Diffusion Model Simulation Research Articles

Reconstruction of the Chemical Gas Concentration Distribution Using Partial Convolution-Based Image Inpainting.

An interpolation method, which estimates unknown values with constrained information, is based on mathematical calculations. In this study, we addressed interpolation from an image-based perspective and expanded the use of image inpainting to estimate values at unknown points. When chemical gas is dispersed through a chemical attack or terrorism, it is possible to determine the concentration of the gas at each location by utilizing the deployed sensors. By interpolating the concentrations, we can obtain the contours of gas concentration. Accurately distinguishing the contours of a contaminated region from a map enables the optimal response to minimize damage. However, areas with an insufficient number of sensors have less accurate contours than other areas. In order to achieve more accurate contour data, an image inpainting-based method is proposed to enhance reliability by erasing and reconstructing low-accuracy areas in the contour. Partial convolution is used as the machine learning approach for image-inpainting, with the modified loss function for optimization. In order to train the model, we developed a gas diffusion simulation model and generated a gas concentration contour dataset comprising 100,000 contour images. The results of the model were compared to those of Kriging interpolation, one of the conventional spatial interpolation methods, finally demonstrating 13.21% higher accuracy. This suggests that interpolation from an image-based perspective can achieve higher accuracy than numerical interpolation on well-trained data. The proposed method was validated using gas concentration contour data from the verified gas dispersion modeling software Nuclear Biological Chemical Reporting And Modeling System (NBC_RAMS), which was developed by the Agency for Defense Development, South Korea.

丘陵山地果园单轨式防霜机的仿真与试验

In order to solve the problems of artificial operation, high labor intensity and low efficiency of frost prevention in hilly orchards, this study takes hilly orchards as the research object. Based on the agronomic requirements of frost prevention in hilly orchards, a smoke anti-frost machine based on the transport track of hilly orchards is designed. With the help of Fluent software, the spatial and temporal distribution characteristics of smoke mass concentration and temperature in the process of smoke diffusion are simulated and analyzed based on the discrete phase model. The results show that the smoke continues to erupt at a speed of 6 m / s, and the plume volume gradually increases. After 9 s, the smoke reaches a stable diffusion state. Based on the established smoke diffusion simulation model, the orthogonal test analysis of the working parameters at the smoke outlet of the frost-proof machine was carried out with the temperature increase near the canopy of the fruit tree as the index. The results showed that the best combination of working parameters at the smoke outlet of the frost-proof machine was the smoke outlet speed of 10 m / s, the smoke outlet angle of 60 °, and the smoke outlet diameter of 140 mm. The field test results show that the frost-proof machine can continuously operate for 0.5 h at a running speed of 0.6 m / s, and the temperature in the height range of 1.5 m to 4 m in the working area can be increased by about 1.7 ° C. This study is of great significance to reduce the frost disaster loss of hilly orchards and improve the economic benefits of hilly orchards.

Multiscale modeling of solute diffusion in triblock copolymer membranes.

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

Industry Cluster Innovation Upgrading and Knowledge Evolution: A Simulation Analysis Based on Small-World Networks

This article focuses on the innovation and knowledge evolution of industry clusters. We examine the effects of the hub firm and the interaction of network member firms on the upgrading of the cluster. Our study is based on two patterns of knowledge learning and innovation, namely, STI (science, technology, and innovation) and DUI (doing, using, and interacting). This article adopts a knowledge diffusion simulation model to study the exchange of knowledge between cluster network actors in the context of small-world networks. The results indicate that we must pay close attention to the influence of hub enterprises on cluster evolution. Although hub companies may have certain innovation capabilities, if knowledge absorption problems among members are not properly resolved in the cluster network, the innovation performance of the local clusters is likely to be weakened, despite the success of the hub firm.

Haze risk: information diffusion based on cellular automata

Negative effects of haze risk can easily spread faster and more widely, however, existing studies rarely investigate the whole period or cycle of diffusion events, which leads to lowered public knowledge, often resulting in exaggerated negative effects. In this paper, the diffusion simulation model based on cellular automata is used to evaluate the diffusion of haze risk information. Firstly, according to the whole-life cycle of emergencies, the public affected by haze risk information is classified by resembling the SEIR infectious disease model. Secondly, a diffusion rule from unknown to exposed individuals is developed based on the theory of cellular automata. Then, according to the individual state transformation at different stages, a whole-life cycle model regarding haze risk information diffusion and propagation model is constructed. Finally, appropriate parameters are selected to calculate the results without intervention. The results show that during the whole evolution process, the unknowns continue to decrease, and the lurkers continue to increase. Due to the existence of the immunization period, the immunized persons reach their maximum number before they are about to lose immunity, and the number of communicators reaches their minimum. Afterward, the number of immunized persons reduced to a stable level, and the number of communicators continues to increase toward an agglomeration benefit. Therefore, in order to achieve effective control of the spread of haze risk information, strong and weak control measures are taken for each type of individuals, and the immunity of unknowns and lurkers is increased for individual type control. For the entire information diffusion control, increase communicator immunity and reduce immunization conversion rate. The study of the spread of haze risk information helps to increase the public's sense of responsibility, helps to improve the government's credibility, and contributes to the establishment of a harmonious society.

Diffusion model simulation of odor pollutants in livestock and poultry farms based on environmental monitoring of the Internet of Things

Livestock pollution is one of the main sources of agricultural pollution, which has a negative impact on the global environment. Monitoring, simulation and early warning of major pollutants emitted from livestock production is of great significance for reducing agricultural pollution. Especially, real-time comprehensive monitoring and early warning of the concentration and distribution of harmful gases could improve the harm of livestock production to people, livestock itself and the environment, and increase the safety level of livestock production. This study focused on the perception and monitoring of the discharge status of livestock farming simulation technics, mainly to carry out Internet of Things-based monitoring of the main components of livestock culture pollutants, and to use odor gas air dispersion software “ModOdor” to simulate the spread of pollutants. This study was aimed to determine the characteristics of pollutant diffusion in typical farms, which could provide decision reference to odor hygienic buffer zone and minimum shelter distance to achieve the ecological and safety objectives of livestock farming.

Investigating the Diffusion of Workload-Induced Stress—A Simulation Approach

Work-induced stress is widely acknowledged as harming physical and psychosocial health and has been linked with adverse outcomes such as a decrease in productivity. Recently, workplace stressors have increased due to the COVID-19 pandemic. This study aims to contribute to the literature base in a couple of areas. First, it extends the current knowledge base by utilising generative additive modelling (GAMs) to uncover the nature of the relationship between workload (a key workplace stressor) and productivity based on real-world event logs. Additionally, it uses recursive partitioning modelling to shed light on the factors that drive the relationship between these variables. Secondly, it utilises a simulation-based approach to investigate the diffusion of workload-induced stress in the workplace. Simulation is a valuable tool for exploring the effect of changes in a risk-free manner as it provides the ability to run multiple scenarios in a safe and virtual environment with a view to making recommendations to stakeholders. However, there are several recognised issues with traditional simulation approaches, such as inadequate resource modelling and the limited use of simulations for operational decision making. In this study, we propose an approach which extracts the required parameters from an event log and subsequently utilises them to initialise a workload-induced stress diffusion simulation model accurately. We also explore the effects of varying the parameters to control the spread of workload-induced stress within the network. With suitable amendments, this approach can be extended to model the spread of disease (e.g., COVID-19), diffusion of ideas, among other things, in the workplace.

Coercivity enhancement of sintered Ce-substituted Nd-Fe-B magnets by grain boundary diffusion process of Tb

Grain boundary diffusion process (GBDP) of Tb on sintered Ce-substituted Nd-Fe-B magnets has been conducted by magnetron sputtering deposition of Tb film for coercivity enhancement. The microstructure and magnetic properties of the diffused (Nd,Ce)FeB magnets have been evaluated and the effect of diffusion temperature, time and Tb coating thickness on the Ce-substituted N35 magnets are discussed. The results show that a core-shell structure is formed around the main grains after GBDP, which promotes the coercivity enhancement of the magnets. A maximum coercivity enhancement from 10.9 kOe to 15.2 kOe is achieved in Ce-N35 magnets by deposition of 10 μm Tb films followed by diffusion processing at 900 °C for 300 min. The inhibition effect of Ce in GBDP of Tb is also evaluated using (Nd,Ce)FeB magnets with different Ce contents and discussed using diffusion model simulation implemented by Tb distributions through GD-OES. The rare earth element (REE)-rich nodes located at the triple junction of the grains may attribute to the Tb aggregation and the formation of TbFe2, resulting in the inhibition of further Tb diffusion.

The Combination of MoS2/WO3 and Its Adsorption Properties of Methylene Blue at Low Temperatures.

It was found previously that neither monomer MoS2 nor WO3 is an ideal material for the adsorption of organic dyes, while MoS2/WO3 composites synthesized by a two-step hydrothermal method have outstanding adsorption effects. In this work, the chemical state of each element was found to be changed after combination by X-ray photoelectron spectroscopy analysis, which lead to their differences in adsorption performance. Moreover, the adsorption test of methylene blue on MoS2/WO3 composites was carried out under a series of temperatures, showing that the prepared composites also had appreciable adsorption rates at lower temperatures. The adsorption process could be well described by the Freundlich isothermal model and the pseudo-second order model. In addition, the particle-internal diffusion model simulation revealed that the internal diffusion of the particles played an important role in the whole adsorption process.

Hydrogen diffusion simulation model considering thermal and stress changes in sport welds.

ABSTRACT High-strength steel sheets have been developed for both reducing automobile weight and keeping passengers’ safety. Delayed fracture due to diffusible hydrogen is known as one of the problems in arc welds of high-strength steel plates, which is often prevented by applying pre/post-heating processes. However, there is little reported regarding hydrogen diffusion behaviour during rapid temperature change in resistance spot welds of steel sheets. Hydrogen diffusion behaviour is affected by thermal and stress fields. Therefore, the effect of rapid change in these fields needs to be properly considered for the elucidation of hydrogen diffusion behaviour in resistance spot welds. This study aims to clarify the delayed fracture mechanism of resistance spot welds. The hydrogen diffusion simulation model considering the effects of thermal and stress changes during welding is constructed. It is assumed in the model that hydrogen solubility and diffusivity in the nugget are larger than those of base metal. A change in fracture pattern dependent on hydrogen environment can be well explained using this model. The numerical simulation predicts that high residual stress leads to hydrogen concentration at the edge of the nugget after welding. This concentration would be the main factor of the delayed fracture in the nugget when the nugget diameter is small. In this situation, hydrogen is introduced from antirust oil, water and contamination on sheet surfaces to the nugget during welding. The numerical simulation assumed in actual use is also performed, in which the hydrogen is introduced from the sheet surfaces. Hydrogen concentrates at the edge of the nugget in a relatively mild hydrogen environment, whereas in the severe environment it highly concentrates in the heat-affected zone around the nugget before concentrating in the nugget. These calculated results can well explain the transition of the fracture pattern from the nugget to theheat-affected zone in the experiment.

Comparison of drift–diffusion model and hydrodynamic carrier transport model for simulation of GaN-based IMPATT diodes

This paper presents a numerical simulation of a Wurtzite-GaN-based IMPATT diode operating at the low-end frequency of terahertz range. Conventional classical drift–diffusion model is independent of the energy relaxation effect at high electric field. However, in this paper, a hydrodynamic carrier transport model including a new energy-based impact ionization model is used to investigate the dc and high-frequency characteristics of an IMPATT diode with a traditional drift–diffusion model as comparison. Simulation results show that the maximum rf power density and the dc-to-rf conversion efficiency are larger for conventional drift–diffusion model because it overestimates the impact ionization rate. Through hydrodynamic simulation we revealed that the impact ionization rates are seriously affected by the high and rapidly varied electric field and the electron energy relaxation effect, which lead to the rf output power density and the dc-to-rf conversion efficiency falls gradually, and a wider operation frequency band is obtained compared with the drift–diffusion model simulation at frequencies over 310 GHz.

Forest Fire Smoke Detection Based on Visual Smoke Root and Diffusion Model

The damage caused by forest fire to forestry resources and economy is quite serious. As one of the most important characters of early forest fire, smoke is widely used as a signal of forest fire. In this paper, we propose a novel forest fire smoke detection method based on computer vision and diffusion model. Unlike the video-based methods that usually rely on image characters extraction, we try to find the shape of smoke that is at the generation stage. To combine vision and diffusion model together, the basic concept of smoke root is proposed. In the frame processing stage, none characters of fire smoke are extracted (like texture, color, frequency information etc.), and continuous frames are only used to extract stable points in dynamic areas as the smoke root candidate points. In the diffusion model simulation stage, all smoke root candidate points information is adopted by the model to generate the simulation smoke. Finally, the match algorithm based on color, dynamic areas and simulation smoke is implemented to get the final results. In order to reduce the complexity of computation, we ignored the simulation process of the smoke details, such as texture and turbulence, and only retained the contour features in two-dimensional form. Experiments show that under the condition of smoke root existing in the frames, the algorithm can obtain stable detection results and low false positive rate in cloudy scenes.

Optimization of ion transport from atmospheric pressure ion sources

An experimental study and numerical calculations of the influence of geometric and electrical parameters of ion sources with ionization at atmospheric pressure on the motion of ions from ionization site to an entrance orifice were carried out. The effect of space charge on the current-voltage characteristics of an electrode configuration of the ion source was experimentally shown. The results of numerical calculations of ion motion with allowance for the space charge in a stationary ion flow based on the statistical diffusion simulation model were shown to agree well with the experiment. The possibility of 100% ion transportation from the ionization area to a collector was demonstrated experimentally and numerically.

Integrated assessment modelling as a positive science: private passenger road transport policies to meet a climate target well below 2 \u2218C

Transport generates a large and growing component of global greenhouse gas emissions contributing to climate change. Effective transport emissions reduction policies are needed in order to reach a climate target well below 2 ∘C. Representations of technology evolution in current integrated assessment models (IAM) make use of systems optimisations that may not always provide sufficient insight on consumer response to realistic policy packages for extensive use in policy-making. Here, we introduce FTT: transport, an evolutionary technology diffusion simulation model for road transport technology, as an IAM sub-component, which features sufficiently realistic features of consumers and of existing technological trajectories that enables to simulate the impact of detailed climate policies in private passenger road transport. Integrated to the simulation-based macroeconometric IAM E3ME-FTT, a plausible scenario of transport decarbonisation is given, defined by a detailed transport policy package, that reaches sufficient emissions reductions to achieve the 2 ∘C target of the Paris Agreement.

Selective adsorption of lithium from high Mg-containing brines using HxTiO3 ion sieve

The prepared titanium type lithium ion sieve HxTiO3 are characterized and tested as adsorbents for selective adsorption of lithium from high Mg-containing brine. The maximum adsorption capacities of Li+, Na+, K+, Ca2+ and Mg2+ ions onto HxTiO3 reached 36.34, 1.93, 1.99, 2.58 and 3.53mg/g, respectively, under the initial pH8.8 at 25°C for 24h. Adsorption isotherms and kinetic analysis indicated that the adsorption of Li+ ions and other cations onto HxTiO3 followed Langmuir isotherm model and pseudo-second order kinetic equation. A diffusion model simulation showed that the Li+ ions had the highest external diffusion coefficients (7.7×10−2cms−1) while the other cations had small effect on the adsorption rate of Li+. The Biot number was >100 signified that these ions adsorption onto HxTiO3 were controlled by the internal diffusion. The separation factors (α) of Li+ over other cations were much >1. In particular, the α value of Li+ over Mg2+ reached 4783, indicating the high selectivity for Li+. In addition, the Li+ ion adsorption capacity kept constant during the six adsorption-desorption cycles and molar ratio of Mg to Li of eluents was as low as 0.08, indicating that the HxTiO3 ion sieve could effectively extract Li+ from the enriched brine.

The calibration model in potassium ion flux non-invasive measurement of plants in vivo in situ

SIET (Self-referencing Ion Electrode Technique) provides a novel electrophysiological tool which can non-invasively measure the dynamic influxes and effluxes of ions caused by the diffusion along the concentration gradients in vivo. However, in this technique ion fluxes are converted to voltage signals using an ion selective microelectrode at a small amplitude of μV, which is easy to be interfered by the ambient noise. Hence, effective solutions to the suppression of noise and calibration of ion flux measurement system are very important for this method. A K+-selective microelectrode was constructed using liquid ion exchangers (LIX) to investigate ion transport over plant tissue. A standard concentration gradient which simulates plant living cells was produced by an electrode with a certain tip diameter, filled with a solution containing a known K+ concentration in 100mmol/L. An ion diffusion simulation model was established. This model evaluated the performance of ion flux measurement system in accuracy and reliability by comparing the consistency of the measured value and the predicted curve. K+ fluxes were measured within 25min at each measuring point of distance 10, 20, 30, 40, 50, 80, and 100μm from the K+ source, respectively. It can be seen that the K+ fluxes changes little, which indicates that ion flux measurement system has a reliable stability. The study provides a theoretical basis for a new non-invasive ion flux measurement method creation and a new sensors design.

Evaluating the efficacy of the Florida Keys’ angler-assisted permit tagging program

The use of angler-based tagging programs in recreational fisheries is on the rise, as they provide managers with effective, inexpensive means of collecting information. However, such programs are rarely evaluated for efficacy during the course of the program. Our study tagging program was the recreational permit Trachinotus falcatus (Linnaeus) fishery in the Florida Keys, which began in 2010. We evaluated the program for potential improvements using two complimentary approaches: an email-based survey of anglers and an advection–diffusion simulation model. Survey respondents reported potential fish stress and poor access to tags as the biggest obstacles to tagging permit. Respondents also cited poor data feedback from scientists as a potential area for improvement. According to the advection–diffusion simulation, the current level of tag returns is insufficient for accurately and precisely estimating movement parameters. The Florida permit tagging program could be greatly improved with greater communication between scientists and anglers. Data feedback and angler rewards could each increase angler buy-in. Tag retention could have a strong effect on the tag return rate and deserves more attention. Tag reporting could also have a strong impact on the tag return rate. The Florida permit tagging program needs more work to become a success, but has the potential to provide benefits for scientists and anglers.

Synthesis and characterization of a chitosan ligand for the removal of copper from aqueous media

ABSTRACTA novel chitosan derivative, 8‐hydroxyquinioline‐2‐carboxaldehydechitosan (CSHQ) Schiff base, was synthesized by a simple condensation process. The structure of the resulting product was confirmed by Fourier transform infrared spectroscopy, solid‐state 13C‐NMR, differential scanning calorimetry, scanning electron microscopy, and energy‐dispersive X‐ray spectroscopy. Further, the synthesized CSHQ Schiff base was used as an adsorbent for the removal of Cu(II) from aqueous solution. The adsorption of Cu(II) onto CSHQ was strongly dependent on the pH value, and the optimum pH value obtained was pH 5. Among the pseudo‐first‐order (PFO), pseudo‐second‐order (PSO), and intraparticle diffusion model simulation, the experimental data followed PSO kinetics well. The Cu(II) adsorption process onto CSHQ obeyed the Langmuir isotherm model well rather than the Freundlich and Sips isotherm models; this suggested that a monolayer adsorption occurred on the surface of CSHQ. From the overall results, we found that Cu(II) was adsorbed onto CSHQ through oxygen, nitrogen and pyridine nitrogen donor atoms and that CSHQ could be a promising adsorbent for water treatment. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 4542–4550, 2013

Effect of surface pressurization on the growth of α-Fe2O3nanostructures

By suitably pressurizing iron substrates under different conditions, the resulting α-Fe(2)O(3) nanostructures, formed by its direct thermal oxidation, can gradually change in succession from nanowires to nanoleaves and to micropillars as the pressure is increased. The inter-relation between the pressure conditions and the resulting nanostructure is studied by density functional calculations using ultrasoft pseudopotentials with a plane-wave basis method and with the generalized gradient approximation (GGA). It is shown that the shape of the formed nanostructures is primarily determined by the anisotropic activation energy and, as the latter is lowered, there is a shape change from wire to pillar. A simulation model of diffusion using the Monte Carlo method is applied in the 3-D (dimensional) case to show how the anisotropic activation energy influences the growth process of the α-Fe(2)O(3) nanostructure. The present study provides a way to control the shape of the nanostructures grown by the thermal-oxidation method.

Discrete event modeling and massively parallel execution of epidemic outbreak phenomena

In complex phenomena such as epidemiological outbreaks, the intensity of inherent feedback effects and the significant role of transients in the dynamics make simulation the only effective method for proactive, reactive or post facto analysis. The spatial scale, runtime speed, and behavioral detail needed in detailed simulations of epidemic outbreaks cannot be supported by sequential or small-scale parallel execution, making it necessary to use large-scale parallel processing. Here, an optimistic parallel execution of a new discrete event formulation of a reaction–diffusion simulation model of epidemic propagation is presented to facilitate a dramatic increase in the fidelity and speed by which epidemiological simulations can be performed. Rollback support needed during optimistic parallel execution is achieved by combining reverse computation with a small amount of incremental state saving. Parallel speedup of over 5,500 and other runtime performance metrics of the system are observed with weak-scaling execution on a small (8,192-core) Blue Gene/P system, while scalability with a weak-scaling speedup of over 10,000 is demonstrated on 65,536 cores of a large Cray XT5 system. Scenarios representing large population sizes, with mobility and detailed state evolution modeled at the level of each individual, exceeding several hundreds of millions of individuals in the largest cases, are successfully exercised to verify model scalability.