Diffeomorphism-invariant properties for quasi-linear elliptic operators

For quasi-linear elliptic equations we detect relevant properties which remain invariant under the action of a suitable class of diffeomorphisms. This yields a connection between existence theories for equations with degenerate and non-degenerate coerciveness.Comment: 16 page

From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media

The modeling of flow and transport in porous media is of the utmost importance in many chemical engineering applications, including catalytic reactors, batteries, and CO2 storage. The aim of this study is to test the use of fully connected (FCNN) and convolutional neural networks (CNN) for the prediction of crucial properties in porous media systems: The permeability and the filtration rate. The data-driven models are trained on a dataset of computational fluid dynamics (CFD) simulations. To this end, the porous media geometries are created in silico by a discrete element method, and a rigorous setup of the CFD simulations is presented. The models trained have as input both geometrical and operating conditions features so that they could find application in multiscale modeling, optimization problems, and in-line control. The average error on the prediction of the permeability is lower than 2.5%, and that on the prediction of the filtration rate is lower than 5% in all the neural networks models. These results are achieved with at least a dataset of ~ 100 CFD simulations

CFD-PBE modelling of continuous Ni-Mn-Co hydroxide co-precipitation for Li-ion batteries

A modelling framework is proposed to simulate the co-precipitation of Ni-Mn-Co hydroxide as precursor of cathode material for lithium-ion batteries. It integrates a population balance equation with computational fluid dynamics to describe the evolution of the particle size in (particularly continuous) co-precipitation processes. The population balance equation is solved by employing the quadrature method of moments. In addition, a multi-environment micromixing model is employed to consider the potential effect of molecular mixing on the fast co-precipitation reaction. The modelling framework is used to investigate the co-precipitation of Ni0.8Mn0.1Co0.1(OH)2 in a multi-inlet vortex micromixer, as a suitable candidate for the study of fast co-precipitation processes in continuous mode. Finally, the simulation results are discussed, and the role of the different phenomena involved in the formation and evolution of particles is identified by inspecting the predicted trends

CFD-PBM Simulation of Nickel-Manganese-Cobalt Hydroxide Co-precipitation in CSTR

The co-precipitation of Ni 0.8 Mn 0.1 Co 0.1 (OH) 2 in a pilot-scale CSTR is simulated by adopting the CFD-PBM approach combined with the operator-splitting method. It is shown that the excessive total computational time can affect the applicability of the approach, hence necessity of using massive parallel calculations. However, the effectiveness of the parallel calculation is limited unless an algorithm is implemented to balance the load of the source integration across computing processors

The German Shorthair Pointer Dog Breed (Canis lupus familiaris) : Genomic Inbreeding and Variability

The German Shorthaired Pointer (GSHP) is a breed worldwide known for its hunting versatility. Dogs of this breed are appreciated as valuable companions, effective trackers, field trailers and obedience athletes. The aim of the present work is to describe the genomic architecture of the GSHP breed and to analyze inbreeding levels under a genomic and a genealogic perspective. A total of 34 samples were collected (24 Italian, 10 USA), and the genomic and pedigree coefficients of inbreeding have been calculated. A total of 3183 runs of homozygosity (ROH) across all 34 dogs have been identified. The minimum and maximum number of Single Nucleotide Polymorphisms (SNPs) defining all ROH are 40 and 3060. The mean number of ROH for the sample was 93.6. ROH were found on all chromosomes. A total of 854 SNPs (TOP_SNPs) defined 11 ROH island regions (TOP_ROH), in which some gene already associated with behavioral and morphological canine traits was annotated. The proportion of averaged observed homozygotes estimated on total number of SNPs was 0.70. The genomic inbreeding coefficient based on ROH was 0.17. The mean inbreeding based on genealogical information resulted 0.023. The results describe a low inbred population with quite a good level of genetic variability

Efficiency and accuracy of GPU-parallelized Fourier spectral methods for solving phase-field models

Phase-field models are widely employed to simulate microstructure evolution during processes such as solidification or heat treatment. The resulting partial differential equations, often strongly coupled together, may be solved by a broad range of numerical methods, but this often results in a high computational cost, which calls for advanced numerical methods to accelerate their resolution. Here, we quantitatively test the efficiency and accuracy of semi-implicit Fourier spectral-based methods, implemented in Python programming language and parallelized on a graphics processing unit (GPU), for solving a phase-field model coupling Cahn-Hilliard and Allen-Cahn equations. We compare computational performance and accuracy with a standard explicit finite difference (FD) implementation with similar GPU parallelization on the same hardware. For a similar spatial discretization, the semi-implicit Fourier spectral (FS) solvers outperform the FD resolution as soon as the time step can be taken 5 to 6 times higher than afforded for the stability of the FD scheme. The accuracy of the FS methods also remains excellent even for coarse grids, while that of FD deteriorates significantly. Therefore, for an equivalent level of accuracy, semi-implicit FS methods severely outperform explicit FD, by up to 4 orders of magnitude, as they allow much coarser spatial and temporal discretization

Application of a multiscale approach for modeling the rheology of complex fluids in industrial mixing equipment

Many industrial sectors, like the personal care one, make wide use of mixing processes that involve complex fluids. However, modeling the rheology of these fluids is still challenging due to their non-Newtonian behavior, which depends also on the local composition. Computational tools such as dissipative particle dynamics (DPD) have been already used to calculate the equilibrium properties of these systems. Moreover, different works have been focused on the calculation of transport properties from these mesoscale DPD simulations. Multiscale approaches have been proposed to couple rheological information from DPD with computational fluid dynamics (CFD) simulations. The CFD technique reproduces the macroscale piece of equipment, implementing a rheology model built using the Gaussian process regression, a mathematical tool related to machine learning. In this work, such a framework is tested on an industrial process, to assess its performance on a realistic application. The investigated system is a solution at a high concentration of sodium lauryl ether sulfate in water under laminar fluid dynamics regime. The results show that the mixture correctly exhibits a shear-thinning behavior and presents viscosity values in good agreement with rheology experiments. While the feasibility of the coupling approach is shown, further studies on DPD are needed to improve the accuracy and the predictability of the methodology

Coherent Change Detection for repeated-pass interferometric SAR images: An application to earthquake damage assessment on buildings

During disaster response, the availability of relevant information, delivered in a proper format enabling its use among the different actors involved in response efforts, is key to lessen the impact of the disaster itself. Focusing on the contribution of geospatial information, meaningful advances have been achieved through the adoption of satellite earth observations within emergency management practices. Among these technologies, the Synthetic Aperture Radar (SAR) imaging has been extensively employed for large-scale applications such as flood areas delineation and terrain deformation analysis after earthquakes. However, the emerging availability of higher spatial and temporal resolution data has uncovered the potential contribution of SAR to applications at a finer scale. This paper proposes an approach to enable pixel-wise earthquake damage assessments based on Coherent Change Detection methods applied to a stack of repeated-pass interferometric SAR images. A preliminary performance assessment of the procedure is provided by processing Sentinel-1 data stack related to the 2016 central Italy earthquake for the towns of Amatrice and Accumoli. Damage assessment maps from photo-interpretation of high-resolution airborne imagery, produced in the framework of Copernicus EMS (Emergency Management Service - European Commission) and cross-checked with field survey, is used as ground truth for the performance assessment. Results show the ability of the proposed approach to automatically identify changes at an almost individual building level, thus enabling the possibility to empower traditional damage assessment procedures from optical imagery with the centimetric change detection sensitivity characterizing SAR. The possibility of disseminating outputs in a GIS-like format represents an asset for an effective and cross-cutting information sharing among decision makers and analysts

Mobile mapping for disaster relief

This special issue foreword focuses on methods and technologies developed by researchers, practitioners, and decision makers around the world for enabling and using mobile disaster response