Epidemic and pandemic preparedness with rapid outbreak response rely on timely, trustworthy evidence. Mathematical models are crucial for supporting timely and reliable evidence generation for public health decision-making with models spanning approaches from compartmental and metapopulation models to detailed agent-based simulations. Yet, the accompanying software ecosystem remains fragmented across model types, spatial resolutions, and computational targets, making models harder to compare, extend, and deploy at scale. Here we present MEmilio, a modular, high-performance framework for epidemic simulation that harmonizes the specification and execution of diverse dynamic epidemiological models within a unified and harmonized architecture. MEmilio couples an efficient C++ simulation core with coherent model descriptions and a user-friendly Python interface, enabling workflows that run on laptops as well as high-performance computing systems. Standardized representations of space, demography, and mobility support straightforward adaptations in resolution and population size, facilitating systematic inter-model comparisons and ensemble studies. The framework integrates readily with established tools for uncertainty quantification and parameter inference, supporting a broad range of applications from scenario exploration to calibration. Finally, strict software-engineering practices, including extensive unit and continuous integration testing, promote robustness and minimize the risk of errors as the framework evolves. By unifying implementations across modeling paradigms, MEmilio aims to lower barriers to reuse and generalize models, enable principled comparisons of implicit assumptions, and accelerate the development of novel approaches that strengthen modeling-based outbreak preparedness.

Visual analytics tools can help illustrate the spread of infectious diseases and enable informed decisions on epidemiological and public health issues. To create visualisation tools that are intuitive, easy to use, and effective in communicating information, continued research and development focusing on user-centric and methodological design models is extremely important. As a contribution to this topic, this paper presents the design and development process of the visual analytics application ESID (Epidemiological Scenarios for Infectious Diseases). ESID is a visual analytics tool aimed at projecting the future developments of infectious disease spread using reported and simulated data based on sound mathematical-epidemiological models. The development process involved a collaborative and participatory design approach with project partners from diverse scientific fields. The findings from these studies, along with the guidelines derived from them, played a pivotal role in shaping the visualisation tool.

We analyze the relaxation of non-pharmaceutical interventions (NPIs) under an increasing number of vaccinations in Germany. For the spread of SARS-CoV-2 we employ a SIR-type model that accounts for age-dependence and includes realistic contact patterns between age groups. The implementation of NPIs occurs on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. We account for spatial heterogeneity and commuting activities in between regions in Germany, and the testing of commuters is considered as a further NPI. We include the ongoing vaccination process and analyze the effect of the B.1.617.2 (Delta) variant, which is considered to be 40%-60% more infectious then the currently dominant B.1.1.7 (Alpha) variant. We explore different opening scenarios under the ongoing vaccination process by assuming that local restrictions are either lifted in early July or August with or without continued wearing of masks and testing. Our results indicate that we can counteract the resurgence of SARS-CoV-2 despite the Delta variant with appropriate timing for the relaxation of NPIs. In all cases, however, school children are hit the hardest.

Background Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. Conclusions Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.

Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.

In the aerospace sciences we produce huge amounts of data. This data must be arranged in a meaningful order, so that we can analyze or visualize it. In this paper we focus on data that is distributed among computer processes and then needs to be sorted by a single root process for further analysis. We assume that the memory on the root process is too small to hold all sorted data at once, so that we have to perform the sorting and processing of data chunk-wise. We prove the efficiency of our approach in weak scaling tests, where we achieve a near constant bandwidth. Additionally, we obtain a considerable speed up compared to the standard parallel external sort. We also demonstrate the usefulness of our algorithm in a real-life aviation application.

The numerical simulation of droplet impact is of interest for a vast variety of industrial processes, where practical experiments are costly and time-consuming. In these simulations, the dynamic contact angle is a key parameter, but the modeling of its behavior is poorly understood so far. One of the few models which considers the overall physical context of the involved ‘moving contact line problem’ is Shikhmurzaev’s interface formation model (Shikhmurzaev, 2008). In addition to keeping the problem well-posed, all surface and bulk parameters, such as the contact angle, are determined as part of the solution rather than being prescribed functions of contact line speed. In this article, we couple an asymptotic version of the interface formation model with our three-dimensional incompressible two-phase Navier–Stokes solver. Additionally, we employ a simple constant contact angle approach. We compare the results of these two numerical models with those from laboratory experiments for the micron-scale droplet impact on substrates with a variety of wetting characteristics. With our sophisticated asymptotic interface formation model, the droplet shapes, heights and diameters compare very well with practical experiments.

In the DLR-Project VicToria (Virtual Aircraft Technology Integration Platform, 7/2016 – 12/2019) the basis of VAST is being developed. Halfway through the project a prototype version exists that clearly demonstrates the potential of the approach pursued here. This paper describes the concept and goals of the code and shows results of the current development version (see Fig. 1). The principal idea is a coupled system of models that are expressed as state-space models (See Fig. 1) ẋ = F(u,x,t) with the implicit system of coupling equations y = G(u,x,t) being automatically resolved. This approach allows generic methods for solving the system to be developed unlike many other codes in the field which require highly specialized solution procedures and are thus rigid to change. The logic for the model interconnection is kept completely outside of the simulation code and computed as a prestep. The basis of physical models is taken from existing research codes like S4 and the FT-Freewake [2] as well as implementation of proven numerical models like a dynamic wake model [3] and its recent extensions. The structural dynamics is calculated using an internal structural model using rigid multi body dynamics as well as an adaptive coupling to SIMPACK building on [4]. First applications to the free flying helicopter and the wind tunnel case are shown and discussed

One of the main targets of DLR's project Victoria is the development of a platform for multi-disciplinary analysis and optimization (MDA/O) on massively parallel High Performance Computing (HPC) clusters. The activity consists of three main parts: FlowSimulator as integration platform for multiple disciplines, Flucs (Flexible Unstructured CFD Software) as new Computational fluid Dynamics (CFD) capability targeting HPC efficiency as well as a consistent implementation of both established and new methodologies including high order discretization schemes, and Spliss (Sparse Linear System Solver) as a dedicated HPC-driven linear solver library for CFD applications.

The modeling of two-phase flows in computational fluid dynamics is still an area of active research. One popular method is the coupling of level-set and volume-of-fluid (CLSVOF), which benefits from the advantages of both approaches and results in improved mass conservation while retaining the straightforward computation of the curvature and the surface normal. Despite its popularity, details on the involved complex computational algorithms are hard to find and if found, they are mostly fragmented and inaccurate. In contrast, this article can be used as a comprehensive guide for an implementation of CLSVOF into the existing level-set Navier-Stokes solvers on Cartesian grids in three dimensions.

We perform 3D incompressible two-phase flow simulations of rising droplets. Based on a similar 2D benchmark, a 3D benchmark configuration with two test cases is formulated in which we compare the flow solvers DROPS, NaSt3DGPF and OpenFOAM. All codes adopt different numerical techniques. We define several quantities of interest and investigate their temporal evolution in both test cases. For most benchmark variables we obtain a high level of agreement and establish reference data for other flow solvers.

The simulation of the liquid composite molding process is an important step for the manufacturing of composites with textile reinforcements. However, its fast direct numerical simulation is impossible on the microscale due to computational complexity reasons. We have to resort to the macroscale instead. Here, an essential task is the fast and accurate prediction of the permeability of textile microstructures. For textiles composed of impermeable yarns, we discuss the homogenization of the Stokes equations. This leads to Darcy's law on the macroscale and gives the permeability via the so-called unit cell problems. These equations are then discretized and solved numerically. For textiles composed of permeable yarns, the porosity on both scales is accounted for by a fictitious domain approach in the form of the Stokes/Brinkman equations. The results of our permeability computations in textiles are validated and compared to experimental data.

The permeability of textile reinforcements is a crucial input for the simulation of the impregnation stage of a composite material fabrication process. In this paper, we present a fast and accurate simulation method for the permeability of a textile reinforcement, based on a finite difference discretisation of the Stokes equations. Results for single layer, multi-layer and sheared models are discussed. The influence of intra-yarn flow and periodic or wall boundary conditions are considered. We compare the numerically computed permeability values with experimental data.

SUMMARY: A fast an accurate simulation tool for the permeability of textiles is presented, based on a finite difference discretisation of the Stokes equations. Results for single layer, multi layer and sheared models are discussed. The influence of intra-yarn flow and periodic respectively wall boundary conditions are considered. Simulated permeability values are compared with experimental data. For the creation of the textile model the WiseTex software is used, which implements a generalised description of internal structure of textile reinforcements on the unit cell level. A finite difference Navier-Stokes solver, NaSt3DGP, was developed at the Institute for Numerical Simulation at the University of Bonn. The flow solver employs a Chorin projection on a staggered grid for the solution of the Navier-Stokes. In the staggered grid approach, the pressure is discretised at the centre of the cells, while the velocities are discretised on the edges. This discretisation leads to a strong coupling between pressure and velocities, and therefore avoids the occurrence of unphysical oscillations in the pressure. The experimental validation is performed with a highly automated central injection rig PIERS.

For the manufacturing of composite materials with textile reinforcement, the permeability of the textile is a key characteristic. Using Darcy's law the permeability can be derived from a numerical simulation of the fluid flow, i.e. by solving the Navier-Stokes or Navier-Stokes/Brinkman equations for a unit cell problem. In this paper we present the results of simulations with two different flow solvers: a finite difference Navier-Stokes/Brinkman solver and a lattice Boltzmann solver. The results are validated with theory and experimental data.

For the simulation of the impregnation process of Resin Transfer Moulding, the permeability of the textile is a key input parameter. Using Darcy's law, the permeability can be derived from a numerical simulation of the fluid flow for a unit cell problem. In this paper we present the results of simulations with a Stokes solver, implemented in the permeability predicting software FlowTex. The results are compared with those of a Navier-Stokes solver and validated using theoretical results for model problems and with experimental data for real textiles.

Numerical computation of textile permeability is important for composite manufacturing. Using Darcy’s law, permeability can be derived from a simulation of the fluid flow, i.e. after solving the Stokes, Navier-Stokes or Brinkman equations. The latter allow to model intra-yarn flow in case of permeable yarns. In this paper we present a numerical method for the calculation of the permeability of textile models based on a finite difference discretisation of the partial differential equations. Two different formulas for the calculation of the local permeability are discussed. Theoretical, numerical and in particular experimental validation is presented.

Summary. For the manufacturing of composite materials with textile reinforce- ment, the permeability of the textile is a key characteristic. Using the law of Darcy, permeability can be derived from a numerical simulation of the uid o w, i.e. by solving the Navier-Stokes or Brinkman equations. In this paper we present the results of simulations with two dieren t o w solvers: a nite dierence Navier- Stokes/Brinkman solver and a lattice Boltzmann solver. The results are validated with theory and experimental data.