Mathias J. Krause

Simon Leistikow, Thomas Miro, Adrian Kummerländer et al.

Background and Objective: Hemodynamic analysis of blood flow through arteries and veins is critical for diagnosing cardiovascular diseases, such as aneurysms and stenoses, and for investigating cardiovascular parameters, such as turbulence and wall shear stress. For subject-specific analyses, the anatomy and blood flow of the subject can be captured non-invasively using structural and 4D Magnetic Resonance Imaging (MRI). Computational Fluid Dynamics (CFD), on the other hand, can be used to generate blood flow simulations by solving the Navier-Stokes equations. To generate and analyze subject-specific blood flow simulations, MRI and CFD have to be brought together. Methods: We present an interactive, customizable, and user-oriented visual analysis tool that assists researchers in both medicine and numerical analysis. Our open-source tool is applicable to domains such as CFD and MRI, and it facilitates the analysis of simulation results and medical data, especially in hemodynamic studies. It enables the creation of simulation ensembles with a high variety of parameters. Furthermore, it allows for the visual and analytical examination of simulations and measurements through 2D embeddings of the similarity space. Results: To demonstrate the effectiveness of our tool, we applied it to three real-world use cases, showcasing its ability to configure simulation ensembles and analyse blood flow dynamics. We evaluated our example cases together with MRI and CFD experts to further enhance features and increase the usability. Conclusions: By combining the strengths of both CFD and MRI, our tool provides a more comprehensive understanding of hemodynamic parameters, facilitating more accurate analysis of hemodynamic biomarkers.

Dennis Teutscher, Tyll Weber-Carstanjen, Stephan Simonis et al.

Efficient solid-liquid separation is crucial in industries like mining, but traditional chamber filter presses depend heavily on manual monitoring, leading to inefficiencies, downtime, and resource wastage. This paper introduces a machine learning-powered digital twin framework to improve operational flexibility and predictive control. A key challenge addressed is the degradation of the filter medium due to repeated cycles and clogging, which reduces filtration efficiency. To solve this, a neural network-based predictive model was developed to forecast operational parameters, such as pressure and flow rates, under various conditions. This predictive capability allows for optimized filtration cycles, reduced downtime, and improved process efficiency. Additionally, the model predicts the filter mediums lifespan, aiding in maintenance planning and resource sustainability. The digital twin framework enables seamless data exchange between filter press sensors and the predictive model, ensuring continuous updates to the training data and enhancing accuracy over time. Two neural network architectures, feedforward and recurrent, were evaluated. The recurrent neural network outperformed the feedforward model, demonstrating superior generalization. It achieved a relative $L^2$-norm error of $5\%$ for pressure and $9.3\%$ for flow rate prediction on partially known data. For completely unknown data, the relative errors were $18.4\%$ and $15.4\%$, respectively. Qualitative analysis showed strong alignment between predicted and measured data, with deviations within a confidence band of $8.2\%$ for pressure and $4.8\%$ for flow rate predictions. This work contributes an accurate predictive model, a new approach to predicting filter medium cycle impacts, and a real-time interface for model updates, ensuring adaptability to changing operational conditions.