Simulation Of Spread Research Articles

A pattern‐oriented simulation for forecasting species spread through time and space: a case study on an ecosystem engineer on the move

Modelling the spread of introduced ecosystem engineers is a conservation priority due to their potential to cause irreversible ecosystem‐level changes. While existing models predict potential distributions and spread capacities, new approaches that simulate the trajectory of a species' spread over time are needed. We developed novel simulations that predict spatial and temporal spread, capturing both continuous diffusion‐dispersal and occasional long‐distance leaps. We focused on the introduced population of superb lyrebird Menura novaehollandiae in Tasmania, Australia. Initially introduced as an insurance population, lyrebirds have become novel bioturbators, spreading across key natural areas and becoming ‘unwanted but challenging to eradicate'. Using multi‐scale ecological data, our research 1) identified broad and fine‐scale correlates of lyrebird occupation and 2) developed a spread simulation guided by a pattern‐oriented framework. This occurrence‐based modelling framework is useful when demographic data are scarce. We found that the cool, wet forests of western Tasmania with open understoreys offer well‐connected habitats for lyrebird foraging and nesting. By 2023, lyrebirds had reached quasi‐equilibrium within a core range in southern Tasmania and were expanding northwest, with the frontier reaching the western coast. Our model forecasts that by 2085, lyrebirds will have spread widely across suitable regions of western Tasmania. By pinpointing current and future areas of lyrebird occupation, we provide land managers with targeted locations for monitoring the effects of their expansion. Further, our area of applicability (AOA) analysis identified regions where environmental variables deviate from the training data, guiding future data collection to improve model certainty. Our findings offer an evidence‐based approach for future monitoring and provide a framework for understanding the dynamics of other range‐expanding species with invasive potential.

A simultaneous simulation of human behavior dynamics and epidemic spread: A multi-country study amidst the COVID-19 pandemic.

The transmission dynamics of infectious diseases and human responses are intertwined, forming complex feedback loops. However, many epidemic models fail to endogenously represent human behavior change. In this study, we introduce a novel behavioral epidemic model that incorporates various behavioral phenomena into SEIR models, including risk-response dynamics, shifts in containment policies, adherence fatigue, and societal learning, alongside disease transmission dynamics. By testing our model against data from 8 countries, where extensive behavioral data were available, we simultaneously replicate death rates, mobility trends, fatigue levels, and policy changes, both in-sample and out-of-sample. Our model offers a comprehensive depiction of changes in multiple behavioral measures along with the spread of the disease. We assess the explanatory power of each model mechanism in capturing data variability. Our findings demonstrate that the comprehensive model that includes all mechanisms provides the most insightful perspective for understanding the influence of human behavior during pandemics.

An offline coupling of fire spread models to simulate the 2021 Marshall Fire

Background Existing fire spread models focus exclusively on wildland or urban fire simulation. Aims This study aims at an offline coupling of two fire spread models to enable a continuous simulation of a wildfire incident transitioning from wildland into wildland–urban interface (WUI) communities, evaluate the effects of wind input on simulation results and study the influence of building types on fire spread patterns. Methods The selected models are WRF-Fire, a wildland fire behaviour simulation platform, and SWUIFT, a model for fire spread inside the WUI. The 2021 Marshall Fire serves as the case study. A map of the fire’s timeline and location is generated using public information. Three simulation scenarios are analysed to study the effects of wind input resolution and building type on the predicted fire spread and damage. Key results The most accurate results are obtained using a high-resolution wind input and when incorporating different building types. Conclusions The offline coupling of models provides a reliable solution for fire spread simulation. Fire-resistant buildings likely helped limit community fire spread during the Marshall Fire. Implications The research is a first step toward developing simulation capabilities to predict the spread of wildfires within the wildland, WUI and urban environments.

Hybrid PDE–ODE models for efficient simulation of infection spread in epidemiology

This article introduces a novel hybrid mathematical modelling approach that effectively couples partial differential equations (PDEs) with ordinary differential equations (ODEs), exemplified through the simulation of epidemiological processes. The hybrid model aims to integrate the spatially detailed representation of disease dynamics provided by PDEs with the computational efficiency of ODEs. In the presented epidemiological use-case, this integration allows for the rapid assessment of public health interventions and the potential impact of infectious diseases across large populations. We discuss the theoretical formulation of the hybrid PDE–ODE model, including the governing equations and boundary conditions. The model’s capabilities are demonstrated through detailed simulations of disease spread in synthetic environments and realworld scenarios, specifically focusing on the regions of Lombardy, Italy and Berlin, Germany. Results indicate that the hybrid model achieves a balance between computational speed and accuracy, making it a valuable tool for policymakers in real-time decisionmaking and scenario analysis in epidemiology and potentially in other fields requiring similar modelling approaches.

The influence of small mass loss rate peaks on the rate of spread of predictive flame spread simulations: A theoretical study

The influence of small mass loss rate peaks on the rate of spread of predictive flame spread simulations: A theoretical study

Wildfire spread prediction using geostationary satellite observation data and directional ROS adjustment factor

This paper introduces a novel data-driven approach to enhance the prediction accuracy of regional-scale wildfire spread simulators using geostationary satellite wildfire detection data. First, we propose a new algorithm that maximizes accuracy by innovatively utilizing the conventional uniform Rate of Spread (ROS) adjustment factor. The ROS adjustment factor is estimated by a genetic algorithm based on near-real-time observations from geostationary satellites and the FARSITE wildfire simulator. The accuracy of wildfire spread prediction is improved by applying the estimated ROS adjustment factor within FARSITE. In addition, this paper proposes a Directional ROS adjustment factor approach, which applies different ROS adjustment factors to a given fuel model depending on their location, thereby allowing for a more accurate representation of a wildfire's perimeter in FARSITE-based simulations. The proposed methodology is demonstrated through the 2020 Creek Fire in California, USA. The results demonstrate that the proposed algorithm significantly enhances accuracy. Moreover, incorporating the directional ROS adjustment factor leads to more precise predictions than the conventional uniform ROS adjustment factors. The proposed methodology offers significant advantages in developing targeted wildfire response strategies, particularly under conditions where external environmental conditions make high-resolution data collection challenging. This approach enhances the accuracy and reliability of response planning, enabling more effective allocation of resources and potentially reducing the overall impact of wildfires.

Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation.

Alternative wetting boundary condition for binary fluids based on phase-field lattice Boltzmann method

Based on the phase-field theory, a new wetting boundary condition (WBC) scheme is proposed to describe the fluid–solid interaction of binary fluids. Different from the common linear, cubic and sine form of surface energy wetting conditions, we adopt a mixed cubic and sine form of free energy in the present scheme. Two conditions are given to ensure that the spurious film at the solid surface disappears and the reasonable boundary condition is obtained. Combined with the wetting scheme and lattice Boltzmann (LB) method based on phase-field theory, numerical simulation of droplet spreading on a cylindrical surface is carried out to verify the performance of the present WBC. It is found that the present wetting scheme can offer considerable accuracy for predicting a static contact angle, which means that it can be used to study the wetting boundary problems of binary fluids.

Numerical simulation of fire spread in a large-scale open CLT compartment

Recent experiments have shown that exposed cross-laminated timber (CLT) can have a significant effect on the fire dynamics of large compartments. A simulation with the Fire Dynamic Simulator has been conducted to better understand the fire behaviour of open-plan compartments with exposed CLT. The simulation was set up to replicate a large-scale experiment, FRIC-02, with exposed CLT on the back wall and ceiling. The compartment was 95 m2 (18.8 m × 5.0 m × 2.5 m), with one long wall open (opening factor 0.18 m1/2). A continuous wood crib was used as the variable fuel load.The characteristic results of FRIC-02 with a rapid fire development and non-symmetrical external flames were successfully reproduced. With the wind coming diagonally from behind, as in FRIC-02, the external flames emerged mainly out of one window. The flames covered the entire window height, which effectively inhibited the inflow of air through that window. The imbalance in air supply also created large temperature differences throughout the compartment. With no implementation of wind, external flames and temperatures were more symmetrical. Despite a good match to FRIC-02, the method still has several limitations, including the adaption of the burning rate to the feedback from surroundings.

Towards predictive engineering-type simulations of upward flame spread in SBI scenarios

Towards predictive engineering-type simulations of upward flame spread in SBI scenarios

UPI-LT: Enhancing Information Propagation Predictions in Social Networks Through User Influence and Temporal Dynamics

The TEST pervasive use of social media has highlighted the importance of developing sophisticated models for early information warning systems within online communities. Despite the advancements that have been made, existing models often fail to adequately consider the pivotal role of network topology and temporal dynamics in information dissemination. This results in suboptimal predictions of content propagation patterns. This study introduces the User Propagation Influence-based Linear Threshold (UPI-LT) model, which represents a novel approach to the simulation of information spread. The UPI-LT model introduces an innovative approach to consider the number of active neighboring nodes, incorporating a time decay factor to account for the evolving influence of information over time. The model’s technical innovations include the incorporation of a homophily ratio, which assesses the similarity between users, and a dynamic adjustment of activation thresholds, which reflect a deeper understanding of social influence mechanisms. Empirical results on real-world datasets validate the UPI-LT model’s enhanced predictive capabilities for information spread.

A waypoint based approach to visibility in performance based fire safety design

In performance based fire safety design, ensuring safe egress, e.g. by visibility of safety signs, is a crucial safety goal. Compliance with the building requirements is often demonstrated by simulations of smoke spread. Numerical models like the Fire Dynamics Simulator generally compute visibility as a local quantity using the light extinction coefficient, without the consideration of the actual light path to a safety sign. Here, visibility maps are introduced, providing an approach for post-processing fire simulation data. They indicate safe areas along egress routes, with respect to visibility. At each location, the available visibility is calculated using Jin’s empirical relation, as an integrated value of the extinction coefficient along the line of sight to the closest exit sign. The required visibility results from the distance between those points. Additional parameters like view angle or visual obstructions are considered. The presented method allows for temporal visibility assessment, e.g. in an ASET-RSET analysis.

Analysis and Simulation of Misinformation Spread in Social Networks: A Hybrid Stochastic-Deterministic Approach with NEAB Model

This paper examined and simulated the dynamics of misinformation spread within social networks using the NEAB model [32]. In this model, (N) represents non-believers, (E) denotes individuals exposed to the information but undecided, (A) refers to those who accept the information and may propagate it, and (B) are the believers. Similar to epidemiological models like SEIR (Susceptible-Exposed-Infected-Recovered), the NEAB model adapts to study the spread of various types of information and behaviors within a population [20]. By combining elements of network theory [28] and epidemiology, the model investigates how behaviors and network structures influence information transmission. Our simulations, particularly when applied to real-world scenarios like misinformation spread during events such as COVID-19 or natural disasters, highlight key factors influencing dissemination [7]. Rates of exposure to misinformation, the transition of individuals from exposed to active spreaders, and the rate of recovery all play crucial roles in shaping how misinformation spreads [33]. Misinformation propagates more easily when exposure rates are high, while higher recovery rates help limit its spread. Sensitivity analysis shows that variations in (β) and (δ) significantly impact the spread, emphasizing the importance of targeted interventions during these critical stages [22]. The objective of this study is to evaluate strategies for reducing the effects of misinformation on public discourse and health outcomes while offering insights into the key factors that drive its dissemination based on model simulations.

A coupled local front reconstruction and immersed boundary method for simulating 3D multiphase flows with contact line dynamics in complex geometries

Multi-phase flows in the presence of complex solid geometries are encountered widely throughout industrial processes to intensify mass and heat transfer processes. The efficiency of these processes relies largely on the predominant fluid dynamics regime inside the reaction vessel (e.g. trickle bed, bubble column reactor). The prevailing flow structure is influenced by many factors including the nature of the fluid-solid interactions (i.e. wetting or non-wetting). Such flows are often studied using front-capturing techniques to capture the fluid-fluid interface in combination with an auxiliary model to account for fluid-solid interactions. However, these front-capturing methods have difficulties in accurately representing the interface. Therefore, we adopted a front-tracking method: the Local Front Reconstruction Method (LFRM). In this study, LFRM is coupled with the Immersed Boundary method to incorporate the no-slip boundary condition at the solid surface. The model performance is assessed using droplet spreading simulations, where the equilibrium droplet shape (i.e. radius, height, and outline), the interfacial pressure difference, and the surface tension force are compared to analytical solutions and literature data. The results show an excellent match with the analytical solutions, and additionally superior performance to state-of-the-art volume tracking models.

Static analysis-based rapid fire-following earthquake risk assessment method using simple building and GIS information

After the occurrences of large-scale earthquakes, secondary damage (e.g., fire following earthquake) can result in tremendous losses of life, properties, and buildings. To reduce these disaster risks, fire following earthquake assessment methods composed of ignition and fire-burned rate estimation models have been utilized. However, previous methods required for large amounts of building and GIS information, and complex modeling and analysis processes, leading to significant time consumption. This paper proposed a static analysis-based rapid fire following earthquake assessment method using simple information and implemented it in Pohang City, South Korea. Based on previous studies, the best-fit model for the ignition rate estimation was selected, and a cluster-based fire-burned rate estimation model was developed using simple building information (e.g., construction year, building occupancy, story, and total floor area) from the public building database (e.g., building registration data). For the fire-burned rate estimation model, fire-resistant structure types were defined using simple building information, and this was utilized to generate clusters of buildings at a regional level by comparing fire-spread distances for each fire-resistant structure type with adjacent distances among the buildings. This proposed method was applied to Pohang City, South Korea, and validated as follows: (1) the selected ignition rate model predicted similar ignition numbers to the actual reported number (actual number of ignitions = 4 vs. predicted number of ignitions = 3), and (2) the fire-burned rate model estimated fire-burned areas with a marginal difference compared to the fire spread simulation (fire-burned area using the proposed model = 13,703.6 m2 vs. results of fire spread simulation = 16,800.0 m2, with an error of approximately 18%).

Effect of powder properties, process parameters, and recoating speed on powder layer properties measured by in-situ laser profilometry and part properties in laser powder bed fusion

In laser-based powder bed fusion of metals (PBF-LB/M), the powder layer is the link between the powder properties and the resulting part quality. Powder layer quality is a key metric related to powder spreadability and ultimately part quality, yet it is still unclear how it can be quantified. This is due to the difficulty of studying powder layer properties during the process. This study investigates the influence of powder properties, process parameters, and recoating speed on the surface roughness of the powder layer and the part, as well as on the effective thickness of the powder layer and solidified layer, and the resulting relative part density. Utilizing in-situ laser profilometry, high-resolution topographical data of the powder layer and the part surface were acquired, with minimal interference to the PBF-LB/M process. Six AlSi10Mg powders with varying particle size distribution, morphology, and flowability were processed using a wide range of recoating speeds and scan speeds to create powder layers with a wide range of properties. The results reveal a strong correlation between energy input and the effective powder layer thickness where lower scan speed results in an increased effective powder layer thickness due to material losses. Additionally, faster recoating decreases the powder layer density, which is moderated by the median particle size where the effect is strongest for fine powders. The surface roughness of the powder layer and top part surface are influenced by the recoating speed, energy input, and particle size, and they are strongly linked to each other. This highlights the importance of considering realistic substrate surface roughnesses in both powder spreading experiments and simulations. Finally, layer properties affect the process stability, resulting in small differences in relative part density.

Effect of Flame Retardant Additives on the Flammability and Flame Spread Rate of Glass-Fiber-Reinforced Epoxy Resin with Varying Binder Content: An Experimental and Numerical Study

ABSTRACT This paper presents a study of flammability and downward flame spread rate in an opposed oxidizer flow over glass fiber-reinforced epoxy resin (GFRER) with the added flame retardants 6,6′-((methylenebis(4,1-phenylene))bis(azanediyl)) bis(6 H-dibenzo[c,e][1,2]oxaphosphinine 6-oxide) (DDM-DOPO) and graphene and with a binder content (BC) of ~ 35 wt% and ~ 52 wt%; the mass ratio of the glass fiber to the binder in the composite was 2:1 and 1:1, respectively. To evaluate the flammability and thermal stability of the obtained materials, the LOI test, the UL-94HB test, and thermogravimetric analysis were conducted. The effective DDM-DOPO concentration for decreasing the flammability of GFRER was found based on the LOI results. During the flame spread experiment with an opposed oxidizer flow, the addition of flame retardants resulted in an increase in the limiting oxygen concentration (LOC). Oxygen concentration increase in the oxidizer flow led to a decrease in the flame retardant effect on the rate of flame spread (ROS) for samples with a BC of ~ 35 wt%. The flame retardant effectiveness for samples with a BC of ~ 52 wt% remained almost the same at 40–60 vol% O2 concentrations. The relationship among the LOI, LOC, and ROS was experimentally established. A numerical simulation of flame spread over reinforced material was performed using a coupled gas-solid heat and mass transfer model to predict the ROS over GFRER with and without flame retardants. The model correctly predicted the ROS for GFRER with ~ 35 wt% BC, while for samples with ~ 52 wt% BC, the model gave lower ROS compared to experiment.

Computational models for improving surveillance for the early detection of direct introduction of cassava brown streak disease in Nigeria.

Cassava is a key source of calories for smallholder farmers in sub-Saharan Africa but its role as a food security crop is threatened by the cross-continental spread of cassava brown streak disease (CBSD) that causes high yield losses. In order to mitigate the impact of CBSD, it is important to minimise the delay in first detection of CBSD after introduction to a new country or state so that interventions can be deployed more effectively. Using a computational model that combines simulations of CBSD spread at both the landscape and field scales, we model the effectiveness of different country level survey strategies in Nigeria when CBSD is directly introduced. We find that the main limitation to the rapid CBSD detection in Nigeria, using the current survey strategy, is that an insufficient number of fields are surveyed in newly infected Nigerian states, not the total number of fields surveyed across the country, nor the limitation of only surveying fields near a road. We explored different strategies for geographically selecting fields to survey and found that early and consistent CBSD detection will involve confining candidate survey fields to states where CBSD has not yet been detected and where survey locations are allocated in proportion to the density of cassava crops, detects CBSD sooner, more consistently, and when the epidemic is smaller compared with distributing surveys uniformly across Nigeria.

Discrete element simulation of powder layer spreading by blade sliding: packing factor, mechanism, and optimization

Discrete element simulation of powder layer spreading by blade sliding: packing factor, mechanism, and optimization

Effect of different boundary conditions on the combustion properties and fire characteristics of typical woods

To provide guidance on the bioenergy potential of wood and its fire spread numerical simulation, insights into the combustion properties and fire characteristics of one hardwood (sassafras) and two softwoods (pine and fir) were investigated. The effect of different boundary conditions are focused. The condensed phase decomposition in thermogravimetric analysis indicated that the main degradation processes for all wood combustion could be divided into two stages, and the corresponding demarcation point of conversion rate is 0.4 and 0.35 for hardwood and softwood, respectively. Three specific regions could further be established based on the activation energies variations in Stage Ⅰ. The governing reaction mechanism of sassafras wood is determined to be the diffusion model connect with order-based model, while the reaction mechanism of softwoods (pine and fir) can be considered as the reaction order (1st to three-halves) type followed by diffusion model. The gas phase flaming in FPA combustion experiments suggest that fir and pine wood have a faster charring rate and produce less CO and CO2 during pyrolysis and combustion. Sassafras requires more time to be ignited owing to its high density, which causes higher thermal inertia therefore decelerating the temperature increase on the sample surface. In addition, the dense structure of sassafras leads to an early appearance of the second HRR and MLR peaks. As hardwood has a higher density than softwood, the thermal insulation in hardwood delays char cracking on sassafras wood surface and enhances the duration between ignition and flameout. Such findings should provide guidance for wood combustion modeling and flame spread simulation in future works.