This deliverable presents the final software release of Kratos Multiphysics, together with the XMC library, Hyperloom and PyCOMPSs API definitions [13]. This release also contains the latest developements on MPI parallel remeshing in ParMmg. This report is meant to serve as a supplement to the public release of the software. Kratos is “a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface”. XMC is “a Python library for parallel, adaptive, hierarchical Monte Carlo algorithms, aiming at reliability, modularity, extensibility and high performance“. Hyperloom and PyCOMPSs are environments for enabling parallel and distributed computation. ParMmg is an open source software which offers the parallel mesh adaptation of three dimensional volume meshes.
This deliverable presents the software release of Kratos Multiphysics, together with the XMC library, Hyperloom and PyCOMPSs API definition [8]. This report is meant to serve as a supplement to the public release of the software. Kratos is “a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface”. XMC is a python library for hierarchical Monte Carlo algorithms. Hyperloom and PyCOMPSs are environments for enabling parallel and distributed computation.
We introduce a novel hybrid methodology that combines classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems. The residual from finite element methods and custom loss functions from neural networks are merged to form the algorithm. The Finite Element Method-enhanced Neural Network hybrid model (FEM-NN hybrid) is data-efficient and physics-conforming. The proposed methodology can be used for surrogate models in real-time simulation, uncertainty quantification, and optimization in the case of forward problems. It can be used to update models for inverse problems. The method is demonstrated with examples and the accuracy of the results and performance is compared to the conventional way of network training and the classical finite element method. An application of the forward-solving algorithm is demonstrated for the uncertainty quantification of wind effects on a high-rise buildings. The inverse algorithm is demonstrated in the speed-dependent bearing coefficient identification of fluid bearings. Hybrid methodology of this kind will serve as a paradigm shift in the simulation methods currently used.
This work shows the application of Multi-fidelity Uncertainty Quantification to Wind Engineering problems. As test case a rectangular shape is used, with a fillet radius, in order to represent the geometrical variations that can affect buildings or other bluff bodies. The rectangular cylinder used has a chord-to-thickness ratio 5:1. This rectangular shape is an important basic shape for wind engineering tasks, e.g. in case of buildings or other bluff bodies exposed to the flow. Moreover it is well investigated and documented.Coarse and fine meshes are used as low and high fidelity models respectively. To perform CFD simulations, the stabilized finite element methods are used in both the high and low fidelity model with a CFD code developed by TUM and the International Center for Numerical Methods in Engineering. The underlying UQ framework is based on a Sparse Arbitrary Moment Based Algorithm (SAMBA) developed at ICL. In the formulation the number of simulations is reduced using a Smolyak sparsity model.The multi-fidelity extension, with application to wind engineering problems is discussed and presented in this work. The overall goal of such formulation is to gain an accuracy of mixed lowhigh fidelity simulations comparable to the ones obtained with only high fidelity simulations, at a fraction of the computational cost.
This document presents a simple and ecient strategy for adaptive mesh renement (AMR) and a posteriori error estimation for the transient incompressible Navier{Stokes equations. This strategy is informed by the work of Prudhomme and Oden [22, 23] as well as modern goal-oriented methods such as [5]. The methods described in this document have been implemented in the Kratos Multiphysics software and uploaded to https://zenodo.org [27].1 This document includes: A review of the state-of-the-art in solution-oriented and goal-oriented AMR. The description of a 2D benchmark model problem of immediate relevance to the objectives of the ExaQUte project. The denition and a brief mathematical summary of the error estimator(s). The results obtained. A description of the API.
The implementation of machine learning for the real-time prediction of the suitable value of the damping ratio of a semi-active tuned mass damper (SA-TMD) is investigated to ensure enhanced vibration control in vehicle-bridge interaction (VBI) problems. The response assessment of the uncontrolled, tuned mass damper (TMD)-controlled, and SA-TMD-controlled bridge models is performed under the Japanese SKS (Shinkansen) train model. The energy-based predictive (EBP®) control algorithm is implemented for the bridge fitted with the SA-TMD. The EBP algorithm-controlled SA-TMD results in more effective suppression of the bridge vibration as compared to the passive TMD. However, the effectiveness of the EBP algorithm reduces for more complex VBI systems because of the increased computational time delay. To circumvent the effect of the delay, a control strategy is proposed based on the weighted random forest (WRF) algorithm. The WRF algorithm is trained based on the data obtained from the EBP algorithm-controlled bridge and implemented to suppress the vehicle-induced vibration of the bridge using SA-TMD. The results demonstrate that the implementation of the newly proposed WRF algorithm-based control strategy nullifies the effects of the computational time delay. Furthermore, it is established that the WRF algorithm suppresses the bridge vibration more effectively than the EBP algorithm.
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