Eldorado - Repositorium der TU Dortmund

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Bei diesem Service handelt es sich um das Institutionelle Repositorium der Technischen Universität Dortmund. Hier werden Ressourcen aus und für Lehre, Studium und Forschung gespeichert, erschlossen und der Öffentlichkeit zugänglich gemacht.

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Aktuellste Veröffentlichungen

High Speed Forming 2025
(2025-08) Korkolis, Yannis P.; Daehn, Glenn S.
Modeling and contribution of flexible heating systems for transmission grid congestion management
(Elsevier BV, 2024-06-26) Kröger, David; Teodosic, Milijana; Rehtanz, Christian
The large-scale integration of flexible heating systems in the European electricity market leads to a substantial increase of transportation requirements and consecutively grid congestions in the continental transmission grid. Novel model formulations for the grid-aware operation of both individual small-scale heat pumps and large-scale power-to-heat (PtH) units located in district heating networks are presented. The functionality of the models and the contribution of flexible heating systems for transmission grid congestion management is evaluated by running simulations for the target year 2035 for the German transmission grid. The findings show a decrease in annual conventional redispatch volumes and renewable energy sources (RES) curtailment resulting in cost savings of approximately 5% through the integration of flexible heating systems in the grid congestion management scheme. The analysis suggests that especially large-scale PtH units in combination with thermal energy storages can contribute significantly to the alleviation of grid congestion and foster RES integration.
Mixed bioconvection of nanofluid of oxytactic bacteria through a porous cavity with inlet and outlet under periodic magnetic field using artificial intelligence based on LightGBM algorithm
(Elsevier BV, 2024-04-24) Hussain, Shafqat; Öztop, Hakan F.; Alsharif, Abdullah Madhi; Ertam, Fatih
The comprehension of microorganisms’ responses to magnetic field and fluid motion offers potential for the advancement of targeted drug delivery systems or medical interventions reliant on biological fluids. In this paper, the mixed bioconvection flow of nanofluid of oxytactic bacteria has been investigated through a porous cavity with inlet and outlet ports under the impact of periodic magnetic field. All the walls of the cavity are fixed at the constant high temperature. The proposed problem has been modeled first and then simulated using the finite element method. The computed computational fluid dynamics results have been analyzed for the several important controlling parameters. It is observed that there is at least 65% increment on heat transfer between the lowest and highest Peclet numbers. Further, regression analysis was conducted using the LightGBM algorithm. Careful parameter tuning was performed to avoid overfitting, ensuring that the model did not memorize the training data. According to the ℝ² values used for regression analysis, in the 18 datasets used for the performance metric comparison of the artificial intelligence model, a total of 18 targets were tried to be predicted for different Richardson number (0.1, 1, and 10) and Lewis Number (0.1, 1, and 10) values for each motile microorganisms, temperature and oxygen concentration values specified in the datasets, and a minimum accuracy of 95% and a maximum accuracy of 98% were obtained. These findings demonstrate the capability of the LightGBM algorithm to accurately predict the target variable within a high range of accuracy for the given datasets.
MaRTIn – Massive Recursive Tensor Integration
(Elsevier BV, 2024-09-10) Brod, Joachim; Hüdepohl, Lorenz; Stamou, Emmanuel; Steudtner, Tom
We present MaRTIn, an extendable all-in-one package for calculating amplitudes up to two loops in an expansion in external momenta or using the method of infrared rearrangement. Renormalisable and non-renormalisable models can be supplied by the user; an implementation of the Standard Model is included in the package. In this manual, we discuss the scope and functionality of the software, and give instructions of its use.
Microscopic understanding of NMR signals by dynamic mean-field theory for spins
(Elsevier BV, 2024-05-24) Gräßer, Timo; Hahn, Thomas; Uhrig, Götz S.
A recently developed dynamic mean-field theory for disordered spins (spinDMFT) is shown to capture the spin dynamics of nuclear spins very well. The key quantities are the spin autocorrelations. In order to compute the free induction decay (FID), pair correlations are needed in addition. They can be computed on spin clusters of moderate size which are coupled to the dynamic mean fields determined in a first step by spinDMFT. We dub this versatile approach non-local spinDMFT (nl-spinDMFT). It is a particular asset of nl-spinDMFT that one knows from where the contributions to the FID stem. We illustrate the strengths of nl-spinDMFT in comparison to experimental data for CaF₂. Furthermore, spinDMFT provides the dynamic mean fields explaining the FID of the nuclear spins of ¹³C in adamantane up to some static noise. The spin Hahn echo in adamantane is free from effects of static noise and agrees excellently with the spinDMFT results without further fitting.