Fine Time Step Research Articles

FLORAH: a generative model for halo assembly histories

ABSTRACT The mass assembly history (MAH) of dark matter haloes plays a crucial role in shaping the formation and evolution of galaxies. MAHs are used extensively in semi-analytic and empirical models of galaxy formation, yet current analytic methods to generate them are inaccurate and unable to capture their relationship with the halo internal structure and large-scale environment. This paper introduces florah (FLOw-based Recurrent model for Assembly Histories), a machine-learning framework for generating assembly histories of ensembles of dark matter haloes. We train florah on the assembly histories from the Gadget at Ultra-high Redshift with Extra Fine Time-steps and vsmdplN-body simulations and demonstrate its ability to recover key properties such as the time evolution of mass and concentration. We obtain similar results for the galaxy stellar mass versus halo mass relation and its residuals when we run the Santa Cruz semi-analytic model on florah-generated assembly histories and halo formation histories extracted from an N-body simulation. We further show that florah also reproduces the dependence of clustering on properties other than mass (assembly bias), which is not captured by other analytic methods. By combining multiple networks trained on a suite of simulations with different redshift ranges and mass resolutions, we are able to construct accurate main progenitor branches with a wide dynamic mass range from $z=0$ up to an ultra-high redshift $z \approx 20$, currently far beyond that of a single N-body simulation. florah is the first step towards a machine learning-based framework for planting full merger trees; this will enable the exploration of different galaxy formation scenarios with great computational efficiency at unprecedented accuracy.

A high-order multi-time-step scheme for bond-based peridynamics

A high-order multi-time-step scheme (MTS) for the bond-based peridynamic (PD) model, an extension of classical continuous mechanics widely used for analyzing discontinuous problems like cracks, is proposed. The MTS scheme discretizes the spatial domain with a meshfree method and advances in time with a high-order Runge–Kutta method. To effectively handle discontinuities (cracks) that appear in a local subdomain in the solution, the scheme employs the Taylor expansion and Lagrange interpolation polynomials with a finer time step size, that is, coarse and fine time step sizes for smooth and discontinuous subdomains, respectively, to achieve accurate and efficient simulations. By eliminating unnecessary fine-scale resolution imposed on the entire domain, the MTS scheme outperforms the standard STS scheme for PD by significantly reducing computational costs, particularly for problems with discontinuous solutions, as demonstrated by comprehensive theoretical analysis and numerical experiments.

SkipDiff: Adaptive Skip Diffusion Model for High-Fidelity Perceptual Image Super-resolution

It is well-known that image quality assessment usually meets with the problem of perception-distortion (p-d) tradeoff. The existing deep image super-resolution (SR) methods either focus on high fidelity with pixel-level objectives or high perception with generative models. The emergence of diffusion model paves a fresh way for image restoration, which has the potential to offer a brand-new solution for p-d trade-off. We experimentally observed that the perceptual quality and distortion change in an opposite direction with the increase of sampling steps. In light of this property, we propose an adaptive skip diffusion model (SkipDiff), which aims to achieve high-fidelity perceptual image SR with fewer sampling steps. Specifically, it decouples the sampling procedure into coarse skip approximation and fine skip refinement stages. A coarse-grained skip diffusion is first performed as a high-fidelity prior to obtaining a latent approximation of the full diffusion. Then, a fine-grained skip diffusion is followed to further refine the latent sample for promoting perception, where the fine time steps are adaptively learned by deep reinforcement learning. Meanwhile, this approach also enables faster sampling of diffusion model through skipping the intermediate denoising process to shorten the effective steps of the computation. Extensive experimental results show that our SkipDiff achieves superior perceptual quality with plausible reconstruction accuracy and a faster sampling speed.

Model study of the earth’s ionosphere electric currents system at high latitudes

This paper presents a numerical study of the response of high- latitudinal Earth’s ionosphere electric currents system to different magne- tospheric inputs. The ionosphere states were modeled using the Upper At- mosphere Model (UAM). The magnetospheric inputs were specified using two models of field-aligned currents (FACs), namely, the classical Iijima & Potemra model (UAM-IP variant) vs. the MFACE model (UAM- MFACE variant). Model results showed that the UAM-IP variant did not allow specifying FACs independently at each hemisphere while the UAM- MFACE version did. The inclusion of the IMF dependence in the UAM extended the modeling facility. The UAM-MFACE version reproduced the high latitudinal ionosphere currents and the geometry of the auroral oval better than the UAM-IP version. At the same time, UAM-MFACE required an extended list of input parameters, a specific spatial grid, and finer time-steps.

Optimal artificial neural network configurations for hourly solar irradiation estimation

<span lang="EN-US">Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.</span>

Novel Girsanov correction based Milstein schemes for analysis of nonlinear multi-dimensional stochastic dynamical systems

This work proposes Girsanov corrected explicit, semi-implicit, and implicit Milstein approximations for the solution of nonlinear stochastic differential equations. The solution trajectories provided by the Milstein schemes are corrected by employing the change of measures, aimed at removing the error associated with the diffusion process incurred due to the transformation between two probability measures. The change of measures invoked in the Milstein schemes ensures that the solution from the mapping is measurable with respect to the filtration generated by the error process. The proposed scheme incorporates the error between the approximated mapping and the exact representation as an innovation that is accounted in the Milstein trajectories as an additive term. Numerical demonstrations using parametrically and non-parametrically excited stochastic oscillators, a practical problem involving ring type gyroscope, and a 7-degree-of-freedom nonlinear structural system subjected to both stationary and non-stationary excitation demonstrate the improvement in the solution accuracy for the proposed schemes with much coarser time steps when compared with the classical Milstein approximation with finer time steps.

Convergence analysis of single rate and multirate fixed stress split iterative coupling schemes in heterogeneous poroelastic media

AbstractRecently, the accurate modeling of flow‐structure interactions has gained more attention and importance for both petroleum and environmental engineering applications. Of particular interest is the coupling between subsurface flow and reservoir geomechanics. Different single rate and multirate iterative and explicit coupling schemes have been proposed and analyzed in the past. In addition, Banach fixed point contraction results were obtained for iterative coupling schemes, and conditionally stable results were obtained for explicit coupling schemes. In this work, we will consider the mathematical analysis of the single rate and multirate fixed stress split iterative coupling schemes for spatially heterogeneous poroelastic media. We will re‐establish the contractivity for both schemes in the localized case, and we will show that heterogeneities come at the expense of imposing more restricted conditions on the number of fine flow time steps that can be taken within one coarse mechanics time step in the multirate case. Our mathematical analysis is supplemented by numerical simulations validating our derived upper bounds. To the best of our knowledge, this is the first rigorous mathematical analysis of the multirate fixed‐stress split iterative coupling scheme in heterogeneous poroelastic media.

Rainfall variability assessment in the hydrological catchment of Addis Ababa

The Intensity-Duration-Frequency (IDF) relationship is a key input for defining the hydrograph and peak flow discharge required for the hydraulic design. The IDF curves describe the relationship between rainfall intensity, rainfall duration, and return period. The objective of this study was to develop an accurate rainfall analysis in the Addis Ababa catchment. To compute reliable IDF curves, long rainfall data at fine time step scale and uniformly distributed are necessary. In Ethiopia, there is limited availability of data at sub-hourly and at spatial scales. To cope with these issues, (1) stochastic disaggregation techniques have been employed to disaggregate daily observed data into sub-hourly data and (2) to guarantee homogeneity in the project area, the merge of satellite data and observed gauged data have been considered. In this study, IDF curves have been computed for the entire hydrological catchment of Addis Ababa (~1430 km2). The results of the study include tabular and graphical presentation of IDF curves under changed climatic conditions, for return periods of 2, 5, 10, 25, 50 and 100 years for the durations of 15,30 min and 1, 3, 6, 12 and 24 hours.

Assessing the impact of climate change on Combined Sewer Overflows based on small time step future rainfall timeseries and long-term continuous sewer network modelling

The evolution of the climate in the future will probably lead to an increase in extreme rainfall events, particularly in the Mediterranean regions. This change in rainfall patterns will have impacts on combined sewer systems operation with a possible increase of spilled flows, leading to an increase of untreated water volumes released to the receiving water. Due to the impact of overflows on the water cycle, local authorities managing combined sewer systems are wondering about the extent of these changes and the possibility of taking it into account in stormwater management structure design. To do this, rainfall data with a fine time step are required to better master the shape of the hyetographs that are crucial to get a relevant rainfall/runoff relationship in an urban environment. However, there are currently no simulations of future rainfall series available at a time step compatible with the needs in urban drainage field. In this work, future rainfall time series with a fine time step are elaborated with the aim to be used in urban hydrology. The proposed approach is based on simulations results from five regional climate models in the framework of the Euro-Cordex program. It consists in a spatial downscaling step followed by a temporal disaggregation. The rainfall time series obtained are then used as input for a calibrated and validated hydrological model to investigate the evolution of annual CSO volumes and frequencies by 2100. The results show an increase of annual spilled volumes between 13% and 52% according to the considered climate model. This increase will most likely be a problem regarding compliance of sewer networks in line with the water framework directive, particularly the current French regulations. No clear trends were observed on the CSO frequencies. If there is a consensus for all the carried-out simulations to conclude that the CSO volumes will increase, we must remember that actual regional climate models suffer from limited spatial and temporal resolution and don't explicitly solve convection processes. Due to this point uncertainty concerning the evolution rate remains important particularly for intense rainfall episodes. New generations of climate models are needed to accurately predict intense episodes.

Fully discrete approximation schemes for rate-independent crack evolution.

Over the past two decades, several distinct solution concepts for rate-independent evolutionary systems driven by non-convex energies have been suggested in an attempt to model properly jump discontinuities in time. Much attention has been paid in this context to the modelling of crack propagation. This paper studies two fully discrete (in time and space) approximation schemes for the rate-independent evolution of a single crack in a two-dimensional linear elastic material. The crack path is assumed to be known in advance, and the evolution of the crack tip along it relies on the Griffith theory. On the time-discrete level, the first scheme is based on local minimization, whereas the second scheme is a regularized version of the first one. The crucial feature of the schemes is their adaptive time-stepping nature, with finer time steps at those points where the evolution of the crack tip might develop a discontinuity. The set of discretization parameters includes the mesh size, crack increment, locality parameter and regularization parameter. In both cases, we explore the interplay between the discretization parameters and derive sufficient conditions on them ensuring the convergence of discrete interpolants to parametrized balanced viscosity solutions of the continuous model. To illustrate the performance of the approximation schemes, we support our theoretical analysis with numerical simulations. This article is part of the theme issue 'Non-smooth variational problems and applications'.

Demonstration of combined reduced order model and deep neural network for emulation of a time-dependent reactor transient

In this paper, a Reduced-Order Model (ROM) was constructed to approximate nuclear reactor neutronic transient behaviour. The ROM was developed by first using the Principal-Component Analysis (PCA) to reduce the output space of the response of interest, i.e, the time-dependent reactor power. A Deep-Neural Network (DNN) is then employed to build a surrogate model that maps the model inputs, i.e, the kinetic data, to the reduced-order output. While many previous applications of machine learning have focused on using algorithms to generate the response of singular Figures of Merit (such as peak clad or peak fuel temperatures over an entire transient) relatively few works have developed methods that can replicate the transient output of computer codes over the duration of the simulations. The difficulty in using machine learning to reconstruct the entire transient response is that, given the duration of the transient and the relatively fine time steps used, the output space can be very large thus making standard machine learning methods difficult to apply. In this work, PCA is first used to identify the key components of the output responses such that DNN can then be used to map the input parameters to the Principal component time-dependent behaviour. Then the neural network can reconstruct these components and emulate the output transient response based on the input parameters selected. Thus the methodology can provide time-dependent output results rather than single figures of merit. In this work, the LRA benchmark was used as a demonstration problem. The Detran time-dependent diffusion solver was used to generate the training and test data for a power control-rod ejection scenario. The results show that the combined ROM-DNN methodology is able to reconstruct the output power trajectory with high accuracy and with significantly reduced computational overhead.

An adaptive method for calculation of iron losses in switched reluctance motors using a minimum number of magnetostatic finite element simulations

PurposeThis paper aims to present an adaptive method based on finite element analysis to calculate iron losses in switched reluctance motors (SRMs). Calculation of iron losses by analytical formulas has limited accuracy. On the other hand, its estimation in rotating electrical machines through fully dynamic simulations with a fine time-step is time-consuming. However, in the initial design process, a quick and sufficiently accurate method, i.e. a value close to that of iron losses, is always welcome. The method presented in this paper is a semi-analytical approach. The main problem is that iron losses depend on d B/d t. Therefore, the accuracy of the calculation of iron losses depends on the accuracy of the calculation of the first derivative of the flux density waveform. When adopting a magnetostatic model to estimate the iron losses, an important question arises: by how many magnetostatic simulations can the iron losses be estimated within the desired accuracy? In the proposed algorithm, the aim is not to accurately calculate the value of iron losses in SRMs. The objective is to find a numerical error criterion to calculate iron losses in SRMs with a minimum number of magnetostatic simulations.Design/methodology/approachA finite element solver is developed by authors in MATLAB to solve the 2 D nonlinear magnetostatic problem using the Newton–Raphson method. A parametric program is developed to create geometry and mesh. The proposed method is implemented in MATLAB using the developed solver. Counterpart simulations are done in the ANSYS Maxwell software to validate the accuracy of the results generated by the developed solver.FindingsThe performance of the proposed method is studied on a 12/8 (500 W) SRM. Three scenarios are studied. The first one is the calculation of iron losses by uniform refinement, and the second one is by adaptive refinement, and the last one is by adaptive refinement started by particular initial points (switching points). According to the results, the proposed method substantially reduces the number of magnetostatic simulations without sacrificing accuracy.Originality/valueThe main novelty of this paper is introducing an error criterion to find the minimum number of magnetostatic simulations that are needed to calculate iron losses with the desired accuracy.

Robust Convergence of Parareal Algorithms with Arbitrarily High-Order Fine Propagators

The aim of this paper is to analyze the robust convergence of a class of parareal algorithms for solving parabolic problems. The coarse propagator is fixed to the backward Euler method and the fine propagator is a high-order single step integrator. Under some conditions on the fine propagator, we show that there exists some critical $J_*$ such that the parareal solver converges linearly with a convergence rate near $0.3$, provided that the ratio between the coarse time step and fine time step named $J$ satisfies $J \ge J_*$. The convergence is robust even if the problem data is nonsmooth and incompatible with boundary conditions. The qualified methods include all absolutely stable single step methods, whose stability function satisfies $|r(-\infty)|<1$, and hence the fine propagator could be arbitrarily high-order. Moreover, we examine some popular high-order single step methods, e.g., two-, three- and four-stage Lobatto IIIC methods, and verify that the corresponding parareal algorithms converge linearly with a factor $0.31$ and the threshold for these cases is $J_* = 2$. Intensive numerical examples are presented to support and complete our theoretical predictions.

Acoustic wave propagation with new spatial implicit and temporal high-order staggered-grid finite-difference schemes

AbstractThe implicit staggered-grid (SG) finite-difference (FD) method can obtain significant improvement in spatial accuracy for performing numerical simulations of wave equations. Normally, the second-order central grid FD formulas are used to approximate the temporal derivatives, and a relatively fine time step has to be used to reduce the temporal dispersion. To obtain high accuracy both in space and time, we propose a new spatial implicit and temporal high-order SG FD stencil in the time–space domain by incorporating some additional grid points to the conventional implicit FD one. Instead of attaining the implicit FD coefficients by approximating spatial derivatives only, we calculate the coefficients by approximating the temporal and spatial derivatives simultaneously through matching the dispersion formula of the seismic wave equation and compute the FD coefficients of our new stencil by two schemes. The first one is adopting a variable substitution-based Taylor-series expansion (TE) to derive the FD coefficients, which can attain (2M + 2)th-order spatial accuracy and (2N)th-order temporal accuracy. Note that the dispersion formula of our new stencil is non-linear with respect to the axial and off-axial FD coefficients, it is complicated to obtain the optimal spatial and temporal FD coefficients simultaneously. To tackle the issue, we further develop a linear optimisation strategy by minimising the L2-norm errors of the dispersion formula to further improve the accuracy. Dispersion analysis, stability analysis and modelling examples demonstrate the accuracy, stability and efficiency advantages of our two new schemes.

Implicit Space-Time Domain Decomposition Approach for Solving Multiphase Miscible Flow: Accuracy and Scalability

Summary In this paper, we propose a fully implicit space-time multiscale scheme to improve computational efficiency in solving nonlinear multiphase flow in porous media. Here, error estimators are used for adaptively changing the spatial-temporal mesh. This algorithm applied to the black-oil model is compared to a standard control volume approach using a fine time and spatial mesh. Error estimators are introduced to determine subdomains of the reservoir in which high nonlinearity hinders Newtonian convergence. This is followed by applying local fine timesteps to these marked regions, whereas the remaining regions retain the coarse time scale. Once a temporal discretization is determined for different parts of the reservoir, the spatial mesh is refined for treating saturation fronts. The nonmatching interfaces arising from different temporal and spatial scales are resolved by the enhanced velocity method, which enforces strongly the continuity of fluxes. This whole system is solved monolithically. Results from a three-phase black-oil model are described. The multiscale solution is compared to a uniformly fine spatial mesh and fine timestepping solution to confirm accuracy. Solutions from both Gaussian and channelized permeability fields are presented. In the multiscale solution, we observe temporal refinements being applied to the water and gas saturation fronts by reducing the timestep size to guarantee Newtonian convergence. We also observe that the extent of refinements is balanced between the two saturation fronts based on their respective nonlinearity. For Gaussian permeability fields, the algorithm only treats saturation fronts spatially, whereas for channelized permeability fields, the geological features are also considered. Production profiles for the three phases match well between the two solutions under specific refinement criteria. We also investigate the improvement in computational efficiency, as well as the algorithm scalability in regard to increasing problem sizes. We observe a speedup of approximately 10 for solving the linear system, and such speedup increases as the problem size expands. In reservoir simulation, schemes that attempt to decouple the diffusion and advection process become less robust as the physics becomes more complex. Our approach, which is focused on handling the high nonlinearities, can be treated fully implicitly and provide a reduction in simulation costs. This approach can also be applied to model order reduction in providing accurate snapshot solutions. NOTE: This paper is published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

The Advanced Multilevel Predictor-Corrector Quasi-Static Method for Pin-Resolved Neutron Kinetics Simulation

The Advanced Multilevel Predictor-Corrector Quasi-static Method (AML-PCQM) is proposed in this work. The four computational levels, including transport, Multi-Group (MG) Coarse Mesh Finite Difference (CMFD), One-Group (1G) CMFD, and Exact Point-Kinetics Equation (EPKE), are coupled with a new dynamic iteration strategy. In each coupling algorithm, the original Transient Fixed Source Problem (TFSP) is solved in the predictor process using coarse time step, and then the flux distribution is factorized to the functions of amplitude and shape in the next corrector process. Finally, multiple fine time steps are used to adjust the predicted solution. Two heterogeneous single assembly problems with the prompt control rod withdrawal event are used to verify the AML-PCQM scheme’s accuracy and efficiency. The numerical results obtained by different cases are compared and analyzed. The final results indicate that the AML-PCQM performs the remarkable advantages of efficiency and accuracy with the reference cases.

Fine-scale leaf chlorophyll distribution across a deciduous forest through two-step model inversion from Sentinel-2 data

Leaf chlorophyll content (LCC) is a key physiological trait and is crucial for monitoring plant health and accurately modeling the terrestrial carbon cycle. However, spatially-continuous information on LCC variability at fine time-steps, and at fine spatial resolutions across regional spatial extents, is sparse. In this study, we improved a physically-based, two-step inversion approach by using an advanced canopy-to-leaf reflectance conversion model to estimate LCC at fine spatial resolution (20 m) from Sentinel-2 Multi-Spectral Instrument (MSI) data. The first step is to convert MSI canopy reflectance to leaf reflectance using look-up tables constructed from a geometric optical model (4-Scale). The second step is to estimate LCC from the modeled leaf reflectance using the PROSPECT-5 leaf optical model. Both leaf reflectance and LCC derived from MSI were validated against field measurements at a mixed temperate forest site in Canada to examine the accuracy of leaf area index (LAI) and LCC retrievals. The results demonstrate robust canopy-level inversions with strong relationships between measured and MSI-derived leaf reflectance (R2 = 0.995, p < 0.001, RMSE = 0.0143). The modeled LCC results were also strong when compared to measured LCC samples: R2 = 0.849, p < 0.001, and RMSE = 0.304 μg/cm2, respectively. The most important Sentinel-2 MSI band for LAI and LCC derivation was centered at 705 nm (Band 5). Importantly, this two-step radiative transfer inversion approach substantially improved upon the current LAI and LCC algorithms adopted by the Sentinel-2 Application Platform, which underestimated LAI by 52.93% and overestimated LCC by 44.45%. This work highlights the potential of the physically-based two-step inversion method for deriving leaf and canopy traits from Sentinel-2 at very fine spatial and temporal resolutions, for a wide range of terrestrial ecological applications.

A discontinuity in the luminosity–mass relation and fluctuations in the evolutionary tracks of low-mass and low-metallicity stars at the Gaia M-dwarf gap

Context. The Gaia M-dwarf gap is a recently discovered feature in the colour-magnitude diagram that shows a deficiency of low-mass and low-metallicity stars at the lower end of the main sequence. Aims. We aim at performing theoretical stellar modelling at low metallicities using a fine mass step and a fine time step, looking specifically for the transition of models from partially to fully convective since the convective kissing instability that occurs at this transition is believed to be the cause of the gap. Methods. Stellar evolution models with metallicities of Z = 0.01, Z = 0.001, and Z = 0.0001 are performed using MESA, with a mass step of 0.00025 M⊙ and a time step of 50 000 years. Results. The small time step produced models that experience loops in their evolutionary tracks in the Hertzsprung-Russell (HR) diagram. The fluctuations in effective temperature and luminosity correspond to repeated events in which the bottom of the convective envelope merges with the top of the convective core, transporting 3He from the core to the surface. In addition to the episodes of switching from partially to fully convective, several near-merger events that produced low amplitude fluctuations were also found. Low-metallicity models undergo the convective kissing instability for longer portions of their lifetime and with higher fluctuation amplitudes than models with higher metallicities. The small mass step used in the models revealed a discontinuity in the luminosity–mass relation at all three metallicities. Conclusions. The repeated merging of the convective core and envelope, along with several near-merger events, removes an abundance of 3He from the core and temporarily reduces nuclear burning. This results in fluctuations in the model’s luminosity and effective temperature, causing loops in the evolutionary track in the HR diagram and leading to the deficiency of stars at the M-dwarf gap, as well as a discontinuity in the luminosity–mass relation.

A robust method to develop future rainfall IDF curves under climate change condition in two major basins of Iran

Current rainfall Intensity-Duration-Frequency (IDFs) curves generally were developed based on stationary extreme value assumption. However, climate change is expected to alter extreme rainfalls and invalidate the stationary assumption. So, it is crucial to develop future rainfall IDFs taking into account the impacts of climate change. Difficulty in preparing reliable rainfall time series with fine time step and capturing extremes for future climate scenarios has led to limited studies aimed at investigating the change of IDF curves due to climate change. In this paper, a robust stochastic rainfall model (NSRP) is employed to produce future rainfall IDFs for five stations across Karkheh and Karun basins in Iran. NSRP can generate long-term realistic rainfall series containing extremes. Moreover, it applies changes in different rainfall statistical characteristics, projected by GCMs, in the downscaling procedure. For each station, the model was calibrated using observed rainfalls series. Then, 3000-year daily rainfall series were generated, and historical IDFs were developed. Consequently, NSRP parameters were perturbed based on GCM projections. Then, 3000-year future rainfall series were generated, and future IDFs were developed. The climate change uncertainties were represented by employing two GCM (CanESM2 and HadGem2) and three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The results showed NSRP is able to reproduce observed extreme rainfalls in a wide range of time scales. Also, climate change will lead to a considerable increase in future extreme rainfall intensity in the study basins. As averages of all considered scenarios, rainfall intensities will increase between 22 and 206% in the future.

Benchmark for three-dimensional explicit asynchronous absorbing layers for ground wave propagation and wave barriers

This paper presents a benchmark for three-dimensional explicit asynchronous absorbing layers for modeling unbounded domains through a standard displacement-based finite element method: Three-dimensional Perfectly Matched Layers (PML) is compared to recently proposed Absorbing Layers with Increasing Damping (ALID) based on Rayleigh and Kosloff damping formulations.The coupled problem, including the interior subdomain and the absorbing layers, is implemented in the framework of Heterogeneous Asynchronous Time Integrator (HATI), enabling the absorbing layers to be handed with an explicit Central Difference scheme, with fine time steps, independently from the time stepping procedure adopted in the interior subdomain. Simple 3D Lamb’s tests are considered by using different kinds of absorbing layers. The superiority of the PML in terms of the accuracy and computation time is highlighted. Finally, realistic 3D applications are investigated, such as the Lamb’s test and the study of the screening effect provided by a horizontal wave barrier in mitigating the ground surface vibration generated by an excited plate.