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Increasing the model resolution is expected to be one way for improving air quality forecasts in urban areas. In this study, we evaluate the model performance in a large city at various resolutions to examine the best resolution for air pollution simulations. The comparison with measurements at a station near the traffic emissions shows the advantage of using high resolutions for capturing the extreme values. The statistical evaluation indicates that the highest model resolution (33 m) provides the best results for NOX concentration distributions near the traffic roads, while the improvement for roadside O3 with decreasing grid spacing stops at a certain point. The best model performance for the areas with a distance to the pollution sources is with the resolution of 100–300 m, at which the transport errors are equivalent to the emission biases. Plain Language Summary As the increasing needs in the air quality forecasting in large cities, there is a trend in decreasing the model grid spacing to obtain more detailed pollutants distributions between neighborhoods or at street levels. To determine at which resolution the model can obtain the best representation of the pollutants' concentrations, we evaluate the model performance at different resolutions taking Hong Kong as an illustration. The analysis shows that the improvement with increasing model resolution is not monotonic for the areas far away from the intense emissions; however, the model with the highest resolution (33 m) reproduces the best results for the short‐lived species near the pollution sources. Key Points Increasing horizontal resolution to 33 m improves the prediction of NOX near the traffic emissions The threshold of the model resolution is around 300 m for areas with a distance to the pollution sources The changes of model performances with varied resolutions are different for NOX and O3

ABSTRACT Introduction Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi‐modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). Methods We enhance large‐scale whole‐brain simulation in TVB with a cause‐and‐effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB‐simulated local field potentials (LFP) for ML classification. Results The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1‐score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD‐typical spatial distribution. Discussion The cause‐and‐effect implementation of local hyperexcitation caused by Aβ can improve the ML–driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity‐based brain simulation.

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based model to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data are extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimises the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimising computational performance compared to other models.

Corona discharge is a typical discharge in gas-insulated equipment; however, the correlation between microscopic discharge process and macroscopic electromagnetic (EM) wave signals excited by discharge remains unclear. Therefore, this study innovatively employs the space current pulse as a bridge to reveal their relationship through the multi-scale simulation. First, the needle-plate discharge process in SF 6 gas is simulated based on a fluid dynamics model. Then, the effects of voltage, temperature, and the curvature of needle tip on the space current pulse are investigated. Lastly, the current pulses generated under varying conditions serve as excitation sources, and the finite-difference time-domain (FDTD) method is utilized to establish correlations between the corona discharge stages and discharge conditions and the amplitude-frequency characteristics of excited EM waves. The simulation results indicate that in the rising and falling stages of current pulse, the spectral energy is predominantly concentrated in the high frequency band (2.3–3.0 GHz) of the ultra-high-frequency (UHF) range, whereas the spectral energy constitutes the highest proportion within the mid-high frequency band (1.6–2.3 GHz) in the stabilization stage. As voltage, temperature, or the curvature of needle tip increases, there is a corresponding rise in the proportion of EM energy within both the low frequency band (0.2–0.9 GHz) and the mid-low frequency band (0.9–1.6 GHz), as well as in the mid-high frequency band; conversely, the proportion of energy within the high frequency band diminishes. The proposed multi-scale simulation method provides a novel way to obtain the characteristics of EM waves induced by partial discharge (PD) in gas.

Numerical simulations consist of many components that affect the simulation accuracy and the required computational resources. However, finding an optimal combination of components and their parameters under constraints can be a difficult, time-consuming and often manual process. Classical adaptivity does not fully solve the problem, as it comes with significant implementation cost and is difficult to expand to multi-dimensional parameter spaces. Also, many existing data-based optimization approaches treat the optimization problem as a black-box, thus requiring a large amount of data. We present a constrained, model-based Bayesian optimization approach that avoids black-box models by leveraging existing knowledge about the simulation components and properties of the simulation behavior. The main focus of this paper is on the stochastic modeling ansatz for simulation error and run time as optimization objective and constraint, respectively. To account for data covering multiple orders of magnitude, our approach operates on a logarithmic scale. The models use a priori knowledge of the simulation components such as convergence orders and run time estimates. Together with suitable priors for the model parameters, the model is able to make accurate predictions of the simulation behavior. Reliably modeling the simulation behavior yields a fast optimization procedure because it enables the optimizer to quickly indicate promising parameter values. We test our approach experimentally using the multi-scale muscle simulation framework OpenDiHu and show that we successfully optimize the time step widths in a time splitting approach in terms of minimizing the overall error under run time constraints.

The addition of a hydrophobic agent to fiber concrete can realize the overall hydrophobic of the material, which can prevent damage to cementing material due to its porous and hydrophilic properties. However, the impact of varying fiber content on the mechanical properties of these materials remains unclear, limiting their large-scale application in extreme environments. Mechanical experiments were conducted to obtain the material’s elastic modulus, compressive strength, and Poisson’s ratio, aiming to explore the reinforcing effect and mechanism of fibers on mechanical properties. The mechanical parameters of hydrophobic basalt fiber cement-based materials with different fiber content were calculated by Mori–Tanaka homogenization theory calculation and mesoscopic numerical simulation. Scanning electron microscopy results displayed the binding between the fiber and the gelling material was good, there was no obvious alkali-silicon reaction damage, and the homogeneity analysis could be carried out. When the fiber content was below 1.5%, there was good agreement among the experimental, finite element, and numerical simulation data. When the fiber content was 2%, deviations in numerical values occurred due to fiber agglomeration failure. These findings provided a foundation for optimizing fiber content in hydrophobic basalt fiber cement-based materials, supporting their broader application in durable concrete structures.

The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity and multi-scale behavior. Traditional computational methods, while effective, often struggle to balance accuracy with computational efficiency, especially when bridging the atomistic to macroscopic scales. Recently, physics-informed neural networks (PINNs) have emerged as a promising tool that integrates data-driven learning with the governing physical laws of the system. This review discusses the development and application of PINNs in the context of polymer science. It summarizes the recent advances, outlines the key methodologies, and analyzes the benefits and limitations of using PINNs for polymer property prediction, structural design, and process optimization. Finally, it identifies the current challenges and future research directions to further leverage PINNs for advanced polymer modeling.

Purpose Depending on an experimental approach to find optimal parameters for producing fully dense (relative density > 99%) Inconel 718 (IN718) components in the selective laser melting (SLM) process is expensive and offers no guarantee of success. Accordingly, this study aims to propose a multi-scale simulation framework to guide the choice of processing parameters in a more pragmatic manner. Design/methodology/approach In the proposed approach, a powder layer, ray tracing and heat transfer simulation models are used to calculate the melt pool dimensions and evaporation volume corresponding to a small number of laser power and scanning speed conditions within the input design space. A layer-heating model is then used to determine the inter-layer idle time required to maximize the temperature convergence rate of the solidified layer beneath the power bed. The simulation results are used to train surrogate models to construct SLM process maps for 3,600 pairs of the laser power and scanning speed within the input design space given three different values of the underlying solidified layer temperature (i.e., 353 K, 673 K and 873 K). The ideal selection of laser power and scanning speed of each process map is chosen based on four quality-related criteria listed as follows: without the appearance of key-hole melting; an evaporation volume less than the volume of the d90 powder particles; ensuring the stability of single scan tracks; and avoiding a weak contact between the melt pool and substrate. Finally, the optimal laser power and scanning speed parameters for the SLM process are determined by superimposing the optimal regions of the individual process maps. Findings The feasibility of the proposed approach is demonstrated by fabricating IN718 test specimens using the optimal processing conditions identified by the simulation framework. It is shown that the maximum density of the fabricated parts is 99.94%, while the average density is 99.88% and the standard deviation is less than 0.05%. Originality/value The present study proposed a multi-scale simulation model which can efficiently predict the optimal processing conditions for producing fully dense components in the SLM process. If the geometry of the three-dimensional printed part is changed or the machine and powder material is altered, users can use the proposed method for predicting the processing conditions that can produce the high-density part.

Primary dendrite arm spacing (PDAS) is a crucial microstructural feature in nickel-based superalloys produced by laser cladding. In order to investigate the effects of process parameters on PDAS, a multi-scale model that integrates a 3D transient heat and mass transfer model with a quantitative phase-field model was proposed to simulate the dendritic growth behavior in the molten pool for laser cladding Inconel 718. The values of temperature gradient (G) and solidification rate (R) at the S/L interface of the molten pool under different process conditions were obtained by multi-scale simulation and used as input for the quantitative phase field model. The influence of process parameters on microstructure morphology in the deposition layer was analyzed. The result shows that the dendrite morphology is in good agreement with the experimental result under varying laser power (P) and scanning velocity (V). PDAS was found to be more sensitive to changes in laser scanning velocity, and as the scanning velocity decreased from 12 mm/s to 4 mm/s, the PDAS increased by 197% when the laser power was 1500 W. Furthermore, smaller PDAS can be achieved by combining higher scanning velocity with lower laser power.

Precipitation hardening stainless steels have attracted extensive interest due to their distinguished mechanical properties. However, it is necessary to further uncover the internal quantitative relationship from the traditional standpoint based on the statistical perspective. In this review, we summarize the latest research progress on the relationships among the composition, microstructure, and properties of precipitation hardened stainless steels. First, the influence of general chemical composition and its fluctuation on the microstructure and properties of PHSS are elaborated. Then, the microstructure and properties under a typical heat treatment regime are discussed, including the precipitation of B2-NiAl particles, Cu-rich clusters, Ni3Ti precipitates, and other co-existing precipitates in PHSS and the hierarchical microstructural features are presented. Next, the microstructure and properties after the selective laser melting fabricating process which act as an emerging technology compared to conventional manufacturing techniques are also enlightened. Thereafter, the development of multi-scale simulation and machine learning (ML) in material design is illustrated with typical examples and the great concerns in PHSS research are presented, with a focus on the precipitation techniques, effect of composition, and microstructure. Finally, promising directions for future precipitation hardening stainless steel development combined with multi-scale simulation and ML methods are prospected, offering extensive insight into the innovation of novel precipitation hardening stainless steels.