Component-based Simulation Framework Research Articles

Application of micro computed tomography for adjustment of model parameters for discrete element method

Nowadays the Discrete Element Method (DEM) is widely applied for the numerical simulation of granular materials. However, for the effective application of DEM detailed information about material properties is needed. Some material parameters of individual particles can be directly measured using various experimental set-ups like shear cell, compression tester, electron microscopy, etc. Nevertheless, in the majority of cases such measurements do not suffice to get overall information about the material. Therefore, various macroscopic experiments like determination of static or dynamic angle of repose are often used to validate DEM simulations and to adjust model parameters. In this contribution, an alternative non-intrusive technique for the estimation of unknown parameters, which is based on particle tracking in sets of the micro computed tomography images, is proposed. In comparison to other methods this technique allows to obtain detailed information about particle motion inside the granular material and does not only rely on the surface properties.In the presented contribution experiments and simulation studies were performed using a cylindrical mixer apparatus filled with polyethylene microspheres. Based on the analysis of obtained images, the exact 3D-positions of all particles in the mixing volume were determined and extracted. For numerical calculations the DEM implemented into component-based simulation framework MUSEN was used.

Reusability of model components for environmental simulation – Case studies for integrated coastal zone management

Model constructs in environmental models are seldom reused beyond the project lifetime or in other modelling studies. A library of reusable model components could facilitate the maintenance of existing models and make the design of new models more efficient. Although component-based design is the common standard in software engineering and manufacturing few examples are yet found in environmental science. The multi-disciplinary project SPICOSA used a common, component-based simulation framework for environmental modelling, based on 18 case studies through Europe. The development of high-quality model components with potential for reuse turned out to be a challenge despite of the guidelines and tutorial examples provided. Well-designed components are of appropriate granularity, encapsulated, with a limited use of connectors and proper data handling. Ultimately, the success of a model library depends on a sufficient set of quality components with complementary functionalities, a framework for quality control, and support of the environmental modelling community.

ON DESIGN OF A COUPLING COMPONENT FOR PARALLEL MULTIMODELING

A component-based simulation framework is a favorable choice for parallel multiscale simulations, which require a dedicated coupling component to support flexible data communication/exchange, computational parallelism, and on-demand dynamic load balancing. In this paper, a coupling component (the Coupler) was designed to support integrated parallel multimodeling. An integrated ecological multimodeling, part of the across trophic level system simulation (ATLSS) for Everglades ecosystem restoration, has been used to demonstrate the advantage of the Coupler for natural resource management applications.

On Parallelization of a Spatially-Explicit Structured Ecological Model for Integrated Ecosystem Simulation

Spatially explicit landscape population models are widely used to analyze the dynamics of an ecological species over a realistic landscape. These models may be data intensive applications when they include the age and size structure of the species in conjunction with spatial information coming from a geographic information system (GIS). We report on parallelization of a spatially explicit landscape model (PALFISH), in a component-based simulation framework, utilizing different parallel architectures. A multithreaded programming language (Pthread) is used to deliver high scalability on a symmetric multiprocessor (SMP), and a message-passing library is deployed for parallel implementation on both an SMP and a commodity cluster. The PALFISH model delivers essentially identical results as a sequential version but with high scalability: yielding a speedup factor of 12 as the runtime is reduced from 35 hours (sequential ALFISH) to 2.5 hours on a 14processor SMP. Hardware performance data were collected to better characterize the parallel execution of the model on the different architectures. This is the first documentation of a high performance application in natural resource management that uses different parallel computing libraries and platforms. Due to the diverse needs for computationally intensive multimodels in scientific applications, our conclusions arising from a practical application which brings the software component paradigm to highperformance scientific computing, can provide guidance for appropriate parallelization approaches incorporating multiple temporal and spatial scales.

Concepts and architecture of a simulation framework based on the JavaBeans component model

We report on a combination of system theoretic simulation modeling methodology with the JavaBeans component model as a basis for a component-based simulation framework. While system theory formalisms can serve as formal, mathematical foundations for modular, hierarchical modeling and simulation, the JavaBeans component model provides the appropriate implementation base. The result of the synergism is a powerful component-based simulation framework. In this paper we present the basic concepts and overall architecture of our JavaBeans modeling and simulation framework. We review the underlying system modeling formalisms for simulation modeling, sketch the layered architecture of the framework, and show elementary simulation programming and interface-based, hierarchical coupling of simulation components in more detail. Finally, we show the current state of implementation and demonstrate how simulation models can be developed using standard bean builder tools.