In this section, you will find an overview of the applications in OPTIMA project and their work.
Underground analysis applications are both very useful and very demanding in terms of processing. Some examples indicating the importance of underground analysis applications are the simulation of the withdrawal/injection of subsurface resources (such as oil and gas, water, CO2, etc.), the evaluation of the induced and/or triggered seismicity due to fault reactivation and/or fracture generation caused by reservoir activities, the prediction of the flow field in variably-saturated or multi-aquifer systems both at the well and at the basin scale, the evaluation of the aquifer artificial recharge by superficial basins and wells, the quantification of saltwater intrusion in coastal aquifers, all of which can have a strong impact on the environment. Its computational complexity stems from the requirement for accurate, complex and reliable numerical simulations. In this context, the Finite Element Method (FEM) remains the most widely used approach.
The numerical simulation of these problems requires the development of very accurate FE models, usually characterized by a high level of complexity due to various factors: a wide simulation domain, a strong heterogeneity in material properties, coupled Multiphysics (e.g. solid mechanics with fluid/pressure diffusion) and so on. In particular, an accurate predictive model requires fine discretization of the domain, with a total number of elements up to hundreds of millions.
For all the above-mentioned considerations, the computational time and memory storage for running numerical simulations are the two main bottlenecks. Approaching these problems with coarser models and/or fewer investigated scenarios results in reduced accuracy and confidence in the quality of the results.
Hence, in geomechanical and underground applications, it is of paramount importance to utilize very powerful HPC systems, in order to maintain accuracy and result precision, while reducing the simulation time to acceptable levels.Within the Optima Project the Atlas (a FEM geomechanical code) and Chronos (a library of sparse linear solver) will be ported and deployed in FPGA systems.
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CFD codes nowadays are becoming more and more demanding in computer resources due to the number of equations per cell that needs to be solved for coupled problems.
Lattice Boltzmann methods (LBM) The lattice Boltzmann method (LBM)models fluids with fictive particles performing propagation and collision processes over a discrete lattice mesh. LBM offers advantages over traditional Navier-Stokes equation solvers in the form of exceptional scalability, robust treatment of complex boundaries, and the capacity to take greater time steps.
The LBM code used is a general purpose fluid dynamics solver optimized for modern multi-core processors, especially Graphics Processing Units (GPUs). The solver is based on the Lattice Boltzmann Method, which is conceptually quite simple to understand and which scales very well with increasing computational resources.
Within the OPTIMA project the code will be ported and deployed in FPGA systems in order to accelerate the solution and benefit from multi FPGAs systems.
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The simulation software MESHFREE has been developed since 1999 at the Fraunhofer ITWM with regards to industrial applications. It is a meshfree approach to simulate flows and continuum mechanic processes. By not using a numeric meshing the method is versatile and efficient regarding simulations with moving boundary elements, free surfaces, boundary curves or fluid-structure-interaction.
In 2018 the software was extended by the solver library SAMG which is developed by the Fraunhofer SCAI. In the course of the OPTIMA project, some tasks within the software will be ported to FPGA systems for accelerating the computation.
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Cyberbotics Ltd. (CYB) develops an open source robot simulation software named Webots. This software is used to develop intelligent robotics appliances in simulation before transferring the results to real robotics systems (see Figure below).
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Robotics appliances require an increasing level of autonomy and hence well-trained Artificial Intelligence (AI) systems, including sensor processing, image recognition, learning abilities, etc. Training and calibrating such AI systems involve running a large number of simulations. These simulations include the generation of simulated sensor data, the actuators dynamics, and the environment dynamics, in order to close the control loop. The same robotics simulation scenarios may be run millions of times together with Machine Learning algorithms (for example evolutionary algorithms) to train intelligent robot controllers.
Such simulations are heavily used for example in the automotive industry to simulate autonomous cars and train intelligent controllers to react to unexpected events. It is also used to validate existing intelligent controllers on a wide range of simulation generated scenarios. Simulations are also heavily used for example to optimize the traffic of a large number of heterogeneous trucks in surface mines.
Several parts of the Webots simulator were selected within the OPTIMA project, so as to be ported and optimized for the OPTIMA FPGA-based HPC systems.
The first implementation of Webots on HPC systems aims at accelerating a deep learning-based simulation featuring an autonomous car. Running on CPU, the speed of the simulation is strongly slowed down by the large number of operations required by the robot controller for the inference calculation. The deployment of the neural network part on FPGAs allows to accelerate the execution of the simulation.
