Large-scale Simulations Research Articles

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

Large-scale simulations of Floquet physics on near-term quantum computers

Periodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High-Frequency Floquet Simulation (QHiFFS) algorithm as a method to simulate fast-driven quantum systems on quantum hardware. Central to QHiFFS is the concept of a kick operator which transforms the system into a basis where the dynamics is governed by a time-independent effective Hamiltonian. This allows prior methods for time-independent simulation to be lifted to simulate Floquet systems. We use the periodically driven biaxial next-nearest neighbor Ising (BNNNI) model, a natural test bed for quantum frustrated magnetism and criticality, as a case study to illustrate our algorithm. We implemented a 20-qubit simulation of the driven two-dimensional BNNNI model on Quantinuum’s trapped ion quantum computer. Our error analysis shows that QHiFFS exhibits not only a cubic advantage in driving frequency ω but also a linear advantage in simulation time t compared to Trotterization.

Learning macroscopic equations of motion from dissipative particle dynamics simulations of fluids

Macroscopic descriptions of both natural and engineered materials usually include a number of phenomenological parameters that have to be estimated from experiments or large-scale microscopic simulations. When dealing with advanced complex materials, these descriptions are sometimes not a priori available or not even known. Using sparsity-promoting techniques one can extract macroscopic dynamic models directly from particle-based simulations. In this work, we showcase such an approach on a simple fluid and test its robustness. We introduce a novel measure for automatic macroscopic model selection that combines stability and accuracy of a model. Using this measure and employing only a few physics-based assumptions, we are able to infer both the mass continuity equation and an equation for the conservation of linear momentum. Moreover, the extracted phenomenological and non-phenomenological parameters agree well with their numerically measured values and the well-known semi-empirical estimates. The presented model selection framework can be applied to simulations or experimental data of more complex systems, described in general by a rich set of coupled nonlinear macroscopic equations.

From Fractional Quantum Anomalous Hall Smectics to Polar Smectic Metals: Nontrivial Interplay Between Electronic Liquid Crystal Order and Topological Order in Correlated Topological Flat Bands.

Symmetry-breaking orders can not only compete with each other, but also be interwined, and the interwined topological and symmetry-breaking orders make the situation more intriguing. This work examines the archetypal correlated flat band model on a checkerboard lattice at filling ν = 2/3 and we find the unique interplay between smectic charge order and topological order gives rise to two novel quantum states. As the interaction strength increases, the system first transitions from a Fermi liquid into FQAH smectic (FQAHS) state, where FQAH topological order coexists cooperatively with smectic charge order with enlarged ground-state degeneracy and interestingly, the Hall conductivity is σxy = ν = 2/3, different from the band-folding or doping scenarios. Further increasing the interaction strength, the system undergoes another quantum phase transition and evolves into a polar smectic metal (PSM) state. This emergent PSM is an anisotropic non-Fermi liquid, whose interstripe tunneling is irrelevant while it is metallic inside each stripe. Different from the FQAHS and conventional smectic orders, this PSM spontaneously breaks the two-fold rotational symmetry, resulting in a nonzero electric dipole moment and ferroelectric order. In addition to the exotic ground states, large-scale numerical simulations are also used to study low-energy excitations and thermodynamic characteristics. We find the onset temperature of the incompressible FQAHS state, which also coincides with the onset of non-polar smectic order, is dictated by the magneto-roton modes. Above this onset temperature, the PSM state exists at intermediate-temperature regime. Although the T = 0 quantum phase transition between PSM and FQAHS is first order, the thermal FQAHS-PSM transition could be continuous. We expect the features of the exotic states and thermal phase transitions could be accessed in future experiments.

Improved phase-state identification bypass approach of the hydrocarbons-CO2-H2O system for compositional reservoir simulation

CO2 injection is a highly effective technique to enhance oil recovery, achieved through continuous or alternative injection. However, the intricate interactions between different phases within porous media present significant challenges when predicting the performance of CO2 injection. To address this, it is crucial to employ compositional simulation, which accounts for the multiphase multicomponent transport. Nonetheless, conventional multiphase flash calculations can be computationally inefficient for large-scale reservoir simulations. Therefore, it is necessary to accelerate the Equation-of-State (EoS)-based compositional simulation, given the widespread use of CO2 enhanced oil recovery (CO2-EOR) in recent years. The phase-state identification bypass method has proven to be superior to other methods in terms of efficiency. However, this approach struggles with regions near phase boundaries, resulting in reduced computational efficiency in those areas.In this study, an enhanced phase-state identification bypass approach is developed to address this limitation. The first step involves discretising the pressure-temperature space using rectangular grids. Additionally, the tie-simplexes, which represent regions defined by the maximum number of phases formed by the fluid under consideration, are discretized in the phase-fraction space at the pressure and temperature of each discretization node. Subsequently, the discretization grid associated with the given point (the overall composition, pressure, and temperature) is located, and the phase states of the grid nodes are determined using the conventional multiphase flash method. If all nodes exhibit the same phase state, that phase state is assigned to the given point. However, if multiple phase states are obtained, a novel process is proposed to determine the phase state of the given point. To validate this improvement to the phase-state identification bypass method, phase diagram calculations and simulation cases are conducted, and the results demonstrate the robustness of the proposed method and its superior computational efficiency compared to the previous method.

Exploring the constitutive activation mechanism of the class A orphan GPR20.

GPR20, an orphan G protein-coupled receptor (GPCR), shows significant expression in intestinal tissue and represents a potential therapeutic target to treat gastrointestinal stromal tumors. GPR20 performs high constitutive activity when coupling with Gi. Despite the pharmacological importance of GPCR constitutive activation, determining the mechanism has long remained unclear. In this study, we explored the constitutive activation mechanism of GPR20 through large-scale unbiased molecular dynamics simulations. Our results unveil the allosteric nature of constitutively activated GPCR signal transduction involving extracellular and intracellular domains. Moreover, the constitutively active state of the GPR20 requires both the N-terminal cap and Gi protein. The N-terminal cap of GPR20 functions like an agonist and mediates long-range activated conformational shift. Together with the previous study, this study enhances our knowledge of the self-activation mechanism of the orphan receptor, facilitates the drug discovery efforts that target GPR20.

Large-scale molecular dynamics simulation and aggregate behavior research on asphalt

In this comprehensive study, a molecular dynamics (MD) model consisting of approximately 150,000 atoms was developed within the Strategic Highway Research Program (SHRP) framework to investigate the behavior of AAA asphalt, focusing on asphaltene aggregate dynamics behavior over a simulation time span of 0.282 microseconds. The findings reveal that the model achieves a stable state in terms of both energy and microstructure without significant changes or the formation of densely clustered molecules, despite the extensive simulation period. Notably, the patterns of asphaltene aggregation were found to significantly influence the MD system energy, with variations ranging from loosely organized structures due to dispersed asphaltenes to more structured, energy-efficient clusters formed by encapsulated asphaltene pentamers. The size of asphaltene aggregates emerged as a pivotal factor, affecting their encapsulation and leading to distinct formation patterns. Furthermore, the research highlighted that increasing shear rates prompt the depolymerization of asphaltene aggregates, shifting from large-scale structures to smaller ones and adapting dynamically to the flow conditions by aligning parallel to the shear direction. This study underscores the critical impact of asphaltene aggregate behavior and shear rates on the structural integrity and distribution within the asphalt model, offering profound insights into the MD of asphalt materials.

Computational fluid dynamics (CFD)- deep neural network (DNN) model to predict hydrodynamic parameters in rectangular and cylindrical bubble columns

Bubble columns are omnipresent in the chemical, bio-chemical, petrochemicals, petroleum industries, but their design and scale-up is complex owing to its complex hydrodynamics. Liquid velocity and gas holdup is one of the critical hydrodynamic parameters which effects the mixing, heat and mass transfer in bubble columns. CFD is widely recognized as a powerful tool for estimating critical hydrodynamic parameters but requires significant computational resources, time and expertise. These limitations restrict its practical use in hydrodynamic simulations that need real-time processing involving large-scale simulations of bubble columns. To overcome these limitations, CFD-DNN model is developed to predict the time averaged gas holdup and axial liquid velocity at various operating conditions. The DNN model was trained using CFD data that was produced for rectangular (with dimensions L=0.2 m, W=0.05 m, H=1.2 m) and cylindrical (with a diameter of 0.19 m) bubble columns. The data covers a range of operating conditions and various flow regimes. The superficial gas velocity for the rectangle column was selected at 1.33 and 7.3 mm/s, whereas for the cylindrical bubble column, it was fixed at 0.02 and 0.12 m/s. The CFD-DNN model was validated against the experimental and the CFD data from the literature. Further, the model was tested for new data that the CFD-DNN model has not seen with existing literature and showed good agreement with their data and it reflects the excellent generalization ability of the model. The proposed CFD-DNN approach improves current CFD models by providing shorter computing time, decreasing computational expenses, and reducing the expertise in CFD simulations. The accuracy of the developed CFD-DNN model was evaluated using different metrics for gas holdup and axial liquid velocity. For rectangular bubble columns, the model achieved MSE of 0.0001 for gas holdup and 0.0007 for axial liquid velocity. Similarly, for cylindrical bubble columns, the MSE values were 0.0009 for gas holdup and 0.0006 for axial liquid velocity.

Large-Scale Physical Simulation Experiment of Water Invasion Law for Multi-Well Development in Sandstone Gas Reservoirs with Strong Water Drive

In order to clarify the water invasion law and residual gas distribution characteristics in edge and bottom water gas reservoirs with multi-well development, a large-scale three-dimensional physical simulation model was developed and a physical simulation experiment method for the water invasion law of multi-well development in sandstone gas reservoirs with strong water drives was established. Water invasion physical simulation experiments of multi-well development under the conditions of different water body multiples and production systems were conducted. The results show the following: (1) Gas wells near fractures and high-permeability zones experience the earliest water breakthrough. The larger the water body multiple, the faster the rate of water invasion, the earlier the water breakthrough time of gas wells, the more severe the degree of water invasion in gas reservoirs, and the lower the ultimate recovery rate. (2) Shutting in low-position gas wells immediately after water breakthrough reduces the overall water production of the gas reservoir and extends the overall water-free gas production period. However, the ultimate recovery rate is lower than when the wells are not shut in. (3) The residual gas in the fracture model is mainly distributed around the fracture and the edge of the gas reservoir, with the ultimate recovery rate ranging from 38.5% to 58.2%. The residual gas in the fracture–high-permeability zone model is mainly distributed around the fracture–high-permeability zone and the edge of the gas reservoir, with the ultimate recovery rate ranging from 28.32% to 41.8%. The experimental results have important guiding significance for the economical and efficient development of similar gas reservoirs.

Molecular dynamics simulation for phase transition of CsPbI3 perovskite with the Buckingham potential.

The CsPbI3 perovskite is a promising candidate for photovoltaic applications, for which several critical phase transitions govern both its efficiency and stability. Large-scale molecular dynamics simulations are valuable in understanding the microscopic mechanisms of these transitions, in which the accuracy of the simulation heavily depends on the empirical potential. This study parameterizes two efficient and stable empirical potentials for the CsPbI3 perovskite. In these two empirical potentials, the short-ranged repulsive interaction is described by the Lennard-Jones model or the Buckingham model, while the long-ranged Coulomb interaction is summed by the damped shifted force method. Our molecular dynamics simulations show that these two empirical potentials accurately capture the γ ↔ β ↔ α and δ → α phase transitions for the CsPbI3 perovskite. Furthermore, they are up to two orders of magnitude more efficient than previous empirical models, owing to the high efficiency of the damped shifted force truncation treatment for the Coulomb interaction.

A methodology for appropriate withdrawal of tsunami warnings based on numerical simulations

This study performed large-scale numerical simulations for predicting the attenuation of tsunamis caused by the 2011 Tohoku, Japan, and 2010 Maule, Chile earthquakes, recorded at Japanese tide gauges. Tsunami amplitude waveforms were generated by computing the moving root-mean-square of the data for quantitative analyses. Sensitivity analysis showed that tsunami nonlinearity and computational grid intervals near the tide gauges significantly impact the prediction of tsunami attenuation. The predicted withdrawal times of tsunami warnings agreed with the observations; however, time discrepancies were observed for advisory withdrawals at several stations. Using the proposed method, we predicted the warning period of a great interplate earthquake in the Nankai Trough to be approximately one day. These findings can provide critical information for disaster prevention because the withdrawal of warnings is directly related to permission to enter coastal areas affected by the tsunami, whereas unnecessarily long warnings hinder rescue operations.

How Does the Ocean Melt Antarctic Ice Shelves?

The present-day state and future of the Antarctic Ice Sheet depend on the rate at which the ocean melts its fringing ice shelves. Ocean heat must cross many physical and dynamical barriers to melt ice shelves, with the last of these being the ice-ocean boundary layer. This review summarizes the current understanding of ice-ocean boundary-layer dynamics, focusing on recent progress from laboratory experiments, turbulence-resolving numerical simulations, novel observations, and the application to large-scale simulations. The complex interplay between buoyant meltwater and external processes such as current shear leads to the emergence of several melting regimes that we describe, as well as freezing processes. The remaining challenges include developing new parameterizations for large-scale ice-ocean models based on recent advances and understanding the coevolution of melt and basal topography.

Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems

This paper focuses on the construction of accurate and predictive data-driven reduced models of large-scale numerical simulations with complex dynamics and sparse training datasets. In these settings, standard, single-domain approaches may be too inaccurate or may overfit and hence generalize poorly. Moreover, processing large-scale datasets typically requires significant memory and computing resources, which can render single-domain approaches computationally prohibitive. To address these challenges, we introduce a domain-decomposition formulation into the construction of a data-driven reduced model. In doing so, the basis functions used in the reduced model approximation become localized in space, which can increase the accuracy of the domain-decomposed approximation of the complex dynamics. The decomposition furthermore reduces the memory and computing requirements to process the underlying large-scale training dataset. We demonstrate the effectiveness and scalability of our approach in a large-scale three-dimensional unsteady rotating-detonation rocket engine simulation scenario with more than 75 million degrees of freedom and a sparse training dataset. Our results show that compared to the single-domain approach, the domain-decomposed version reduces both the training and prediction errors for pressure by up to 13% and up to 5% for other key quantities, such as temperature, and fuel, and oxidizer mass fractions. Lastly, our approach decreases the memory requirements for processing by almost a factor of four, which in turn reduces the computing requirements as well.

QSOFEED: Relationship between star formation and active galactic nuclei feedback

Context. Large-scale cosmological simulations suggest that feedback from active galactic nuclei (AGN) plays a crucial role in galaxy evolution. More specifically, outflows are one of the mechanisms by which the accretion energy of the AGN is transferred to the interstellar medium (ISM), heating and driving out gas and impacting star formation (SF). Aims. The purpose of this study is to directly test this hypothesis utilising SDSS spectra of a well-defined sample of 48 low-redshift (z < 0.14) type 2 quasars (QSO2s). Methods. By exploiting these data, we were able to characterise the kinematics of the warm ionised gas by performing a non-parametric analysis of the [OIII]λ5007 emission line. We also constrained the properties of the young stellar populations (YSP; tysp < 100 Myr) of their host galaxies via spectral synthesis modelling. Results. These analyses revealed that 85% of the QSO2s display velocity dispersions in the warm ionised gas phase greater than that of the stellar component of their host galaxies, indicating the presence of AGN-driven outflows. We compared the gas kinematics with the intrinsic properties of the AGN and found that there is a positive correlation between gas velocity dispersion and 1.4 GHz radio luminosity – but not with the AGN bolometric luminosity or Eddington ratio. This either suggests that the radio luminosity is the key factor driving outflows or that the outflows themselves are shocking the ISM and producing synchrotron emission. We found that 98% of the sample host YSPs to varying degrees, with star formation rates (SFRs) of 0 ≤ SFR ≤ 92 M⊙ yr−1, averaged over 100 Myr. We compared the gas kinematics and outflow properties to the SFRs to establish possible correlations that could suggest that the presence of the outflowing gas could be impacting SF, but we found that no such correlation exists. This leads us to the conclusion that on the scales probed by the SDSS fibre (between 2 and 7 kpc diameters), AGN-driven outflows do not impact SF on the timescales probed in this study. However, we find a positive correlation between the light-weighted stellar ages of the QSO2s and the black hole mass, which might indicate that successive AGN episodes lead to the suppression of SF over the course of galaxy evolution.

Discrete differential geometry-based model for nonlinear analysis of axisymmetric shells

In this paper, we propose a novel one-dimensional (1D) discrete differential geometry (DDG)-based numerical method for geometrically nonlinear mechanics analysis (e.g., buckling and snapping) of axisymmetric shell structures. Our numerical model leverages differential geometry principles to accurately capture the complex nonlinear deformation patterns exhibited by axisymmetric shells. By discretizing the axisymmetric shell into interconnected 1D elements along the meridional direction, the in-plane stretching and out-of-bending potentials are formulated based on the geometric principles of 1D nodes and edges under the Kirchhoff–Love hypothesis, and elastic force vector and associated Hessian matrix required by equations of motion are later derived based on symbolic calculation. Through extensive validation with available theoretical solutions and finite element method (FEM) simulations in literature, our model demonstrates high accuracy in predicting the nonlinear behavior of axisymmetric shells. Importantly, compared to the classical theoretical model and three-dimensional (3D) FEM simulation, our model is highly computationally efficient, making it suitable for large-scale real-time simulations of nonlinear problems of shell structures such as instability and snap-through phenomena. Moreover, our framework can easily incorporate complex loading conditions, e.g., boundary nonlinear contact and multi-physics actuation, which play an essential role in the use of engineering applications, such as soft robots and flexible devices. This study demonstrates that the simplicity and effectiveness of the 1D discrete differential geometry-based approach render it a powerful tool for engineers and researchers interested in nonlinear mechanics analysis of axisymmetric shells, with potential applications in various engineering fields.

High proportion of new energy power system frequency and voltage support and regulation technology based on doubly fed induction generator

Abstract With the increasing proportion of new energy in power systems, issues regarding system voltage, inertia, and primary frequency regulation have become increasingly severe. In response to control and grid safety issues, a high proportion of new energy power system frequency and voltage support and regulation technology based on a doubly fed induction generator (DFIG) is proposed. The voltage-magnetic link equation of DFIG is theoretically derived, and the control principles of active and reactive power of DFIG are proposed. The support and regulation characteristics of improving the voltage of new energy substations are studied. Verification is conducted through large-scale grid simulation and on-site measurements.

An analytic traction-displacement model for a reinforcing ligament bridging a crack at an arbitrary angle, including elastic, frictional, snubbing, yielding, creep, and fatigue phenomena

A micromechanical model is developed that generates analytic expressions for the crack displacement vector u given an arbitrary far-field stress state σa for a crack that is bridged by an array of ligaments oriented at an arbitrary angle with respect to the crack plane. The model is applicable to various materials, e.g., fibrous ceramic composites, or polymer composites reinforced by stitches or z-pins or woven tows, and deals with interfacial friction, enhanced friction due to increased contact pressure (“snubbing”), and the possibility of ligament deflection enabled by yield or damage. The model also conveniently incorporates ligament failure and rate dependent phenomena (fatigue or creep). Adaptability of the model is enabled by the definition of a standard Reference Model, which generates analytic expressions for the crack displacement for given possible yield, ligament deflection, and friction and snubbing effects and is invariant for all geometrical and material choices. The switching on or off and the strengths of all phenomena are governed by assigning values to a handful of material parameters. The material parameters will generally be calibrated against data in a top-down strategy, the model thereby mapping material selection onto engineering fracture via the predicted bridging relationship u[σa]. The relationship u[σa] can depend strongly on bi-angular ligament orientation. Yield and deflection can change u[σa] qualitatively, e.g., by creating fracture surface contact even when σa includes substantial opening tension. Snubbing has significant effects, including possible stabilization of the pullout of a finite ligament. Since model output is computed via analytic expressions, its speed will support the model's use in large-scale material simulations or as constraining physical information in machine learning algorithms.

Development and deployment of data-driven turbulence model for three-dimensional complex configurations

Abstract In recent years, the synergy between artificial intelligence and turbulence big data has given rise to a new data-driven paradigm in turbulence research. Data-driven turbulence modeling has emerged as one of the forefront directions in fluid mechanics. Most existing studies focus on feature construction, selection, and the development of modeling frameworks, often overlooking the practical deployment and application of trained models. This paper examines the entire process from model construction to real-world deployment, using data-driven turbulence modeling for high Reynolds number flows over complex three-dimensional configurations as a case study. Key stages include data generation, input-output feature construction, model training, model compilation and optimization, deployment, and validation. We successfully implemented the entire workflow in a heterogeneous supercomputing environment and, through mixed programming techniques, integrated the resulting turbulence model into the Platform for Hybrid Engineering Simulation of Flows (PHengLEI) open-source software framework. This allowed for mixed-precision simulations, with the main equations solved in double precision and the turbulence model in half precision. The new computational framework was validated through large-scale parallel numerical simulations on grids with tens of millions of elements for three-dimensional complex configurations. The results highlight the efficiency of our model deployment, with overall computational efficiency improving by 13.35% and the turbulence model’s solution speed increasing by approximately 3.9 times. The accuracy of the computations was also confirmed, with the average relative error in the lift and drag coefficients calculated by the data-driven turbulence model within 3%. Across various computing nodes, the relative error in the computed aerodynamic coefficients remained within 1%, demonstrating the framework’s scalability. Notably, our contributions have been incorporated as a case study in the latest PHengLEI open-source project5 5 https://forge.osredm.com/PHengLEI/PHengLEI-TestCases/tree/master/Y02_ThreeD_M6_Unstruct_Branch_Ascend. .

JuMonC: A RESTful tool for enabling monitoring and control of simulations at scale

As systems and simulations grow in size and complexity, it is challenging to maintain efficient use of resources and avoid failures. In this scenario, monitoring becomes even more important and mandatory. This paper describes and discusses the benefits of the advanced monitoring and control tool JuMonC, which runs under user control alongside HPC simulations and provides valuable metrics via REST-API. In addition, plugin extensibility allows JuMonC to go a step further and provide computational steering of the simulation itself. To demonstrate the benefits and usability of JuMonC for large-scale simulations, two use cases are described employing nekRS and ICON on JURECA-DC, a supercomputer located at the Jülich Supercomputing Centre (JSC). Furthermore, a large-scale use case with nekRS on JSC’s flagship system JUWELS Booster is described. Finally, the interplay between JuMonC and LLview (a standard monitoring tool for HPC systems) is presented using a simple and secure JuMonC-LLview plugin, which collects performance metrics and enables their analysis in LLview. Overall, the portability and usefulness of JuMonC, together with its low performance impact, make it an important application for both current and future generations of exascale HPC systems.

Artificial neural network enabled photovoltaic-thermoelectric generator modelling and analysis

Photovoltaic-Thermoelectric Generator (PV-TEG) system has emerged as a promising approach to significantly enhance the efficiency of conventional PV cells. However, optimizing the performance of these hybrid systems presents a formidable challenge due to their complex structure and multitude of design parameters. This study tackles such challenge by developing a machine learning based Artificial Neural Network (ANN) model which comprises two sub-ANN models that can work independently for PV and TEG modules or in combination through a cyclic approach for the hybrid PV-TEG system. The model demonstrates remarkable versatility, allowing control over various parameters such as PV coating, device geometry, and environmental conditions. Compared to COMSOL simulations, the ANN model achieves over 97.6 % accuracy with a 6000-fold increase in simulation speed, enabling extensive parameter sweeps and insightful system analysis. Within 18 min, the model conducted a real-time simulation using 8712 weather data entries from Singapore in 2022 and predicted that the hybrid PV-TEG system would generate a total power of 265 kWh/m2, 6.4 % more than that of the standalone PV system with an average system temperature reduction of 7 K. The model's rapid processing capabilities and high accuracy are particularly beneficial for large-scale simulations and practical applications in renewable energy technology.