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Although the electromagnetic and muonic components of extensive air showers have been studied in great detail, no comprehensive simulation study of the neutron component is available. This is related to the complexity of neutron transport processes that is typically not treated in standard simulation tools. In this work we use the Monte Carlo simulation package Fluka to study the production and the transport of neutrons in extensive air showers over the full range of neutron energies, extending down to thermal neutrons. The importance of different neutron production mechanisms and their impact on predicted neutron distributions in energy, lateral distance, atmospheric depth, and arrival time are discussed. In addition, the dependencies of the predictions on the properties of the primary particle are studied. The results are compared to the equivalent distributions of muons, which serve as reference., Comment: 18 pages, 14 figures, 2 tables, prepared for submission to Phys. Rev. D

We present a full model of surface-detector responses to extensive air showers. The model is motivated by the principles of air-shower universality and can be applied to different types of surface detectors. Here we describe a parametrization for both water-Cerenkov detectors and scintillator surface detectors, as for instance employed by the upgraded detector array of the Pierre Auger Observatory. Using surface detector data, the model can be used to reconstruct with reasonable precision shower observables such as the depth of the shower maximum $X_\text{max}$ and the number of muons $R_\mu$., Comment: 15 pages, 10 figures, prepared for submission to Phys.Rev.D

The landscape of ultrafast structured light pulses has recently evolved driven by the capability of high-order harmonic generation (HHG) to up-convert orbital angular momentum (OAM) from the infrared to the extreme-ultraviolet (EUV) spectral regime. Accordingly, HHG has been proven to produce EUV vortex pulses at the femtosecond timescale. Here we demonstrate the generation of attosecond vortex pulse trains, i.e. a succession of attosecond pulses with a helical wavefront, resulting from the synthesis of a comb of EUV high-order harmonics with the same OAM. By driving HHG with a polarization tilt-angle fork grating, two spatially separated circularly polarized high-order harmonic beams with order-independent OAM are created. Our work opens the route towards attosecond-resolved OAM light-matter interactions., Comment: Main text (1-14 pages) and supplemental material (14-16 pages)

High-fidelity computational fluid dynamics (CFD) simulations for design space explorations can be exceedingly expensive due to the cost associated with resolving the finer scales. This computational cost/accuracy trade-off is a major challenge for modern CFD simulations. In the present study, we propose a method that uses a trained machine learning model that has learned to predict the discretization error as a function of largescale flow features to inversely estimate the degree of lost information due to mesh coarsening. This information is then added back to the low-resolution solution during runtime, thereby enhancing the quality of the under-resolved coarse mesh simulation. The use of a coarser mesh produces a non-linear benefit in speed while the cost of inferring and correcting for the lost information has a linear cost. We demonstrate the numerical stability of a problem of engineering interest, a 3D turbulent channel flow. In addition to this demonstration, we further show the potential for speedup without sacrificing solution accuracy using this method, thereby making the cost/accuracy trade-off of CFD more favorable., Comment: 7 pages, under review at ASME IMECE 2023 conference

In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular operation. For this, we introduce Mel Frequency Cepstral Coefficients (MFCCs) as features, which form the input to a simple Multi-Layer Perceptron classifier. The proposed method is evaluated with real-world data that was obtained for state-of-the-art commuter railway vehicles in a measurement campaign. The experiments show that with the chosen MFCC features bearing faults can be reliably detected even for bearing damages that were not included in training., Comment: Accepted at the ICPHM 2023

In many places, tectonic tremor is observed in relation to slow slip and can be used as a proxy to study slow slip events of moderate magnitude where surface deformation is hidden in Global Navigation Satellite System (GNSS) noise. However, when no clear relationship between tremor and slow slip occurrence is observed, these methods cannot be applied, and we need other methods to be able to better detect and quantify slow slip. Wavelets methods such as the Discrete Wavelet Transform (DWT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) are mathematical tools for analyzing time series simultaneously in the time and the frequency domain by observing how weighted differences of a time series vary from one period to the next. We use wavelet methods to analyze GNSS time series of slow slip events in Cascadia. We use detrended GNSS data, apply the MODWT transform and stack the wavelet details over several nearby GNSS stations. As an independent check on the timing of slow slip events, we also compute the cumulative number of tremor in the vicinity of the GNSS stations, detrend this signal, and apply the MODWT transform. In both time series, we can then see simultaneous waveforms whose timing corresponds to the timing of slow slip events. We assume that there is a slow slip event whenever there is a positive peak followed by a negative peak in the wavelet signal. We verify that there is a good agreement between slow slip events detected with only GNSS data, and slow slip events detected with only tremor data. The wavelet-based detection method effectively detects events of magnitude higher than 6 as determined by independent event catalogs. As a demonstration of using the wavelet analysis in a region without significant tremor, we also analyze GNSS data from New Zealand and detect slow slip events that are spatially and temporally close to those detected previously by other studies., Comment: This paper appeared in Bulletin of the Seismological Society of America 112 (5): 2408-2424

In early May 2022, the Terra ecosystem collapsed after the algorithmic stablecoin failed to maintain its peg. Emergency measures were taken by Terraform Labs (TFL) in an attempt to protect Luna and UST, but then were abruptly abandoned by TFL for Luna 2.0 several days later. At this time, the Luna Classic blockchain has been left crippled and in limbo for the last two months. In the face of impossible odds, the Luna Classic community has self organized and rallied to build and restore the blockchain. This technical document outlines the steps we, the community, have taken towards the emergency management of the Luna Classic blockchain in the weeks after the UST depeg. We outline precisely what would be implemented on-chain to mitigate the concerns of affected stakeholders, and build trust for external partners, exchanges, and third-party developers. For the Luna Classic community, validators, and developers, this outlines concrete steps on how passed governance can and will be achieved. We openly audit our own code and welcome any feedback for improvement. Let us move forward together as the true community blockchain., Comment: 22 pages

In fluid physics, data-driven models to enhance or accelerate solution methods are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for the Navier-Stokes equations to approximate spatio-temporal turbulent fluid dynamics. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous Neural Ordinary Differential Equations for tracking the evolution of the differential equation. We demonstrate this technique on three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically-interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity., Comment: Submitted to Physical Review Fluids

Modelling the sudden depressurisation of superheated liquids through nozzles is a challenge because the pressure drop causes rapid flash boiling of the liquid. The resulting jet usually demonstrates a wide range of structures, including ligaments and droplets, due to both mechanical and thermodynamic effects. As the simulation comprises increasingly numerous phenomena, the computational cost begins to increase. One way to moderate the additional cost is to use machine learning surrogacy for specific elements of the calculations. The present study presents a machine learning-assisted computational fluid dynamics approach for simulating the atomisation of flashing liquids accounting for distinct stages, from primary atomisation to secondary break-up to small droplets using the ${\Sigma}$-Y model coupled with the homogeneous relaxation model. Notably, the model for the thermodynamic non-equilibrium (HRM) and ${\Sigma}$-Y are coupled, for the first time, with a deep neural network that simulates the turbulence quantities, which are then used in the prediction of superheated liquid jet atomisation. The data-driven component of the method is used for turbulence modelling, avoiding the solution of the two-equation turbulence model typically used for Reynolds-averaged Navier-Stokes simulations for these problems. Both the accuracy and speed of the hybrid approach are evaluated, demonstrating adequate accuracy and at least 25% faster computational fluid dynamics simulations than the traditional approach. This acceleration suggests that perhaps additional components of the calculation could be replaced for even further benefit., Comment: 12 pages, 11 figures

Past Lagrangian/Eulerian modeling has served as a poor match for the mixing limited physics present in many sprays. Though these Lagrangian/Eulerian methods are popular for their low cost, they are ill-suited for the physics of the dense spray core and suffer from limited predictive power. A new spray model, based on mixing limited physics, has been constructed and implemented in a multi-dimensional CFD code. The spray model assumes local thermal and inertial equilibrium, with air entrainment being limited by the conical nature of the spray. The model experiences full two-way coupling of mass, momentum, species, and energy. An advantage of this approach is the use of relatively few modeling constants. The model is validated with three different sprays representing a range of conditions in diesel and gasoline engines., Comment: Submitted to International Journal of Multiphase Flow

The realization that interfacial features play little role in diesel spray vaporization and advection has changed the modus operandi for spray modeling. Lagrangian particle tracking has typically been focused on droplet behavior, with sub-models for breakup, collision, and interfacially limited vaporization. In contrast, the mixing-oriented spray models are constructed so that gas entrainment is the limiting factor in the evolution of momentum and energy. In this work, a new spray model, ELMO (Eulerian-Lagrangian Mixing-Oriented), is implemented in a three-dimensional CFD code with two-way coupling with the gas phase. The model is verified and validated with three canonical sprays including spray A, H, and G from the ECN database., Comment: Presented at the ILASS-Americas 31st Annual Conference on Liquid Atomization and Spray Systems, May 2021. arXiv admin note: text overlap with arXiv:2107.03508