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- Fachbereich Ingenieurwissenschaften und Kommunikation (87)
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Keywords
Quality diversity algorithms can be used to efficiently create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing 100.000s of evaluations. Even with the assistance of surrogate models, quality diversity needs 100s or even 1000s of evaluations, which can make it use infeasible. In this study we try to tackle this problem by using a pre-optimization strategy on a lower-dimensional optimization problem and then map the solutions to a higher-dimensional case. For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints. For a diverse set of building designs, by sampling the space of 2D footprints with a quality diversity algorithm, a predictive model can be trained that is more accurate than when trained on a set of footprints that were selected with a space-filling algorithm like the Sobol sequence. Simulating only 16 buildings in 3D, a set of 1024 building designs with low predicted wind nuisance is created. We show that we can produce better machine learning models by producing training data with quality diversity instead of using common sampling techniques. The method can bootstrap generative design in a computationally expensive 3D domain and allow engineers to sweep the design space, understanding wind nuisance in early design phases.
Heutzutage werden alternative Mobilitätslösungen immer wichtiger. Dabei haben eBikes ihr Potential längst unter Beweis gestellt. Der zugehörige Markt ist über die letzten 10 Jahre enorm gewachsen und gleichermaßen auch die Erwartungen an das Produkt, wie bspw. eine Fahrt ohne störende Vibrationen und Geräusche zu haben. Der Motorfreilauf leistet dabei einen maßgeblichen Einfluss auf das dynamische Verhalten. In diesem Beitrag soll daher eine methodische Vorgehensweise vorgestellt werden, um mittels Versuch und Simulation den Einfluss des Motorfeilaufs auf das dynamische Verhalten der eBike Antriebseinheit zu bestimmen.
The elucidation of conformations and relative potential energies (rPEs) of small molecules has a long history across a diverse range of fields. Periodically, it is helpful to revisit what conformations have been investigated and to provide a consistent theoretical framework for which clear comparisons can be made. In this paper, we compute the minima, first- and second-order saddle points, and torsion-coupled surfaces for methanol, ethanol, propan-2-ol, and propanol using consistent high-level MP2 and CCSD(T) methods. While for certain molecules more rigorous methods were employed, the CCSD(T)/aug-cc-pVTZ//MP2/aug-cc-pV5Z theory level was used throughout to provide relative energies of all minima and first-order saddle points. The rPE surfaces were uniformly computed at the CCSD(T)/aug-cc-pVTZ//MP2/aug-cc-pVTZ level. To the best of our knowledge, this represents the most extensive study for alcohols of this kind, revealing some new aspects. Especially for propanol, we report several new conformations that were previously not investigated. Moreover, two metrics are included in our analysis that quantify how the selected surfaces are similar to one another and hence improve our understanding of the relationship between these alcohols.
Transition point prediction in a multicomponent lattice Boltzmann model: Forcing scheme dependencies
(2018)
How self-reliant Peer Teaching can be set up to augment learning outcomes for university learners
(2022)
Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force field parameters need to be adjusted. This force field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties’ errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the overall error is reduced. One interesting outcome is a large variety in the resulting optimized force field parameters (FFParams) and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.
AErOmAt Abschlussbericht
(2020)
Das Projekt AErOmAt hatte zum Ziel, neue Methoden zu entwickeln, um einen erheblichen Teil aerodynamischer Simulationen bei rechenaufwändigen Optimierungsdomänen einzusparen. Die Hochschule Bonn-Rhein-Sieg (H-BRS) hat auf diesem Weg einen gesellschaftlich relevanten und gleichzeitig wirtschaftlich verwertbaren Beitrag zur Energieeffizienzforschung geleistet. Das Projekt führte außerdem zu einer schnelleren Integration der neuberufenen Antragsteller in die vorhandenen Forschungsstrukturen.