectrum and bispectrum covariance on all scales. Furthermore, we show that a conditional GAN can smoothly interpolate between samples conditioned on redshift. Deep generative models, such as the ones described in this work, provide great promise as fast, low-memory, high-fidelity forward models of large-scale structure.