Plasma flows and magnetic field interplay during the formation of a pore

We studied the formation of a pore in AR NOAA 11462. We analysed data obtained with the IBIS at the DST on April 17, 2012, consisting of full Stokes measurements of the Fe I 617.3 nm lines. Furthermore, we analysed SDO/HMI observations in the continuum and vector magnetograms derived from the Fe I 617.3 nm line data taken from April 15 to 19, 2012. We estimated the magnetic field strength and vector components and the LOS and horizontal motions in the photospheric region hosting the pore formation. We discuss our results in light of other observational studies and recent advances of numerical simulations. The pore formation occurs in less than 1 hour in the leading region of the AR. The evolution of the flux patch in the leading part of the AR is faster (< 12 hour) than the evolution (20-30 hour) of the more diffuse and smaller scale flux patches in the trailing region. During the pore formation, the ratio between magnetic and dark area decreases from 5 to 2. We observe strong downflows at the forming pore boundary and diverging proper motions of plasma in the vicinity of the evolving feature that are directed towards the forming pore. The average values and trends of the various quantities estimated in the AR are in agreement with results of former observational studies of steady pores and with their modelled counterparts, as seen in recent numerical simulations of a rising-tube process. The agreement with the outcomes of the numerical studies holds for both the signatures of the flux emergence process (e.g. appearance of small-scale mixed polarity patterns and elongated granules) and the evolution of the region. The processes driving the formation of the pore are identified with the emergence of a magnetic flux concentration and the subsequent reorganization of the emerged flux, by the combined effect of velocity and magnetic field, in and around the evolving structure.Comment: Accepted for publication in Astronomy and Astrophysic

A phenomenological model for predicting the early development of the flame kernel in spark-ignition engines

This work presents a simple and effective phenomenological model for the prediction of the early growth of the flame kernel in SI engines, including its initiation as a result of the electrical breakdown of the fuel/air mixture between the spark plug electrodes. The present model aims to provide an improved description of the ignition-affected early phases of flame kernel development compared to the majority of models currently available in literature. In particular, these models focus on electrical energy supply and turbulence, whereas the stretch-induced kernel growth slowdown is quantified with linear models that are inconsistent with the small kernel radius. For the flame kernel initiation, this model replaces the current methods that rely on 1D heat diffusion within a plasma column with a more consistent analysis of post-breakdown conditions. Concerning the kernel growth, the present model couples the mass and energy conservation equations of a spherical kernel with the species and temperature profiles outside of it. This combination leads to a non-linear description of the flame stretch, according to which the kernel development is controlled by the Lewis-number-dependent balance between the heat gained via combustion and the heat lost via thermal diffusion. As a result, the kernel temperature differs from the adiabatic flame temperature, causing the laminar flame speed to change from its adiabatic value and ultimately affecting the overall kernel development. Kernel growth predictions are conducted for laminar flames and compared to literature data, showing a satisfactory agreement and highlighting the ability to describe the stretch-induced kernel slowdown, up to its possible extinction. A good agreement with literature data is also obtained for kernel expansions under moderately turbulent conditions, typical of internal combustion engines. The simple formulation of the present model enables swift integration into phenomenological combustion models for spark-ignition engines, while simultaneously offering useful insight into the early kernel development even for CFD-based approaches

BIM-ORIENTED ALGORITHMIC RECONSTRUCTION OF BUILDING COMPONENTS FOR EXISTING HERITAGE

Abstract. This study is part of a more complex research aimed at establishing guidelines to simplify the digitalization process used to manage existing building heritage. Working in a BIM environment, this paper will present two different algorithms: a modelling algorithm, a data analysis algorithm, and relative applications in the digitalization of a contemporary building. All the archival data required for the digitalization process was collected and those in two-dimensional digital vector format have aroused particular interest because they enabled initiation of the reconstruction process of the BIM model. One of the two algorithms allowed us to identify recurrent elements in a CAD drawing, based on geometric 2D primitives. The final outcome of the first phase involves quadrilateral or circular surfaces and can be viewed in algorithmic environment. The next phase involves applying a unique coloured sign to the identified sections and then export them all in a BIM software. This tool produced unexpected positive results: the presence of a small coloured grid emphasized the discrepancies created between the two-dimensional drawings and the vertical elements. We were thus able to identify the objects with these inconsistencies: they were verified using accurate surveys and then corrected.</p

Analysis and Geographical Representation of Cilento’s Monastic Architecture

This paper is part of a wider research on the Cilento monastic architec-tures of Italo-Greek origin located in southern Campania (Italy). The investigationconcentrates on the study, updating and analysis of the existing constructions forthe enrichment of the geographical information databases of the Cilento. On thisopportunity, the analysis focuses specifically on two monuments of Basilian foun-dation: the Abbey of Santa Maria di Pattano, in Vallo della Lucania, and the churchof San Nicola di Myra in Sacco Vecchia. The first case study presents superfe-tations that make it difficult to read the architectural languages and to interpretits conformation. On the other hand, the church of San Nicola in Myra, despitebeing located in one of the most famous ghost towns of the Cilento countrysideand showing important deterioration, still preserves its original morphology, char-acterized by a splendid hieratic character that is completely Basilian. The study ofthese constructions was carried out with digital models and geographic informa-tion systems, in order to obtain the original conformation of the Badia of Pattano.The comparative analysis of the information gathered on the other monument wasused to obtain the necessary data to clarify and identify the main constructionpatterns of the Byzantine and Basilian architectures of the area. These data willserve to enrich the current information and, furthermore, to develop more specificmultidisciplinary analyses in the future

Impact of the laminar flame speed correlation on the results of a quasi-dimensional combustion model for Spark-Ignition engine

Abstract In the present study, the impact of the laminar flame speed correlation on the prediction of the combustion process and performance of a gasoline engine is investigated using a 1D numerical approach. The model predictions are compared with experimental data available for full- and part-load operations of a small-size naturally aspirated Spark-Ignition (SI) engine, equipped with an external EGR circuit. A 1D model of the whole engine is developed in the GT-Power™ environment and is integrated with refined sub-models of the in-cylinder processes. In particular, the combustion is modelled using the fractal approach, where the burning rate is directly related to the laminar flame speed. In this work, three laminar flame speed correlations are assessed, including both experimentally- and numerically-derived formulations, the latter resulting from the fitting of laminar flame speeds computed by a chemical kinetic solver. Each correlation is implemented within the combustion sub-model, which is properly tuned to reproduce the experimental performance of the engine at full load. Then, the reliability of the considered flame speed formulations is proved at part-loads, even under external EGR operations

Forecasting SYM-H Index: A Comparison Between LongShort-Term Memory and Convolutional Neural Networks

Forecasting geomagnetic indices represents a key point to develop warning systems for the mitigation of possible effects of severe geomagnetic storms on critical ground infrastructures. Here we focus on SYM‐H index, a proxy of the axially symmetric magnetic field disturbance at low and middle latitudes on the Earth's surface. To forecast SYM‐H, we built two artificial neural network (ANN) models and trained both of them on two different sets of input parameters including interplanetary magnetic field components and magnitude and differing for the presence or not of previous SYM‐H values. These ANN models differ in architecture being based on two conceptually different neural networks: the long short‐term memory (LSTM) and the convolutional neural network (CNN). Both networks are trained, validated, and tested on a total of 42 geomagnetic storms among the most intense that occurred between 1998 and 2018. Performance comparison of the two ANN models shows that (1) both are able to well forecast SYM‐H index 1 h in advance, with an accuracy of more than 95% in terms of the coefficient of determination R2; (2) the model based on LSTM is slightly more accurate than that based on CNN when including SYM‐H index at previous steps among the inputs; and (3) the model based on CNN has interesting potentialities being more accurate than that based on LSTM when not including SYM‐H index among the inputs. Predictions made including SYM‐H index among the inputs provide a root mean squared error on average 42% lower than that of predictions made without SYM‐H

Draft Genome Sequences of Three Enterococcus casseliflavus Strains Isolated from the Urine of Healthy Bovine Heifers (Gyr Breed)

Enterococcus casseliflavus is a commensal bacterium present in the intestinal microbiota of different animals. Previous studies have found that strains isolated from livestock are often resistant to many different antibiotics. Here, we present three E. casseliflavus strains, UFMG-H7, UFMG-H8, and UFMG-H9, isolated from urine collected from healthy dairy heifers in Brazil

Draft Genome Sequence of Aeromonas caviae UFMG-H8, Isolated from Urine from a Healthy Bovine Heifer (Gyr Breed)

Aeromonas caviae is an emerging pathogen in humans, causing intestinal infections. Here, we report Aeromonas caviae strain UFMG-H8, isolated from the urine of a healthy heifer (Gyr breed)