Dynamic Field Research Articles

Floquet engineering of a dynamical Z2 lattice gauge field with ultracold atoms

Abstract Gauge field theory is a fundamental concept in modern physics, attracting many theoretical and experimental efforts towards its simulation. In this paper we propose that a simple model, in which fermions coupled to a dynamical lattice gauge field, can be engineered via the Floquet approach. The model possesses both an independent Maxwell term and local Z 2 gauge symmetry. Our proposal relies on a species-dependent optical lattice, and can be achieved in one, two or three dimensions. By a unitary transformation, this model can be mapped into a non-interacting composite fermion system with fluctuating background charge. With the help of this composite fermion picture, two characteristic observations are predicted. One is radio-frequency spectroscopy, which exhibits no dispersion in all parameter regimes. The second is dynamical localization, which depends on the structure of the initial states.

Modelling study on pedestrian evacuation dynamics considering exit selection behaviour under flood disaster

Flood spreading in metro stations is a dangerous hazard that frequently causes casualties and property losses. However, pedestrian evacuation under flood spreading scenarios has not been fully investigated. To fill this gap,we propose an extend Floor-Field model incorporating flood spreading and exit selection behaviour of individuals. In the model, the dynamic flood spreading process on metro platforms is expressed by using a simulation software named Mike21. The optimal exit selected by pedestrians in each step is decided on three factors, that is pedestrian density around the exit, pedestrian distance to the exit, and water depth at the exit. Meanwhile, the movement speed of pedestrain also changes with the water depth at each time step. Based on this, the transition probability to neighbor cell including static field, dynamic field, height field, and water flow field is calculated. This results in a flood evacuation model considering the pedestrian's behaviour when choosing an exit. The effects of flooding, pedestrian density, different exit selection behaviours, water flow field, and inlet flow on evacuation were thoroughly analysed. The analysis demonstrates that the likelihood of flooding significantly impacts the evacuation of pedestrians from the subway station; the more flooding is taken into account, the more influential the impact. In addition, the effect of inlet flow on evacuation is also noteworthy; considering exit selection behaviour can significantly increase the effectiveness of evacuation. The study's findings can be used to develop evacuation plans for flooded metro stations.

Antibody-Drug Conjugates in Breast Cancer: Toward a Molecular Perspective Into Clinical Practice.

Antibody-drug conjugates (ADCs) are at the forefront of cancer therapy, combining targeted precision with potent cytotoxicity. Conceived by Paul Ehrlich in the early 1900s, the concept of a magic bullet selectively eliminating cancer cells has evolved alongside bioengineering and cancer biology advancements. ADCs consist of a monoclonal antibody, linker, and cytotoxic payload, designed to target specific antigens on tumor cells while minimizing collateral damage. Mechanistically, ADCs are internalized via endocytosis, releasing the cytotoxic payload within the lysosome, potentially affecting neighboring tumor cells. ADC development has progressed through multiple generations, each addressing limitations of its predecessors. From gemtuzumab ozogamicin to trastuzumab emtansine (T-DM1), and now to third-generation agents such as trastuzumab deruxtecan (DS-8201) and disitamab vedotin (RC48), improvements have been made in target selectivity, potency, linker stability, and reduced off-target effects. Significant success has been seen in ADCs targeting human epidermal growth factor receptor 2 and trophoblast cell-surface antigen 2 antigens, especially in patients with breast cancer, including those resistant to previous therapies. The future of ADCs includes exploring new surface antigens, bispecific antibodies, immune-activating antibodies, radiopharmaceutical-loaded ADCs, and masked ADCs for tissue-specific activation. Ongoing research aims to optimize treatment efficacy while minimizing toxicity, expanding the potential of combination therapy. ADCs represent a promising frontier in precision cancer treatment, with continued research enhancing their potential in breast cancer and beyond. This review provides a comprehensive exploration of ADCs' evolution in breast cancer therapy, offering a molecular perspective to inform clinical practice and update colleagues on this dynamic field.

Numerical Approach of the First Instability Appearance in Inclined Taylor–Couette System

The present study numerically investigates the three-dimensional forced and mixed convection heat transfer in an inclined Taylor–Couette system (η=0.5 and Γ=20) submitted to a radial temperature gradient. The main objective of this study is to determine the effect of the angle inclination duct on the thermal and dynamic fields. Several cases have been dealt with depending on the inclination angle to detect the critical Reynolds number in each case. The model of the conservation equations with their boundary conditions is numerically solved by the finite volume method with a second-order spatiotemporal discretization. The results show that, in forced convection, the effect of the inclination angle is inexistent on the velocity field. However, with the presence of buoyancy effects, which impact flow stability and the transition to turbulence, the inclination influences both velocity and temperature fields. It also shows that selecting the vertical position of the annulus is preferable to obtain hydrodynamic stability in mixed convection. At the same time, from the thermal point of view, it is preferable to select the horizontal position to get dynamic and thermal stability.

Effects of velocity regularization on neural network performance in processing particle images

Recent studies have witnessed remarkable progress in harnessing convolutional neural networks (CNNs) to overcome the inherent limitations of conventional particle image velocimetry (PIV) methods. Traditional PIV techniques often suffer from compromised resolution and precision, hindering their ability to capture the complexities of fluid dynamics within the observation frame. While CNNs offer promise in addressing these challenges, they face obstacles such as limited accuracy, weak generalization, and a dearth of physical interpretability. In our prior research, we presented a CNN architecture that incorporates optical flow algorithms as supplementary physical constraints, thereby bolstering the model interpretability and precision. Nevertheless, the practical implications of this approach, especially when dealing with multi-dimensional, low-quality particle image data and restricted training sets, have yet to be fully explored. To address this knowledge gap, we have assembled a comprehensive dataset that simulates a wide array of experimental scenarios. We have systematically assessed the influence of velocity regularization on neural network performance, taking into account variations in image quality and the size of training datasets. The results underscore the pivotal importance of velocity regularization in enhancing the predictive prowess of neural networks, particularly when dealing with poor image quality and smaller data sizes. This study provides useful insights into the effective application of CNNs with velocity regularization in the field of experimental fluid dynamics.

Broadband elastic energy harvesting based on achromatic meta-grating

Energy harvesting exploiting the inverse piezoelectric effect has been the subject of much attention and discussion in the field of elastic and structural dynamics. Recently, the ongoing development of elastic metamaterials and metasurfaces has opened up a new way to improve the quality of energy harvesting. Here, we proposed a new strategy for harvesting elastic energy in a plate, which is the use of the inverse piezoelectric effect to convert the elastic energy into electrical energy after the achromatic meta-grating has focused broadband flexural waves. A new theoretical method to design the achromatic meta-grating is proposed based on derived analytical expression of the phase shift of subunit. When a meta-grating, a thin plate and a piezoelectric patch are combined into an energy harvesting system, the elastic energy can be converted into electric energy by the system, and the output voltage can be amplified by twice that of the system without the meta-grating. A theoretical framework is built to analyze the performance of the energy harvesting system, and variational parametric analyses are carried out to obtain the optimal resistance, the optimal length, thickness and position of piezoelectric patch, which are 870Ω, 18mm, 0.2mm and 30mm, respectively. For the optimized system, the power harvested rate of the system is close to 4 in the frequency band of 6-8kHz. Finally, the design of the system based on the wave focusing principle is extended, and energy harvesters are designed for different frequency bands, which can all work under different excitation conditions (a local and a base excitations). Our work opens up a new route for elastic energy harvesting and may have broad application prospects in the development of self-powered sensors.

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain. This approach provides significant advantages in addressing diverse difficulties in the field of complex fluid dynamics. We thoroughly investigated the design of the model architecture, the optimization of the convergence rate, and the development of computational modules for PINNs. However, efficiently and accurately utilizing PINNs to resolve complex fluid dynamics problems remain an enormous barrier. For instance, rapidly deriving surrogate models for turbulence from known data and accurately characterizing flow details in multiphase flow fields present substantial difficulties. Additionally, the prediction of parameters in multi-physics coupled models, achieving balance across all scales in multiscale modeling, and developing standardized test sets encompassing complex fluid dynamic problems are urgent technical breakthroughs needed. This paper discusses the latest advancements in PINNs and their potential applications in complex fluid dynamics, including turbulence, multiphase flows, multi-field coupled flows, and multiscale flows. Furthermore, we analyze the challenges that PINNs face in addressing these fluid dynamics problems and outline future trends in their growth. Our objective is to enhance the integration of deep learning and complex fluid dynamics, facilitating the resolution of more realistic and complex flow problems.

Skyrmion dynamics in attractive and repulsive local magnetic fields

The study of the behavior of magnetic skyrmions in local magnetic fields’ nanometric length-scale has gained increasing interest in recent years due to the theoretical proposal of magnetic skyrmion–superconducting vortex pairs as potential hosts for topologically protected bound states, which hold high promise for applications in quantum computing. From a magnetic interaction point-of-view, the key interest lies in understanding the skyrmion dynamics triggered by the magnetic energy landscape generated by the superconducting vortex. Here, we present a micromagnetic study of the dynamics of nanometric skyrmions inside a Gaussian magnetic field profile, which is used as a simplified version of the vortex magnetic flux. On the one hand, our calculations show that local non-linear magnetic fields can be very effective in controlling the dynamics of magnetic skyrmions; in particular, they offer the appealing possibility to manipulate skyrmions in a two dimensional space. On the other hand, they also show that the dynamics of a skyrmion in a local magnetic field can be manipulated via a uniform external magnetic field without any change in the magnetic field gradient. An analytical expression for the skyrmion velocity is given, and the corresponding microscopic dynamics are confirmed by the micromagnetic simulations. This work is expected to motivate more theoretical and experimental studies of the behavior of magnetic skyrmions in proximity to superconducting vortices.

A Two-dimensional Numerical Characterization on the Droplet Dynamics in the Electric Field by VOSET Method

A coupled volume-of-fluid and level set (VOSET) model is extended to the simulation of electro-hydrodynamic (EHD) flow. The good accuracy of proposed model is validated by comparing with previous results. Although the electrostrictive force might be greater than the Coulomb and dielectric forces under certain conditions, it has no influence on droplet dynamic behaviors except the pressure distribution. The electro-coalescence of droplet pair is systematically investigated. In addition to the coalescence and repulsion, two droplets might neither coalesce nor repulse with the repulsive hydrodynamic force and attractive electric force strike a balance. The electro-coalescence of two droplets always happens as long as the electric conductivity ratio is smaller than the permittivity ratio. The critical permittivity ratio separating the coalescence and repulsion of droplets increases as the increase of electric conductivity ratio. The electro-coalescence time of two droplets decreases as the permittivity ratio and electric capillary number increase. Nevertheless, the electro-coalescence time shows different variation tendency as the increase of electric conductivity ratio with different permittivity ratios and electric capillary numbers.

Gravitational Wave Ringdown Analysis Using the F -statistic

After the final stage of the merger of two black holes (BHs), the ringdown signal takes an important role in providing information about the gravitational dynamics in strong field. We introduce a novel time-domain (TD) approach, predicated on the F -statistic, for ringdown analysis. This method diverges from traditional TD techniques in that its parameter space remains constant irrespective of the number of modes incorporated. This feature is achieved by reconfiguring the likelihood and analytically maximizing over the extrinsic parameters that encompass the amplitudes and reference phases of all modes. Consequently, when performing the ringdown analysis under the assumption that the ringdown signal is detected by the Einstein Telescope, the parameter estimation computation time is shortened by at most 5 orders of magnitude compared to the traditional TD method. We further establish that traditional TD methods become difficult when including multiple overtone modes due to close oscillation frequencies and damping times across different overtone modes. Encouragingly, this issue is effectively addressed by our new TD technique. The accessibility of this new TD method extends to a broad spectrum of research and offers flexibility for various topics within BH spectroscopy applicable to both current and future gravitational wave detectors.

ReLU surrogates in mixed-integer MPC for irrigation scheduling

Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problems in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing Rectified Linear Unit (ReLU) surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times—by up to 99.5%—while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach achieves enhanced performance in terms of irrigation water savings and IWUE compared to the triggered approach.

Data-driven MHD Simulation of the Formation of a Magnetic Flux Rope and an Inclined Solar Eruption

Solar energetic events are caused by the release of magnetic energy accumulated in the solar atmosphere. To understand their initiating physical mechanisms, the dynamics of the coronal magnetic fields must be studied. Unfortunately, the dominant mechanisms are still unclear due to a lack of direct measurements. Numerical simulations based on magnetohydrodynamics (MHD) can reproduce the dynamical evolution of solar coronal magnetic field, providing a useful tool to explore flare initiation. Data-driven MHD simulations, in which the time-series observational data of the photospheric magnetic field is used as the simulation boundary condition, can explore different mechanisms. To investigate the accumulation of free magnetic energy through a solar eruption, we simulated the first of several large flares in NOAA active region 11283. We used a data-driven model that was governed by zero-beta MHD, focusing on the free magnetic energy accumulation prior to the M5.3 flare (2011 September 6 at 01:59 UT). We reproduced the flare-associated eruption following the formation of twisted magnetic fields, or a magnetic flux rope (MFR), formed by photospheric motions at its footpoints. We found that the eruption was first triggered by the growth of the torus instability. The erupting MFR caused magnetic reconnections with neighboring magnetic field lines located along the direction of the eruption. Using the simulation results and an axial-radial decay index centered on the MFR, we find a natural explanation for the inclination of the eruption and a possible approach to predict the direction of solar eruptive events.

Surrogate Model of Shale Stress Based on Plackett-Burman and Central Composite Design

Summary Multifactor analysis and accurate prediction of dynamic stress in shale reservoirs are of great practical significance for designing hydraulic fracturing. In this paper, a surrogate model for the rapid prediction of shale stress is proposed by considering the geomechanical heterogeneity and multiscale seepage mechanism of shale gas. The Plackett-Burman method is used to compare the influence of different parameters on shale stress, and significant parameters are selected as decision variables for establishing a surrogate model. The surrogate model for predicting stress is obtained by central composite design fitting, and the interaction of significant factors on shale stress is studied. The results show that after considering the heterogeneity, the minimum horizontal stress fluctuation range is 20.25 to 44.03 MPa and the maximum horizontal stress fluctuation range is 26.46 to 49.77 MPa in the area controlling hydraulic fracture. The initial reservoir pressure, as well as the length and width of hydraulic fractures, are the key factors influencing reservoir stress. The analysis of variance demonstrates that the proposed method is effective for predicting shale stress. The research results are helpful for gaining a deeper understanding of the evolution mechanism of dynamic stress fields in shale reservoirs and provide guidance for treatment design and dynamic optimization of gas wells.

Fireball distribution characteristics and thermal radiation effects in the explosion of aviation kerosene storage tank

Aviation kerosene acts as the main fuel for civil and military aircrafts. In order to study the explosion fireball distribution and thermal radiation effects of aviation kerosene storage tank, the dynamic explosion test system was established. The development law of explosion fireball and the thermal radiation characteristics in the presence of various oil volumes (empty, bottom, half and full oil) under the dynamic impact of projectile were studied. The dynamic explosion process was divided into four stages: projectile explosion, aviation kerosene jet flow, aviation kerosene deflagration and pool fire. The evaluation model (Y=aXb) for maximum diameter, maximum height, duration of fireball and oil mass was established. The fireball temperature showed a trend of ‘rise-fall-rise-oscillation attenuation’. The maximum fireball temperature and average temperature increased first and then decreased as increasing the oil volumes, with the maximum values at 1534.67 K and 1285.78 K, respectively. Based on the fireball dynamic model, the damage effect of fireball thermal radiation on personnel was analyzed, the safety radius and injury probability were obtained, and the dynamic explosion fireball thermal radiation field was constructed.

Systematic simulation of tumor cell invasion and migration in response to time-varying rotating magnetic field.

Cancer invasion and migration play a pivotal role in tumor malignancy, which is a major cause of most cancer deaths. Rotating magnetic field (RMF), one of the typical dynamic magnetic fields, can exert substantial mechanical influence on cells. However, studying the effects of RMF on cell is challenging due to its complex parameters, such as variation of magnetic field intensity and direction. Here, we developed a systematic simulation method to explore the influence of RMF on tumor invasion and migration, including a finite element method (FEM) model and a cell-based hybrid numerical model. Coupling with the data of magnetic field from FEM, the cell-based hybrid numerical model was established to simulate the tumor cell invasion and migration. This model employed partial differential equations (PDEs) and finite difference method to depict cellular activities and solve these equations in a discrete system. PDEs were used to depict cell activities, and finite difference method was used to solve the equations in discrete system. As a result, this study provides valuable insights into the potential applications of RMF in tumor treatment, and a series of in vitro experiments were performed to verify the simulation results, demonstrating the model's reliability and its capacity to predict experimental outcomes and identify pertinent factors. Furthermore, these findings shed new light on the mechanical and chemical interplay between cells and the ECM, offering new insights and providing a novel foundation for both experimental and theoretical advancements in tumor treatment by using RMF.

Charge self-consistent dynamical mean field theory calculations in combination with linear combination of numerical atomic orbitals framework based density functional theory

Charge self-consistent dynamical mean field theory calculations in combination with linear combination of numerical atomic orbitals framework based density functional theory

Station keeping control method based on deep reinforcement learning for stratospheric aerostat in dynamic wind field

Aiming at the problem of long-endurance station keeping of stratospheric aerostats under energy constraints in dynamic wind environment, a stratospheric aerostat station keeping controller was designed using the D3QN (Dueling Double Deep Q Network) algorithm of deep reinforcement learning in this article. The performance of the station keeping controllers are evaluated by the average station keeping radius and station keeping effective time ratio. Meanwhile, the effects of different reward functions on the performance of station keeping controller are studied. The simulation results of typical scenarios with European Centre for Medium-Range Weather Forecasts (ECMWF) data show that: under the task constraints of three days of station keeping time and 50 km of station keeping radius, the average station keeping radius of the stratospheric aerostat without power propulsion is 28.25 km, the station keeping time ratio is 90.56%, and the State of Charge (SOC) of the battery is more than 50%. Under the condition of propulsion, the control effect of the average station keeping radius of stratospheric aerostat is significantly improved. The control effect of adding dynamic propulsion in the north-south direction is better than that in the east-west direction, which is close to the dual-channel control effect. At the same time, this study verifies that the station keeping controller based on deep reinforcement learning has strong robustness, and demonstrates that the station keeping controller can be designed with different reward functions for the requirements of different station keeping task.

Electronic, transport, and thermodynamic properties of plutonium monoxide: Density functional theory combined with dynamical mean field theory

Electronic, transport, and thermodynamic properties of plutonium monoxide: Density functional theory combined with dynamical mean field theory

An Efficient Method for Identifying Inter-Well Connectivity Using AP Clustering and Graphical Lasso: Validation with Tracer Test Results

Identifying inter-well connectivity is crucial for optimizing reservoir development and facilitating informed adjustments. While current engineering methods are effective, they are often prohibitively expensive due to the complex nature of reservoir conditions. In contrast, methods that utilize historical production data to identify inter-well connectivity offer faster and more cost-effective alternatives. However, when faced with incomplete dynamic data—such as long-term shut-ins and data gaps—these methods may yield substantial errors in correlation results. To address this issue, we have developed an unsupervised machine learning algorithm that integrates sparse inverse covariance estimation with affinity propagation clustering to map and analyze dynamic oil field data. This methodology enables the extraction of inter-well topological structures, facilitating the automatic clustering of producers and the quantitative identification of connectivity between injectors and producers. To mitigate errors associated with sparse production data, our approach employs sparse inverse covariance estimation for preprocessing the production performance data of the wells. This preprocessing step enhances the robustness and accuracy of subsequent clustering and connectivity analyses. The algorithm’s stability and reliability were rigorously evaluated using long-term tracer test results from a test block in an actual reservoir, covering a span of over a decade. The results of the algorithm were compared with those of the tracer test to evaluate its accuracy, precision rate, recall rate, and correlation. The clustering results indicate that wells with similar characteristics and production systems are automatically grouped into distinct clusters, reflecting the underlying geological understanding. The algorithm successfully divided the test block into four macro-regions, consistent with geological interpretations. Furthermore, the algorithm effectively identified the inter-well connectivity between injectors and producers, with connectivity magnitudes aligning closely with actual tracer test data. Overall, the algorithm achieved a precision rate of 79.17%, a recall rate of 90.48%, and an accuracy of 91.07%. This congruence validates the algorithm’s effectiveness in the quantitative analysis of inter-well connectivity and demonstrates significant potential for enhancing the accuracy and efficiency of inter-well connectivity identification.

A multi-scale method for the study of deep drying of ethylene with 3A zeolite

Coal-based ethanol to ethylene (ETE) is an emerging production pathway with the advantages of a short construction period, high product purity, and few by-products. N2 is commonly used in industry to regenerate zeolites during the ETE process, with the problems of excessive material consumption, energy consumption and environmental pollution. To solve this problem, this paper proposed a method of using the ethylene product as regeneration gas to regenerate the zeolite and recover the desorbed gas. In this work, we adopted a multi-scale simulation method combining microscale apparent diffusivity and macroscale mass transfer models. Reactive force field and molecular dynamics (ReaxFF-MD) were used to obtain an equation for the diffusion coefficient versus temperature. The start-up of the twin-tower temperature and pressure swing adsorption (TPSA) process was simulated using Computational Fluid Dynamics (CFD). The TPSA process achieved an outlet water content below 5 ppm, fulfilling the process specifications.