Comprehensive Modeling Approach Research Articles

Assessment of socio-economic strategies for managing regional flood risk in an urban coastal catchment

With the cascading flooding events over the past decades, several structural and non-structural measures have been effectively implemented for improved flood management. However, the uneven impacts of the flooding events have highlighted that socio-economic inequality amongst the community needs to be addressed along with hazards to devise long term and sustainable flood management strategies. This study aims to evaluate the impact of socio-economic adaptive scenarios over an urban coastal catchment for reduction in flood risk. Flood risk is estimated utilizing socio-economic aspects (vulnerability) and flood inundation (hazard), for the highly flood-prone coastal city of Mumbai, India. A multivariate approach is used for assessing socio-economic vulnerability, starting with Principal Component Analysis followed by Data Envelopment Analysis, for various socio-economic scenarios, i.e., by reducing percentage of certain feasible socio-economic sensitivity indicators, to identify the key indicators, which contribute to significant change in vulnerability. The flood hazard is assessed through a comprehensive hydrodynamic flood modelling approach to quantify the flood inundation depth and extent. The flood risk is, thereafter, evaluated by combining socio-economic vulnerability and flood hazard using bivariate risk mapping approach. The results indicate that, with the decrease in alteration percentage of certain feasible socio-economic sensitivity indicators, the overall vulnerability and flood risk may be reduced. The areas under very high and high vulnerability classes reduce gradually for all scenarios as the percentage reduction for various indicators is varied from 5 % to 15 %, total illiteracy being the most significant indicator. This study marks the first effort to discern socio-economic vulnerability indicators that have the potential to significantly diminish flood risk, thereby constituting a viable long-term flood risk mitigation strategy. This novel framework is generic and applicable to any flood prone catchments, to devise appropriate long-term flood management measures.

Dynamic Modeling of a HeXe-Cooled Mobile Nuclear Reactor with Closed Brayton Cycle

Helium-xenon (HeXe)-cooled mobile nuclear reactors have promising potential in future low-carbon energy systems. However, there is currently a lack of fast and reliable tools for analyzing the complicated dynamic characteristics of such systems. In this study, we developed a comprehensive dynamic modeling approach for a HeXe-cooled nuclear power system coupled with a closed Brayton cycle (CBC). The system’s key components, including the reactor, printed circuit heat exchanger (PCHE), and turbomachinery, are lumped-modeled to capture their time-varying behavior. A step-solving algorithm that incorporates HeXe mass conservation iteration is designed. The verification results demonstrate that the dynamic program is robust and reliable, with each time step converging within 25 iterations and the HeXe mass remaining within the range of 3.755 ± 0.01 kg throughout the simulation meeting the law of mass conservation. Then, a 1500 s frozen start-up simulation for the coupled system is conducted, in which the CBC is started in the first 500 s by increasing the main shaft speed to 40% of the rated value, and then the reactor is started by inserting external reactivity between 500 and 800 s. Both the dynamic process and the steady-state performance after the start-up are analyzed. The results show that the system achieved a stable electrical output of 5.7 MWe with a thermal efficiency of 32.5%. This study lays a solid foundation for future work aimed at improving the overall efficiency and performance of HeXe-cooled nuclear power systems.

Recent studies on multiscale modeling of carbon fiber-reinforced composites

Multiscale modeling has garnered attention as an analytical method for investigating the mechanical behavior of composite materials. This paper introduces a comprehensive multiscale modeling approach developed by the authors’ group, which enables comprehensive analysis of the material properties from the atomic-scale reaction characteristics to laminate-scale fracture analysis; an overview of relevant literature is included as well. First, the methods of analysis at the atomic/molecular, microscopic, and mesoscopic scales are separately explained. Finally, a multiscale modeling approach that integrates these models and bridges the gaps between the scales is presented. The multiscale model is used to predict the fracture behavior of carbon fiber-reinforced composites based on the mechanical properties of epoxy resin.

Comprehensive biomarker and modeling approach to support dose finding for BI 836880, a VEGF/Ang-2 inhibitor

BackgroundBI 836880 is a humanized bispecific nanobody® that binds to and blocks vascular endothelial growth factor (VEGF) and angiopoietin-2 (Ang-2). A comprehensive biomarker and modeling approach is presented here that supported dose finding for BI 836880.MethodsTwo Phase I dose-escalation studies (1336.1 [NCT02674152], 1336.6 [NCT02689505]) assessed BI 836880 in adults with confirmed locally advanced or metastatic solid tumors, refractory to standard therapy or for which standard therapy was not reliably effective. Two dosing schedules were investigated, 3 weeks (q3w) or once weekly (qw), starting at a dose of 40 mg. In a comprehensive biomarker approach, soluble pharmacodynamic markers (free and total plasma VEGF-A and Ang-2), as well as circulating angiogenic factors (soluble VEGF3, soluble Tie2 and placenta growth factor, amongst others) were analyzed to assess target engagement in peripheral blood for q3w doses. A Population based pharmacokinetics/pharmacodynamics (PopPK/PD) model was built using the limited Phase I dataset to support dose finding by simulations. In order to demonstrate drug activity in the tumor, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was applied.ResultsDCE-MRI scans supported target engagement in the tumor. Free VEGF-A was depleted at all doses, whereas free Ang-2 decreased dose-dependently, reaching depletion in most patients from 360 mg q3w onwards. While total VEGF-A levels increased in a dose-dependent manner, reaching saturation at 360 mg q3w, total Ang-2 levels increased, but did not plateau. Angiogenic biomarkers showed changes from doses ≥ 360 mg q3w. PopPK/PD modeling showed that doses ≥ 360 mg q3w led to > 90% inhibition of free Ang-2 at steady-state in most patients. By increasing the dose to ≥ 500 mg q3w, > 90% of patients are expected to achieve this level.ConclusionsThe comprehensive analyses of multiple target engagement markers support BI 836880 720 mg q3w as a biologically relevant monotherapy dose schedule.Trial registration: NCT02674152 and NCT02689505.

Hail hazard modeling with uncertainty analysis and roof damage estimation of residential buildings in North America

This research presents a statistical approach for hail risk modeling that incorporates the uncertainties of hail model prediction to provide insight into assessing the roof damage of a residential house in hail events. By quantifying the inherent uncertainties in evaluating hailstorm characteristics, this study extends the current existing hail models. The hail data are sourced from the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) in the U.S. In the modeling process, the largest hail diameter reported in the CoCoRaHS database serves as a primary input variable to estimate the number of observations for the largest hail diameter, hailstorm duration, and hit rate. The assessment of hail risk in this study focuses on the probability of hail damage and resultant repair costs for five types of roofs in North America (unrated roof and impact-resistant roofs with UL 2218 rating classes 1 to 4). The probability of hail damage is calculated as the failure probability by integrating all individual hailstone hits having variable diameters during a hailstorm with fragility curves, which estimate the probability that hailstones will fracture asphalt shingles (allowing water infiltration) or that they dislodge enough granules to cause visible damage requiring replacement for aesthetic reasons. The results reveal that an impact-resistant roof (impact-resistant rating classes 1 to 4) is associated with lower hail risks, with 60 % to 98 % reduction on average compared to unrated roofs. This study provides a comprehensive uncertainty modeling approach for hail hazard and risk, enabling better-informed decision-making and risk management strategies.

Analysing Double Shearing Mechanism in Fiberglass Rock Bolting Systems: a Comprehensive Analytical Model and Numerical Simulation Approach

Analysing Double Shearing Mechanism in Fiberglass Rock Bolting Systems: a Comprehensive Analytical Model and Numerical Simulation Approach

Helioseismic Properties of Dynamo Waves in the Variation of Solar Differential Rotation

Solar differential rotation exhibits a prominent feature: its cyclic variations over the solar cycle, referred to as zonal flows or torsional oscillations, are observed throughout the convection zone. Given the challenge of measuring magnetic fields in subsurface layers, understanding deep torsional oscillations becomes pivotal in deciphering the underlying solar dynamo mechanism. In this study, we address the critical question of identifying specific signatures within helioseismic frequency-splitting data associated with the torsional oscillations. To achieve this, a comprehensive forward modeling approach is employed to simulate the helioseismic data for a dynamo model that, to some extent, reproduces solar-cycle variations of magnetic fields and flows. We provide a comprehensive derivation of the forward modeling process utilizing generalized spherical harmonics, as it involves intricate algebraic computations. All estimated frequency-splitting coefficients from the model display an 11 yr periodicity. Using the simulated splitting coefficients and realistic noise, we show that it is possible to identify the dynamo wave signal present in the solar zonal flow from the tachocline to the solar surface. By analyzing observed data, we find similar dynamo wave patterns in the observational data from the Michelson Doppler Imager, Helioseismic Magnetic Imager, and Global Oscillation Network Group. This validates the earlier detection of dynamo waves and holds potential implications for the solar dynamo theory models.

Promoting digital twin technology application for process industry: A novel generation modelling platform and its implementations

AbstractOne key concept of Industry 4.0 is the industrial digital twin (DT)—a virtual replica of physical assets, processes, and systems. DTs optimise operations, enhance productivity, and facilitate innovation across various industries, including manufacturing, energy, healthcare, and transportation. This paper presents a multi‐domain and multi‐time scale modelling and simulation platform, featuring built‐in model libraries that represent diverse physical, chemical, and behavioural properties. A comprehensive system modelling approach and a closed‐loop industrial control system based on DTs are developed, showcasing advanced control, optimisation, and system security research capabilities. The architecture and advantages of the novel generation modelling platform are detailed, with specific applications highlighted, underscoring its contributions to the process industry.

Developing an optimization framework for capacity planning of hydrogen-based residential energy hub

— This paper introduces a comprehensive modeling approach for optimizing the capacities of various energy converters and storage systems within a residential Energy Hub (EH). The model focuses on minimizing energy supply costs to customers while maximizing the utilization of renewable and hydrogen-based technologies. Through the optimization process, we determined that implementing CHP units in the EH could reduce total costs by approximately 78% with green tax incentives and by 46.7% without them. Additionally, the inclusion of an incentive to the EH resulted in a 27.2% reduction in total costs imposed on subscribers.The study also evaluates the impact of uncertainties in photovoltaic (PV) electricity generation, demonstrating the benefits of using stochastic optimization over deterministic methods. The results indicate that the economic advantage of this approach over deterministic methods is 86.75%–95.74% greater, depending on the scenario. Sensitivity analysis further reveals that the investment costs of fuel cells significantly influence the optimal capacity design and economic viability of the EH model.

From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions.

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation

BackgroundIncreasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases.ResultsWe collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays.ConclusionsOur findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.

Optimizing Mid-Temperature Sodium Iodine Batteries for Sustainable Large-Scale Energy Storage: A Comprehensive Modeling Approach and Experimental Validation

The need for safe, eco-friendly, low-cost batteries for large scale implementation rises in the presence of the expansions of renewable energy sources. High-temperature liquid sodium batteries (e.g. sodium-sulfur) are a well-established technology for large-scale grid storage. Combining the molten sodium anode with an aqueous iodine cathode overcomes the problems of thermal loses and sealing by reducing the operating temperature to about 100°C. This leads to higher cost efficiency, energy density and a simplified cell design.Here we present a novel modelling approach for complex carbon-based cathode structures and its limits and abilities on enhancing the utilizable capacity. Due to their sustainability and high efficiency, which is comparable to Li-Ion –batteries, mid-temperature sodium-iodine batteries are a promising candidate for stationary energy storage applications. With a high specific energy of around 200 Wh kg-1, sodium-iodine batteries have the ability to meet the growing demand of short-term energy storage caused by the integration of renewable energy sources into the power grid. Up to this day, the detailed processes inside the battery are not well-understood. Quantities like initial species concentrations, C-rate, cell design and cathode geometry have large impacts on the overall battery performance. A deeper understanding of these processes is inevitable for enhancing the battery performance by choosing the optimal abovementioned quantities. We set up a fully resolved 3D Simulation model to gain insight into microstructural processes inside the battery, as experimental studies are limited to macroscopic quantities like electronic current and potential. The methodology is based on continuum hypothesis by using an open-source CFD software, which is rewritten accordingly. The proposed spatially resolved three-dimensional simulation model takes charge and mass transport, heterogeneous and homogeneous reactions as well as the electrochemical processes into account. Results show that transport limitations confine the utilizable capacity of the battery [1].This contribution focuses on enhancing the battery energy density and efficiency by including open-pored glassy carbon foam structures in the cathode half-cell [2, 3]. We state simulation predicted, optimal morphology properties of such a structure. Furthermore, we describe the evaluation of improved thermodynamic data of the aqueous iodine solution by refitting simulation results to polarization curves. We utilize a novel impedance spectroscopy simulation method for sodium iodine batteries to improve parameterization of the underlying electrochemical model by comparing the results with experimental data. References Gerbig, F., Cernak, S., Nirschl, H. (2021). 3D Simulation of Cell Design Influences on Sodium–Iodine Battery Performance. Energy Technology, vol. 9, no. 6, p. 2000857Gerbig, F., Cernak, S. and Nirschl H. (2021), Towards a Novel Sodium-Iodine Battery with an Aqueous Catholyte: Numerical Investigations of Complex Cathode Structures, ECS Trans., vol. 104, no. 1, pp. 123–130Gerbig, M. Holzapfel, and H. Nirschl, Simulating the Impact of Glassy Carbon Foam Electrodes on the Performance of Sodium Iodine Batteries, J. Electrochem. Soc., vol. 170, no. 4, p. 40517, 2023, doi: 10.1149/1945-7111/accab7. Figure 1

Influence of Mechanical Degradation in Polycrystalline NMC Particles on the Electrochemical Performance of Lithium-Ion Batteries

Lithium-ion batteries (LIBs) have gained widespread popularity for energy storage systems owing to their high coulombic efficiency and energy density. Within the cathode electrode materials of LIBs, the structural integrity of secondary particles, composed of randomly oriented single-crystal primary active particles, is crucial for sustained performance. The fracture of these particles can occur due to both mechanical stress and chemical interactions within the solid. Modeling LIBs presents a multiphysics challenge as it involves addressing the electro-chemo-mechanical phenomena and their interactions across various length scales. During electrochemical cycling, volume changes in active particles induce mechanical stresses, while the mechanical state of the battery influences the diffusion of lithium.This study proposes a numerical modeling framework to investigate the degradation of active material particles and its impact on electrochemical performance. The model integrates mechanical and electrochemical processes, solving nonlinear equations related to surface charge transfer, lithium diffusion, and mechanical stresses. By incorporating phase field damage, the model tracks crack evolution and mechanical failure of active particles. The coupled equations are solved within a finite element framework using COMSOL Multiphysics.Notably, the model offers numerical insights into intergranular and transgranular fracture within secondary active material particles. The infiltration of liquid electrolyte into cracks is identified as a positive effect, reducing electrochemical overpotential by increasing electrochemically active surface area from particle cracking. However, prolonged cycling with particle cracking poses a notable threat to battery performance and capacity. This comprehensive numerical modeling approach provides valuable insights into the intricate interplay of mechanical and electrochemical factors governing LIB performance and degradation.

Estimation of mode I fracture toughness of rocks exposed to different environmental conditions using simple and linear multiple regression

AbstractMode I fracture toughness (Kıc) is a critical parameter in rock mechanics that is essential for understanding how rocks behave under tensile loading and crucial for applications ranging from safety assessments to structural design in geotechnical engineering. This study comprehensively investigates the influence of various environmental conditions (dry, saturated, frozen, thermal shock and thermal aging) on the physico-mechanical properties and Kıc of rocks. The primary novelty of this study lies in its comprehensive modeling approach under diverse environmental conditions, providing a nuanced understanding of factors influencing rock fracture toughness. By extending analysis to less-studied conditions such as freezing and thermal shock cycles, the study enhances the predictive capacity of fracture toughness models in practical geotechnical applications. Physico-mechanical properties, including uniaxial compressive strength, point load strength, Brazilian tensile strength (BT), Schmidt hardness, and ultrasonic wave velocity were evaluated across different environmental scenarios. Simple and linear multiple regression models were developed using these properties to predict Kıc. Notably, BT emerged as a significant predictor in the simple regression analyzes. Ten linear multiple regression models were formulated using SPSS 20, combining mechanical tests (UCS, BT, PL) with non-destructive testing methods (Vp, Vs, SH), demonstrating robust predictive capabilities with R2 values exceeding 0.95. Performance metrics (mean absolute error, mean absolute percentage error, root mean square error) were used to verify the accuracy of the model.

Advanced Modeling of Electro‐Optic Sampling: Nonlinear Vectoral‐Field Solutions to Maxwell's Equations

AbstractA comprehensive modeling approach for elucidating the intricacies of electro‐optic (EO) sampling is presented, which fully encapsulates the EO sampling process by accounting for all linear and second‐order nonlinear optical effects. The developed approach implements a multi‐step procedure, involving a vectorial‐field modeling of the second‐order nonlinear interactions occurring within the EO crystal, followed by a theoretical evaluation of the subsequent optical EO sampling components. To assess the efficacy of the approach, it is used for the sampling of terahertz electric fields (<1–>60 THz) within the bulk crystals of ZnTe and LiNbO3, as well as a ZnTe‐based waveguide. Nonetheless, this versatile method enables the investigation of EO sampling within any crystal incorporated within any geometry. This model provides a powerful tool for the in‐depth exploration of EO sampling, paving the way for advancements in various applications.

Fuel Cell System Modeling Dedicated to Performance Estimation in the Automotive Context

In this paper, a meticulous modeling approach is proposed not only for a fuel cell stack itself but also for all auxiliary components that collectively form the fuel cell system. This comprehensive modeling approach encompasses a wide range of components, including, but not limited to, the hydrogen recirculation pump and the air compressor. Each component is thoroughly analyzed and modeled based on the detailed specifications provided by suppliers. This involves considering factors such as efficiency, operating parameters, response times, and interactions with other system elements. By integrating these detailed models, a holistic understanding of the entire fuel cell system’s performance can be attained. Such an approach enables engineers and designers to simulate various operating scenarios, predict system behavior under different conditions, and optimize the system design for maximum efficiency and reliability. Moreover, it allows for informed decision-making throughout the system’s development, deployment, and operational phases, ultimately leading to more robust and effective energy systems. The model validation is performed by comparing experimental data to theoretical results, and the observed difference does not exceed 3%.

Coupled thermo-elastic dynamics of rotating pre-twisted blades exposed to thermal shock including nonlinear rotational effects

Rotating blades operating under high-temperature experience intense mechanical and thermal stresses induced by centrifugal forces and thermal shock. In this research, to address this critical yet understudied topic, coupled thermo-elastic dynamics of rotating blades incorporating precise rotational effects is investigated. A comprehensive modeling approach, characterizing blade geometry in terms of pre-twist and pre-set angles is employed based on the theory of surfaces. Thermal strain–displacement field is defined based on third-order shear deformation theory in the model integrated with a third-order expansion of temperature change distribution within the blade. Rotational factors related to Coriolis, centrifugal stiffening, and rotational softening effects are considered. In the case of stiffening effects, two different modeling approaches including direct integration of centrifugal forces (DICFs), and pre-stressed dynamics about steady-state equilibrium deformations (SSEDs) are employed. When incorporating large-amplitude SSEDs in the latter, nonlinear rotational effects are incorporated into the model. To overcome the complexity arising from coupled thermo-elasticity, and rotational motion, the spectral Chebyshev technique is applied to the derived integral boundary value problems and coupled energy equations. Finally, natural frequencies, and thermal and centrifugal deformations are investigated comprehensively by including DICFs and pre-stressed dynamics. Results show that thermo-elastic dynamics of the system is highly affected by the modeling approaches used for centrifugal stiffening effects.

Improving the performance and economics of reversible solid oxide cell-based hydrogen energy storage systems through the utilization of renewable alternative fuels

Reversible solid oxide cells (RSOCs) represent a promising solution for addressing the future challenge of large-scale renewable energy storage. However, the performance and economic viability of current RSOC systems are hindered by the large overpotential losses in electrolysis and apparent fuel (H2) costs associated with power generation. This study employs a comprehensive numerical modeling approach to analyze the potential of renewable fuel (glycerol) in assisting hydrogen production performance and reducing operating costs within RSOC systems. The detailed comparative analyses and parametric studies are conducted on H2-based and glycerol-based RSOC systems at typical operating conditions. Notably, it is found that the economic viability of hydrogen-based RSOC systems is hindered by the remarkable fuel (H2) costs during nighttime despite of the favorable effects of larger stack capacity and higher temperature. While incorporating glycerol as an alternative fuel in RSOC systems offers significant advantages, including doubling the electrolytic H2 production (from 585.92 Nm3·h−1 to 1191.24 Nm3·h−1) at a relatively low cost and substantially reducing the fuel expenses (from 594.30 $·h−1 to 30.57 $·h−1) for nighttime power generation, thus leading to competitive electrolytic hydrogen production cost (2.982 $·kg−1) and noteworthy enhancement in economic viability (LCOE of 0.090 $·kWh−1 and 0.304 $·kWh−1 for electrolysis and power generation, respectively). The findings of this study provide valuable insights that contribute to the advancement of RSOC systems, thus facilitating the development of efficient, cost-effective, and sustainable energy storage and conversion technologies.

A Comprehensive Threat Modelling Analysis for Distributed Energy Resources

The exponential rise in popularity of Distributed Energy Resources (DERs) is attributed to their numerous benefits within the power sector. However, the risks that new DERs pose to the power grid have not yet been closely assessed, exposing a gap in the literature. This paper addresses this gap by presenting a comprehensive threat model of the DER architecture, combining the MITRE ATT&CK catalogue for Industrial Control Systems (ICS), and the IDDIL/ATC threat model, to create a hybrid approach. Our first contribution is to propose criteria derived from seven metrics to evaluate and compare the efficacy and usability of threat modelling frameworks for DER systems, allowing more informed framework selection. Our second contribution is to develop a comprehensive hybrid threat modelling approach based on IDDIL/ATC and MITRE ATT&CK and organise attack paths chronologically using the Cyber Kill Chain methodology to categorise attacker techniques. Our third contribution is to perform a comprehensive DER architecture system decomposition, elaborating assets, trust levels, entry points, data, protocols, and entity relations to identify the threat landscape. Our final contribution is to apply the proposed approach to the Distribution System Operator (DSO), mapping potential attacker techniques and illustrating a ransomware attack chain on the DSO’s Energy Management System, with proposed mitigations.

A comprehensive approach for assessing social flood vulnerability and social flood risk: The case of Denmark

Flood risk assessment approaches have traditionally been dominated by measures of economic damage. However, the importance of understanding the social impacts of flooding are increasingly being acknowledged. Social vulnerability indices have been constructed in diverse geographical contexts to understand relative susceptibility of different social groups to flood hazards. However, integrated assessments of social vulnerability, exposure, and hazard information are lacking. Here, we construct a national social vulnerability index (SVI) for Denmark and combine this with direct and indirect social exposure data and coastal flood hazard data to construct a national social flood risk index (SFRI). Results show the spatial distribution of social flood vulnerability and social flood risk in Denmark. Our findings illustrate that including social data in flood risk assessment could significantly change our understanding of flood risk on a national scale. Methodologically, our work introduces a comprehensive flood risk modelling approach that explicitly considers the social impacts of flooding in all model components. The application of this model in Denmark reveals that the social impacts of flooding extend far beyond flooded areas, thus highlighting the importance of explicitly considering direct and indirect social exposure in addition to social vulnerability in flood risk assessment. By introducing a comprehensive, socially specific approach to flood risk assessment that is usable within existing risk management frameworks such as the EU Floods Directive, our work aims to mainstream social wellbeing, resilience, and justice as central considerations in decision making on flood risk management.