Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
10,893
result(s) for
"Multiscale"
Sort by:
Mastering the scales: a survey on the benefits of multiscale computing software
In the last few decades, multiscale modelling has emerged as one of the dominant modelling paradigms in many areas of science and engineering. Its rise to dominance is primarily driven by advancements in computing power and the need to model systems of increasing complexity. The multiscale modelling paradigm is now accompanied by a vibrant ecosystem of multiscale computing software (MCS) which promises to address many challenges in the development of multiscale applications. In this paper, we define the common steps in the multiscale application development process and investigate to what degree a set of 21 representative MCS tools enhance each development step. We observe several gaps in the features provided by MCS tools, especially for application deployment and the preparation and management of production runs. In addition, we find that many MCS tools are tailored to a particular multiscale computing pattern, even though they are otherwise application agnostic. We conclude that the gaps we identify are characteristic of a field that is still maturing and features that enhance the deployment and production steps of multiscale application development are desirable for the long-term success of MCS in its application fields. This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.
Journal Article
Multiscale space–time fluid–structure interaction techniques
We present the multiscale space–time techniques we have developed for fluid–structure interaction (FSI) computations. Some of these techniques are multiscale in the way the time integration is performed (i.e. temporally multiscale), some are multiscale in the way the spatial discretization is done (i.e. spatially multiscale), and some are in the context of the sequentially-coupled FSI (SCFSI) techniques developed by the Team for Advanced Flow Simulation and Modeling
. In the multiscale SCFSI technique, the FSI computational effort is reduced at the stage we do not need it and the accuracy of the fluid mechanics (or structural mechanics) computation is increased at the stage we need accurate, detailed flow (or structure) computation. As ways of increasing the computational accuracy when or where needed, and beyond just increasing the mesh refinement or decreasing the time-step size, we propose switching to more accurate versions of the Deforming-Spatial-Domain/Stabilized Space–Time (DSD/SST) formulation, using more polynomial power for the basis functions of the spatial discretization or time integration, and using an advanced turbulence model. Specifically, for more polynomial power in time integration, we propose to use NURBS, and as an advanced turbulence model to be used with the DSD/SST formulation, we introduce a space–time version of the residual-based variational multiscale method. We present a number of test computations showing the performance of the multiscale space–time techniques we are proposing. We also present a stability and accuracy analysis for the higher-accuracy versions of the DSD/SST formulation.
Journal Article
Guided filter-based multi-scale super-resolution reconstruction
The learning-based super-resolution reconstruction method inputs a low-resolution image into a network, and learns a non-linear mapping relationship between low-resolution and high-resolution through the network. In this study, the multi-scale super-resolution reconstruction network is used to fuse the effective features of different scale images, and the non-linear mapping between low resolution and high resolution is studied from coarse to fine to realise the end-to-end super-resolution reconstruction task. The loss of some features of the low-resolution image will negatively affect the quality of the reconstructed image. To solve the problem of incomplete image features in low-resolution, this study adopts the multi-scale super-resolution reconstruction method based on guided image filtering. The high-resolution image reconstructed by the multi-scale super-resolution network and the real high-resolution image are merged by the guide image filter to generate a new image, and the newly generated image is used for secondary training of the multi-scale super-resolution reconstruction network. The newly generated image effectively compensates for the details and texture information lost in the low-resolution image, thereby improving the effect of the super-resolution reconstructed image.Compared with the existing super-resolution reconstruction scheme, the accuracy and speed of super-resolution reconstruction are improved.
Journal Article
Time Series Analysis Using Composite Multiscale Entropy
Multiscale entropy (MSE) was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn) of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE) is introduced to overcome this difficulty. Simulation results on both white noise and 1/f noise show that the CMSE provides higher entropy reliablity than the MSE approach for large time scale factors. On real data analysis, both the MSE and CMSE are applied to extract features from fault bearing vibration signals. Experimental results demonstrate that the proposed CMSE-based feature extractor provides higher separability than the MSE-based feature extractor.
Journal Article
Multiscale computing for science and engineering in the era of exascale performance
In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a multiscale model and simulating it on available computing infrastructure, and argue that we could rely on it both on the conceptual modelling level and also when actually executing the multiscale simulation, and maybe should further develop generic frameworks and software tools to facilitate multiscale computing. Next, we focus on simulating multiscale models on high-end computing resources in the face of emerging exascale performance levels. We will argue that although applications could scale to exascale performance relying on weak scaling and maybe even on strong scaling, there are also clear arguments that such scaling may no longer apply for many applications on these emerging exascale machines and that we need to resort to what we would call multi-scaling . This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.
Journal Article
A poroelastic master curve for time-dependent and multiscale mechanics of hydrogels
Mechanical properties of hydrogels are of considerable interest for applications including tissue engineering and drug delivery. However, mechanical characterization of hydrogels is inherently challenging due to their multiphasic construction. Under mechanical loading, internal fluid redistribution affects the gel response, leading to a time- and length-scale-dependent material behavior, known as poroelasticity. Traditional mechanical tests are effective for determining instantaneous flow-independent gel response, and they are limited in characterizing poroelastic behavior as a function of loading time- and length-scales. Here, micro- and nanoindentation experiments are combined to characterize the full range of poroelastic behavior of a hydrogel. A master curve is presented to demonstrate that the relative competition of poroelastic relaxation time with ramp loading time determines gel response across different time- and length-scales. The master curve provides a novel mechanism to establish the instantaneous and equilibrium limits on the elastic modulus for a material, useful for designing hydrogel biomaterials.
Journal Article
mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale
Geographically weighted regression (GWR) is a spatial statistical technique that recognizes that traditional ‘global’ regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity by allowing effects to vary over space. To do this, GWR calibrates an ensemble of local linear models at any number of locations using ‘borrowed’ nearby data. This provides a surface of location-specific parameter estimates for each relationship in the model that is allowed to vary spatially, as well as a single bandwidth parameter that provides intuition about the geographic scale of the processes. A recent extension to this framework allows each relationship to vary according to a distinct spatial scale parameter, and is therefore known as multiscale (M)GWR. This paper introduces mgwr, a Python-based implementation of MGWR that explicitly focuses on the multiscale analysis of spatial heterogeneity. It provides novel functionality for inference and exploratory analysis of local spatial processes, new diagnostics unique to multi-scale local models, and drastic improvements to efficiency in estimation routines. We provide two case studies using mgwr, in addition to reviewing core concepts of local models. We present this in a literate programming style, providing an overview of the primary software functionality and demonstrations of suggested usage alongside the discussion of primary concepts and demonstration of the improvements made in mgwr.
Journal Article
Refined time-shift multiscale slope entropy: a new nonlinear dynamic analysis tool for rotating machinery fault feature extraction
Slope entropy (SlE) is an effective nonlinear dynamic analysis method, which has been used in mechanical fault diagnosis field. However, SlE only analyzes the time series on a single scale with much important information on other scales being ignored. Inspired by the multiscale analysis, the multiscale slope entropy (MSlE) is developed to extract the multiscale features of time series. Nevertheless, MSlE is susceptible to the loss of important information of original time series due to insufficient coarse-graining. In this paper, a novel algorithm termed refined time-shift multiscale slope entropy (RTSMSlE) is further proposed for enhancing the performance of MSlE. RTSMSlE changes the original coarse-grained computation and effectively improves the nonlinear analysis performance of MSlE, which has higher discriminating power and is less affected by mutant signals. After that, a novel fault diagnosis method for rotating machinery is proposed based on the RTSMSlE and DBO–SVM classifier. The effectiveness and superiority of the proposed fault diagnosis method is verified via the simulated signals and the measured data analysis with comparison to the MSlE, refined time-shift multiscale sample entropy (RTSMSE), refined time-shift multiscale fuzzy entropy (RTSMFE), refined composite multiscale sample entropy (RCMSE) and refined composite multiscale dispersion entropy (RCMDE). The analysis results show that the proposed method provides better diagnostic effect and more stable performance in analyzing the vibration signals of rotating machinery than the compared methods.
Journal Article