Diagonal Components Research Articles

Quarkonium polarization in medium from open quantum systems and chromomagnetic correlators

We study the spin-dependent in-medium dynamics of quarkonia by using the potential nonrelativistic QCD (pNRQCD) and the open quantum system framework. We consider the pNRQCD Lagrangian valid up to the order rM0=r and r0M=1M in the double power counting. By considering the Markovian condition and applying the Wigner transformation upon the diagonal spin components of the quarkonium density matrix with the semiclassical expansion, we systematically derive the Boltzmann transport equation for quarkonia with polarization dependence in the quantum optical limit. Unlike the spin-independent collision terms governed by certain chromoelectric field correlators, new gauge invariant correlators of chromomagnetic fields determine the recombination and dissociation terms with polarization dependence at the order we are working. We also derive a Lindblad equation describing the in-medium transitions between spin-singlet and spin-triplet heavy quark-antiquark pairs in the quantum Brownian motion limit. The Lindblad equation is governed by new transport coefficients defined in terms of the chromomagnetic field correlators. Our formalism is generic and valid for both weakly coupled and strongly coupled quark gluon plasmas. It can be further applied to study spin alignment of vector quarkonia in heavy ion collisions. Published by the American Physical Society 2024

Subgrid-scale model considering the inverse energy cascade using an artificial neural network

For the closure of the subgrid-scale (SGS) stress tensor, an artificial neural network (ANN)-based SGS model that takes account of the inverse energy cascade in isotropic turbulence is developed. The data required for training this ANN-based SGS model are provided by direct numerical simulation of isotropic turbulence with an inverse energy cascade. Two input features, the root mean square of the rate-of-strain tensor and the product of the eigenvalues of the rate-of-strain tensor, are employed to characterize the inverse energy cascade. An a priori test reveals that the ANN-based model adequately predicts the SGS stress tensor in the backward energy transfer process, and the predictive capability of the gradient model is found to be slightly poorer than that of the ANN-based model, while that of the Smagorinsky model is not satisfactory. In comparison with the gradient model, the ANN-based model even predicts a few backward energy transfer events in the stage of excessive energy dissipation. In addition, the off-diagonal component of the SGS stress tensor, rather than the diagonal component, may be intimately associated with the inverse energy cascade. The ANN-based SGS model presented here is expected to provide inspiration for future investigations of the construction of SGS models that take account of the inverse energy cascade.

Group-random algorithm to generate representative volume element models for composites

One of the most commonly used methods for characterizing the mechanical properties of discontinuous fiber reinforced composites (DFRC) is to establish a Representative Volume Element (RVE) model and perform finite element (FE) analysis. However, FE analysis on RVE models established by traditional sampling algorithms is often computationally expensive due to the large size of RVE that is required to be statistically representative of the composite. To address this issue, this paper proposes a new approach for constructing RVE models with more accurate description of fiber orientation, aiming to make the FE modelling more efficient by using an RVE with small size. When establishing RVE models with given target fiber orientation tensor, it is very challenging to accurately capture the orientation of fibers. In order to mitigate the error between the orientation tensor reconstructed by fibers generated in the RVE and the target orientation tensor, a group-random algorithm is proposed in the current work to generate RVE models. Unlike the traditional algorithm, in which fibers are sampled one by one in the RVE, the group-random algorithm samples a group of four fibers at one time in order to eliminate the error of the off-diagonal components of the reconstructed orientation tensor in the principal coordinate system. Then a modification tensor is further introduced to mitigate the error of the diagonal components of the reconstructed orientation tensor. Simulation results show that the orientation tensor error could be significantly reduced by the group-random algorithm even for the RVE with low number of fibers. The merits of the group-random algorithm are also witnessed by the stability and accuracy of predicting the elastic constants of composite materials through RVE modeling. It is thus concluded that the major advantage of this work is to provide an alternatively feasible strategy to substantially improve computational efficiency of RVE modelling.

Design of broad quasi-zero stiffness platform metamaterials for vibration isolation

Adaptability and reliability are challenges in designing vibration isolation structures, and mechanical metamaterials featuring broad quasi-zero stiffness (QZS) platforms are among the most promising candidates for addressing this issue. This paper proposes a novel design of vibration isolation metamaterials featuring a broad QZS platform to achieve vibration control in complex environments. The metamaterial unit cells are designed by integrating horizontal and diagonal beams based on the mechanism combining Euler buckling and flexural deformation. Herein, the component made of diagonal beams is configured to exhibit negative stiffness behavior, while the designed support components aim to relax the boundary constraints of the diagonal beam component, thereby mitigating the negative stiffness effect. By tuning the synergistic effects between horizontal and diagonal beams, QZS features can be achieved over a broad range of displacements. A combination of theoretical analysis, finite element method and experiment is employed to investigate the payload and QZS features of metamaterials comprehensively. Notably, the designed unit cell maintained a considerably broad QZS platform, with static experiments revealing that this platform accounts for approximately 55 % of the total loading range. Furthermore, the designed metamaterials exhibit excellent vibration isolation performance in the low-frequency range, with vibration experiments demonstrating that the unit cell can effectively shield vibrations across almost the entire range when the support mass corresponds to the QZS payload. The geometric parameters of the metamaterial configuration that influence the range of the QZS platform are also explored. In conclusion, the proposed mechanical metamaterials have a tunable and broad QZS platform with considerable potential for use in customized low-frequency vibration isolation applications.

Understanding Nonlinear Optical Phenomena in N-Pyrimidinyl Stilbazolium Crystals via a Self-Consistent Electrostatic Embedding - DFT Approach.

Density functional theory (DFT) and time-dependent density functional theory (TD-DFT) have been used to investigate the nonlinear optical (NLO) properties of phenolic N-pyrimidinyl stilbazolium cationic chromophore in its corresponding noncentrosymmetric crystals. Such a cationic chromophore, the OPR (4-(4-hydroxystyryl)-1-(pyrimidin-2-yl)pyridinium), consists of a strong electron donor, the 4-hydroxyphenyl group, and a strong electron acceptor, the N-pyrimidinylpyridinium group based on two electron-withdrawing groups. The in-crystal NLO properties were determined by applying a supermolecule approach in combination with an iterative electrostatic scheme, in which the surrounding molecules of a unit cell are represented by point charges. With CAM-B3LYP, our absolute estimates for the largest diagonal component of the second-order nonlinear susceptibility tensor of OPR-based crystals range from 64.00 to 80.34 pm/V in the static regime and from 162.09 to 175.52 pm/V at 1907 nm. These values are significant when compared to those of benchmark stilbazolium-based crystals. Furthermore, the third-order susceptibility, which is related to the nonlinear optical process of the intensity-dependent refractive index, is also significant compared to the results for other organic crystals, such as chalcone derivatives. With TD-CAM-B3LYP, the two-state model effectively explains the similarity in the first hyperpolarizability values in the crystalline phase. This similarity arises from the combination of the oscillator strength and the charge transfer of the crucial transition. Therefore, phenolic organic salt crystals show great promise for various nonlinear optical applications.

Improved gravity field estimation by incorporating the Tiangong Space Station and next-generation CAI satellite gravity mission

The next-generation gravity satellite mission equipped with the Cold Atom Interferometry (CAI) gradiometer has great potential for the Earth's gravity field estimation. Deploying a CAI gradiometer on the Chinese Tiangong Space Station launched for long-term Earth science research not only reduces the cost compared to a dual-satellite constellation but also enhances interdisciplinary collaboration in the Earth's gravity field detection. In this study, we conducted gravity gradient-based simulations to assess the contribution of deploying a CAI gradiometer on the Tiangong Space Station to collaboratively observe the Earth's gravity field with a polar-orbit gravity satellite. The simulation results demonstrate that whether utilizing Vyy component, three diagonal components or full components, the derived gravity field models show significant improvements within 100 degree and above 200 degree after incorporating Tiangong Space Station. In particular, the gravity field solution recovered from three diagonal components achieves the best accuracy. In the case of using diagonal components, the collaboration observation scheme effectively reduced the cumulative geoid height error by approximately 5.3cm (300 d/o). In the spatial domain, the incorporation of the Tiangong Space Station primarily impacts the estimated gravity field within the orbital coverage area of the space station, and this effect is particularly pronounced when just employing Vyy component. However, due to the limitation of angular velocity observation inaccuracy associated with the CAI gradiometer in nadir mode, there is no substantial accuracy improvement observed above 200 degree when adding gradient components.

Pulsar as a Weber detector of gravitational waves and a probe to its internal phase transitions

It is believed that cores of neutron stars provide a natural laboratory where exotic high baryon density phases of quantum chromo dynamics (QCD) may exist. In fact, the theoretically well-established neutron superfluid phase is also believed to be found only inside neutron stars. Focus on neutrons stars has tremendously intensified in recent years with the direct detection of gravitational waves (GWs) by LIGO/Virgo from binary neutron star (BNS) merger events which has allowed the possibility of directly probing the properties of the interior of a neutron star. A truly remarkable phenomenon manifested by rapidly rotating neutron stars is in their avatar as Pulsars. The accuracy of pulsar timing can reach the level of one part in 10[Formula: see text], comparable to that of atomic clocks. Indeed, it was such a great accuracy which had allowed the first indirect detection of GWs from a BNS system. Such an incredible accuracy of pulse timings points to a very interesting possibility. Any deformation of the pulsar, even if it is extremely tiny, has the potential of leaving its imprints on the pulses through introduction of tiny perturbations in the entire moment of inertia (MI) tensor. While, the diagonal components of perturbed MI tensor affect the pulse timings, the off-diagonal components lead to wobbling of pulsar, directly affecting the pulse profile. This opens up a new window of opportunity for exploring various phase transitions occurring inside a pulsar core, through induced density fluctuations, which may be observable as perturbations in the pulse timing as well as its profile. Such perturbations also naturally induce a rapidly changing quadrupole moment of the star, thereby providing a new source of GW emission. Another remarkable possibility arises when we consider the effect of an external GW on neutron star. With the possibility of detecting any minute changes in its configuration through pulse observations, the neutron star has the potential of performing as a Weber detector of GW. This brief review will focus on these specific aspects of a pulsar. Specifically, the focus will be on the type of physics which can be probed by utilizing the effect of changes in the MI tensor of the pulsar on pulse properties.

The solution of the nonlinear Poisson-Boltzmann equation using lattice-Boltzmann

In this paper we present the solution of the Poisson-Boltzmann equation using the Lattice-Boltzmann method. In order to obtain the solution, we use a redefinition of tensor n°, which is declared as a symmetric tensor whose diagonal components are chosen as the second derivative in time of the first moment of the distribution function, and the components outside of the diagonal give account of the nonlinear terms. The results are presented in two dimensions employing the D2Q9 lattice velocity scheme. We obtain results for the scalar field and its gradient for several kinds of initial conditions.

Three-dimensional biphase fabric estimation from 2D images by deep learning

A pruned VGG19 model subjected to Axial Coronal Sagittal (ACS) convolutions and a custom VGG16 model are benchmarked to predict 3D fabric descriptors from a set of 2D images. The data used for training and testing are extracted from a set of 600 3D biphase microstructures created numerically. Fabric descriptors calculated from the 3D microstructures constitute the ground truth, while the input data are obtained by slicing the 3D microstructures in each direction of space at regular intervals. The computational cost to train the custom ACS-VGG19 model increases linearly with p (the number of images extracted in each direction of space), and increasing p does not improve the performance of the model - or only does so marginally. The best performing ACS-VGG19 model provides a MAPE of 2 to 5% for the means of aggregate size, aspect ratios and solidity, but cannot be used to estimate orientations. The custom VGG16 yields a MAPE of 2% or less for the means of aggregate size, distance to nearest neighbor, aspect ratios and solidity. The MAPE is less than 3% for the mean roundness, and in the range of 5-7% for the aggregate volume fraction and the mean diagonal components of the orientation matrix. Increasing p improves the performance of the custom VGG16 model, but becomes cost ineffective beyond 3 images per direction. For both models, the aggregate volume fraction is predicted with less accuracy than higher order descriptors, which is attributed to the bias given by the loss function towards highly-correlated descriptors. Both models perform better to predict means than standard deviations, which are noisy quantities. The custom VGG16 model performs better than the pruned version of the ACS-VGG19 model, likely because it contains 3 times (p = 1) to 28 times (p = 10) less parameters than the ACS-VGG19 model, allowing better and faster cnvergence, with less data. The custom VGG16 model predicts the second and third invariants of the orientation matrix with a MAPE of 2.8% and 8.9%, respectively, which suggests that the model can predict orientation descriptors regardless of the orientation of the input images.

Relations between Reynolds stresses and their dissipation rates during premixed flame–wall interaction within turbulent boundary layers

A direct numerical simulation (DNS) database for head-on quenching of premixed flames propagating across turbulent boundary layers representative of friction Reynolds numbers, Reτ, of 110 and 180 has been utilized to analyze the interrelation between Reynolds stresses and their dissipation rates during flame–wall interaction. The Reynolds stresses and their dissipation rates exhibit significant deviations from the corresponding non-reacting flow profiles within the flame brush and in the burned gas region. This behavior is prominent for the components in the wall-normal direction because the mean direction of flame normal acceleration due to thermal expansion aligns with the wall-normal direction in this configuration. The anisotropy of Reynolds stresses and their dissipation rate tensors have been found to be qualitatively similar, but the anisotropic behavior weakens with increasing Reτ. However, the components of the anisotropy tensors of Reynolds stresses and viscous dissipation rate are not related according to a linear scaling, and thus, the models based on this assumption do not successfully capture the viscous dissipation rate components obtained from the DNS data. By contrast, a model, which includes the invariants of the anisotropy tensor of Reynolds stresses and satisfies the limiting conditions, has been found to capture especially the diagonal components of the viscous dissipation rate tensor more successfully for both non-reacting and reacting cases considered in this work. However, the quantitative prediction of this model suffers for the components in the wall-normal direction for lower values of Reτ, but the performance of this model improves with an increase in Reτ.

Stresses in silicon dioxide films deposited from dielectric targets: results of atomistic modeling

The previously proposed method of molecular dynamics simulation of the deposition of thin films from metal targets is adapted to the case of dielectric targets and applied to silicon dioxide films. The possibility of not only silicon atoms leaving the target, but also clusters with oxygen atoms was taken into account by adding O=Si=O molecules to the flow of deposited atoms. Atomistic film clusters were obtained by high-energy and low-energy deposition at different percentages of molecules in the flow of deposited atoms. The values of the stress tensor components are calculated. With high-energy deposition, compressive stresses are observed, with low-energy deposition, tensile stresses are observed. The absolute values of the diagonal components of the stress tensor increase with increasing proportion of molecules in the flow of deposited atoms.

Convergence properties of eigenfrequencies in RUS

An in-depth exploration of the asymptotic behavior of resonant frequencies in Resonant Ultrasound Spectroscopy (RUS), a non-destructive method for material property evaluation, is presented in this work. This work delves into the asymptotic analysis using Legendre polynomials for cuboid samples and examines the impact of increasing elements on eigenfrequencies. In this examination, we noted various elements concerning the computational boundaries and the influence of diagonal components on the asymptotes. The presence of a boundary for the asymptotes indicates limited information beyond a certain point. Additionally, how elastic constants influence these eigenfrequencies is discussed.

Accounting for mesoscale geometry and intra-yarn fiber volume fraction distribution on 3D angle-interlock fabric permeability

Understanding resin flows within 3D interlock fabrics involves addressing a dual-scale flow problem. Indeed, the fluid can flow through inter-yarn channels, characterized by a unit cell mesoscale morphology, but also within yarns considered as homogeneous equivalent porous media. The former is assumed to follow the Stokes’ law while the latter be related to the Darcy’s law, directly linked to the microscopic intra-yarn fiber volume fraction (FVF) field. In this work, a coupled Stokes–Darcy steady-state flow within a 3D woven textile, modeled at mesoscale, is solved through a finite element monolithic approach with a mixed velocity-pressure formulation, stabilized by the Variational MultiScale Method (VMS) and implemented in the Z-set software. The fabric permeability tensor is then computed without any assumptions on its nature and analyzed both through its diagonal components, its eigenvectors and eigenvalues. The main contribution of this study lies in examining the influence of variations of the inter-yarn porous medium morphology, through textile compaction and geometrical reduction to prevent edge flows, and the intra-yarn permeability (from complete field to unique value) on the overall fabric permeability.

Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis.

Graphs adequately represent the enormous interconnections among numerous entities in big data, incurring high computational costs in analyzing them with conventional hardware. Physical graph representation (PGR) is an approach that replicates the graph within a physical system, allowing for efficient analysis. This study introduces a cross-wired crossbar array (cwCBA), uniquely connecting diagonal and non-diagonal components in a CBA by a cross-wiring process. The cross-wired diagonal cells enable cwCBA to achieve precise PGR and dynamic node state control. For this purpose, a cwCBA is fabricated using Pt/Ta2O5/HfO2/TiN (PTHT) memristor with high on/off and self-rectifying characteristics. The structural and device benefits of PTHT cwCBA for enhanced PGR precision are highlighted, and the practical efficacy is demonstrated for two applications. First, it executes a dynamic path-finding algorithm, identifying the shortest paths in a dynamic graph. PTHT cwCBA shows a more accurate inferred distance and ≈1/3800 lower processing complexity than the conventional method. Second, it analyzes the protein-protein interaction (PPI) networks containing self-interacting proteins, which possess intricate characteristics compared to typical graphs. The PPI prediction results exhibit an average of 30.5% and 21.3% improvement in area under the curve and F1-score, respectively, compared to existing algorithms.

Magnetic impedance in nonstichiometric manganese sulfide

The role of defects on the dynamic characteristics of manganese sulfide is studied by impedance spectroscopy in the frequency range 102–106 Hz and temperatures 80–500 K. Nonstoichiometry plays an important role in the formation of new transport and magnetic properties, as it leads to electrically inhomogeneous states. The phase composition and crystal structure of nonstoichiometric manganese sulfide were studied on a DRON-3 X-ray unit using CuKα – radiation at room temperature. According to X-ray diffraction analysis, the synthesized compound is single-phase and has a NaCl-type cubic lattice. From the frequency dependences of the impedance components measured in the absence of a field and in a magnetic field, the relaxation time of the current carriers in the Debye model is found. A sharp decrease in the relaxation time and its correlation with conductivity were found. The contribution to the impedance of the active and reactive parts of the impedance at frequencies below and above the relaxation time is established. The capacitance from the impedance hodograph in the equivalent circuit model is determined. In defective manganese sulfide, the temperature-dependent impedance has an activation character. The activation energy is determined in the range 250–500 K, which is attributed to the excitation energy of lattice polarons. The effect of a magnetic field on the dynamic characteristics of current carriers was studied as a result of a change in the impedance components in a magnetic field at fixed temperatures. The impedance increases in a magnetic field and reaches a maximum in the temperature range of charge ordering of vacancies. An increase in the impedance in a magnetic field is explained by a decrease in the diagonal component of the permittivity in a magnetic field in an electrically inhomogeneous medium. The experimental data are explained in the Debye model.

Quality analysis of distributed max-plus algebra based morphological wavelet transform

This proposed technique employs a disseminated data embedding watermarking strategy that is based on a morphological wavelet transform and uses Max-plus algebra to achieve high image quality, big data capacity, and reasonable operational cost. Previously, we had to use a simple MMT watermarking methodology and estimated that using image quality parameters, primarily considering the diagonal component (HH). For embedding watermarks after the decomposition process carried through MMT, the presented distributed data embedding strategy, however, emphasises on middle frequency signal-groups. We have used hypothesized watermarking technique to process five benchmark images during the experiments to examine the scheme’s capability. The PSNR and the SSIM values are elevated by the proposed approach. The combined (high and low) frequency’s PSNR value is preferable towards the high frequency’s. It is also remarked that the middle-frequency SSIM value is higher than the high-frequency value.

High-frequency transport properties of a 2D electron gas with spin-orbit interaction under magnetic field driven topological transition

We study the transport properties of a two-dimensional electron system with an essential spin-orbit interaction under topological phase transition due to changing in-plane magnetic field. We have analyzed the contribution of diagonal and off-diagonal components of density matrix with respect to number of spin zone in the high-frequency conductivity. Analytical formulas for the conductivity tensor have been obtained. A numerical analysis giving full enough representation about the dependence of the conductivity on frequency and magnetic field is presented. We have shown that the contribution of the off-diagonal elements of density matrix is the greater than the higher the frequency of the alternating electromagnetic field. It has been established that for magnetic field value at which the minimum electron energy of upper spin zone exceeds the Fermi energy, the conductivity as a function of magnetic field change in discrete step.

Colossal Linear Magnetoelectricity in Polar Magnet Fe_{2}Mo_{3}O_{8}.

The linear magnetoelectric effect is an attractive phenomenon in condensed matters and provides indispensable technological functionalities. Here a colossal linear magnetoelectric effect with diagonal component α_{33} reaching up to ∼480 ps/m is reported in a polar magnet Fe_{2}Mo_{3}O_{8}. This effect can persist in a broad range of magnetic field (∼20 T) and is orders of magnitude larger than reported values in literature. Such an exceptional experimental observation can be well reproduced by a theoretical model affirmatively unveiling the vital contributions from the exchange striction, while the sign difference of magnetocrystalline anisotropy can also be reasonably figured out.

Achieving controllable multifunctionality through layer sliding

Dynamical variation of physical properties in a controllable fashion provides exciting possibilities to obtain multifunctional materials. In this work, layer-sliding is employed to modify the structural, interfacial electronic and optical properties of unintercalated and Mg-intercalated two-dimensional (2D) van der Waals heterostructure (vdW-HS) consisting of buckled silicene and hexagonal boron nitride (hBN). The most stable stacking configuration of silicene over hBN is screened out and then intercalated with Mg at the interface. Dynamical-dependent changes in the properties of vdW-HS are performed by sliding silicene over hBN monolayer in the absence and presence of the intercalant. Layer-sliding is carried out in equal length intervals, and various parametric quantities related to the physical characteristics of the vdW-HS are repeatedly calculated and compared. Apart from various parametric quantities, stability of unintercalated and Mg-intercalated vdW-HS is also checked by means of relative total energies, binding energies and vdW gaps along the sliding pathway. Comparison of binding energies shows that the un-slided, half-slided, and fully-slided Mg-intercalated vdW-HS are 1.52, 1.44 and 1.42 eV more stable than the unintercalated vdW-HS. Opening of a small band gap of 12, 31 and 28 meV for un-slided, half-slided and fully-slided unintercalated vdW-HS, respectively, is worth mentioning. To study the interfacial electronic behavior, planar average charge density difference (Δρ) and charge transfer (ΔQ) are also calculated and varied via layer-sliding. Further, we calculated diverse optical spectra such as the complex dielectric function (DF), electron energy loss function [L(ω)], diagonal components of dielectric tensor [ε(iω)], refractive index [n(ω)], extinction coefficient [k(ω)], absorption coefficient [α(ω)], and reflectivity [R(ω)] for un-slided, half-slided and fully-slided unintercalated and Mg-intercalated vdW-HS. Interestingly, the polarization and energy losses have been reduced in the case of Mg-intercalated vdW-HS. The suggested layer-sliding method can be established as a general scheme for bringing multifunctionality into a layered material.

Graph neural network for predicting the effective properties of polycrystalline materials: A comprehensive analysis

We develop a polycrystal graph neural network (PGNN) model for predicting the effective properties of polycrystalline materials, using the Li7La3Zr2O12 ceramic as an example. A large-scale dataset with >5000 different three-dimensional polycrystalline microstructures of finite-width grain boundary is generated by Voronoi tessellation and processing of the electron backscatter diffraction images. The effective ion conductivities and elastic stiffness coefficients of these microstructures are calculated by high-throughput physics-based simulations. The optimized PGNN model achieves a low error of <1.4% in predicting all three diagonal components of the effective Li-ion conductivity matrix, outperforming a linear regression model and two baseline convolutional neural network models. Sequential forward selection method is used to quantify the relative importance of selecting individual grain (boundary) features to improving the property prediction accuracy, through which both the critical and unwanted node (edge) feature can be determined. The extrapolation performance of the trained PGNN model is also investigated. The transfer learning performance is evaluated by using the PGNN model pretrained for predicting conductivities to predict the elastic properties of the same set of microstructures.: