Phenomenology at the LHC of composite particles from strongly interacting Standard Model fermions via four-fermion operators of NJL type

A new physics scenario shows that four-fermion operators of Nambu-Jona-Lasinio (NJL) type have a strong-coupling UV fixed point, where composite fermions $F$ (bosons $\Pi$) form as bound states of three (two) SM elementary fermions and they couple to their constituents via effective contact interactions at the composite scale $\Lambda \approx {\cal O}$(TeV). We present a phenomenological study to investigate such composite particles at the LHC by computing the production cross sections and decay widths of composite fermions in the context of the relevant experiments at the LHC with $pp$ collisions at $\sqrt{s}={\rm 13}$ TeV and $\sqrt{s}={\rm 14}$ TeV. Systematically examining all the different composite particles $F$ and the signatures with which they can manifest, we found a vast spectrum of composite particles $F$ that has not yet been explored at the LHC. Recasting the recent CMS results of the resonant channel $pp\rightarrow e^+F \rightarrow e^+e^- q\bar{q}'$, we find that the composite fermion mass $m_F$ below 4.25 TeV is excluded for $\Lambda$/$m_F$ = 1. We further highlight the region of parameter space where this specific composite particle $F$ can appear using 3 ab$^{-1}$, expected by the High-Luminosity LHC, computing 3 and 5 $\sigma$ contour plots of its statistical significance.Comment: To appear in EPJC. This revised version expands the search for composite fermion F considering all its possible flavors and topologies and highlighting the signatures not yet investigated at LH

Effect of welding parameters on mechanical and microstructural properties of AA6082 jointsproduced by friction stir welding

The effect of processing parameters on mechanical and microstructural properties of AA6082 joints produced by friction stir welding was analysed in the present study. Different welded specimens were produced by employing a fixed rotating speeds of 1600rpm and by varying welding speeds from 40 to 460 mm/min. The joints mechanical properties were evaluated by means of tensile tests at room temperature. In addition, fatigue tests were performed by using a resonant electro-mechanical testing machine under constant amplitude control up to 250 Hz sinusoidal loading. The fatigue tests were conducted in axial control mode with R = min/max = 0.1, for all the welding and rotating speeds used in the present study. The microstructural evolution of the material was analysed according to the welding parameters by optical observations of the jointed cross-sections and SEM observations of the fractured surfaces were done to characterize the weld performances

fatigue damage on cfrp plates under bending by thermographic and ut analysis aided with fem dic prediction

Abstract In this article, a combination of numerical and experimental methodology for delamination evolution analysis on unidirectional CFRP elements under fatigue is suggested. Fiber-reinforced composite structures exhibit continuous damage accumulation with degradation of effective mechanical properties during cyclic HCF loads. Since advanced composites applications are allowed after extensive experimental certification tests, proposed work is based on experimental procedures to better detect and predict damage initiation and growth, monitoring static displacements and strains under 4-points bending by digital image correlation and measuring compliance variation under fatigue. In addition, non-destructive investigation of composite plates is conducted during cycling tests by using infrared thermography and ultrasonic measurements to detect damage location and validate FEM predictions. Experimental results are analyzed and compared, employing also digital image correlation technique; in addition, thermographic and ultrasonic monitoring inspection with Matlab elaborated measurements are implemented to check results for fatigue damage analysis of same specimens

A general approach to dropout in quantum neural networks

In classical machine learning (ML), “overfitting” is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in ML is the so called “dropout,” which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of quantum neural networks (QNNs) as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here, a generalized approach is presented to apply the dropout technique in QNN models, defining and analyzing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. This study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the QNN models, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations and may pave the way to efficiently employing deep quantum machine learning (QML) models based on state-of-the-art QNNs

Evaluation of the Stress State in Aluminium Foam Sandwiches

In this paper a discussion about the determination of the stress state corresponding to the application of four-points bending load on a sandwich panel having a core made of closed cell aluminium foam is reported. An analytical model based on laminated plate classical theory is compared to a more complex FEM model, considering the effect of geometric parameters of panels, like core and plate thickness, and of loading mode, like span length. The results show the difficulties to define a reliable model to calculate stress state in this kind of composite material

A General Approach to Dropout in Quantum Neural Networks

In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in Machine Learning is the so called "dropout", which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of Quantum Neural Networks as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here we present a generalized approach to apply the dropout technique in Quantum Neural Network models, defining and analysing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. Our study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the Quantum Neural Networks model, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations, and may pave the way to efficiently employing deep Quantum Machine Learning models based on state-of-the-art Quantum Neural Networks

Characterization of steel welded joints with hybrid projection and capacitor discharge welding (CDW) processes

This work studies the improved hybrid Capacitor Discharge Welding process (CDW), based on projection welding principles applied to stainless steel AISI 304. The innovative idea is to modify the igniting points geometry on the section to be welded and optimize the weld characteristics in order to guide the local fusion processes more uniformly on the whole area and enhance the weld properties. Eight different profile geometries for the contact surfaces have been realized in order to evaluate the geometry influence on joints quality and according to process parameters influence. The mechanical behavior of the welds has been verified with static characterization at room temperature and fatigue tests for welded samples with the better observed microstructure

Mechanical and microstructural behaviour of 2024–7075 aluminium alloy sheets joined by friction stir welding

The aim of the present work is to investigate on the mechanical and microstructural properties of dissimilar 2024 and 7075 aluminium sheets joined by friction stir welding (FSW). The two sheets, aligned with perpendicular rolling directions, have been successfully welded; successively, the welded sheets have been tested under tension at room temperature in order to analyse the mechanical response with respect to the parent materials. The fatigue endurance (S–N) curves of the welded joints have been achieved, since the fatigue behaviour of light welded sheets is the best performance indicator for a large part of industrial applications; a resonant electro-mechanical testing machine load and a constant load ratio RZsmin/smaxZ0.1 have been used at a load frequency of about 75 Hz. The resulted microstructure due to the FSW process has been studied by employing optical and scanning electron microscopy either on ‘as welded’ specimens and on tested specimen after rupture occurred

real time monitoring of damage evolution by nonlinear ultrasonic technique

Abstract In this work, the ultrasound technique was used to monitor the damage of material subjected to fatigue loads. Prediction of structural damage is critical for safe and reliable operation of engineered complex systems. In these measurements, conventional ultrasonic probes (transmitter and receiver) were stably fixed to the tested samples with steel brackets, in order to eliminate ever possible variability associated with the coupling of probes. The transmitted and received ultrasonic signals were recorded and analyzed using a digital oscilloscope. The data were converted into the frequency domain using an algorithm developed in Matlab based on Fast Fourier Transform (FFT) for received signal in dependence of the applied stress level and the accumulated fatigue damage was deeply studied in order to recognize quantitative effects, suitable for an experimental prediction of the integrity of the material. The acquired data were compared with the reference signal, at the beginning of the fatigue tests. Particular care has been paid to UT signal attenuation and to the study of the frequency spectrum as the number of load cycles varies. The applied experimental technique has proved efficient for detecting damage induced by mechanical stress

Thermoelasticity and CCD analysis of crack propagation in AA6082 friction stir w elded joints

The advantages of friction stir welding (FSW) process compared to conventional fusion welding technologies have been clearly demonstrated in recent years. In the present study, AA6082 FSW joints were produced by employing different welding parameters. The principal aim of this work is to apply thermoelastic stress analysis (TSA) to study crack propagation characteristics of friction stir welded aluminum sheets, during cyclic fatigue tests. The crack propagation experiments were performed by employing single edge notched specimens; fatigue tests were performed under tension with load ratio R = 0.1. All the mechanical tests were conducted up to failure. The TSA measurement system allowed crack evolution to be observed in real-time during fatigue cycles and stress fields to be derived on the specimens from the measured temperature variation. The thermoelastic data were used to analyse principal stresses and principal strains on the specimens surface and the crack growth rate during tests. In addition, it was possible to evaluate all the joints defects effects, as a function of welding parameters, correlating effects on different crack growth rate and instabilities. The achieved results were compared with those obtained by classical CCD camera monitoring of crack front propagation during cyclic loading and all the results were validated by employing finite element analysis performed with ABAQUS software