Accurate Simulation Research Articles

Machine learning-assisted microscopic public transportation simulation: Two coupling strategies

Evaluating the performance of public transportation, such as bus lines for example, is a major issue for Public Transportation operators. To be able to integrate specific and local behaviors, microscopic line simulations, modeling each buses on a daily basis, provide actual added value in terms of precision and quality. Carrying out more realistic and accurate simulations requires the use of appropriate parameters. To achieve this, machine learning models trained on real-world data can be used to feed and parameterize simulation models. To address this scientific question, it is necessary to determine how to efficiently integrate machine learning and simulation models. This study aims to couple machine learning and microscopic simulation models using various strategies, evaluate their accuracy and performance and discuss the advantages and drawbacks of each. A case study involving three bus lines was conducted, with results validated against real-world data, showing a good fit for both online and offline strategies. With the best simulation time, good accuracy and adequate travel times and bus punctuality, an offline strategy seems to stand out from other coupling strategies.

Impact of key parameters on the numerical modelling of Phase-Changing phenomena in horizontal latent heat thermal energy storage

Numerous studies have focused on numerical investigations of the performance of the shell-and-tube Latent Heat Thermal Energy Storage (LHTES). However, many studies have often overlooked rigorous assessment of key parameters’ impact on the numerical simulation accuracy relative to experimental results. Therefore, this paper aims to develop a numerical model to track the solid–liquid interface during phase-changing phenomena in a horizontal LHTES system and investigate the effects of key parameters on the simulation accuracy. For this purpose, an experimental facility was built, and the numerical model was developed in ANSYS using the enthalpy-porosity method. During the simulation of the computational model, consideration was given to thermophysical properties (constant versus variable) of phase change material, contact resistance (zero versus nonzero), and the mushy zone constant (105, 5 × 105, and 106). The simulation results were compared with experimental data obtained from the in-house studies. The results showed that, compared to using constant thermophysical properties, employing variable thermophysical properties more accurately tracked the solid–liquid interface during melting. It substantially improved the simulation accuracy with a Root Mean Square (RMS) error less than 5 % for most of the temperature-measuring locations. Conversely, variable properties did not make a significant difference in the solidification process. The contact resistance due to the air gap was determined to be 0.035 m2·K/W for the current study. Furthermore, incorporating this resistance resulted in a more accurate approximation of the solid–liquid interface during melting, with RMS errors below 5 % across all locations. The study comparing different mushy zone constants recommended utilizing a slightly higher value of 106 for melting, whereas its impact on solidification was found to be insignificant.

Failure analysis of Nehbandan granite under various stress states and strain rates using a calibrated Riedel–Hiermaier–Thoma constitutive model

This study presents a comprehensive procedure for determining, calibrating, and validating the Riedel–Hiermaier–Thoma (RHT) material model parameters for granite. The process involves collecting a comprehensive dataset of conventional mechanical tests conducted on various types of granite worldwide. Based on this dataset, one set of RHT material model parameters is determined. Additionally, a specific granite sample from Iran, known as Nehbandan granite, is characterized through physical and mechanical testing to obtain another set of parameters. The challenging task of determining the third set of parameters, which are difficult to obtain analytically and experimentally, is accomplished through a calibration process that iteratively adjusts the parameters based on comparisons between numerical simulation results and experimental data. To validate the determined parameters, a series of tests, including uniaxial compressive strength (UCS), triaxial compressive strength (TCS), Brazilian tensile strength, and dynamic Brazilian using split Hopkinson pressure bar (SHPB) tests, are conducted on the Nehbandan granite. These tests are also simulated using LS-Dyna software, and the numerical simulation results are compared with the corresponding experimental test results. The comparison between the numerical and experimental data serves as a means of validating and verifying the accuracy and reliability of the determined RHT material model parameters for granite. The results demonstrate the successful determination and calibration of the RHT material model parameters for granite. The model exhibits effectiveness in predicting the behavior of granite under various loading conditions. The validation process confirms the accuracy and reliability of the determined parameters through a close agreement between numerical simulations and experimental data. The findings contribute to a better understanding of granite's mechanical response and provide a reliable tool for simulating and predicting its behavior in engineering applications. The validated RHT material model parameters offer a robust framework for accurate numerical simulations, enabling engineers to make informed decisions in rock engineering projects.

Numerical analysis of fractional‐order Euler–Bernoulli beam model under composite model

The primary objective of this study is to develop a new constitutive model by combining a fractional‐order Kelvin–Voigt model with an Abel dashpot element in parallel. Subsequently, this new model will be incorporated into the Euler–Bernoulli beam's governing equation, utilizing shifted Legendre polynomials as basis functions, a classical orthogonal polynomial system, to solve the fractional‐order partial differential equations. By comparing the numerical solutions with the analytical solutions, we aim to evaluate the applicability of shifted Legendre polynomials in solving such problems and the accuracy of the obtained numerical solutions. Furthermore, we will investigate the performance of viscoelastic HDPE beams under different loading conditions and conduct a comparative analysis of the displacements of HDPE beams under the new constitutive model and the traditional fractional‐order Kelvin–Voigt model. Through this research, we hope to gain a deeper understanding of the characteristics of fractional‐order phenomena and provide more accurate and efficient numerical simulation and analysis methods for the field of structural mechanics, promoting the development of related engineering applications.

Tsunami propagation and flooding maps: An application for the Island of Lampedusa, Sicily Channel, Italy

AbstractThe Mediterranean coastlines are densely populated zones which host key socio‐economic and commercial activities. For this reason, coastal areas are vulnerable sites in case of natural disasters as tsunamis that can strike coasts causing widespread damage to the population and facilities. For these reasons, several studies were performed over the last decade to study the impact of tsunami waves on the coasts. This research assessed the inundation risk due to a tsunami wave which can hit the southeastern coast of Lampedusa Island. The coastal low‐lying geomorphological setting of the southeastern part of the island led to significant socio‐economic growth, but Lampedusa falls within the Mediterranean Sea, a high‐tsunamigenic area, therefore, the need to investigate tsunami propagation and coastal flooding of this sensitive site emerged. For this scope, a calculation chain model was implemented incorporating three steps: the DELFT‐3D software for earthquake effects modelling, MIKE 21 Flow Model FM for nearshore propagation and HEC‐RAS for onshore tsunami inundation modelling. The simulations illustrate the impact of three tsunami scenarios with different magnitudes (Mw 8.5, 7.5, 6.5) generated by hypothetical earthquakes in the Hellenic Arc. In the Mw 8.5 magnitude scenario, significant flooding occurs in the harbour region, with maximum water depths reaching approximately 3.5 m. The maximum water velocity in this scenario reaches about 15 m/s in the eastern portion, adjacent to cliffs impacted by the tsunami wave. In contrast, the Mw 7.5 magnitude scenario demonstrates reduced flooded areas, with the cliffs containing the waves and preventing further flooding. Water depths and velocities in the Mw 7.5 scenario remain minimal. Changes in both propagation and flooding are not significant between scenarios Mw 7.5 and Mw 6.5. This methodology can be employed for more accurate tsunami wave simulations not only in the Mediterranean region but also in various case studies.

Fast Computer Model Calibration using Annealed and Transformed Variational Inference

Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models through statistical inference. While Bayesian inference is the standard approach for this task, employing Markov chain Monte Carlo methods often encounters computational hurdles due to the costly evaluation of likelihood functions and slow mixing rates. Although variational inference (VI) can be a fast alternative to traditional Bayesian approaches, VI has limited applicability due to boundary issues and local optima problems. To address these challenges, we propose flexible VI methods based on deep generative models that do not require parametric assumptions on the variational distribution. We embed a surjective transformation in our framework to avoid posterior truncation at the boundary. Additionally, we provide theoretical conditions that guarantee the success of the algorithm. Furthermore, our temperature annealing scheme and fine-tuning can prevent being trapped in local optima through a series of intermediate posteriors and weight adjustment. We apply our method to infectious disease models and a geophysical model, illustrating that the proposed method can provide fast and accurate inference compared to its competitors.

Applying double cropping and interactive irrigation in the North China Plain using WRF4.5

Abstract. Irrigated cultivation exerts a significant influence on the local climate and the hydrological cycle. The North China Plain (NCP) is known for its intricate agricultural system, marked by expansive cropland, high productivity, compact rotation, a semi-arid climate, and intensive irrigation practices. As a result, there has been considerable attention on the potential impact of this intensive irrigated agriculture on the local climate. However, studying the irrigation impact in this region has been challenging due to the lack of an accurate simulation of crop phenology and irrigation practices within the climate model. By incorporating double cropping with interactive irrigation, our study extends the capabilities of the Weather Research Forecast (WRF) model, which has previously demonstrated commendable performance in simulating single-cropping scenarios. This allows for two-way feedback between irrigated crops and climate, further enabling the inclusion of irrigation feedback from both ground and vegetation perspectives. The improved crop modeling system shows significant enhancement in capturing vegetation and irrigation patterns, which is evidenced by its ability to identify crop stages, estimate field biomass, predict crop yield, and project monthly leaf area index. The improved simulation of large-scale irrigated crops in the NCP can further enhance our understanding of the intricate relationship between agricultural development and climate change.

Effect of cloud chemistry on seasonal variations of sulfate and its precursors in China

Cloud chemistry is of paramount importance in the secondary production of atmospheric aerosols, influencing the spatial-temporal distribution of gases and aerosols in the atmosphere. Using WRF/CUACE (China Meteorological Administration Unified Atmospheric Chemistry Environment), this study assesses the seasonal impacts of cloud chemistry on the concentrations of SO2, sulfate, as well as two oxidizers, H2O2 and O3, in the most east-central areas of China, including four key pollution zones (the North China Plain (NCP), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Sichuan Basin (SCB)). Near the surface, H2O2-oxidation was the dominant pathway for cloud chemistry in four key pollution zones in four seasons. H2O2 consumption is most pronounced in summer, especially in the SCB and NCP, while O3 consumption peaks in autumn, particularly in the PRD and southeastern coastal areas. While at higher altitudes, oxidation by O3 and H2O2 is compatible with the cloud chemistry process. Near the surface, cloud chemistry consumes SO2 ranging from approximately 0.1 ppb–5.0 ppb, resulting in the generation of about 6.0–25.0 μg m−3 of sulfate. Higher SO2 reduction and sulfate increase are in both summer and winter, especially for the SCB and NCP in summer, and the SCB in winter. Vertically, the cloud chemistry process primarily concentrates its influence on SO2 and sulfate concentrations below 5 km, particularly within the turbulent zone of the troposphere below 2 km in all the four pollution zones and four seasons. The most notable seasonal variation occurs in the NCP compared to other zones. This study also shows that cloud chemistry effectively improves the seasonal simulation accuracy of SO2 and sulfate, resulting in improved correlation and a notable reduction in RMSE.

Numerical analysis of nonlinear Klein–Gordon equations by a meshless superconvergent finite point method

In this paper, we introduce a meshless method for numerical simulation of nonlinear Klein–Gordon equations. The method begins with a temporal discretization to address time derivatives. The stability and error of the temporal discretization scheme are theoretically analyzed. Subsequently, meshless algebraic systems of Klein–Gordon solitons are established by using the superconvergent finite point method (SFPM) for spatial discretization. The moving least squares approximation and its smoothed derivatives are adopted in the SFPM to ensure the high accuracy and remarkable superconvergence. Accuracy and convergence of the meshless numerical simulation for nonlinear Klein–Gordon equations are analyzed in theory. Numerical results validate the superconvergence and effectiveness of the method and confirm the theoretical analysis.

Process-based reconstruction of digital rock based on discrete element method considering thermal-mechanical coupling effect and actual particle shape

The digital rock is a crucial platform for numerical simulations of pore-scale flow in various fields such as geo-energy, carbon (CO2) sequestration, and hydrogen (H2) storage. Under these conditions, rocks deform, and pore structures change due to temperature and stress effects. However, existing methods for reconstructing digital rocks, such as physical experimental, stochastic simulation, and machine learning, can not consider the influence of high temperature and stress. Therefore, a process-based reconstruction method based on the discrete element method (DEM) considering the thermal-mechanical coupling effect is proposed in this paper. Initially, the computed tomography (CT) images are segmented based on the watershed algorithm, and a contour database is established using the spherical harmonic analysis method. A clump template library is then constructed in PFC3D (Particle Flow Code in 3 Dimensions). Subsequently, the DEM model is established using clumps from the template library according to porosity and particle radius distribution, and the model's accuracy is evaluated based on two-point and linear path correlation functions. Micro-mechanical and thermal parameters between particles are calibrated, and different temperature and stress boundary conditions are applied to obtain digital rocks under varying conditions. Finally, digital rocks' geometric and topological structures under different conditions are analyzed, and permeability and relative permeability are calculated. Using Bentheim sandstone as an example, digital rocks are constructed under various temperature and stress conditions. The research findings indicate that high temperatures and stress result in decreased pore and throat radii, elongated throats, deteriorated connectivity, reduced porosity, and permeability, making the digital rocks more water-wet. This study provides theoretical guidance for the accurate pore-scale flow simulation of geo-energy fluids, CO2, and H2.

A real-time temperature field prediction method for steel rolling heating furnaces based on graph neural networks

In the billet reheating process during steel rolling, the real-time and accurate prediction of the temperature field is a prerequisite for the dynamic regulation of the heating process, which is crucial for ensuring the quality of billet reheating and reducing the energy consumption of the reheating furnace. The most commonly used finite element thermal field simulation and analysis methods are unable to meet the demand for real-time prediction under dynamic working conditions. Furthermore, traditional machine learning methods struggle to handle irregular data that includes spatial location information, resulting in inaccurate temperature field predictions, which in turn affect the furnace control efficiency and the quality of the product. In light of these limitations, this study proposes a Graph Neural Network (GNN)-based temperature field prediction method for steel rolling reheating furnaces. This method considers the interactions between the nodes within the reheating furnace and constructs a temperature field topology graph to effectively capture these interactions, including heat conduction and convection. Additionally, in the feature fusion and state updating mechanism of the model, we introduce process parameters, such as the air-fuel ratio, as additional inputs to enhance the ability of the GNN to handle complex variables. This enables real-time accurate simulation of the internal temperature distribution of the reheating furnace. The experimental results demonstrate that the model prediction error is controlled within 2.9 % and the response time is maintained within 20 ms, thereby validating the reliability and efficacy of the method in practical applications.

Improving UASS pesticide application: optimizing and validating drift and deposition simulations.

As unmanned aerial spraying systems (UASS) usage grows rapidly worldwide, a critical research study was conducted to optimize the simulation of UASS applications, aiming to enhance pesticide delivery efficiency and reduce environmental impact. The study examined several key aspects for accurate simulation of UASS application with lattice Boltzmann method (LBM). Based on these discussions, the most suitable grid size and simulation parameters were selected to create a robust model for optimizing UASS performance in various pest management scenarios, potentially leading to more targeted and sustainable pest control practices. The effect of stability parameter, grid size around the rotor and near ground, and parameters at wake flow were carefully analyzed to improve the precision of pesticide drift predictions and deposition patterns. Optimal grid sizes were identified as 0.2 m generally, 0.025 m near rotors, and a 0.1 + 0.2 m scheme for ground proximity, with finer grids improving accuracy but increasing computation time. Wake resolution and threshold significantly influenced simulation results, while wake distance had minimal impact beyond a certain point. The LBM's accuracy was validated by comparing simulated downwash flow and droplet deposition with field test data. This study optimized UASS simulation parameters, balancing computational efficiency with accuracy. The validated model enhances our ability to design more effective UASS for pest management, potentially leading to more precise and targeted pesticide applications. These advancements contribute to the development of sustainable pest control strategies, aiming to reduce pesticide usage and environmental impact while maintaining crop protection efficacy. © 2024 Society of Chemical Industry.

Optical Investigation of Sparks to Improve Ignition Simulation Models in Spark-Ignition Engines

The use of renewable fuels in place of fossil fuels in internal combustion engines is regarded as a viable method for achieving zero-impact-emission powertrains. However, to achieve the best performance with these fuels, these engines require further optimization, which is achieved through new combustion strategies and the use of advanced ignition systems such as prechambers. Since simulations greatly accelerate this development, accurate simulation models are needed to accurately predict the combustion phenomenon, which requires a deep understanding of the ignition phenomenon as it significantly affects combustion. This work presents a comprehensive experimental methodology to study sparks under engine conditions, providing quantitative data to improve and validate ignition simulation models. The goal was to determine the volume generated by sparks under engine conditions that can initiate combustion and use this information to improve simulation results to match the experimental results. The visible sparks were observed with high-speed cameras to understand their time-resolved evolution and interaction with the flow. The heat transfer from the plasma was also visualized using a modified Background-Oriented Schlieren technique. The information gained from the experimental observations was used to improve an ignition simulation model. Since the velocity of the plasma was found to be slower than the surrounding flow, a user-defined parameter was included to calibrate the velocity of the simulated plasma particles. This parameter was calibrated to match the simulated spark length to the experimental spark length. In addition, since the previous simulation model did not take the heat transfer from the plasma into account, the simulated plasma particles were coupled to have heat transfer to the surroundings. Based on a comparison of the simulation results with the experimental results, the improved approach was found to provide a better physical representation of the spark ignition phenomenon.

Research on the mechanisms of 2D road runoff pollution migration and the influence of pipeline overflow onto roads

In this paper, a novel numerical model capable of high-resolution, accurate simulation of the accumulation, wash-off, and migration of nonpoint source (NPS) pollutants on roads is proposed, effectively addressing the challenge of limited pipe network data for high-density urban building communities. This approach is based on a 1D-2D hydrodynamic and water quality dynamic bidirectional coupling model: GAST-SWMM. The calculation accuracy of the GAST two-dimensional road NPS wash-off model is validated via comparison with experimental data. The obtained Nash-Sutcliffe efficiency (NSE) is greater than 0.8. Moreover, the model was used to simulate the NPSs in a densely populated urban region of Xi'an, China, lacking building community pipeline data. The NPS pollutant transport and fate under the influence of both road runoff and the building community hydrodynamic water quality during rainfall events with a specific return period were examined. The proposed model can effectively and accurately replicate the accumulation and removal of NPS pollutants on a two-dimensional road and their dynamic interaction with the drainage network. With increasing rainfall return period, the peak time of the surface contaminant total load is postponed. The maximum surface pollutant load durations during rainfall events with 2-, 10-, and 50-year return periods are 60, 75, and 80 min, respectively. During the peak surface pollutant load time, the overflow pollutant fraction can exceed 85% for a 50-year rainfall return period. The simulation method presented in this paper accurately captures the spatial and temporal variations in NPS pollutants in densely populated urban areas, even when pipe network data for building communities are lacking. This method offers valuable technical assistance for urban environmental management and water quality protection.

Problem of Low-Frequency Motion Cueing Along Roll on Flight Simulators

An innovative approach to improve the fidelity of low-frequency lateral motion cueing within full-flight simulators for non-maneuvering aircraft is presented. On the basis of the perception peculiarities by the human vestibular system of movement along the roll, the problem of low-frequency motion cueing of lateral movement by motion systems of flight simulators was formulated and solved. The critical challenge of simulating the set of angular motion cues that pilots perceive during flights is decided. This research highlights two key outcomes. The fourth-order low-pass filter effectively extracts low-frequency motion cues from aircraft motion kinematic parameters, enhancing simulation accuracy. Secondly, proposed method significantly expands the range of simulated motion cues while ensuring their synchronization with high-frequency motion cues along relevant degrees of freedom. This formulation of the problem increases the range of simulated motion cues to ±0,3nz, which practically corresponds to the range of simulated motion cues of a transport aircraft, and thus increases the quality of motion cueing. The implementation of the developed method on the An-72TK-200 full flight simulator confirmed its effectiveness. In conclusion, this study introduces a promising methodology that enhances the quality of motion cueing, thus rendering flight simulations more realistic and beneficial for both pilots and aerospace engineering researchers in the real of non-maneuvering aircraft flight simulators.

Effect of slope angle on fractured rock masses under combined influence of variable rainfall infiltration and excavation unloading

Intense precipitation infiltration and intricate excavation processes are crucial factors that impact the stability and security of towering and steep rock slopes within mining sites. The primary aim of this research was to investigate the progression of cumulative failure within a cracked rock formation, considering the combined effects of precipitation and excavation activities. The study was conducted in the Huangniuqian eastern mining area of the Dexing Copper Mine in Jiangxi Province, China. An engineering geological investigation was conducted, a physical model experiment was performed, numerical calculations and theoretical analysis were conducted using the matrix discrete element method (MatDEM), and the deformation characteristics and the effect of the slope angle of a fractured rock mass under different scenarios were examined. The failure and instability mechanisms of the fractured rock mass under three slope angle models were analyzed. The experimental results indicate that as the slope angle increases, the combined effect of rainfall infiltration and excavation unloading is reduced. A novel approach to simulating unsaturated seepage in a rock mass, based on the van Genuchten model (VGM), has been developed. Compared to the vertical displacement observed in a similar physical experiment, the average relative errors associated with the slope angles of 45°, 50°, and 55° were 2.094%, 1.916%, and 2.328%, respectively. Accordingly, the combined effect of rainfall and excavation was determined using the proposed method. Moreover, the accuracy of the numerical simulation was validated. The findings contribute to the seepage field in a meaningful way, offering insight that can inform and enhance existing methods and theories for research on the underlying mechanism of ultra-high and steep rock slope instability, which can inform the development of more effective risk management strategies.

Topological models of yttrium aluminosilicate glass based on molecular dynamics and structure characterization analysis

AbstractTopological constraint theory (TCT) and molecular dynamics simulations (MD) are utilized to study the effect of Y2O3/SiO2 substitution on the short‐ and medium‐range structures and properties in aluminosilicate glasses containing Y2O3. Experimental methods have been applied to characterize the structures of the as‐prepared glasses to verify the accuracy of MD simulations. It was found that the Y–O bond distance is around 2.38 Å and the Y average coordination number is around 5.78. The introduction of Y2O3 disrupts the Si–O and Al–O bonds in the network structure and elevates the proportion of non‐bridging oxygen. Meanwhile, the calculation results of Si–O and Al–O bond distances show Y shortens the distance between Si, Al cations and oxygen ions, improving the network structure tightness. Furthermore, the glass transition temperature (Tg), Vickers hardness (HV), and elastic modulus (E) of glass increase with the increase of Y2O3 content, while the viscosity decreases. TCT was used to analyze the relationship among glass compositions, structures, and properties, and the properties prediction models for Tg, HV, and E were established. The effectiveness of TCT prediction models was proved by the comparison between the results predicted by the models and the data reported in the literature.

A stacking sequence optimization strategy for process‐induced deformation of multi‐layer thick asymmetric laminates

AbstractThe occurrence of process‐induced deformations of composites laminates is challenging for assembly accuracy and may lead to a service life reduction of parts. However, it can be obviously mitigated through different optimization strategies on the basis of the accurate curing process simulation. In this study, a stacking sequence optimization strategy is proposed and applied to multi‐layer thick asymmetric laminates. The shapes of deformed laminate plates are experimentally investigated in virtue of the three‐dimensional coordinate measuring machine. The thermo‐chemical–mechanical behaviors of plates are first verified through the comparisons of model predicted and experimental process‐induced deformations. Then the nonlinear control formula achieved through the regression model is proposed for the direct relationship between stacking sequences and process‐induced deformations. Finally, the required solutions are generated by solving the control formula. With the comparisons between the average deformations before and after optimizations, it is found that the magnitudes of deformations are significantly reduced, especially when the unoptimizated deformations are large.Highlights The thermo‐chemical–mechanical behavior can be described in the curing process simulation model. The deformed shapes of multi‐layer thick asymmetric laminate plates are measured by a three‐dimensional coordinate measuring machine. The stacking sequence optimization strategy is implemented by introducing a nonlinear control formula.

Application of graph neural networks to predict explosion-induced transient flow

We illustrate an application of graph neural networks (GNNs) to predict the pressure, temperature and velocity fields induced by a sudden explosion. The aim of the work is to enable accurate simulation of explosion events in large and geometrically complex domains. Such simulations are currently out of the reach of existing CFD solvers, which represents an opportunity to apply machine learning. The training dataset is obtained from the results of URANS analyses in OpenFOAM. We simulate the transient flow following impulsive events in air in atmospheric conditions. The time history of the fields of pressure, temperature and velocity obtained from a set of such simulations is then recorded to serve as a training database. In the training simulations we model a cubic volume of air enclosed within rigid walls, which also encompass rigid obstacles of random shape, position and orientation. A subset of the cubic volume is initialized to have a higher pressure than the rest of the domain. The ensuing shock initiates the propagation of pressure waves and their reflection and diffraction at the obstacles and walls. A recently proposed GNN framework is extended and adapted to this problem. During the training, the model learns the evolution of thermodynamic quantities in time and space, as well as the effect of the boundary conditions. After training, the model can quickly compute such evolution for unseen geometries and arbitrary initial and boundary conditions, exhibiting good generalization capabilities for domains up to 125 times larger than those used in the training simulations.

Accurate Simulation of Efimov Physics in Ultracold Atomic Gases with Realistic Three-Body Multichannel Interactions

We give a detailed and self-contained description of a recently developed theoretical and numerical method for the simulation of three identical bosonic alkali-metal atoms near a Feshbach resonance, where the Efimov effect is induced. The method is based on a direct construction of the off-shell two-body transition matrix from exact eigenfunctions of the embedded two-body Hamiltonians, obtained using realistic parameterizations of the interaction potentials which accurately reproduce the molecular energy levels. The transition matrix is then inserted into the appropriate three-body integral equations, which may be efficiently solved on a computer. We focus especially on the power of our method in including rigorously the effects of multichannel physics on the three-body problem, which are usually accounted for only by various approximations. We demonstrate the method for 7Li, where we recently showed that a correct inclusion of this multichannel physics resolves the long-standing disagreement between theory and experiment regarding the Efimovian three-body parameter. We analyze the Efimovian enhancement of the three-body recombination rate on both sides of the Feshbach resonance, revealing strong sensitivity to the spin structure of the model thus indicating the prevalence of three-body spin-exchange physics. Finally, we discuss an extension of our methodology to the calculation of three-body bound-state energies.