The European Physical Journal Plus最新文献

This paper aims to analyze the adoption of physics-informed neural networks (PINNs) in solving the Allen–Cahn equation, which represents a fundamental model in phase field dynamics that captures processes such as phase segregation and interface dynamics in materials. To solve the Allen–Cahn equating system under three different initial conditions, PINNs have been employed and shown to achieve very reflective solutions with few numbers of iterations. A comparison with standard numerical solutions verifies the good accuracy of PINN in modeling the nonlinear dynamics of complicated systems. The results indicate that initial conditions play an important role in the rate and nature of phase evolution: lower amplitude initial perturbations reach equilibrium configurations more quickly with minimum interface roughness, whereas higher initial amplitudes represent multi-stage complex interface evolution. It is evident that the dynamics of the Allen–Cahn equation force the phase field toward equilibrium by minimizing the interfacial energy in time. This study further examines the influence of the mobility (L) and interface (ϵ) thickness on phase evolution. Higher mobility accelerates interface migration, thereby enhancing phase separation, although rapidly changing initial conditions present an exception, temporarily increasing interfacial complexity. Similarly, the impact of the interface thickness varies with the initial profile, offering uniform phase separation for smoother configurations, but exhibiting spatially uneven effects when the initial profile contains abrupt variations. These findings highlight PINNs as a highly effective tool for phase field modeling, capable of simulating dynamic systems with accuracy and computational efficiency, thus extending the scope of PINNs in kinetic-controlled applications such as alloy solidification and polymer phase separation.

This paper presents a detailed numerical study of airflow and heat transfer around a heated square obstacle controlled by three partitions in a horizontal channel at a fixed Reynolds number ((text{Re} = 150)). The numerical approach employed is the lattice Boltzmann method (LBM). The primary objective is to examine the influence of key geometric parameters, namely the gap spacing g between the cylinder and the partitions, and the partition’s length (Lp), on both drag reduction and heat exchange enhancement. The results highlight that when the partitions are positioned upstream of the obstacle, a significant reduction in the drag coefficient is achieved due to the disruption of the approaching boundary layer, which weakens the vortex shedding behind the cylinder. The peak drag reduction of (66.10%) is observed at (g=1.5d), as the partitions effectively mitigate the adverse pressure gradient in the wake region. Further increasing the partition length to (Lp=2.5d) enhances this effect, leading to a maximum drag reduction of (72.68%). This configuration also promotes better thermal mixing, resulting in a uniform and consistent heat transfer enhancement across the obstacle’s surfaces. In contrast, when the partitions are placed downstream of the obstacle, the reduction in drag is less pronounced, reaching a maximum of (31.45%) at (g=2.5d). This is because the vortex shedding remains active, albeit with reduced intensity. However, this setup significantly enhances convective heat transfer, increasing the Nusselt number by (14%) compared to the case without partitions. The downstream partitions serve as flow stabilizers, promoting heat advection away from the heated surfaces and reducing thermal recirculation zones. The most efficient configuration combines both upstream and downstream partitions, leading to an optimal aerodynamic and thermal performance. In this case, the upstream partitions effectively weaken the vortex shedding, while the downstream partitions act as additional flow stabilizers, further reducing pressure drag. This synergistic effect results in a maximal drag reduction of (78.31%), coupled with a (15.35%) improvement in the Nusselt number. The presence of both partitions ensures a more uniform temperature distribution and enhances convective heat dissipation, demonstrating the effectiveness of flow control strategies in optimizing both aerodynamic and thermal characteristics.

In this investigation, we study the dynamics of a diffusive prey-predator system in a time-periodic environment, incorporating prey refuge and supplementary food resources for predators. We establish solution’s boundedness, and derive the conditions for population persistence and extinction. We find that whenever both prey and predator populations persist, a unique periodic solution arises that is globally asymptotically stable. Our numerical results show that increasing prey refuge (or alternative food) stabilizes the system, shifting it from oscillatory behavior to stable coexistence. However, excessive alternative food availability can drive prey species to extinction, leaving predator as the sole surviving species. Further, we observe that a time-periodic environment significantly affects oscillatory patterns, driving transitions between uniform bulk oscillations and oscillating Turing patterns. Overall, our study highlights the crucial influence of time-periodic environmental factors on prey-predator dynamics, considering prey refuge and additional food for predators, and stresses the need to account for such variability in ecological systems.

The generalized uncertainty principle (GUP), as a key model in quantum gravity, is crucial for exploring cosmological properties and related challenges. In this paper, we investigate the effects of a higher-order GUP on primordial Big Bang nucleosynthesis (BBN). First, using a new higher-order GUP, we derive the modified Friedmann equations and the corresponding thermodynamic properties of the universe within the quantum gravity framework. Next, we analyze BBN under these modifications. Finally, by incorporating observational data of BBN and the primordial light element abundances, respectively, we establish constraints on the GUP parameter. The results reveal a significant impact of the GUP on the BBN processes in the early universe. Notably, the GUP parameter is found to take both positive and negative values, which is different from the classical case.

We investigate the propagation characteristics of apertured Gaussian and Bessel beams in a nontrivial spacetime background characterized by a constant 4-vector. Such 4-vector introduces a privileged direction in spacetime, thus breaking Lorentz invariance. The scalar beam is modeled by a massless scalar field theory, which corresponds to the scalar sector of the standard model extension. Assuming an axially symmetric Lorentz-violating background, we employ the Green’s function method to investigate the propagated field for particular input fields. We investigate the impact of the Lorentz-violating parameter upon the intensity and focalization of the beams. Finally, we discuss possible realizations in optically transparent multiferroic materials.

Ebola virus disease, often referred to as Ebola hemorrhagic fever, is one of the deadliest viral infections, posing a severe global health threat. It typically originates from human contact with domestic or wild animals and spreads through direct and indirect human contact, making containment highly challenging. Managing and controlling the spread of Ebola disease remains a significant challenge in epidemic response efforts. This study introduces a novel compartmental model to examine Ebola disease transmission dynamics and the effectiveness of control strategies. We conduct a mathematical analysis to ensure the model’s well-posedness and explore its stability properties. The theoretical results are verified using three numerical methods: the Euler’s method, the fourth-order Runge–Kutta method, and the non-standard-finite-difference method. Furthermore, the impact of time-invariant vaccination and quarantine rates on the epidemic is analyzed using the non-standard-finite-difference approach. A sensitivity analysis is conducted on the model to identify the most influential parameters affecting disease transmission. Additionally, we formulate an optimal control problem to identify effective, time-dependent strategies for Ebola vaccination and quarantine measures. As a novel contribution, our findings emphasize the potential of these control strategies in reducing both infection rates and associated costs, with a particular focus on the most reliable non-standard finite difference scheme. The application of forward and backward-in-time non-standard finite difference method ensures numerical stability and preserves essential biological properties. Numerical simulations demonstrate that a combination of effective vaccination and quarantine measures, and public awareness can accelerate the control of Ebola virus disease. Overall, this study provides a comprehensive approach to modeling, analyzing, and controlling Ebola virus disease by integrating advanced mathematical techniques with practical disease management strategies.

This study examines the thermal properties of phase change materials (PCMs) using molecular dynamics simulation, concentrating on a cylindrical system including octadecane and water as PCMs. The study was executed in two phases: atomic structure equilibration and thermal analysis. During equilibration, the system's potential and total energy reached stability at 10 ns, confirming thermodynamic equilibrium for future study. The thermal analysis phase investigated the impact of altering the initial temperature (IT) on the system's charging and discharging behavior. Raising the initial temperature from 300 to 350 K resulted in a decrease in charging time, from 6.58 to 6.16 ns, attributable to improved atomic mobility and increased frequency of atomic collisions. At elevated temperatures, atoms gain more kinetic energy, promoting accelerated energy transfer and enhanced energy absorption efficiency. The raised temperature induced alterations in the system's atomic characteristics: atomic density diminished owing to thermal expansion, while the average atomic velocity and system temperature escalated in reaction to heightened atomic energy. Furthermore, the elevated temperature enhanced the thermal efficiency of the system, as seen by augmented heat flow and thermal conductivity. These enhancements resulted from accelerated atomic motion and more effective energy dispersion inside the material. The study shows that elevating the initial temperature significantly improved the thermal efficiency of PCM systems, providing critical insights for the optimization of thermal storage and heat management systems.

This work examines how complexity affects time-independent, spherical symmetric celestial systems using a radial metric distortion approach (commonly referred to be minimal geometric deformation) in f(T) theory, where T is torsion scalar. We illustrate that the complexity factor, a scalar function derived by dividing the Riemann tensor perpendicularly, has a supplementary feature. The entire complexity of an entity with two associated fluid distributions is just a combination of the complexities associated with each fluid. This work uses the radial metric distortion method to create astrophysically feasible models of anisotropic matter, based on the Tolman and Buchdahl models. The two frameworks generate qualitatively equivalent features for every non-null value of the decoupling constant ((0le beta <1)), while the magnitudes may differ significantly. Remarkably, both models maintain their anisotropic characteristics even after approaching the zero-complexity condition ((beta =1)). In conclusion, we investigate the possible accuracy of these new solution categories in representing actual compact structures by delving into their physical ramifications.

Ion microbeam facility is a highly effective tool for precise sample irradiation, ion beam micro-modification, ion beam analysis, and other applications at micron and nanometer scale. However, achieving high-resolution beam spots requires meticulous adjustment of the microslit setting, beam transport and magnetic focusing field, which is even time-consuming for well-trained technicians. Nowadays, most of the beamline instruments and power supplies support remote control and automatic adjustment, which promotes the application of artificial intelligence to microbeam formation. In this work, we simulated the 50 MeV proton microbeam system with Oxford triplet lens configuration using a homemade ion optics package, which can generate data about any number of ions passing through quadrupole magnets. Then, an agent interacted with the system and generated large amounts of data. The data was used to train a deep Q-Network (DQN) model. Ultimately, we used the model to accomplish the intelligent focusing function on the simulated microbeam system. Comparative results show that the error between our model and the classic method is less than 0.3%.

In this work, we analyze a quantum cosmology model with three components: radiation fluid; negative cosmological constant term; and a Bose–Einstein condensate described by the Pöschl–Teller potential. At the classical level, all cosmological solutions of the model exhibit big bang-type singularities. Through quantization in minisuperspace, the singularities are removed by wavefunction solutions of the Universe in the form of wormholes from the Wheeler–DeWitt equation. These solutions and the corresponding energy spectrum are obtained using the Galerkin method. We also construct normalized wave packets and verify, within the many-worlds interpretation of quantum mechanics, that the expected value of the Universe’s scale factor never vanishes.