Advanced Theory and Simulations最新文献

The possibility of treating a molecular liquid in an open region at ab initio electronic resolution embedded in a classical reservoir of energy and particles, is investigated. Because of its challenging properties and its relevance in many field of current research, the system chosen as prototype of molecular liquid is water at room conditions. A numerical protocol based on the mathematical model of open particle system is applied and the results are compared with results of a full ab initio simulation of reference. The key conclusion is that one can claim the existence of a mandatory minimal size of the quantum region in which structural and electronic properties reproduce those of reference and, at the same time, the exchange of molecules with the environment takes place as expected. This work provides a proof of concept about the possibility to systematically define a physically well founded open quantum system embedded in a classical environment. In turn, the proof of concept is a key information for the design of numerically efficient algorithms for ab initio molecular dynamics simulations of open systems.

Comprehending and measuring heat transfer (HT) mechanisms in groundwater systems is crucial for tackling diverse issues and maximizing the use of subterranean resources while reducing ecological consequences. Groundwater flow is frequently simulated and predicted using mathematical models, such as Darcy's law, which governs the movement of fluids through porous media. Thus, a theoretical analysis of HT is therefore carried out for a time-independent 3D power-law (PL) nanofluid (NF) flow on the stretching rotating porous disc near the stagnation region, subject to convective boundary condition, using the MHD, heat source/sink, and thermal radiation effects. A numerical simulation via the Keller Box method is performed using PDEs as the mathematical model for the suggested problem. Investigations are conducted on how several classes of pertinent characteristics affect temperature, velocity, surface drag forces, and HT rate. It has been observed that the radial velocity of the disc increases with an escalation in the permeability of the porous media whereas the azimuthal velocity, however, tends to decrease. Additionally, the rate at which heat is transferred escalates as the radiation and heat source/sink parameter's strength increases whereas it decays along the Prandtl and Biot numbers. Lastly, the present study's results can be applied to understand the thermal impact on seepage of groundwater, geothermal energy extraction, containment systems for landfills and waste, design of subsurface infrastructure, aquifer thermal energy storage, and impact assessment against climate change.

Lithium-ion batteries have been a major energy source in electric vehicles because of their strong adaptability in operating conditions. Accurate estimation of state of charge (SOC) for lithium-ion batteries can efficiently improve the efficiency of battery energy utilization. However, SOC estimation is complicated in operating conditions with unknown model parameters. This study proposes an adaptive square root central difference Kalman filter (ASRCDKF) algorithm based on the equivalent circuit model to achieve high-precision estimation of SOC. First of all, to avoid an open-circuit voltage test, a linear Kalman filter is constructed to realize real-time estimation of unknown parameters in the measurement equation. Then, to improve the stability of the algorithm, a square root method is used to ensure a positive semi-definite of the error covariance matrix that is based on the adaptive central difference Kalman filter algorithm. Model parameters are considered as the state to be estimated, and the joint estimation of the model parameters and SOC is realized by an ASRCDKF algorithm. After that, the linear Kalman filter is coupled with the ASRCDKF to realize the accurate estimation of SOC in the case of both the state equation and the measurement equation including unknown parameters. Last, the ASRCDKF algorithm is compared with the adaptive central difference Kalman filter algorithm and the adaptive cubature Kalman filter algorithm under two sets of operating conditions. The results show that the SOC estimation of the ASRCDKF algorithm is more significantly accurate than other algorithms under different operating conditions.

Optical field enhancement maximization has been the ultimate objective of applications covering random lasers, spectroscopy, and -importantly- targeted drug delivery. Consequently, scientists resorted to plasmonic based approaches, which rendered the entire approach inapplicable due to biodegradability concerns. In another work, an experimental realization for a method of magneto-optical transmission maximization is reported. However, possible limitations on the higher excitation power needed for biomedical applications are still questionable. Furthermore, a comprehensive, quantitative understanding of all material and design related parameters influencing this enhancement is still needed for complete control over possible applications. Therefore, successfully derives a model for the magneto-optical transmission under a time-varying 0–4 kHz magnetic field, exhaustively accounting for material and design related phenomena; birefringence of hematite, dissipation, randomness, and anisotropy on the dielectric function, scattering cross-section, and polarizability, for the first time. The model achieves an accuracy of 99.99% over the band 300–1100 nm and exhausts the model limitations to the decay time constant of Cotton–Mouton co-effects. The dynamics of the problem are also derived, accounting for the influence of the magnetic field on the viscosity of the ferrofluid, which leads to an in-depth, required understanding of the magneto-optic interactions with ferrofluids for efficient applicability.

The report carried out detailed first-principle calculations of Mercury chalcogenides (HgX; X = Te, Se and S) using density functional theory, verifying the bulk band inversion property with different exchange-correlation functionals. The Wannier function method is used to study the non-trivial topology of HgX systems, spin Berry curvature, and intrinsic spin Hall conductivity. Quantized intrinsic spin Hall conductivity is observed in the HgX systems. Large intrinsic spin Hall conductivity is found in the systems due to a strong spin Berry curvature accumulation near the triply degenerate points in the Brillouin zone. Calculation shows that the intrinsic spin Hall conductivity for all three HgX systems has stable plateaus, with Mercury Telluride having a maximum width of up to 1.05 eV. The maximum intrinsic spin Hall conductivity of –931$�\hslash$/e ($�{\mathrm{Scm}}^{�-�1�}$) is obtained in mercury sulfide, higher than the reported values for spin Hall conductivity and the plateau width in typical topological insulators such as $��{\text{Bi}}_2�{\text{Se}}_3�$, $��{\text{Bi}}_2�{\text{Te}}_3�$, and $��{\text{Sb}}_2�{\text{Se}}_3�$ as well as in transition metal pnictides (TaX, X = As, P and N) and transition metal iridates.

In this study, SiC quantum dots (SiC-QD's) are studied, and some roundish SiC-QD's with the incorporation of defects by removing a carbon or silicon atom are considered. Fourteen configurations are modeled in which the position of the silicon or carbon defect for each configuration is changed, considering that due to the chemical composition, it allows more Si atoms or more C atoms on the QD surface. All calculations are performed using the Density Functional Theory (DFT) methodology. The electronic exchange correlation is treated using the Generalized Gradient Approximation (GGA) with the Revised Perdew–Burke–Ernzerhof (RPBE) functional. The electronic energy levels of each configuration are calculated as well as the partial density of states to know the origin of the energy gap in each quantum dot. The final step is to analyze the energy formation to determine chemical stability.

In recent years, machine learning (ML) has emerged as a potential tool in the exploration of thermoelectric (TE) materials. This study exploits the StarryData2 public database to construct an ML model for predicting the figure-of-merit ZT of TE materials. The original dataset from StarryData2 (372,480 datapoints) underwent systematic cleaning, resulting in a refined dataset of 18,126 instances with 2,761 unique compounds. The cleaned data is employed to train an XGBoost regressor model, utilizing chemical formulas of TE compounds as features to predict ZT at given temperatures. The XGBoost regressor exhibited high prediction accuracy, achieving the coefficient of determination (R²) scores of 0.815 and mean absolute error (MAE) of 0.103 for the test set, further evaluated through cross-validation across 5 folds. The learning curve analysis demonstrated improved model performance with increased training data. Furthermore, the contributions of different chemical descriptors to ZT are analyzed based on feature importance analysis. Beyond conventional TE families in the training set, the trained model is applied to predict ZT for promising unexplored TE materials and estimate optimal doping concentrations. This comprehensive study shows the impact of ML on TE material research, offering valuable insights and accelerating the discovery of materials with enhanced TE properties.