Large-scale Simulation Models Research Articles

CO2 enrichment accelerates alpine plant growth via increasing water-use efficiency

Phenological changes in global vegetation are often attributed to climate warming. However, climate warming and elevated atmospheric CO2 concentration (eCO2) are two co-occurring global change factors, and how eCO2 would affect vegetation phenology has received less attention. The partial pressure of atmospheric CO2 on the Tibetan Plateau (TP) is lower than that in regions of lower altitudes. Consequently, the growth and phenology of alpine plants in this region could be more sensitive to eCO2, but this hypothesis is not yet supported by empirical evidence. Here we explored the effect of eCO2 on plant phenology (including phenophases of green-up, budding, and flowering) through a 5-year field manipulation experiment in a high-altitude (4600 m above sea level) alpine grassland on the TP. Our results showed that eCO2 significantly advanced the spring phenology of an early-flowering species (Kobresia pygmaea), while it had no impact on the phenology of two mid-flowering species (Potentilla saundersiana and Potentilla cuneata). Compared to other low-altitude regions, plant phenology on the TP underwent greater alterations under eCO2, which supports our hypothesis that the growth of high-altitude plants is more sensitive to eCO2. Furthermore, we found that eCO2 significantly reduced the overlapping of flowering between contrasting plant species, mainly due to the phenological advancement of the K. pygmaea induced by eCO2. The observed advancement of the spring phenology in K. pygmaea under eCO2 was associated with increasing ecosystem water-use efficiency (WUE), thereby advancing its subsequent phenological development, such as budding and flowering. Our findings provide experimental evidence that atmospheric CO2 enrichment can accelerate plant growth processes in high-altitude regions, and suggest that large-scale model simulations should consider the effects of elevated atmospheric CO2 concentration on plant growth and phenology.

Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates

Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their parameters from empirical data. Most simulation models do not possess closed-form expressions for their likelihood function, requiring the use of simulation-based inference, such as simulated method of moments, indirect inference, likelihood-free inference or approximate Bayesian computation. However, given the high computational requirements of large-scale models, it is often difficult to run these estimation methods, as they require more simulated runs that can feasibly be carried out. The aim is to address the problem by providing a full Bayesian estimation framework where the true but intractable likelihood function of the simulation model is replaced by one generated by a surrogate model trained on the limited simulated data. This is provided by a Linear Model of Coregionalization, where each latent variable is a sparse variational Gaussian process, chosen for its desirable convergence and consistency properties. The effectiveness of the approach is tested using both a simulated Bayesian computing analysis on a known data generating process, and an empirical application in which the free parameters of a computationally demanding agent-based model are estimated on US macroeconomic data.

Identifying composition-mechanics relations in human brain tissue based on neural-network-enhanced inverse parameter identification

The mechanical properties of human brain tissue remain far from being fully understood. One aspect that has gained more attention recently is their regional dependency, as the brain’s microstructure varies significantly from one region to another. Understanding the correlation between tissue components and the mechanical behavior is an important step toward better understanding how human brain tissue properties change in space and time and to develop highly spatially resolved constitutive models for large-scale brain simulations. Here, we analyze the correlation between human brain tissue components quantified through enzyme-linked immunosorbent assays (ELISA) and material parameters obtained through an inverse parameter identification scheme based on a hyperelastic Ogden model and multimodal mechanical testing data for eight regions of the brain. We use neural networks as a metamodel to save computational costs. The networks are trained on finite element simulation outputs and are able to replace the simulations in the initial optimization step. We identified strong dependencies between mechanical properties and Iba1 associated with microglia cells, collagen VI, GFAP associated with astrocytes, and collagen IV. These results advance our understanding of microstructure-mechanics relations in human brain tissue and will contribute to the development of highly spatially resolved microstructure-informed constitutive models.

A Predictive Out-of-Step Identification Method Based on Wide-Area Velocity-Acceleration Trajectory

Online identification of the transient instability is essential in power system analysis, as it facilitates the grid operator to decide and coordinate system failure correction control actions. The rapid technological development of phasor measurement units (PMUs) provides an opportunity for online transient out-of-step identification in real-time. This paper analyzes the characteristics of angular velocity-acceleration trajectories in detail, and then proposes a predictive transient out-of-step identification method among coherent groups of generators based on the velocity-acceleration trajectory sampled by PMUs. To prove that the method can distinguish the transient out-of-step event following disturbance reliably, a mathematical proof of the criterion extended to multi-machine system is made, and the influences of the exciter and emergency control are taken into consideration to a certain extent. Finally, the effectiveness and predictability of the proposed method are demonstrated on a 39-bus New England test system and two large-scale power system simulation models of China by numerical studies.

Numerical investigation on aerodynamic characteristics of a high-speed train travelling through a bridge–tunnel section under non-uniform airflows caused by mountain terrain effect

A thorough understanding of aerodynamic characteristics of a high-speed train in a bridge–tunnel section is of extremely importance to ensure the vehicle safety of driving. This study presents a numerical investigation on aerodynamic characteristics of a high-speed train travelling through a bridge–tunnel section under non-uniform airflows caused by mountain terrain effect. First, the wind field distribution of the section is obtained from a large-scale numerical simulation model of a mountain terrain. Second, a high-fidelity small-scale model is established to simulate the aerodynamic characteristics. It is validated by the wind tunnel test. Third, the influence of non-uniform wind speeds and wind attack angles airflows on the aerodynamic characteristics are investigated. Finally, a derived empirical equation is introduced to predict the aerodynamic coefficients of the vehicle under non-uniform airflows caused by the mountain terrain. The results demonstrate that due to the effect of mountain terrain, the trend of aerodynamic coefficients varies slowly when the vehicle travels in the bridge–tunnel section. Meanwhile, when the vehicle travels near the tunnel exit, the jumping property of the pressure along the vehicle longitudinal axis is reduced. Furthermore, the predictions of the overturning moment coefficients via the simple empirical equation are consistent with the numerical simulation results.

Large-scale agent-based simulation model of pedestrian traffic flows

Mobility patterns of pedestrians at a very high spatial and temporal resolution support urban planning strategies and facilitate a better understanding of the transport system. In this study, we develop an agent-based model that simulates disaggregated pedestrian traffic flows at a regional scale. This model is designed to overcome limitations of existing approaches in pedestrian traffic modelling by incorporating several decision processes that are defined by probabilistic rules. These rules include activity type, mode, and route choices. The results of a case study in Salzburg (Austria) show traffic flows concentrated in the city centre and along the river. Performed complexity analysis justifies the structure and logic of the modelled system by showing improvements in results during the stepwise implementation of model concepts. We also performed uncertainty analysis to provide information on the accuracy of the modelled results, showing strong and moderate correlations against observational data.

Development, Calibration, and Validation of a Large-Scale Traffic Simulation Model: Belgium Road Network

Development of large-scale traffic simulation models have always been challenging for transportation researchers. One of the essential steps in developing traffic simulation models, which needs lots of resources, is travel demand modeling. Therefore, proposing travel demand models that require less data than classical travel demand models is highly important, especially in large-scale networks. This paper first presents a travel demand model named as probabilistic travel demand model, then it reports the process of development, calibration and validation of Belgium traffic simulation model. The probabilistic travel demand model takes cities' population, distances between the cities, yearly vehicle-kilometer traveled, and yearly truck trips as inputs. The extracted origin-destination matrices are imported into the SUMO traffic simulator. Mesoscopic traffic simulation and the dynamic user equilibrium traffic assignment are used to build the base case model. This base case model is calibrated using the traffic count data. Al-so, the validation of the model is performed by comparing the real (extracted from Google Map API) and simulated travel times between the cities. The validation results ensure that the model is a superior representation of reality with a high level of accuracy. The model will be helpful for road authorities, planners, and decision-makers to test different scenarios, such as the im-pact of abnormal conditions or the impact of connected and autonomous vehicles on the Belgium road network.

Climate change drives rapid warming and increasing heatwaves of lakes

Climate change could seriously threaten global lake ecosystems by warming lake surface water and increasing the occurrence of lake heatwaves. Yet, there are great uncertainties in quantifying lake temperature changes globally due to a lack of accurate large-scale model simulations. Here, we integrated satellite observations and a numerical model to improve lake temperature modeling and explore the multifaceted characteristics of trends in surface temperatures and lake heatwave occurrence in Chinese lakes from 1980 to 2100. Our model-data integration approach revealed that the lake surface waters have warmed at a rate of 0.11 °C 10a−1 during the period 1980–2021, being only half of the pure model-based estimate. Moreover, our analysis suggested that an asymmetric seasonal warming rate has led to a reduced temperature seasonality in eastern plain lakes but an amplified one in alpine lakes. The durations of lake heatwaves have also increased at a rate of 7.7 d 10a−1. Under the high-greenhouse-gas-emission scenario, lake surface temperature and lake heatwave duration were projected to increase by 2.2 °C and 197 d at the end of the 21st century, respectively. Such drastic changes would worsen the environmental conditions of lakes subjected to high and increasing anthropogenic pressures, posing great threats to aquatic biodiversity and human health.

Programmable large-scale simulation of bosonic transport in optical synthetic frequency lattices

Photonic simulators using synthetic frequency dimensions have enabled flexible experimental analogues of condensed-matter systems. However, so far, such photonic simulators have been limited in scale, yielding results that suffer from finite-size effects. Here we present an analogue simulator capable of simulating large two-dimensional (2D) and 3D lattices, as well as lattices with non-planar connectivity. Our simulator takes advantage of the broad bandwidth achievable in photonics, allowing our experiment to realize programmable lattices with over 100,000 lattice sites. We showcase the scale of our simulator by demonstrating the extension of bandstructure spectroscopy from 1D to 2D and 3D lattices. We then report the direct observation of time-reversal symmetry-breaking in a triangular lattice in both momentum and real space, as well as site-resolved occupation measurements in a tree-like geometry that serves as a toy model in quantum gravity. Moreover, we demonstrate a method to excite arbitrary multisite states, which we use to study the response of a 2D lattice to both conventional and exotic input states. Our work highlights the scalability and flexibility of optical synthetic frequency dimensions. Future experiments building on our approach will be able to explore non-equilibrium phenomena in high-dimensional lattices and to simulate models with nonlocal higher-order interactions. Analogue photonic simulators have so far suffered from severe finite size effects and limited programmability. Now, a frequency-mode photonic simulator enables the simulation of large-scale models in two and three dimensions.

Evaluation of early single dose vaccination on swine influenza A virus transmission in piglets: From experimental data to mechanistic modelling.

Swine influenza A virus (swIAV) is a major pathogen affecting pigs with a huge economic impact and potentially zoonotic. Epidemiological studies in endemically infected farms permitted to identify critical factors favoring on-farm persistence, among which maternally-derived antibodies (MDAs). Vaccination is commonly practiced in breeding herds and might be used for immunization of growing pigs at weaning. Althoughinterference between MDAs and vaccination was reported in young piglets, its impact on swIAV transmission was not yet quantified. To this aim, this study reports on a transmission experiment in piglets with or without MDAs, vaccinated with a single dose injection at four weeks of age, and challenged 17days post-vaccination. To transpose small-scale experiments to real-life situation, estimated parameters were used in a simulation tool to assess their influence at the herd level. Based on a thorough follow-up of the infection chain during the experiment, the transmission of the swIAV challenge strain was highly dependent on the MDA status of the pigs when vaccinated. MDA-positive vaccinated animals showed a direct transmission rate 3.6-fold higher than the one obtained in vaccinated animals without MDAs, estimated to 1.2. Vaccination nevertheless reduced significantly the contribution of airborne transmission when compared with previous estimates obtained in unvaccinated animals. The integration of parameter estimates in a large-scale simulation model, representing a typical farrow-to-finish pig herd, evidenced an extended persistence of viral spread when vaccination of sows and single dose vaccination of piglets was hypothesized. When extinction was quasi-systematic at year 5 post-introduction in the absence of sow vaccination but with single dose early vaccination of piglets, the extinction probability fell down to 33% when batch-to-batch vaccination was implemented both in breeding herd and weaned piglets. These results shed light on a potential adverse effect of single dose vaccination in MDA-positive piglets, which might lead to longer persistence of the SwIAV at the herd level.

Melting behavior prediction of latent heat storage materials: A multi-pronged solution

Latent heat storage is considered a viable solution for augmenting the thermal behavior of various energy storage systems such as solar technologies. Due to complex heat transfer processes that need to be resolved, performing numerical simulations on these devices are computationally intensive. In this study, a novel predictive model is proposed that is situated at the confluence of lattice Boltzmann method (LBM), proper orthogonal decomposition (POD), and artificial neural networks (ANN). The proposed model is subsequently applied to predicting melting behavior and temperature evolution of a substance within a square cavity. To the best of the authors’ knowledge, such an approach has not been previously applied to the study of latent heat storage materials. The benefits of the proposed method are: (a) reduced computational cost of modeling the thermal performance by POD; (b) robust treatment of complex BCs and geometries by LBM, which is well suited for parallel computing of large-scale simulation models and (c) providing a framework by ANN for establishing the temporal behavior in a robust manner. Finally, the predictions were compared with that of existing benchmark data, and it was found that the results were in good agreement with literature with maximum deviation of 10%.

The relationship between compartment models and their stochastic counterparts: A comparative study with examples of the COVID-19 epidemic modeling.

Deterministic compartment models (CMs) and stochastic models, including stochastic CMs and agent-based models, are widely utilized in epidemic modeling. However, the relationship between CMs and their corresponding stochastic models is not well understood. The present study aimed to address this gap by conducting a comparative study using the susceptible, exposed, infectious, and recovered (SEIR) model and its extended CMs from the coronavirus disease 2019 modeling literature. We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations. Based on this equivalence, we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment. The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics. However, it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs. Additionally, we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents. This model offered a balance between computational efficiency and accuracy. The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling. Furthermore, the results had implications for the development of hybrid models that integrated the strengths of both frameworks. Overall, the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Realization of Large-Scale Social Simulation Models by an Agent-Based Approach

Realization of Large-Scale Social Simulation Models by an Agent-Based Approach

Enhanced gas production efficiency of class 1,2,3 hydrate reservoirs using hydraulic fracturing technique

Current gas hydrate production test indicates that it is difficult to reach the commercial level for low permeability hydrate-bearing formation by depressurization method. Hydraulic fracturing in hydrate-bearing sediments (HBS) is a promising method to enhance gas production efficiency. In this study, the use of hydraulic fracturing and depressurization method to improve the recovery rate for class 1,2,3 hydrate reservoirs is studied. The three-dimensional large-scale HBS simulation model is firstly established with the single bi-wing vertical fracture. The gas production behavior for cases with different fracture parameters is investigated including the fracture conductivity, position, scale, and length. The simulation results show that fracture parameters have significant influence on Class 2 and Class 3 hydrate reservoirs. By enlarging the fracture conductivity, fracture length, and fracture scale, the flow resistance around the well can remarkably decrease which is beneficial to the pressure spread and hydrate dissociation. The peak rate of gas production increases and the peak time arrives early. The production feature is analyzed based on the test data in Shenhu Area of the South China Sea (SCS) by numerical simulation method. The cumulative gas production substantially increases by using hydraulic fracturing technique compared to that without fracturing.

Depth- averaged large eddy simulation of shallow turbulent mixing layer

This paper presents the DA-LES large-scale simulation model of turbulent shallow flows, which is based on dual filtering, the first filter removes vertical scales and the second filtering, as a filter spatial, removes all scales from the movement of sizes smaller than the mesh size. To validate this model, we consider the mixing layer problem in a shallow flow of two different velocities imposed on the input Uijttewaal, Booij,Van Prooijen and Uijttewaal [1,2]. In this work, we consider the finite volume method based on an unstructured mesh for turbulent equations, the numerical results are presented and compared to experimental data.

Intelligent meta-model construction and global stochastic sensitivity analysis based on PSO-CNN

A large-scale simulation model is needed for the stochastic sensitivity analysis of structural uncertainty. The finite element method often exceeds the computing power and cannot meet the analysis requirements and computational efficiency of stochastic sensitivity analysis. With the development of artificial intelligence, the calculation efficiency problem can be solved by a combination of the finite element model and a mathematical model, but the accuracy of the analysis results depends on the accuracy of the mathematical model. However, using artificial neural networks to construct mathematical models also has numerous problems, such as falling into local extrema. Thus, to address the computational efficiency and calculation accuracy problems, the particle swarm optimization (PSO)-convolutional neural network (CNN) meta-model is presented in this paper. This model was used to improve the training and prediction accuracy of a small-sample CNN, where the initial learning rate of the CNN was optimized by PSO. Additionally, the PSO was combined with the Adam optimizer to automatically screen the optimal network structure. The global stochastic sensitivity analysis method based on the Sobol sensitivity method and the Monte Carlo method was used to quantify the degree of influence of the input parameters on the model output parameters and to analyze the influence trend of the parameter changes on the structural response combined with the positive and negative Spearman’s rank correlation coefficients. The accuracy, good adaptability, and extension ability of the PSO-CNN meta-model was verified using the high-dimensional function examples and the structural models. Global stochastic sensitivity analysis was carried out on the joint of an extended end plate. The influence degree of the parameters on the initial rotational stiffness of the joint was determined, and corresponding design suggestions were obtained. These suggestions were consistent with the design specifications and the research results of other scholars, which verified the reliability of this method and could be applied to other types of structural sensitivity analyses.

Analysis and Visualization of High-Dimensional Dynamical Systems’ Phase Space Using a Network-Based Approach

The concept of attractors is considered critical in the study of dynamical systems as they represent the set of states that a system gravitates toward. However, it is generally difficult to analyze attractors in complex systems due to multiple reasons including chaos, high-dimensionality, and stochasticity. This paper explores a novel approach to analyzing attractors in complex systems by utilizing networks to represent phase spaces. We accomplish this by discretizing phase space and defining node associations with attractors by finding sink strongly connected components (SSCCs) within these networks. Moreover, the network representation of phase space facilitates the use of well-established techniques of network analysis to study the phase space of a complex system. We show the latter by introducing a new node-based metric called attractivity which can be used in conjunction with the SSCC as they are highly correlated. We demonstrate the proposed method by applying it to several chaotic dynamical systems and a large-scale agent-based social simulation model.

Substituting petro-elastic model with a new proxy to assimilate time-lapse seismic data considering model errors

Dynamic data from oil and gas reservoirs (such as well-production or 4D seismic data) have been used often to reduce uncertainty in reservoir simulation models. These data are assimilated in the simulation models separately or jointly. Assimilation of 4D seismic data conventionally involves with a petro-elastic model (PEM) to transform outputs from the simulation models to elastic properties. The PEM is a set of different equations with uncertain parameters and its inclusion in assimilation algorithms calls on multidisciplinary teams of geoscientists and engineers. Moreover, PEM requires extraction of different outputs from the simulation models for the seismic forward model calculations. The extraction process can be costly for large-scale simulation models of giant reservoirs. This research presents a new petro-elastic proxy model (named DAI-Proxy) with a novel formulation to substitute the PEM and integrate 4D seismic data. DAI-Proxy relates time-lapse acoustic impedance to a summation of saturation and pressure changes with two coefficients which are functions of porosity. As the proxy is an approximation of the PEM, its application is affected by model error. We introduce two approaches to account for the proxy model error: (1) considering uncertain coefficients for the DAI-Proxy and (2) using fixed coefficients in the proxy while estimating model errors statistics from the prior ensemble of models. We incorporate these two approaches with a data assimilation algorithm to assimilate simultaneously 4D seismic and well-production data. A benchmark case is used with different cases of data assimilation to compare the DAI-Proxy and the PEM applications. Results show that data match quality for 4D seismic and well-production have similar responses for the PEM and DAI-Proxy implementations. In terms of production forecast, using fixed coefficients in the proxy with its model error treatment create a data assimilation framework comparable to the PEM case. Our results indicate that the traditional PEM application to integrate jointly 4D seismic and well-production data can be replaced with our new DAI-Proxy application. Given the degree of uncertainty in the PEM, related to the rock and fluid models, our proxy provides similar results with fewer uncertain inputs. The proxy offers further advantage as it needs less outputs from the simulation models for seismic forward model calculations. In addition, it helps petroleum engineers to use a computationally less expensive model (light model) as a substitute for the PEM to assimilate 4D seismic data.

A Joint Optimization of Strategic Workforce Planning and Preventive Maintenance Scheduling: A Simulation–Optimization Approach

This paper proposes a Simulation–Optimization framework, where a maintenance system of high-value assets is modelled via a novel large-scale Discrete Event Simulation model. In contrast to the existing simulation models in the maintenance domain, the developed simulation model provides an integrated view of the different aspects of the maintenance system including asset acquisitions, maintenance workforce planning, and scheduling of preventive maintenance activities. Further, the workforce planning studies the technicians’ progression based on their allocated maintenance activities, and the trade-off between their progression and the recruitment of additional technicians, which is infrequently studied in this domain. The developed simulation model is coupled with a Differential Evolution (DE) algorithm, that is further enhanced with a K-means clustering machine learning method. By using this coupling (i.e., simulation-based optimization), we jointly optimize the scheduling frequency of preventive maintenance activities and workforce planning decisions (e.g., recruitment and career progression). The proposed framework proved its superiority in terms of representing the dynamics of the overall system, and providing optimal decisions about the workforce and maintenance schedules. A comprehensive study is proposed and showed the ability of the proposed framework to reduce the total maintenance cost on average of 5.6%, that is around three million currency units.

Law of water migration inside the water-injected coal base on the joint analysis of cross-scale CT images

The size of the CT scan specimen and the analytical scale of the CT image are mutually restricted, making it impossible to achieve high-precision imaging for the larger-sized specimens currently used in the laboratory for water injection experiments. Accordingly, the subsequent simulation analysis mostly studies the micro- and meso-scale models. Research on the macro- and meso-scale models that are close to reality is scarce. To solve the above-mentioned problems, the method of cross-scale CT image joint analysis was used to reconstruct the pore and fracture structural model of a cubic specimen with a side length of 10 cm and a cylindrical specimen with a diameter of 1 cm and a height of 1.5 cm. The cube and cylindrical specimens with the same sampling point are assumed to have the same pore size distribution, and the parameters of the small pores in the unidentified area of the cube specimen are converted. Accordingly, a composite model that combines the geometric structure of the pores and fractures of the cube specimen and the conversion parameter data is formed. The mercury intrusion method was also used to verify the accuracy of the pore parameters of the CT scan reconstruction analysis. Subsequently, the CT images were digitized and imported into the simulation software to establish a large-scale simulation model considering the distribution of pores and fractures and the contribution of small pore parameters and compared with the analytical results of the isotropic model and the connected pore and fracture model. The accuracy of the composite model combining geometric structure and conversion parameter data is determined. The law of water migration in the water injection process of different pore and fracture structure types is also revealed.