The stochastic background of gravitational waves due to the f-mode instability in neutron stars

This paper presents an estimate for the spectral properties of the stochastic background of gravitational waves emitted by a population of hot, young, rapidly rotating neutron stars throughout the Universe undergoing $f$-mode instabilities, formed through either core-collapse supernova explosions or the merger of binary neutron star systems. Their formation rate, from which the gravitational wave event rate is obtained, is deduced from observation-based determinations of the cosmic star formation rate. The gravitational wave emission occurs during the spin-down phase of the $f$-mode instability. For low magnetized neutron stars and assuming 10\% of supernova events lead to $f$-mode unstable neutron stars, the background from supernova-derived neutron stars peaks at $\Omega_{\text{gw}} \sim 10^{-9}$ for the $l=m=2$ $f$-mode, which should be detectable by cross-correlating a pair of second generation interferometers (e.g. Advanced LIGO/Virgo) with an upper estimate for the signal-to-noise ratio of $\approx$ 9.8. The background from supramassive neutron stars formed from binary mergers peaks at $\Omega_{\text{gw}} \sim 10^{-10}$ and should not be detectable, even with third generation interferometers (e.g. Einstein Telescope)

A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark

Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system should be efficiently decomposed into its independent components. However, the target structure may be affected by (damage-related or not) nonlinearities, which appear as noise-like distortions in its vibrational response. This response can be nonstationary as well and thus requires a time-frequency analysis. Adaptive mode decomposition methods are the most apt strategy under these circumstances. Here, a shortlist of three well-established algorithms has been selected for an in-depth analysis. These signal decomposition approaches—namely, the Empirical Mode Decomposition (EMD), the Hilbert Vibration Decomposition (HVD), and the Variational Mode Decomposition (VMD)—are deemed to be the most representative ones because of their extensive use and favourable reception from the research community. The main aspects and properties of these data-adaptive methods, as well as their advantages, limitations, and drawbacks, are discussed and compared. Then, the potentialities of the three algorithms are assessed firstly on a numerical case study and then on a well-known experimental benchmark, including nonlinear cases and nonstationary signals

Detecting mode-shape discontinuities without differentiation - Examining a Gaussian process approach

Detecting damage by inspection of mode-shape curvature is an enticing approach which is hindered by the requirement to differentiate the inferred mode-shape. Inaccuracies in the inferred mode-shapes are compounded by the numerical differentiation process; since these small inaccuracies are caused by noise in the data, the method is untenable for most real situations. This publication proposes a new method for detecting discontinuities in the smoothness of the function, without directly calculating the curvature i.e. without differentiation. We present this methodology and examine its performance on a finite element simulation of a cracked beam under random excitation. In order to demonstrate the advantages of the approach, increasing amounts of noise are added to the simulation data, and the benefits of the method with respect to simple curvature calculation is demonstrated. The method is based upon Gaussian Process Regression, a technique usually used for pattern recognition and closely related to neural network approaches. We develop a unique covariance function, which allows for a non-smooth point. Simple optimisation of this point (by complete enumeration) is effective in detecting the damage location. We discuss extensions of the technique (to e.g. multiple damage locations) as well as pointing out some potential pitfall

Video processing techniques for the contactless investigation of large oscillations

The experimental acquisition of large vibrations presents various technical difficulties. Especially in the case of geometric nonlinearities, dealing with very flexible, very light structures causes minimal variations in mass or stiffness to affect severely the dynamical response. Thus, sensors' added masses change the behaviour of the structure with respect to the unloaded condition. Moreover, the most common tools regularly employed for acquisition in vibration analysis - that is to say, laser vibrometers and accelerometers - are often designed with small amplitudes in mind. Their recordings are known to lack accuracy when the investigated structure undergoes large or very large motions, due to geometrical reasons. Image-based measurement techniques offer a valid solution to this problem. Here, an ensemble of three video processing techniques are benchmarked against each other and tested as viable options for the non-contact dynamic characterisation of slender beam-like structures. The methods have been applied to the case study of an aluminium spar for a highly-flexible airwing prototype and compared to the measurements recorded by a laser velocimeter and several Raspberry PI Inertial Measurement Units (IMUs), which also proved to be minimally invasive

Shape Sensing of Plate and Shell Structures Undergoing Large Displacements Using the Inverse Finite Element Method

The inverse Finite Element Method (iFEM) is applied to reconstruct the displacement field of a shell structure which undergoes large deformations using discreet strain measurements as the prescribed data. The iFEM computations are carried out using an incremental procedure where at each load step, the incremental strains are used to evaluate the incremental displacements which in turn update the geometry of the deformed structure. The efficacy of the proposed approach to predict large displacements is examined using two case studies involving a cantilevered wing-shaped plate and a clamped plate. The incremental iFEM procedure is demonstrated to be sufficiently accurate in terms of reproducing the correct nonlinear character of the load-displacement curve even when a reduced number of strain sensors is used. Therefore, this approach may have important implications for real-time monitoring of aerospace structures that undergo large displacements

Using video processing for the full-field identification of backbone curves in case of large vibrations

Nonlinear modal analysis is a demanding yet imperative task to rigorously address real-life situations where the dynamics involved clearly exceed the limits of linear approximation. The specific case of geometric nonlinearities, where the effects induced by the second and higher-order terms in the strain–displacement relationship cannot be neglected, is of great significance for structural engineering in most of its fields of application—aerospace, civil construction, mechanical systems, and so on. However, this nonlinear behaviour is strongly affected by even small changes in stiffness or mass, e.g., by applying physically-attached sensors to the structure of interest. Indeed, the sensors placement introduces a certain amount of geometric hardening and mass variation, which becomes relevant for very flexible structures. The effects of mass loading, while highly recognised to be much larger in the nonlinear domain than in its linear counterpart, have seldom been explored experimentally. In this context, the aim of this paper is to perform a noncontact, full-field nonlinear investigation of the very light and very flexible XB-1 air wing prototype aluminum spar, applying the well-known resonance decay method. Video processing in general, and a high-speed, optical target tracking technique in particular, are proposed for this purpose; the methodology can be easily extended to any slender beam-like or plate-like element. Obtained results have been used to describe the first nonlinear normal mode of the spar in both unloaded and sensors-loaded conditions by means of their respective backbone curves. Noticeable changes were encountered between the two conditions when the structure undergoes large-amplitude flexural vibrations

Finding Direct-Collapse Black Holes at Birth

Direct-collapse black holes (DCBHs) are currently one of the leading contenders for the origins of the first quasars in the universe, over 300 of which have now been found at $z >$ 6. But the birth of a DCBH in an atomically-cooling halo does not by itself guarantee it will become a quasar by $z \sim$ 7, the halo must also be located in cold accretion flows or later merge with a series of other gas-rich halos capable of fueling the BH's rapid growth. Here, we present near infrared luminosities for DCBHs born in cold accretion flows in which they are destined to grow to 10$^9$ M$_{\odot}$ by $z \sim$ 7. Our observables, which are derived from cosmological simulations with radiation hydrodynamics with Enzo, reveal that DCBHs could be found by the James Webb Space Telescope at $z \lesssim$ 20 and strongly-lensed DCBHs might be found in future wide-field surveys by Euclid and the Wide-Field Infrared Space Telescope at $z \lesssim$ 15.Comment: 5 pages, 2 figures, accepted by ApJ

Shape sensing of plate structures using the inverse Finite Element Method: investigation of efficient strain-sensor patterns

Methods for real-time reconstruction of structural displacements using measured strain data is an area of active research due to its potential application for Structural Health Monitoring (SHM) and morphing structure control. The inverse Finite Element Method (iFEM) has been shown to be well suited for the full-field reconstruction of displacements, strains, and stresses of structures instrumented with discrete or continuous strain sensors. In practical applications, where the available number of sensors may be limited, the number and sensor positions constitute the key parameters. Understanding changes in the reconstruction quality with respect to sensor position is generally difficult and is the aim of the present work. This paper attempts to supplement the current iFEM modeling knowledge through a rigorous evaluation of several strain-sensor patterns for shape sensing of a rectangular plate. Line plots along various sections of the plate are used to assess the reconstruction quality near and far away from strain sensors, and the nodal displacements are studied as the sensor density increases. The numerical results clearly demonstrate the effectiveness of the strain sensors distributed along the plate boundary for reconstructing relatively simple displacement patterns, and highlight the potential of cross-diagonal strain-sensor patterns to improve the displacement reconstruction of more complex deformation patterns