Abstract: Modelling and simulation of multiscale systems constitutes a grand challenge in computational science, and is widely applied in fields ranging from the physical sciences and engineering to the life sciences and the socio-economic domain. To adequately simulate numerous intertwined processes characterized by different spatial and temporal scales (often spanning many orders of magnitude), sophisticated models and advanced computational techniques are required. Additionally, these multiscale models frequently need large scale computing capabilities as well as dedicated software and services that enable the exploitation of existing and evolving computational ecosystems. The aim of the annual Workshop on Multiscale Modelling and Simulation is to facilitate the progress in this multidisciplinary research field http://www.computationalscience.nl/MMS/. In this paper, we reflect on the 12 years of workshop history and glimpse at the latest developments presented in 2015 in Iceland, the Land of Fire and Ice. In Section 6, we invite new workshop co-organizers.

Abstract: This paper presents a systematic survey of open source multiphysics frameworks in the engineering domains. These domains share many commonalities despite the diverse application areas. A thorough search for the available frameworks with both academic and industrial origins has revealed numerous candidates. Considering key characteristics such as project size, maturity and visibility, we selected Elmer, OpenFOAM and Salome for a detailed analysis. All the public documentation for these tools has been manually collected and inspected. Based on the analysis, we built a feature model for multiphysics in engineering, which captures the commonalities and variability in the domain. We in turn validated the resulting model via two other tools; Kratos by manual inspection, and OOFEM by means of expert validation by domain experts.

Abstract: Extensive research efforts have been invested in reducing model errors to improve the predictive ability of biogeochemical earth and environmental system simulators, with applications ranging from contaminant transport and remediation to impacts of biogeochemical elemental cycling (e.g., carbon and nitrogen) on local ecosystems and regional to global climate. While the bulk of this research has focused on improving model parameterizations in the face of observational limitations, the more challenging type of model error/uncertainty to identify and quantify is model structural error which arises from incorrect mathematical representations of (or failure to consider) important physical, chemical, or biological processes, properties, or system states in model formulations. While improved process understanding can be achieved through scientific study, such understanding is usually developed at small scales. Process-based numerical models are typically designed for a particular characteristic length and time scale. For application-relevant scales, it is generally necessary to introduce approximations and empirical parameterizations to describe complex systems or processes. This single-scale approach has been the best available to date because of limited understanding of process coupling combined with practical limitations on system characterization and computation. While computational power is increasing significantly and our understanding of biological and environmental processes at fundamental scales is accelerating, using this information to advance our knowledge of the larger system behavior requires the development of multiscale simulators. Accordingly there has been much recent interest in novel multiscale methods in which microscale and macroscale models are explicitly coupled in a single hybrid multiscale simulation. A limited number of hybrid multiscale simulations have been developed for biogeochemical earth systems, but they mostly utilize application-specific and sometimes ad-hoc approaches for model coupling. We are developing a generalized approach to hierarchical model coupling designed for high-performance computational systems, based on the Swift computing workflow framework. In this presentation we will describe the generalized approach and provide two use cases: 1) simulation of a mixing-controlled biogeochemical reaction coupling pore- and continuum-scale models, and 2) simulation of biogeochemical impacts of groundwater – river water interactions coupling fine- and coarse-grid model representations. This generalized framework can be customized for use with any pair of linked models (microscale and macroscale) with minimal intrusiveness to the at-scale simulators. It combines a set of python scripts with the Swift workflow environment to execute a complex multiscale simulation utilizing an approach similar to the well-known Heterogeneous Multiscale Method. User customization is facilitated through user-provided input and output file templates and processing function scripts, and execution within a high-performance computing environment is handled by Swift, such that minimal to no user modification of at-scale codes is required.

Abstract: Nano- and microscale flow phenomena turn out to be highly non-trivial for simulation and require the use of heterogeneous modeling approaches. While the continuum Navier-Stokes equations and related boundary conditions quickly break down at those scales, various direct simulation methods and hybrid models have been applied, such as Molecular Dynamics and Dissipative Particle Dynamics. Nonetheless, a continuum model for nanoscale flow is still an unsolved problem. We present a model taking into account nonlocal momentum transfer. Instead of a bulk viscosity an improved system of parameters of liquid properties, represented by a spatial scalar function for momentum transfer rate between neighboring volumes, is used. Our model does not require boundary conditions on the channel walls. Common nanoflow models relying on a bulk viscosity in combination with a slip boundary condition can be obtained from the model. The required model parameters can be calculated from momentum density fluctuations obtained by Molecular Dynamics simulations. Thus, our model is multiscale, however, the continuum model is applied in the whole region of the simulation. We demonstrate good agreed with nanoflow in a tube as obtained by complete Molecular Dynamics.

Abstract: Cerebrovascular diseases such as brain aneurysms are a primary cause of adult disability. The flow dynamics in brain arteries, both during periods of rest and increased activity, are known to be a major factor in the risk of aneurysm formation and rupture, although the precise relation is still an open field of investigation. We present an automated ensemble simulation method for modelling cerebrovascular blood flow under a range of flow regimes. By automatically constructing and performing an ensemble of multiscale simulations, where we unidirectionally couple a 1D solver with a 3D lattice-Boltzmann code, we are able to model the blood flow in a patient artery over a range of flow regimes. We apply the method to a model of a middle cerebral artery, and find that this approach helps us to fine-tune our modelling techniques, and opens up new ways to investigate cerebrovascular flow properties.

Abstract: We developed a mathematical model of monochorionic twin pregnancies to simulate both the normal gestation and the Twin-Twin Transfusion Syndrome (TTTS), a disease in which the interplacental anastomose create a flow imbalance, causing one of the twin to receive too much blood and liquids, becoming hypertensive and polyhydramnios (the Recipient) and the other to become hypotensive and oligohydramnios (the Donor). This syndrome, if untreated, leads almost certainly to death one or both twins. We propose a compartment model to simulate the flows between the placenta and the fetuses and the accumulation of the amniotic fluid in the sacs. The aim of our work is to provide a simple but realistic model of the twins-mother system and to stress it by simulating the pathological cases and the related treatments, i.e. aminioreduction (elimination of the excess liquid in the recipient sac), laser therapy (removal of all the anastomoses) and other possible innovative therapies impacting on pressure and flow parameters.

Abstract: Atherosclerosis is the main cause of mortality and morbidity in the western world. Atherosclerosis is a chronic disease defined by life-long processes, with multiple actors playing a role at different biological and time scales. Patient-specific in silico models and simulations can help to understand better the mechanisms of atherosclerosis formation, potentially improving patient management. A conceptual and computational multiscale framework for the modelling of atherosclerosis formation at its early stage was created from the integration of a fluid mechanics model and a biochemical model. The fluid mechanics model describes the interaction between arterial endothelium and blood flow using an artery-specific approach. The low density lipoprotein (LDL) oxidation and consequent immune reaction leading to chronic inflammatory process at the basis of plaque formation was described in the biochemical model. The integration of these modelling approaches led to the creation of a computational framework, an effective tool for the modelling of atherosclerosis plaque development. The model presented in this study was able to capture key features of atherogenesis such as the location of pro-atherogenic areas and to reproduce the formation of plaques detectable from in vivo observations. This framework is being currently tested at University College Hospital (UCH).

Abstract: Tumor growth is a very challenging issue of capital importance to address therapy and patient management. Since it is difficult to follow the cancer natural history in humans, animal models are largely investigated. In particular, xeno-transplants are often performed on previously immune-depressed mice in order to get information about both macroscopic (growth rate) and microscopical (at cellular and genomic level) features. Previous studies showed that following multiple transplants tumors grow faster, and this fact was commonly assumed to prove the occurrence of mutations whose rate depended on the transplantation passage due to some sort of genetic instability. A recent paper reports data from a very interesting experiment, where two different clones are monitored through multipassage xeno-trasplant. We use these data in order to validate a two-population Gompertz model which assume a constant mutation rate but takes into account for the timing of multiple transplants.

Abstract: A multiscale model for an improved description of radial particle transport described by the density continuity equation in large-scale plasma edge simulations for tokamak fusion devices is presented. It includes the effects of mesoscale drift-fluid dynamics on the macroscale profiles and vice versa. The realization of the multiscale model in form of the coupled code system B2-ATTEMPT is outlined. A procedure employed to efficiently determine the averaged mesoscale terms using a nonparametric trend test, the Reverse Arrangements Test, is described. Results of stationary, self-consistent B2-ATTEMPT simulations are compared to prior simulations for experiments at the TEXTOR tokamak, making a first evaluation of the predicted magnitude of radial particle transport possible.

Abstract: A fusion plasma is a complex object involving a wide range of physics phenomena occurring at different scales. When building multiscale or multi-physics applications, an interesting approach (formalized in the Multiscale Modelling and Simulation Framework) consists in coupling single scale components (where a scale can be either spatial, temporal or refer to a different physics or numeric model), making each single component easier to develop, validate and maintain. Such coupling has been investigated within the EFDA ITM-TF task force, by using a common data structure to interface every single scale component, and a workflow manager to pilot the simulation. When such a workflow has to run in parallel, a possible approach consists in running the simulation platform within a regular parallel allocation in a single computer. The Integrated Plasma Simulator has been based on such a principle: it runs in a single (possibly very large) allocation and handles internally the dynamic scheduling of each component considering several layers of abstraction for the parallelism. We have implemented within the IPS platform two fusion workflows with different computational needs: an acyclic (loose-coupling) chain composed of high-resolution equilibrium reconstruction and MHD stability study, and a cyclic (tight-coupling) turbulence – transport time evolution. The acyclic case involves a parameter scan where the runtime of each single case can differ significantly, whereas the cyclic case is composed of codes which have to be executed in sequence with different level of parallelism and computational cost. This contribution presents briefly the characteristics of the IPS platform and compares them to other platforms used in the fusion community (Kepler, Muscle). Then implementation details are given about the wrappers, which are required to embed legacy codes coming from the ITM community into the IPS platform. Finally, targeted cyclic and acyclic workflows and their characteristics are presented as well as their performance in different configurations.

Abstract: A spectral method for kinetic plasma simulations based on the expansion of the velocity distribution function in a variable number of Hermite polynomials is presented. The method is based on a set of non-linear equations that is solved to determine the coefficients of the Hermite expansion satisfying the Vlasov and Poisson equations. In this paper, we first show that this technique combines the fluid and kinetic approaches into one framework. Second, we present an adaptive strategy to increase and decrease the number of Hermite functions dynamically during the simulation. The technique is applied to the Landau damping and two-stream instability test problems. Performance results show 21% and 47% saving of total simulation time in the Landau and two-stream instability test cases, respectively.

Abstract: We study the design of a general, fully explicit numerical method for simulating kinetic equations with an extended BGK collision model allowing for multiple relaxation times. In that case, the problem is stiff and we show that its spectrum consists of multiple separated eigenvalue clusters. Projective integration methods are explicit integration schemes that first take a few small (inner) steps with a simple, explicit method, after which the solution is extrapolated forward in time over a large (outer) time step. They are very efficient schemes, provided there are only two clusters of eigenvalues. Telescopic projective integration methods generalize the idea of projective integration methods by constructing a hierarchy of projective levels. Here, we show how telescopic projective integration methods can be used to efficiently integrate multiple relaxation time BGK models. We show that the number of projective levels only depends on the number of clusters and the size of the outer level time step only depends on the slowest time scale present in the model. Both do not depend on the small-scale parameter. We analyze stability and illustrate with numerical results.

Abstract: Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs – subnetworks – connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node information, mean edge information as well as a mean node degree as functions of model parameters and demonstrate HARBNs ability to describe complex hierarchical systems.

Abstract: This study presents numerical algorithms for solving a class of equations that partly consists of derivatives of the unknown state at previous certain times, as well as an integro-differential term containing a weakly singular kernel. These equations are types of integro-differential equation of the second kind and were originally obtained from an aeroelasticity problem. One of the main contributions of this study is to propose numerical algorithms that do not involve transforming the original equation into the corresponding Volterra equation, but still enable the numerical solution of the original equation to be determined. The feasibility of the proposed numerical algorithm is demonstrated by applying examples in measuring the maximum errors with exact solutions at every computed nodes and calculating the corresponding numerical rates of convergence thereafter.

Abstract: Batteries are highly complex electrochemical systems, with performance and safety governed by coupled nonlinear electrochemical-electrical-thermal-mechanical processes over a range of spatiotemporal scales. We describe a new, open source computational environment for battery simulation known as VIBE - the Virtual Integrated Battery Environment. VIBE includes homogenized and pseudo-2D electrochemistry models such as those by Newman-Tiedemann-Gu (NTG) and Doyle-Fuller-Newman (DFN, a.k.a. DualFoil) as well as a new advanced capability known as AMPERES (Advanced MultiPhysics for Electrochemical and Renewable Energy Storage). AMPERES provides a 3D model for electrochemistry and full coupling with 3D electrical and thermal models on the same grid. VIBE/AMPERES has been used to create three-dimensional battery cell and pack models that explicitly simulate all the battery components (current collectors, electrodes, and separator). The models are used to predict battery performance under normal operations and to study thermal and mechanical response under adverse conditions.

Abstract: A magnetosphere is a region of space filled with plasma around a magnetized object, shielding it from solar wind particles. The shape of a magnetosphere is determined by the microscopic interaction phenomena between the solar wind and the dipolar magnetic field of the object. To correctly describe these interactions, we need to model phenomena occurring over a large range of time and spatial scales. In fact, magnetosphere comprises regions with different particle densities, temperatures and magnetic field where the characteristic time scales (plasma period, electron and ion gyro period) and spatial scales (Debye length, ion and electron skin depth) vary considerably. We simulate the formation of a magnetosphere with an implicit Particle-in-Cell code, called iPIC3D. We used a dipole model to represent the magnetic field of the object, where an interplanetary magnetic field is convected by the solar wind. We carried out global Particle-in-Cell simulations that consist of a complete Magnetosphere system, including magnetopause, magnetosheath and magnetotail. In this paper we describe the new algorithms implemented in iPIC3D to address the problem of modelling multi-scale phenomena in magnetosphere. In particular, we present a new adaptive sub-cycling technique to correctly describe the motion of particles that are close to the magnetic dipole. We also implemented new boundary conditions to model the inflow and outflow of solar wind in the simulation box. Finally, we discuss about the application of these new methods for modelling planetary magnetospheres.