A strategy to control industrial plants in the spirit of Industry 4.0 tested on a fluidic system

<w:PermStart w:id="667698911" w:edGrp="everyone"/>The goal of the paper is to propose a strategy of automating the control of wide spectrum industrial processes plants in the spirit of Industry 4.0. The strategy is based on the creation of a virtual simulator of the operation of the plants involved in the process. Through the digitization of the operational data sheets of the various components, the simulator is able to provide the reference values of the process control parameters to be compared with their actual values, in order to decide the direct inspection and/or the operational intervention on critical components before a possible failure. As example, a simple fluidic thrust plant has been considered, which a mathematical model (simulator) for its optimal operating conditions has been formulated for, by using the digitalized real operational data sheets of its components. The simple thrust system considered consists of a centrifugal pump driven by a three-phase electric motor, an inverter to regulate the rotation of the motor and a proportional valve that simulates the external load acting on the pump.As results, the operational data sheets and principal characteristics of the pump have been reproduced by means of the simulator here developed, showing a very good agreement.</p

Design of a non-invasive sensing system for diagnosing gastric disorders

Gastric disorders are widely spread among the population of any age. At the moment, the diagnosis is made by using invasive systems that cause several side effects. The present manuscript proposes an innovative non-invasive sensing system for diagnosing gastric dysfunctions. The Electro-GastroGraphy (EGG) technique is used to record myoelectrical signals of stomach activities. Although EGG technique is well known for a long time, several issues concerning the signal processing and the definition of suitable diagnostic criteria are still unresolved. So, EGG is to this day a trial practice. The authors want to overcome the current limitations of the technique and improve its relevance. To this purpose, a smart EGG sensing system has been designed to non-invasively diagnose gastric disorders. In detail, the system records the gastric slow waves by means of skin surface electrodes placed in the epigastric area. Cutaneous myoelectrical signals are so acquired from the body surface in proximity of stomach. Electro-gastrographic record is then processed. According to the diagnostic model designed from the authors, the system estimates specific diagnostic parameters in time and frequency domains. It uses Discrete Wavelet Transform to obtain power spectral density diagrams. The frequency and power of the EGG waveform and the dominant frequency components are so analyzed. The defined diagnostic parameters are put in comparison with the reference values of a normal EGG in order to estimate the presence of gastric pathologies by the analysis of arrhythmias (tachygastria, bradygastria and irregular rhythm). The paper aims to describe the design of the system and of the arrhythmias detection algorithm. Prototype development and experimental data will be presented in future works. Preliminary results show an interesting relevance of the suggested technique so that it can be considered as a promising non-invasive tool for diagnosing gastrointestinal motility disorders

Double-peaked Narrow-Line Signatures of Dual Supermassive Black Holes in Galaxy Merger Simulations

We present a first attempt to model the narrow-line (NL) region of active galactic nuclei (AGN) in hydrodynamic simulations of galaxy mergers, using a novel physical prescription. This model is used to determine the origin of double-peaked NL (dNL) AGN in merging galaxies and their connection to supermassive black hole (SMBH) pairs, motivated by recent observations of such objects. We find that dNL AGN induced by the relative motion of SMBH pairs are a generic but short-lived feature of gaseous major mergers. dNL AGN should often be observed in late-stage mergers, during the kpc-scale phase of SMBH inspiral or soon after the SMBH merger. However, even within the kpc-scale phase, only a minority of dNL AGN are directly induced by SMBH motion; their lifetimes are typically a few Myr. Most double peaks arise from gas kinematics near the SMBH, although prior to the SMBH merger up to 80% of all dNL profiles may be influenced by SMBH motion via altered peak ratios or velocity offsets. The total lifetimes of dNL AGN depend strongly on viewing angle and on properties of the merging galaxies. Also, in a typical merger, at least 10-40% of the double peaks induced by SMBH motion have small projected separations, 0.1-1 kpc, such that dual peaks of stellar surface brightness are not easily resolved. Diffuse tidal features can indicate late-stage galaxy mergers, although they do not distinguish SMBH pairs from merged SMBHs. We show that dNL profiles with peak velocity splittings > 500 km s^-1 or with measurable overall velocity shifts are often associated with SMBH pairs. Our results support the notion that selection of dNL AGN is a promising method for identifying dual SMBH candidates, but demonstrate the critical importance of high-resolution, multi-wavelength follow-up observations, and the use of multiple lines of evidence, for confirming the dual nature of candidate SMBH pairs. (Abridged)Comment: 24 pages, 9 figures. Moderate revisions; accepted to MNRA

Wind Reversal in Bubbly Natural Convection

The multi-phase Rayleigh–Bènard convection has been weakly investigated, even though it plays a leading role in the theoretical and applied physics of the heat transfer enhancement. For the case of moderate turbulent convection, a rather unexpected result is an unusual kind of wind reversal, in the sense that the fluid is found to be strongly influenced by the bubbles, whereas the bubbles themselves appear to be little affected by the fluid, despite the relative smallness of the Stokes numbers. The wind reversal induced by the bubbles dispersed in the fluid is a new and remarkable phenomenon in multi-phase flows that provides further perspectives in understanding the complex physics leading the enhancement of thermal convection. For this reason, the fundamental research proposed in this paper aimed to identify a space of control parameters and the physical mechanisms responsible for the wind reversal induced by dispersed bubbles in a confined convective flow. The strength of the following description lies in an innovative numerical approach, based on the multi-scale physics induced by the coupling of the local thermal and mechanical mechanisms arising between each bubble and the surrounding fluid. The continuous phase has been solved numerically using the direct numerical simulation (DNS) technique and each bubble has been tracked by means of a particle Lagrangian model

Automatic defect detection and characterization by thermographic images based on damage classifiers evaluation

In the framework of non-destructive evaluation (NDE), an accurate and precise characterization of defects is fundamental. This paper proposes a novel method for characterization of partial detachment of thermal barrier coatings from metallic surfaces, using the long pulsed thermography (LPT). There exist many applications, in which the LPT technique provides clear and intelligible thermograms. The introduced method comprises a series of post-processing operations of the thermal images. The purpose is to improve the linear fit of the cooling stage of the surface under investigation in the logarithmic scale. To this end, additional fit parameters are introduced. Such parameters, defined as damage classifiers, are represented as image maps, allowing for a straightforward localization of the defects. The defect size information provided by each classifier is, then, obtained by means of an automatic segmentation of the images. The main advantages of the proposed technique are the automaticity (due to the image segmentation procedures) and relatively limited uncertainties in the estimation of the defect size

Multi-Attribute Decision Making: Parametric Optimization and Modeling of the FDM Manufacturing Process Using PLA/Wood Biocomposites

The low carbon footprint, biodegradability, interesting mechanical properties, and relatively low price are considered some of the reasons for the increased interest in polylactic acid-based (PLA-based) filaments supplied with natural fillers. However, it is essential to recognize that incorporating natural fillers into virgin PLA significantly impacts the printability of the resulting blends. The complex inter-relationship between process, structure, and properties in the context of fused deposition modeling (FDM)-manufactured biocomposites is still not fully understood, which thus often results in decreased reliability of this technology in the context of biocomposites, decreased accuracy, and the increased presence of defects in the manufactured biocomposite samples. In light of these considerations, this study aims to identify the optimal processing parameters for the FDM manufacturing process involving wood-filled PLA biocomposites. This study presents an optimization approach consisting of Grey Relational Analysis in conjunction with the Taguchi orthogonal array. The optimization process has identified the combination of a scanning speed of 70 mm/s, a layer height of 0.1 mm, and a printing temperature of 220 °C as the most optimal, resulting in the highly satisfactory combination of good dimensional accuracy (Dx = 20.115 mm, Dy = 20.556 mm, and Dz = 20.220 mm) and low presence of voids (1.673%). The experimentally determined Grey Relational Grade of the specimen manufactured with the optimized set of process parameters (0.782) was in good agreement with the predicted value (0. 754), substantiating the validity of the optimization process. Additionally, the research compared the efficacy of optimization between the integrated multiparametric method and the conventional monoparametric strategy. The multiparametric method, which combines Grey Relational Analysis with the Taguchi orthogonal array, exhibited superior performance. Although the monoparametric optimization strategy yielded specimens with favorable values for the targeted properties, the analysis of the remaining characteristics uncovered unsatisfactory results. This highlights the potential drawbacks of relying on a singular optimization approach

Vibrational signal processing for characterization of fluid flows in pipes

The main idea of this paper is to assess a simpler and faster procedure leading to the evaluation of the fluid flow rate through a pipe. Currently, several methods are available and they involve ad-hoc instruments. All these methods are characterized by high accuracies and dynamic responses, but they are intended to be inserted within the pipe under investigation, bringing to well-known insertion effects, compromising the reliability of the measurements performed. The authors illustrate a newer methodology for the measurement of flow rates by means of the processing of the vibration signals of pipe walls, inferred by the flow turbulence. Previous studies of the same authors showed a linear dependence between the amplitude of the most prominent peak of the vibration spectra and the flow rate. In this work, the authors relate the power content of the processed signals (by introducing the signal Root Mean Square value) to the flow rate

About 3D Incompressible Flow Reconstruction from 2D Flow Field Measurements

In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and computing the third velocity component by means of the continuity equation. A quasi-3D test case of an incompressible flow has been inspected in the wake of a NACA 0012 airfoil immersed in a forced flow of water running in a rectangular open channel. Specifically, starting from a 2D measurement data in planes orthogonal to the stream-wise direction, the computational approach can predict the third flow velocity component. A 3D ADV instrument has been utilized to measure the flow field, but only two velocity components have been considered as measured quantities, while the third one has been considered as reference with which to compare the computed component from the continuity equation to check the accuracy and validity of the hybrid procedure. At this aim, the uncertainties of the quantities have been evaluated, according to the GUM, to assess the agreement between experiments and predictions, in addition to other metrics. This aspect of uncertainty is not a technical sophistication but a substantial way to bring to the use of a 1D and 2D measurement system in lieu of a 3D one, which is costly in terms of maintenance, calibration, and economic issues. Moreover, the magnitude of the most relevant flow indicators by means of experimental data and predictions have been estimated and compared, for further confirmation by means of a supervised learning classification. Further, the sensed data have been processed, by means of a machine learning algorithm, to express them in a 3D way along with accuracy and epoch metrics. Two additional metrics have been included in the effort to show paramount interest, which are a geostatistical estimator and Sobol sensitivity. The statements of this paper can be used to design and test several devices for industrial purposes more easily