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Simulation Workflows

FEM Bone

Simulation technology is indispensable for the solution of complex problems, be it in medical science, in development and production processes as well as in other domains of life. This has enormously influenced developments in economy and science. On the other hand, new developments in modeling and simulation technology as well as hardware components have increased the expectations in science and industry. This is the point the activities of the SimTech cluster join: The status quo with individual strategies of different disciplines, theories and discretization concepts is overcome. The concepts are merged and enhanced to a new class of an integrative simulation environment that covers all aspects ranging from a model to an interactive system, e.g. with the aid of simulation workflows.

Weather Forecast

Weatherforcast Simulation Workflow

Weather forecasts are processes that can be seen as semi-automated simulation workflows. The weather forecase use case is in particular interesting for us because it requires a lot of features that must be provided by a supporting scientific workflow system, e.g. large amounts of data in workflows, integration of sensor data, long-running workflow, or human users.

(1) The weather is predicted for a particular geological area. Hence, the workflow is fed with a model of the geophysical environment of ground, air and water for a requested area. (2) Over a specified period of time (e.g. 6 hours) several different variables are measured and observed. Ground stations, ships, airplanes, weather balloons, satellites and buoys measure the air pressure, air/water temperature, wind velocity, air humidity, vertical temperature profiles, cloud velocity, rain fall, and more. (3) This data needs to be collected from the different sources and stored for later access. (4) The collected data is analyzed and transformed into a common format (e.g. Fahrenheit to Celsius scale). The normalized values are used to create the current state of the atmosphere. (5) Then, a numerical weather forecast is made based on mathematical-physical models (e.g. GFS - Global Forecast System, UKMO - United Kingdom MOdel, GME - global model of Deutscher Wetterdienst). The environmental area needs to be discretized beforehand using grid cells. The physical parameters measured in Step 2 are exposed in 3D space as timely function. This leads to a system of partial differential equations reflecting the physical relations that is solved numerically. (6) The results of the numerical models are complemented with a statistical interpretation (e.g. with MOS - Model-Output-Statistics). That means the forecast result of the numerical models is compared to statistical weather data. Known forecast failures are corrected. (7) The numerical post-processing is done with DMO (Direct Model Output): the numerical results are interpolated for specific geological locations. (8) Additionally, a statistical post-processing step removes failures of measuring devices (e.g. using KALMAN filters). (9) The statistical interpretation and the numerical results are then observed and interpreted by meteorologists based on their subjective experiences. (10) Finally, the weather forecast is visualized and presented to interested people.

Simulation for earthquake disaster assessment

This simulation was developed to have an in depth understanding of the destructions and the decisions to be made in various phases of crisis management (Source: Mahdi Hashemi and Ali A. Alesheikh (2010). "Developing an agent based simulation model for earthquakes in the context of SDI." GSDI 12 World Conference. 19 – 22 October 2010. Singapour). The simulation process contains following major steps:

  • All spatial information including satellite images (before and after the earthquake) and topographic/cadastral maps of the area are mosaicked and georeferenced. The parts of the city that contain various levels of destructions are selected. Three types of features namely buildings, roads and recreational areas are classified and extracted from the satellite images.

  • The governing factors of destructions are identified; a mathematical model that integrates the factors is constructed

  • The simulation is constructed for various parameter values (different earthquake strength, time elapses, etc.)

  Earthquake Simulation Workflow

Simulation of structure changing within a human bone

Human bone

Bone as a living tissue is subject to continuing absorption and restructuring of tissue. Hence, the structure of a human bone is changing over time. Mechanical load on bone such as originated by sporty activities has a substantial influence on the changing and thereby affects bone cell dynamics as well as long-term tissue properties such as strength or density. In order to predict these mechanisms at tissue level, it is necessary to account for biochemical processes, cell population dynamics, and mechanical effects at cell level on a spatially resoluted scale. The figure below shows the relationship between the tissue and the cell level of a human bone.

Coupled Simulation

From Skeleton to Cells

A multi-scale simulation couples the biomechanical (tissue level) and the systems biological part (cell level) to a biomechanical system (coupled tissue and cell level). The systems biological part calculates the individual cell populations, their interactions via secreted biochemical, and the resulting bone material by ordinary differential equations. This includes bone-forming cell types (osteoblasts, osteocytes) as well as bone-resorbing cells (osteoclasts), the impact of effective mechanical stress on these cell populations, and the resulting bone mineral produced by the cells. The time scale of the systems biological part is minute, the spatially scale is millimeter.
The biomechanical part is based on the Theory of Porous Media (TPM) and can be calculated with the aid of the finite element method (FEM) allowing for 2D- and 3D-resolution in space, and is responsible for the spatial distribution of force effects and bone supply material. The time scale is between day and week, the spatially scale is centimeter.
Both parts are coupled by a data transformation service. At equally distributed points (Gauss points) of the biomechanical FEM-Grid, the systems biological equations are solved locally for a finite time horizon.

Simulation Workflow

Three simulation workflows implement the coupled simulation (see figure below):

  • The Pandas-Bone workflow (top) is responsible for the simulation on the tissue level. It implements the FEM framework Pandas. Pandas calculates the structure changing within a human bone first for one FEM element. After that, Pandas combines all elements. Furthermore, the Pandas-Bone workflow controls the execution of both other workflows.

  • The Matlab-Bone workflow (bottom) is responsible for the simulation on the cell level. It implements the scientific environment Matlab. With the aid of Matlab we calculate 8 cells within one FEM element.

  • The Data-Manager workflow (middle) is responsible for the transformation of data that is used at the tissue level into a format that can be used at the cell level and vice versa. It implements a data service that makes use of a database (Bone-DB).