Density Evolution Research Articles

Probabilistic analysis of seismic mitigation of base‐isolated structure with sliding hydromagnetic bearings based on finite element simulations

AbstractBase isolation is one of the most efficient methods for seismic mitigation of building and bridge structures in high‐intensity areas. However, the base‐isolated structure designed according to current codes based on the local seismic hazard and a few station records might not guarantee the performance of the base isolation system and ensure the safety of the structure. Carrying out a probabilistic analysis of seismic mitigation of base‐isolated structures, considering the randomness associated with earthquake ground motions, is thus necessary. To this end, the stochastic seismic response and reliability analysis of base‐isolated structures subjected to stochastic ground motions are conducted in this study, in conjunction with the probability density evolution method and the principle of equivalent extreme value event. The recently developed sliding hydromagnetic bearing is employed to constitute the base isolation layer of the base‐isolated structure. A refined finite element analysis is carried out to accurately reveal the dynamic characteristics of the base‐isolated structure. It is shown that the base‐isolated structure can be well simulated by the finite element model, which is verified through a comparative study with the results of shaking‐table tests. The base‐isolated structure gains a sound seismic mitigation compared to the base‐fixed structure in a probabilistic sense, and can ensure the safety of the structure by deploying a moderately designed base isolation system.

Tremaine-Gunn limit with mass-varying particles

General classical arguments on the time evolution of the phase-space density can be used to derive constraints on the mass of particle candidates for the cosmological dark matter (DM). The resulting Tremaine-Gunn limit is extremely useful in constraining particle DM models. In certain models, however, the DM particle mass varies appreciably over time. In this work, we generalize the phase-space limits on possible DM particle masses to these scenarios. We then examine the ensuing cosmological implications on the effective DM equation of state and indirect DM detection.

Symmetries and Asymmetries in Branching Processes

As is known in stochastic particle theory, the same random process can be described by two different master equations for the evolution of the probability density, namely, by a forward or a backward master equation. These are the generalised analogues of the direct and adjoint equations of traditional transport theory. At the level of the first moment, these two equations show considerable resemblance to each other, but they become increasingly different with increasing moment order. The purpose of this paper is to demonstrate this increasing asymmetry and to discuss the underlying reasons. It is argued that since the reason of the different forms of the forward and the backward equations lies in the lack of invariance of the process to time reversal, the reason for the increasing asymmetry between the two forms for higher-order moments or processes with several variables (particle types) can be related to the increasing level of the violation of the invariance to time reversal, as is illustrated with some examples.

The optimal estimation of delayed flux effect on traffic stability in lattice hydrodynamic model

In the process of traffic information transmission, traffic flux delay often occurs. To solve traffic jams resulting from traffic flux delay, we put forward a novel feedback control mode to establish a lattice model accounting for the difference between the optimal estimation and the delayed flux (called for OEDF model). The stability condition, which is closely related to the OEDF model, is derived according to the linear stability analysis. Moreover, the important positive contribution of the OEDF model is demonstrated via numerical simulation from two aspects including density evolution and hysteresis loop, respectively.

Population inversion mechanism of optically pumped metastable rare gas based on laser-induced preionization

A 532 nm laser was used to induce ionization of Ar–He mixture, and metastable 4s [3/2]2 level Ar atoms were prepared. The interaction mechanism between laser, Ar atoms, He atoms and free electrons in laser-induced plasma (LIP) was revealed. Through time-resolved atomic emission spectroscopy, two regimes separated by a few microseconds were found during temporal evolution of Ar I 811.5 nm (4p [5/2]3→4s [3/2]2) line. It was demonstrated that the first regime was generated by the “photoexcitation + radiation + collisional relaxation” process, while the second regime was generated by the “ion-electron recombination” process. The above conclusion was confirmed again by the temporal evolution of electron temperature and electron density, and it was also demonstrated that the ionization of Ar atoms induces the ionization of He atoms. A 811.5 nm laser was utilized to excite the metastable 4s [3/2]2 level Ar atoms to the 4p [5/2]3 level, when changing the delay time of the 811.5 nm laser lagging behind the 532 nm laser, the generated 912.3 nm (4p [1/2]1→4s [3/2]2) fluorescence intensity also showed different pattern of variations. The analysis indicated that “photoexcitation + radiation + collisional relaxation”, “ion-electron recombination” and quenching processes will affect the density of metastable 4s [3/2]2 level Ar atoms. Amplified spontaneous emission (ASE) signal at 912.3 nm was obtained along the direction of pump laser. This work verifies the feasibility of optically pumped metastable rare gas laser (OPRGL) based on laser-induced preionization, and provides an alternative scheme for the future development of OPRGL.

Twin density and twin thickness evolution in sputtered Al–Mg alloys

Twinned Al–Mg alloys have been reported. However, the role of Mg solute in facilitating the formation of growth twins remains unclear. By using a precession-assisted crystal orientation mapping technique (PACOM) coupled with transmission electron microscopy (known as ASTAR), we examined the evolution of twin boundaries in Al, Al–1Mg, and Al–2.2Mg (at. %) films. The twinned grain fraction elevates with increasing film thickness until it reaches a peak when the film thickness is 120–160 nm. The Al–Mg alloys exhibited greater twinned grain fractions than pure Al. To investigate the fluctuation of twinned grain fraction, two types of twin boundaries were classified including intergranular and intragranular twins. The initial increase in twin density is attributed to the impingement of twinned grains during island coalescence and the twinned grains are more likely to survive during the grain growth process. Whereas the decrease in twinned grain fraction in thicker films is related to the removal of intragranular twins, and a lack of formation mechanisms of new twins.

Analysis of the influence of detouring obstacle avoidance behavior on unidirectional flow

A pedestrian’s active obstacle avoidance behavior has an important impact on crowd safety in heterogeneous crowd movements, but how it affects the movement of individuals and crowd is not yet clear. This study proposes a detour anisotropic social force model (DASFM) that considers active obstacle avoidance behavior for future collision prediction. DASFM prioritizes moving obstacle pedestrians based on velocity obstacle (VO) and combines the anisotropic social force model (ASFM) to achieve the dynamic process of pedestrian active obstacle avoidance behavior. The results show that DASFM enables individuals to safely overtake while avoiding fluctuations in pedestrian speed and distance between pedestrians. The model improved abnormal “strip group” crowd aggregation, individual jostling, and congestion found in other models. It has effective performance in spatial distribution, trajectory, density evolution, and time consumption of crowd movement in unidirectional flow under open conditions. The fundamental diagram generated by DASFM in unidirectional flow under periodic boundary conditions tends to be consistent with the observations and experimental results of other researchers. Instantaneous flow analysis shows that overtaking behavior alleviates local congestion in the low-density crowd and promotes the slow flow of the high-density crowd. However, overtaking behavior also exacerbates the stop-and-go waves of the high-density crowd, posing a threat to crowd safety. This study not only effectively realizes the simulation of overtaking behavior among individuals and crowds, but also discovers the impact patterns of overtaking behavior on low- or high-density crowd. Therefore, measures should be taken to control individual overtaking behavior in high-density crowd to prevent the production of stop-and-go waves.

G-SC-IRSA: Graph-Based Spatially Coupled IRSA for Age-Critical Grant-Free Massive Access

In this paper, we focus on a grant-free massive access setup and analyze its age of information (AoI), where a large number of user equipments (UEs) are randomly activated and attempt transmitting status update packets to a base station (BS) over a common shared channel. To support this age-critical grant-free massive access, we propose a graph-based spatially coupled irregular repetition slotted ALOHA (G-SC-IRSA) random access protocol, which utilizes the pseudo-random access pattern (PRAP), coupled frames, and sliding window decoder (SWD) to improve the packet loss rate (PLR) and AoI performance. Specifically, we derive the approximate expressions to the normalized average AoI (AAoI) as a function of the PRAP and system load. Then, we establish the problem of minimizing the AAoI under the G-SC-IRSA protocol. Furthermore, we utilize the density evolution (DE) with a bipartite graph to evaluate the system load threshold of G-SC-IRSA in asymptotic regime, and achieve an optimal degree distribution via the differential evolution algorithm, and finally obtain the optimal PRAP with PEG algorithm. Simulation results validate the accuracy of our theoretical derivations, and show the G-SC-IRSA can achieve the minimum AAoI with the optimal PRAP, and outperforms the existing benchmark schemes in terms of PLR and AAoI.

Two-dimensional extended Hubbard model at half-filling

We consider the extended Hubbard model on a two-dimensional square lattice at half-filling. The model is investigated using the strong coupling diagram technique. We sum infinite series of ladder diagrams allowing for full-scale charge and spin fluctuations and the actual short-range antiferromagnetic order for nonzero temperatures. In agreement with earlier results, we find the first-order phase transition in the charge subsystem occurring at v = v c ≳ U/4 with v and U the intersite and on-site Coulomb repulsion constants. The transition reveals itself in an abrupt sign change of a sharp maximum in the zero-frequency charge susceptibility at the corner of the Brillouin. States arising at the transition have alternating deviations of electron occupations from the mean value on neighboring sites. Due to fluctuations, these alternating occupation deviations have short-range order. For the considered parameters, such behavior is found for U ≲ 5t with t the hopping constant. For the insulating case U ≳ 6t, in which the transition is not observed, we find a continuous growth of the Mott gap with v. The evolution of the electron density of states with increasing v is also considered.

A robust phenomenological modeling framework based on cross-slip propensity factor for capturing the effect of dynamic strain aging on work hardening behavior of an Al-Mg alloy

In the present work, a new modeling framework for analyzing flow behavior of Al-Mg alloys is proposed by considering both direct and indirect effect of solutes on flow stress. While direct effect addresses the increased slip resistance due to dynamic strain aging (DSA), the indirect effect represents the role of solutes on cross-slip probability. This affects the dislocation annihilation propensity and consequently the work hardening behavior. The dislocation density evolution equation is suitably modified by including a factor based on activation volume and DSA-induced strengthening (σdsa) to account for the effect of solutes on dislocation annihilation propensity and develop a new phenomenological modeling framework for predicting flow curves spanning over a wide range of strain rate (0.00002–0.2 s−1). The proposed modeling framework is shown to be performing better than the existing models which consider only the direct effect of solutes on flow stress. Experimental validation of the proposed concept is shown via dislocation density measurements using X-ray line profile analysis, dislocation substructure analysis via transmission electron microscopy and fractography studies. The new modeling framework is also extended to predict flow curves of several Al-Mg alloys with varying Mg concentration to establish the robustness of the proposed concept.

Analysis of the local growth and density evolution of soot deposits generated under hydrocarbon condensation: 3D simulation and detailed experimental validation

The utilization of the Exhaust Gas Recirculation (EGR) system during atypical engine operating conditions in order to meet future type-approval criteria exposes the internal surfaces of the devices to exhaust gas with elevated concentrations of particulate matter and greater amounts of hydrocarbon species, leading to the formation of dense and wet sludge deposits. To broaden the understanding of this phenomenon and contribute to the development of advanced EGR devices, this study presents an extended Computational Fluid Dynamics (CFD) model that, in addition to simulating the growth of fouling deposits caused by the accumulation of soot particles, also takes into account the condensation of hydrocarbons. Two scenarios with varying hydrocarbon concentrations in the exhaust flow are analysed, and the evolution of the deposit's thickness and density is determined. A sequential validation process is carried out by comparing the numerical results to actual deposit profiles at different stages of the fouling process. Additionally, hyperspectral images of the fouling layer have been acquired and analysed to validate the regions where hydrocarbon condensation is predicted to play a crucial role, enabling the verification of the hydrocarbon condensation phenomenon predicted by the numerical model. The results obtained under the studied conditions indicate that, on average, 77.4% of the analysed area exhibits a low level of relative error, demonstrating that the proposed model and the methodology used serve as a valuable tool for examining the propensity for deposit formation in devices subjected to fouling exacerbated by hydrocarbon condensation.

Observational constraints on two cosmological models of f(Q) theory

In the past few years, f(Q) theories have drawn a lot of research attention in replacing Einstein’s theory of gravity successfully. The current study examines the novel cosmological possibilities emerging from two specific classes of f(Q) models using the parametrization form of the equation of state (EoS) parameter as omega left( zright) =-frac{1}{1+3beta left( 1+zright) ^{3}}, which displays quintessence behavior with the evolution of the Universe. We do statistical analyses using the Markov chain Monte Carlo (MCMC) method and background datasets like Type Ia Supernovae (SNe Ia) luminosities and direct Hubble datasets (from cosmic clocks), and Baryon Acoustic Oscillations (BAO) datasets. This lets us compare these new ideas about the Universe to the Lambda CDM model in a number of different possible ways. We have come to the conclusion that, at the current level of accuracy, the values of their specific parameters are the best fits for our f(Q) models. To conclude the accelerating behavior of the Universe, we further study the evolution of energy density, pressure, and deceleration parameter for these f(Q) models.

An investigation of periodic degradation of axle box vibration spectrum for a high-speed rail vehicle based on Bayesian method

Axle box vibration serves as the main source of excitations for rail vehicles. Due to the wear of wheel/rail contact and the re-profiling procedure, the axle box vibration usually degrades periodically with the increased mileage in the service. This could significantly impact the estimation of vibration fatigue when the component is subjected to the axle box vibration. This paper develop a method to describe the periodic evolution of axle box vibration spectrum to better characterise the vibration spectrum of axle box. In this study, a Wiener process incorporating with four random parameters was employed to model the non-linearity of the degradation process. The maximum likelihood estimation (MLE) algorithm is used to estimate the initial values of the random parameters, and a Bayesian approach is employed to update the parameters based on newly obtained data. Finally, the proposed methodology is tested using long-term field test data from a high-speed train, and the results demonstrate that it accurately estimates the evolution of the axle box acceleration spectral density (ASD) spectrum. This could aid in predicting the residual service life of structures subjected to axle box vibration and further contribute to the development of maintenance strategies and top-down design of the structure.

An Efficient Compressive Sensing Event-Detection Scheme for Internet of Things System Based on Sparse-Graph Codes

This work studied the event-detection problem in an Internet of Things (IoT) system, where a group of sensor nodes are placed in the region of interest to capture sparse active event sources. Using compressive sensing (CS), the event-detection problem is modeled as recovering the high-dimensional integer-valued sparse signal from incomplete linear measurements. We show that the sensing process in IoT system produces an equivalent integer CS using sparse graph codes at the sink node, for which one can devise a simple deterministic construction of a sparse measurement matrix and an efficient integer-valued signal recovery algorithm. We validated the determined measurement matrix, uniquely determined the signal coefficients, and performed an asymptotic analysis to examine the performance of the proposed approach, namely event detection with integer sum peeling (ISP), with the density evolution method. Simulation results show that the proposed ISP approach achieves a significantly higher performance compared to existing literature at various simulation scenario and match that of the theoretical results.

Treatment with mycophenolate mofetil is associated with improved nailfold vasculature in systemic sclerosis.

To investigate the evolution of nailfold capillary density in patients with SSc in relation to immunosuppressive treatment and autoantibodies. This was a prospective study cohort. Consecutive newly diagnosed SSc patients were included into this study who, in a retrospective review, had at least two nailfold capillary microscopy measurements performed during the first 48 months of follow-up. Capillary density per 3 mm was measured with widefield nailfold capillary microscopy. Improvement of capillary density per finger and mean capillary density were analysed. Longitudinal measurements of mean capillary density were analysed by generalized estimating equation. Eighty patients (68 women, 12 men) met the inclusion criteria. The median follow-up time was 27 months. Twenty-eight patients had an improved capillary density in per-finger analysis. MMF was associated with fewer numbers of fingers that had worsened in capillary density. Anti-topoisomerase antibodies were associated with low mean capillary density. Anti-RNA polymerase III antibodies were associated with improvement and anti-centromere antibodies with worsening of capillary density in per-finger analysis. MMF treatment was associated with less steep capillary density decline in a moderated generalized estimating equation model including presence of anti-topoisomerase antibodies and the interaction of MMF with follow-up time. Nailfold capillary density improved over time in a substantial proportion of SSc patients. MMF treatment had a positive impact on the evolution of capillary density in these patients. SSc autoantibody phenotype may affect the capillary density development. The data support previous hypotheses that early immunosuppression may favourably affect vascular regeneration in SSc.

Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach

During the thermomechanical processing of titanium alloys in the β-domain, the β-phase undergoes restoration phenomena. This work describes them by a mean-field physical model that correlates the flow stress with the microstructural evolution. To reduce the computational time of process simulations, metamodels are developed for specific outputs of the mean-field physical model using Artificial Neural Network (ANN) and Decision Tree Regression (DTR). The performance of the obtained metamodels is evaluated in terms of the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean relative error (MRE). No significant difference was observed between R2training and R2testing, meaning that all the metamodels correctly generalise the overall behaviour of the outputs for a wide range of inputs. The evolution of the metamodel outputs is compared with the model predictions in two different situations: 1) at a constant strain rate and temperature, and 2) during Finite Element (FE) simulations of the hot deformation of a hat-shaped sample, where temperature and effective strain rate vary at each element during deformation. The evolution of the outputs at constant and non-constant strain rates and temperature demonstrated the robustness of the metamodels in predicting the heterogeneous deformation within a workpiece. The computational time required by the metamodels to calculate selected outputs can be more than 100 times less than that of the model itself at a constant strain rate using MATLAB® and up to 19% less when coupled with FE simulations. The simulation results combined with microstructural analysis are used to visualise the different restoration mechanisms occurring in different regions of the hat-shaped sample as a function of the local thermomechanical history. The changes in strain rate and temperature during deformation influence the evolution of the wall dislocation density and the immobilisation rate of mobile dislocations at subgrain boundaries, leading to different kinetics of microstructure evolution.

The role of Ca on the microstructure and tensile properties of Mg-Al-Zn-Ca alloys

Synergy between Ca and other alloying elements such as Al and Zn can modify the microstructure and mechanical properties of Mg alloys. In this work, we compared four extrusion alloys: Mg-6Al-2 Zn, Mg-6Al-2Zn-0.5Ca, Mg-6Al-2Zn-1.0Ca, and Mg-6Al-2Zn-2.0Ca (all in wt.%). Tensile properties were remarkably not improved with Ca addition. The underlying reasons were explored using in situ synchrotron X-ray diffraction, electron microscopy, and cyclic loading tests. It is found that solute Ca imposes a softening effect on Mg's slip systems. The slight increase of the yield strength by the formation of Al2Ca particles is fully offset by the reduced Al solute strengthening. Texture weakening and resultant ductility enhancement reported in Mg-Zn-Ca ternary alloys is largely compromised by the high amount of Al. Nevertheless, Al2Ca particles can enhance the materials’ strain hardening by inducing back stress. An elastic-viscoplastic self-consistent (EVPSC) model was employed to simulate the deformation of the four alloys. Discrepancies between the simulation results and the experimental measurements mostly occur in the microscopic level (e.g. lattice strains and dislocation density evolution), and the reasons will be discussed.

EESD special issue: AI and data‐driven methods in earthquake engineering – (Part 1)

EESD special issue: AI and data‐driven methods in earthquake engineering – (Part 1)

Electron transport properties and free electron density evolution during the phase change in VO2 thin films

The experimental results on the phase change behavior of vanadium dioxide (VO2) as a function of temperature are invariably examined in the light of the strong electron correlation-based Mott-Hubbard model or the periodic lattice distortion based Peierls model with limited success. Further, the experimental conditions-based nanostructure imparted to the VO2 thin films also plays an important role. Hence, it has been suggested that the theoretical model that best suits the experimental conditions needs to be applied. In this work, we have examined the electron transport properties of VO2 thin films of various thicknesses all along the reversible phase change cycle. These results have led us to carefully examine the free electron density (ne) evolution all along the phase transition cycle and to establish for the first time the reversible hysteresis cycles of this important parameter. The intimate correlation between the degree of change in ne and the resistance seen in our samples leads us to believe that our results may be in favor of the Mott-Hubbard model of phase transition.

Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models

AbstractThe geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM), a physics‐based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE‐GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long‐Short Term Memory neural networks to perform time‐series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE‐GCM ROPE has similar error to previous linear approaches while improving storm‐time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE‐GCM ROPE can capture the position resulting from TIE‐GCM density with <5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7–18 km.