Schmidt, Alfred
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Item-typ:Veröffentlichung, Deep learning for temporal reconstruction of FESOM-derived sea surface temperature and 3D ocean variables(2025-08-27); ; ; In oceanography, the growing volume of data collection raises the challenge of efficient data storage and computational efficiency for ocean simulations. To address this, we adopted an interpolation-based approach for reducing data storage requirements while ensuring accurate reconstruction of sea surface temperature (SST) and 3D ocean variables, including temperature and horizontal velocities (u and v) derived from the Finite-volumE Sea ice–Ocean Model (FESOM2). Our research explores three distinct strategies: single-step interpolation, temporal-window interpolation, and multi-step interpolation. To achieve these goals within a supervised learning framework, we propose a hybrid CNN-BiLSTM deep learning model for both accurate interpolation and data storage optimization. This model extracts features from time-series data by combining the strengths of convolutional neural networks (CNNs) for capturing spatial patterns and bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies in time-series data. Additionally, our approach uses a multi-input multi-output (MIMO) strategy to improve computational efficiency and prevent error accumulation, resulting in high prediction accuracy. By incorporating neighborhood information and preprocessing data to consider spatial and temporal dependencies, our approach effectively prepares the data for training. We use subsampling to reduce memory usage and introduce diverse, uncorrelated samples. The results show that our model outperforms traditional methods, including linear interpolation (LI) and linear regression (LR), achieving lower mean squared error (MSE) and higher correlation coefficients when reconstructing ocean variables. Single-step interpolation achieved a 75% improvement, temporal-window interpolation demonstrated an overall 46.80% improvement, and multi-step interpolation achieved an overall 59.51% improvement in predictive accuracy. Remarkably, the model predicts multiple time steps for 3D ocean fields without introducing artifacts, achieving this within a single model framework. After training on diverse datasets, the model generalizes well to unseen data without requiring additional computational resources, making it a scalable and sustainable solution for addressing the growing need for data storage efficiency in oceanographic research. To improve model performance, key techniques were applied, including overfitting prevention, balanced batching, multi-GPU training, adaptive learning rate schedules such as polynomial decay and cyclic learning, and fine-tuning. We also demonstrate the practical application of our model by computing ocean heat transport in zonal and meridional directions. The results confirm that our model predicts ocean heat content more accurately than traditional methods. However, the computation of ocean heat transport using mixed-sign velocity values presents challenges, despite individual velocity predictions being highly accurate. This work highlights the potential of advanced neural networks in reconstructing temperature and velocity data, accurately matching observed values, and enhancing our understanding of ocean heat dynamics.Dissertation45 46 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Homogenization and numerical methods for models appearing in grinding processes and phase transitions(2025-12-11); ; 33 48 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Adaptive Gittergenerierung für das Finite-Elemente-Ozean- und Meereismodell FESOM(2020-07-16); ; ; To circumvent the problems which arise at the discretizing stage of ocean general circulation models (OGCM) with the Finite Difference Method and to improve the local conservation properties, the three dimensional Finite Element Sea Ice-Ocean Model (FESOM) was already developed at the Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven. The model discretization of FESOM was done with the Finite Element Method and the underlying three-dimensional mesh consists of an unstructured surface mesh and a structured tetrahedra mesh. In this thesis the mesh generator Meshgen4FESOM (with additional supporting tools) will be presented, which generates iteratively and automatically a FESOM mesh of the above structure. The surface mesh will be generated in an adaptive fashion using local element errors (which are calculated on the basis of the elements local bathymetry) and the corresponding global error in every iteration. Additionally different mesh smoothing techniques will be applied to the surface mesh. The tetrahedra mesh will be constructed iteratively by continuing the triangle elements of the surface mesh to the ocean bottom which yields a prism for every surface element. After that, every prism will be subdivided into smaller prisms at predefined levels and finally every subprism is cut into three tetrahedra. In a last step a remeshing of the tetrahedra in every level will be done, to get an optimal triangulation with respect to the bathymetry and in accordance to the needs of FESOM. It is the main goal of this thesis to develop meshing techniques and create a meshgenerator which constructs a family of surface and tetrahedrameshes which takes account of the bathymetry in an adaptive way, to resolve coasts, straits and other dynamical regions of the oceans and leaves non dynamical bathymetry regions more coarse.Dissertation464 248 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, FE-CutS - Finite Elemente Modell für makroskopische Zerspanprozesse : Modellierung, Anaylse und Simulation(2017-09-18); ; ; The resulting complex thermal and mechanical load pectrum in dry machining processes leads to temperature induced shape deviations of metallic orkpieces which changes its behavior for future use. Research projects try to compensate for manufacturing inaccuracies, resulting from the process, during the planning phase by using simulation-supported methods. The finite element method (FEM) is an appropriate tool to calculate thermomechanical behavior of workpieces by applying thermal and mechanical loads. This thesis describes the modeling and simulation of the thermal and mechanical behavior of workpieces considering material removal during the processes by FEM. In this case the FEM is linked to a dexel model to visualize the geometry change also in the FEM. During the mathematical modeling the heat equation is coupled to the quasi-stationary linear-elastic deformation equation on a time-dependent domain with changing boundaries. Heat fluxes and process forces are given from a process model and exist only during the tool-workpiece-interaction. These informations are project to the time-dependent bounds of the workpiece. Here a new visualization of material removal on unfitted meshes is presented. The mesh is divided into two time-dependent disjoint parts. One for the time-dependent workpiece and one for the removed material. The geometry of the workpiece is approximated on time changing bounds by adjusted adaptive methods. The analysis shows good results for the approximation with a controllable volume error. On thus time-dependent domain the thermal and mechanical workpiece behavior during machining processes could be simulated in a realistic case. During the processes the identification and compensation of shape deviations will be possible. The model can be extended for other processes with geometrically defined edges.Dissertation570 296 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Diffuse Grenzflächen thermodynamisch scharf: Ein voll physikalisch eingebettetes Multiphasenfeldmodell(2010-11-26); ; ; Motivated by the problem of distortion occuring during heat treatment of steel, the mutual influence of phase changes and stresses in metals is examined on the length scale of several grains, the mesoscopic length scale, using a multi phase field method. The metalurgical motivation of this work makes it necessary to derive governing equations from entropy maximization as well as from free energy minimization. The inner energy and the entropy of the system, thus the free energy, are as far as possible put together from well-known physical expressions. Commonly stated thermodynamical relations between the contributions are examined for their domain of validity, then this validities are proven between the chosen contributions to inner energy and entropy where they should account. New insights on the thermodynamics of phase changes are achieved. The phasefield itself is chosen such that one component of it symbolizes one phase in each grain, i.e. two components for each grain are needed. To deal with this amount of components and stresses and temperature, a FEM software with the capacity to deal with an arbitrary number of components is designed. Results of the simulations are presented.Dissertation609 310