Integrated Modeling Framework Research Articles

An Integrated Modeling Framework for Automated Product Design, Topology Optimization, and Mechanical Simulation

The present study introduces an integrated software approach that provides an automated product design toolkit for customized products like knives, incorporating topology optimization (TO) and numerical simulations in order to streamline engineering workflows during the product development procedure. The modeling framework combines state-of-the-art technologies into a single platform, enabling the design and the optimization of mechanical structures with minimal human intervention. In particular, the proposed solution leverages artificial intelligence (AI), shape optimization methods, and computational tools in order to iteratively optimize material utilization as well as the design of products based on certain criteria. By embedding simulation within the design optimization loop, the developed software module ensures that performance constraints are respected throughout the design process. The case studies are concentrated in designing knives, demonstrating the platform’s ability to reduce design time, enhance product performance and provide rapid iterations of structurally optimized geometries. Finally, it should be noted that this research showcases the potential of integrated modeling technologies towards the transformation of traditional design paradigms, in this way contributing to faster, more reliable and efficient product development in various engineering industries through the training and deployment of AI models in these scientific fields.

Integrated modeling of boron powder injection for real-time plasma-facing component conditioning

An integrated modeling framework for investigating the application of solid boron (B) powder injection for real-time surface conditioning of plasma-facing components (PFCs) in tokamak environments is presented. Utilizing the DIII-D impurity powder dropper (IPD) setup, this study simulates B powder injection scenarios ranging from milligrams to tens of milligrams per second, corresponding to boron flux rates of 1020–1021 B/s in standard L-mode conditions. The comprehensive modeling approach combines EMC3-EIRENE for simulating the deuterium plasma background and the Dust Injection Simulator (DIS) for the ablation and transport of the boron powder particles. EMC3 trace impurity fluid modeling results show substantial boron transport to the inboard lower divertor, predominantly influenced by the main ion plasma flow. The dependency on powder particle size (5–250μm) was found to be insignificant for the scenario considered. The effects of erosion and redeposition were considered to reconcile the discrepancies with experimental observations, which saw substantial deposition on the outer divertor plasma-facing components. For this purpose, the WallDYN3D code was updated to include boron sources within the plasma domain and integrated into the modeling framework. The mixed-material migration modeling shows evolving boron deposition patterns, suggesting the formation of mixed B-C layers or predominantly B coverage depending on the powder mass flow rate. While the modeling outcomes at lower B injection rates tend to align with DIII-D experimental observations, the prediction of near-pure boron layers at higher rates has yet to be experimentally verified in the carbon environment of the DIII-D tokamak. The extensive reach of boron layers found in the modeling suggests the need for modeling that encompasses the entire wall geometry for more accurate experimental correlations. This integrated approach sets a precedent for analyzing and applying real-time in-situ boron coating techniques in advanced tokamak scenarios, potentially extendable to ITER.

Forecasting Urban Land Use Dynamics Through Patch-Generating Land Use Simulation and Markov Chain Integration: A Multi-Scenario Predictive Framework

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal’s urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model’s potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally.

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

A comprehensive framework to evaluate the financial impacts of genetic improvement on wood products from planted forests

Increasing the productivity of planted forests may efficiently provide an important part of the world’s growing demand for wood while protecting natural forests. In this study, we developed an integrated modelling framework to evaluate the financial impacts of improving productivity of planted forests by tree breeding. Using this framework, we compared three genetic improvement scenarios of white spruce plantations, a key reforestation species in North America, and evaluated the differences in the derived wood product assortments in terms of quantity, quality, and revenues. Favouring the production of wood volume appears as the best way to enhance financial gains from white spruce plantations in the current market. The scenario that focused on increasing tree height produced a greater volume of wood products and larger lumber pieces, which resulted in the greatest revenues. In comparison, favouring wood stiffness over volume led to poorer results, as the increased product quality was not sufficient to surpass the financial gain associated with greater wood volumes. While we successfully provided an evaluation of the product assortments derived from genetically improved plantations, the proposed framework would benefit from more data input to help maximize financial gains from a range of tree breeding strategies.

Transmission pathways for the stem rust pathogen into Central and East Asia and the role of the alternate host, barberry

Abstract After many decades of effective control of stem rust caused by the Puccinia graminis f.sp. tritici, (hereafter Pgt) the reported emergence of race TTKSK/Ug99 of Pgt in Uganda reignited concerns about epidemics worldwide because ∼90% of world wheat cultivars had no resistance to the new race. Since it was initially detected in Uganda in 1998, Ug99 variants have now been identified in thirteen countries in Africa and the Middle East. Stem rust has been a major problem in the past, and concern is increasing about the risk of return to Central and East Asia. Whilst control programs in North America and Europe relied on the use of resistant cultivars in combination with eradication of barberry (Berberis spp.), the alternate host required for the stem rust pathogen to complete its full lifecycle, the focus in East Asia was principally on the use of resistant wheat cultivars. Here, we investigate potential airborne transmission pathways for stem rust outbreaks in the Middle East to reach East Asia using an integrated modelling framework combining estimates of fungal spore deposition from an atmospheric dispersion model, environmental suitability for spore germination, and crop calendar information. We consider the role of mountain ranges in restricting transmission pathways, and we incorporate a representation of a generic barberry species into the lifecycle. We find viable transmission pathways to East Asia from the Middle East to the north via Central Asia and to the south via South Asia and that an initial infection in the Middle East could persist in East Asia for up to three years due to the presence of the alternate host. Our results indicate the need for further assessment of barberry species distributions in East Asia and appropriate methods for targeted surveillance and mitigation strategies should stem rust incidence increase in the Middle East region.

An integrated approach toward digital design and simulation of the automated overbraiding process for composite manufacturing

The study presents a new integrated hybrid modeling framework that combines kinematic models with Finite Element Analysis (FEA) to simulate the overbraiding process on non-circular and variable cross-section mandrels in composite manufacturing. By combining the computational efficiencies of kinematic models with the detailed physical insights from FEA simulations, this hybrid method provides a holistic solution for designing and prototyping overbraided structures. Specifically applied to a complex rectangular-to-square mandrel, the approach accurately predicts braid angles, validated against physical braiding experiments covering a range of target angles. The achieved results highlight the model's accuracy and emphasize its potential to boost the efficiency and precision of overbraiding in industrial settings, thereby reducing the need for costly and time-consuming physical braiding iterations.

Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030)

Dynamic simulation of carbon emissions (CE) in megacities is crucial for regional carbon reduction management, however, limited simulation accuracy hinders its application in carbon reduction policies. An integrated modeling framework was developed based on high-resolution multi-source data to analyze the street-level carbon emissions in Wuhan from 2015 to 2030. First, we conducted principal components analysis on the 5 driving factors of carbon emissions (including point of interests, electricity consumption, gross domestic product, population, and nighttime light) to delineate single carbon emission character region (CECR). Then, a machine learning method was used to simulating CE and explaining the contributions of different CECRs. Multi-scenarios CE accountings also were conducted with CECR simulations and an improved cellular automata model. Results shows that: (1) CE in Wuhan showed strong aggregation during the historical period. The five CECRs formed a near-concentric circle. The changes of CECR reflected the enhancement of human activity. (2) CE gradually shifted from the central urban areas to the surrounding regions during the scenario period, showing significant spatial spillover effects. Administrative districts with lower CE density exhibited greater scenario variation in total carbon emissions, indicating a higher potential for carbon reductions. (3) The total CE of Wuhan (148.11 Mt) in 2030 is projected to increase by 42.6 % compared to 2015 in the baseline scenario, representing 105 % of the low scenario and 91 % of the high scenario. The growth rate of total CE in Wuhan significantly slows down (<1 %) under all scenarios. The high-resolution dynamic simulation of CE will provide an important scientific basis for low-carbon city management in China.

A New Proton‐Hydrogen‐Electron Transport Model for Simulating Optical Emissions From Proton Aurora and Comparison With Ground Observations

AbstractEnergetic proton precipitation from the magnetosphere plays an important role in the magnetosphere‐ionosphere‐thermosphere coupling and energy transfer. Proton precipitation causes hydrogen emissions, such as Hβ (486.1 nm), and also triggers the excitation of other emission lines such as the blue‐line (427.8 nm) and the green‐line (557.7 nm). In light of the growing availability of ground‐based proton auroral measurements in recent years, we revisit the proton auroral modeling in this study, with more focus on the application for interpreting ground observations. An accurate simulation of these optical emissions requires a comprehensive understanding of particle transport and collisions in the upper atmosphere, where the simultaneous consideration of precipitating protons, newly generated energetic hydrogen atoms, and secondary electrons is critical. For this purpose, we couple a 3D Monte‐Carlo proton transport model and an electron transport model. The integrated model framework can compute the emission rates of most major auroral emission lines/bands resulting from proton precipitation, along with self‐consistent calculation of the ionospheric electron density variations. The model results show improved agreement with ground optical observations in terms of the Hβ yield and the green‐to‐Hβ ratio compared to previous model studies. Our new model is a valuable tool for quantifying excitation and ionization due to proton aurora. It has the potential to leverage ground observations to infer precipitating conditions at high altitudes and even for studying magnetospheric activity.

Integrated modelling of fertilizer and climate change scenario impacts on agricultural production and nitrogen losses in Austria

The European Commission's Farm to Fork strategy aims at reducing nutrient losses and fertilizer use, but has been criticized for its expected negative impacts on European economy, agriculture, and food supply. We apply an integrated modelling framework to analyze potential effects of fertilizer reductions on land use, nitrogen losses, and agricultural output of two fertilizer and four climate change scenarios. The fertilizer scenarios comprise a uniform 20 % reduction of mineral N fertilizer (f20) and a combination of several fertilizer restrictions (fcm). The model results indicate that the restrictions in fertilization lead to decreases in crop production of 6 to 9 %, whereas intensive and extensive grassland production increases. N losses to air, water, and soil are substantially reduced by 9 % (f20) and 20 % (fcm), yet fall short of the intended 50 % reduction. The regional heterogeneity of the model results shows that tailored measures need to be elaborated by taking climate change developments, the regional heterogeneity of prevalent farming systems, and bio-physical conditions into account. Uniform measures applied to the national policy context fall short to attain policy targets cost-effectively. N emission capping, taxes or managerial measures such as crop rotational N balancing are options to be explored in future research.

Hydrological model calibration in data-deficient basins using satellite altimetry and a hydrodynamic model

Large basins with insufficient hydrological station distribution and lacking sufficient meteorological data, such as water level and discharge, pose significant challenges in developing reliable hydrological models. These models are crucial for addressing water resource-related challenges such as climate change (e.g., floods and droughts) and human-induced activities (e.g., dams) impacts. Recent advancements in satellite altimetry offer an efficient alternative by providing water level data; however, integrating this data into hydrological models remains an ongoing challenge. Here we demonstrate that a properly developed hydrodynamic model can effectively translate the satellite-observed water levels at various river cross-sections (hereafter referred to as virtual stations (VSs)) into the required discharge data, facilitating reliable hydrological modeling for data-deficient basins. Our results, based on the assessment of water level data derived from a hydrodynamic model and satellite altimetry, showed high consistency (NSE > 0.96) in the Lancang-Mekong River (LMR) basin as a case study. We found that the distribution of stations along the mainstream, in terms of their number and distance from each other, can significantly impact the performance of hydrological modeling processes. The results revealed that when a large stretch of the river is not monitored by a station, a large uncertainty occurs in runoff generation, at least for that segment of the river. However, the inclusion of virtual stations in the modeling process improved the overall performance, indicating the advantages of satellite altimetry data in better modeling of data-deficient basins. Our findings revealed that virtual stations can be effectively utilized in physically based and spatially distributed hydrological models to develop a sub-basin modeling approach that addresses the regional contribution of sub-basins to downstream river flow. The integrated modeling framework holds potential for hydrological analysis and water resources management for basins lacking sufficient gauging data and provides insight into how the distribution of stations can influence the calibration of hydrological models.

Goals and Pathways of Public Governance Contribution to Achieve Progress in the Quality of Life

This research examines the importance of effective public governance in maximising the well-being of citizens in EU member states. Our research strategy used an integrated modelling framework involving data mapping and an autoregressive model with distributed lags (ARDL) for 2012–2022 in the EU member countries. The results demonstrated that the World Governance Indicator (WGI) has a mixed impact on Quality of Life (QL). In the short term, the impact is positive if there is an increase in the level of the public governance indicator, and in the long term, the effect of public governance on the quality of life is negative due to the challenges associated with implementing reforms. Additionally, our results highlighted that, to improve citizens’ quality of life, policies should focus on increasing the Purchasing Power (PP) and Safety of Society (SS) as well as on issues related to Pollution (P) and the Cost of Living (CL). There are similarities between the long-term ARDL analysis estimate and the short-term one, with the latter pointing out that they will be able to have quick positive effects, while pollution and increased living costs have immediate adverse effects on quality of life.

Health impact of policies to reduce agriculture-related air pollutants in the UK: The relative contribution of change in PM2.5 exposure and diets to morbidity and mortality

Food systems can negatively impact health outcomes through unhealthy diets and indirectly through ammonia emissions originating from agricultural production, which contribute to air pollution and consequently cardiovascular and respiratory health outcomes. In the UK, ammonia emissions from agriculture have not declined in the same way as other air pollutants in recent years. We applied a novel integrated modelling framework to assess the health impacts from six ammonia reduction scenarios to 2030: two agriculture scenarios – a “Current trends” scenario projecting current mitigation measures to reflect a low ambition future, and “High ambition mitigation” based on measures included in the Climate Change Committee's Balanced Pathway to Net Zero; three dietary scenarios – a “Business as usual” based on past trajectories, “Fiscal” applying 20% tax on meat and dairy and 20% subsidy on fruit and vegetables, and “Innovation” applying a 30% switch to plant-based alternatives; one combination of “High ambition mitigation” and “Innovation”. Compared to “Current trends”, the “High ambition mitigation” scenario would result in a reduction in premature mortality of 13,000, increase life years by 90,000 and reduce incidence of respiratory diseases by 270,000 cases over a 30 year period. Compared to Business as Usual, the dietary scenarios would reduce the number of premature deaths by 65,000 and 550,000–600,000 life years gained over 30 years, with most of the benefits gained by reducing ischemic heart disease (incidence reduction: 190,000). The “High ambition combination” would lead to 67,000 deaths averted, 536,000 incidence reductions and 650,000 life-years gained. For all scenarios, older age groups and those living in lower income households would experience the greatest benefits, because of higher underlying mortality rates or higher levels of risk factors. Our study shows that combining mitigation policies targeting agricultural production systems with diet-related policies would lead to significant reductions in emissions and improvement in health outcomes.

Synergies of variable renewable energy and electric vehicle battery swapping stations: Case study for Beijing

Battery swapping technology has emerged as a promising option for simultaneously addressing electric vehicle (EV) range anxiety and uncoordinated charging impacts, thereby enabling a renewable-powered future at the city scale. This study aims to explore the potential synergies between variable renewable energy (VRE), including wind and solar power, and the city-scale operation of battery swapping stations (BSSs) under varying levels of VRE penetration. To this end, an integrated modeling framework that combines multisource traffic data with node-based BSS deployment optimization and hourly power system dispatch simulations was developed. Beijing in 2025 was selected as the case study due to its ambitious EV development goals and the substantial need for VRE integration. The simulation results reveal that system-optimized BSS operations, particularly through bidirectional charging (V2G), can significantly enhance VRE integration, reduce net load fluctuations, and mitigate carbon emissions. Specifically, increasing VRE penetration from 30 % to 70 % reduces VRE curtailment by 1.1 TWh to 6.4 TWh and avoids 3.0 t to 6.3 t of carbon emissions per vehicle annually. The economic analysis further indicates that while current time-of-use electricity pricing leads to higher costs for BSS operations, a real-time pricing mechanism offers a more economically viable solution, benefiting both power system operators and BSS operators. The integrated modeling framework developed in this study not only advances the understanding of city-scale BSS operations but also provides a valuable tool for analyzing the complex interactions between EV infrastructure, VRE integration, and urban power grids.

Integrated Modeling of Flow, Soil Erosion, and Nutrient Dynamics in a Regional Watershed: Assessing Natural and Human‐Induced Impacts

AbstractCurrent integrated modeling frameworks for simulating nutrient sources and dynamics are inadequate for large regional watersheds dominated by groundwater‐surface water interactions due to their simplistic representations of groundwater. In this study, we develop a coupled model that integrates comprehensive surface water, 3‐D groundwater, soil erosion, and nutrient processes. The model is intended to enhance the understanding of nutrient dynamics and sources in the Pearl River Basin (PRB). The model exhibits satisfactory performance in simulating streamflow and sediment transport patterns, capturing essential seasonal variations in water quality indicators. Hydrological budget assessments from 2002 to 2020 in the PRB reveal that 54% of precipitation drains into the South China Sea as surface water, while groundwater discharge as baseflow accounts for 18% of the streamflow. The nutrient budget for the PRB indicates that non‐point sources are the dominant contributors to both nitrogen (N) and phosphorus (P), ranging between 64% and 90%. Improved sewage collection and treatment have reduced point source nutrient contributions over the evaluation period. Groundwater remains a significant and consistent source of N, contributing between 11% and 19%. Natural disturbances and fertilization have led to an upward trend in river N inputs, while afforestation and sewage reduction efforts have resulted in a downward trend in river P inputs. Increased fertilization emerges as a central concern for the PRB, suggesting cost‐effective mitigation of fertilizer usage a pragmatic solution. The coupled simulation model developed in this study offers a novel systems approach for basin‐wide nutrient analysis and pollution control strategies, considering both natural and human‐induced disturbances.

Integrated modelling of tungsten accumulation control with wave heating: validation in ASDEX Upgrade and predictions for ITER

Abstract In present-day fusion devices, central wave heating is crucial to avoid core tungsten (W) accumulation. In this work, we present an integrated modelling framework that reproduces the reduction of core W peaking in ASDEX Upgrade experiments when multiple transport channels are self-consistently evolved, emphasizing the effects of wave heating on turbulent and neoclassical W transport. Predictions for the ITER 15 MA baseline are then provided. We show that the core of a reactor is in a different transport regime for W as compared to present-day tokamaks. The challenges introduced by W arise from global radiation losses that can hinder operation in H-mode, instead of local central accumulation.

Introducing a climate, demographics, and infrastructure multi-module workflow for projected flood risk mapping in the greater Pamba River Basin, Kerala, India

Enhanced understanding of extreme events is essential for comprehensive risk assessment promoting informed decision-making and early warning to ensure the resilience against challenges imposed by changing climate. This study proposes a novel multi-module integration framework for the comprehensive evaluation of future flood risk in the Greater Pamba River basin, Kerala, India. The integrative modelling framework is developed by assembling a Cellular Automata-Markov Chain (CAMC) based and Use/Land Cover (LULC) projections, Reliability Ensemble Averaging (REA) of Global Climate Model (GCM) rainfall projections, Bayesian approach-based population projection, and spatial analysis of projected infrastructure and Digital Elevation Models (DEMs). By seamlessly interweaving the associated datasets, we illustrate the intricacies of flood vulnerability, exposure, and frequency across multiple return periods (2, 5, 10, and 100, 200 and 500 year) leading to the creation of diagnostic and prognostic high-resolution flood risk maps. The resultant flood analytics and analyses shown that around 36 % of the villages/municipalities and between 70 and 80 % of the critical facilities like schools, hospitals, and flood relief camps are falling under high-risk zones, for the 100-year return period flood. This finding is in line with Kerala's devastating flood episode in 2018, which exhibited characteristics of a nearly 100-year return period flood. In light of a changing climate, population growth, and shifting landscapes, these approaches provide a roadmap for more informed decision-making and adaptive flood risk management measures.

Modelling occupant behaviour in residential buildings: A systematic literature review

This systematic literature review examines Occupant Behaviour (OB) modelling in residential buildings, focusing on its role in narrowing the energy performance gap and enhancing occupant comfort. It provides an overview of methodologies from simple statistical models to advanced machine learning algorithms. We conducted a chronological literature review on OB modelling in residential buildings, identifying the algorithms and the main remarks and limitations of each study. We also present sample populations for each study, offering a comprehensive view of OB modelling's evolution. Our study shows a trend towards incorporating intelligent technologies like IoT and Artificial Intelligence, highlighting an evolution towards more advanced and precise techniques. The review evaluates studies utilizing monitoring, diary, or survey data to improve behavioural model accuracy, covering aspects such as occupant presence, window manipulation, lighting and shading regulation, thermostat adjustment, and usage patterns of domestic hot water and appliances. The review underscores the importance of standardized data collection and integrating OB into building standards. It advocates for adaptive OB models leveraging IoT data with privacy considerations and calls for comparative analysis of modelling techniques. Future research should develop adaptable occupancy profile repositories and integrated modelling frameworks to address OB complexities.

Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

A real-time capable core Ion Cyclotron Range of Frequencies (ICRF) heating model on NSTX and WEST is developed. The model is based on two nonlinear regression algorithms, the random forest ensemble of decision trees and the multilayer perceptron neural network. The algorithms are trained on TORIC ICRF spectrum solver simulations of the expected flat-top operation scenarios in NSTX and WEST assuming Maxwellian plasmas. The surrogate models are shown to successfully capture the multi-species core ICRF power absorption predicted by the original model for the high harmonic fast wave and the ion cyclotron minority heating schemes while reducing the computational time by six orders of magnitude. Although these models can be expanded, the achieved regression scoring, computational efficiency and increased model robustness suggest these strategies can be implemented into integrated modeling frameworks for real-time control applications.

Modelling the impacts of climate change on agrochemical fate and transport by water on a catchment scale

The export of agrochemicals and their transformation products (TPs) following their application in the agricultural fields poses a threat to water quality. Future changes in climatic conditions (e.g. extreme weather events such as heavy rainfall or extended dry periods) could alter the degradation and mobility of agrochemicals. In this research, we use an integrated modelling framework to understand the impact of extreme climate events on the fate and transport of the agrochemical S-Metolachlor and two of its TPs (M-OXA, Metolachlor Oxanilic Acid and M-ESA, Metolachlor Ethyl Sulfonic Acid). This is done by coupling climate model outputs to the Zin-AgriTra agrochemical reactive transport model in four simulation scenarios. 1) Reference (2015–2018), 2) Very dry (2038–2041), 3) Very wet (2054–2057) and 4) High temperature (2096–2099) conditions of a selected RCP8.5 based regional climate scenario. The modelling framework is tested on an agricultural catchment, Wulka, in Burgenland, Austria. The model results indicate that 13–14 % of applied S-Metolachlor is retained in the soil, and around 85 % is degraded into TPs in the different scenarios. In very dry and high-temperature scenarios, degradation is higher, and hence, there is less S-Metolachlor in the soil. However, a large share of formed M-OXA and M-ESA are retained in the soil, which is transported via overland and groundwater flow, leading to a build-up effect in M-OXA and M-ESA river concentrations over the years. Though a small share of S-Metolachlor and TPs are transported to rivers, their river export is affected by the intensity and amount of rainfall. The very wet and high-temperature scenarios show higher S-Metolachlor and TP concentrations at the catchment outlet due to higher river discharge. The reference scenario shows higher river peak concentrations associated with higher overland flow caused by measured hourly rainfall compared to disaggregated daily precipitation data in the other scenarios.