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Towards Computational Fluid Dynamics with Tensor-Programmable Quantum Circuits
No full textDer Vortrag präsentiert eine Methode um lineare und nicht-lineare Differentialgleichungen mit einem variationallen Quantenalgorithmus zu lösen. Es werden Ergenisse für die Advection-Diffusion und die Burgersgleichung präsentier
A Survey on Personalized Conflict Resolution Approaches in Air Traffic Control
No full textThe global shortage of air traffic controllers (ATCOs) has led to significant challenges. One of them is the high workload of ATCOs, often resulting in flight delays. This makes it essential to develop solutions that reduce ATCOs' workload in order to increase capacity. One promising approach is the integration of decision-support systems. A typical task for which these systems are used for is the resolution of aircraft conflicts in the upper airspaces. A key challenge in implementing these support systems is to ensure a high acceptance and adoption rate of the proposed advisories. One potential solution to this problem is to personalize the advisories, aligning them with individual ATCOs' preferences and conflict resolution strategies. As this approach offers many promising research directions, this literature review aims to provide a comprehensive overview of existing research in this domain and highlight potential opportunities and open challenges. Overall, 16 papers are discussed in detail to examine the diversity of conflict resolution strategies among ATCOs, the impact of personalization on the acceptance rate of advisories, the technical feasibility of implementing personalization, and the balance between personalized advisories and operational efficiency. Additionally, this paper highlights the opportunities such personalization presents, along with the unresolved challenges that should be addressed in future research
PtAC v0.8.0
No full textPtAC is a Python package to automatically compute walking accessibilities from residential areas to public transport stops for the Sustainable Development Goal 11.2 defined by the United Nations. The goal is to measure and monitor the proportion of the population in a city that has convenient access to public transport (see https://sdgs.un.org/goals/goal11). With this library users can download and process OpenStreetMap (OSM) street networks and population information worldwide. Based on this it is possible to calculate accessibilities from population points to public transit stops based on minimum street network distance
Sustainability through the process chain: turning fibre waste into reinforced filaments for AM with its economic side.
No full textHow do convective cold pools influence the atmospheric boundary layer near two wind turbines in northern Germany?
No full textWith increasing wind energy in the German energy grid, it is crucial to better understand how particular atmospheric phenomena can impact wind turbines and the surrounding atmospheric boundary layer. Deep
convection is one source of uncertainty for wind energy prediction, with the near-surface convective outflow
(i.e., cold pool) causing rapid kinematic and thermodynamic changes that are not adequately captured by operational weather models. Using 1 min meteorological mast and remote sensing vertical profile observations from
the WiValdi research wind park in northern Germany, we detect and characterize 120 convective cold-pool passages over a period of 4 years in terms of their temporal evolution and vertical structure. We particularly focus
on variations in wind-energy-relevant variables (wind speed and direction, turbulence strength, shear, veer, and
static stability) within the turbine rotor layer (34–150 m height) to isolate cold-pool impacts that are critical for
wind turbine operations. Near hub height (92 m) during the gust front passage, there are relatively increased
wind speeds of up to +4 m s−1
in addition to the background flow, a relative wind direction shift of up to +15°,
and increased turbulence strength for a median cold pool. Given hub-height wind speeds lying within the partial
load region of the power curve for the detected cases, there is an increase in estimated power of up to 50 %,
which lasts for 30 min. We find a “nose shape” in relative wind speeds and virtual potential temperature (θv) at
hub height during gust front passages, with larger wind direction changes closer to the surface. This manifests as
asymmetric fluctuations in positive shear, negative veer, and static stability across the rotor layer, with relative
variations below hub height at least twice as large compared with above hub height and temporarily opposite
signs for stability that have complex implications for turbine wakes. Doppler wind lidar profiles indicate that
kinematic changes associated with the gust front extend to a height of 700–800 m, providing an estimate for
cold-pool depth and highlighting that cold-pool impacts would typically extend beyond the height of current
onshore wind turbines. After the cold-pool gust front passage, there is gradually increasing static stability, with
a median decrease in near-surface θv of −2.7 K, a gradual decrease in hub-height turbulence strength, and faster
recovery of wind speed than wind direction
Convolutional Neural Networks for Surrogate Modelling of Tube Bundle Heat Exchangers in Electrified Aircraft Propulsion
No full textThe aviation industry is undergoing a transformation toward sustainable propulsion technologies, with electrified aircraft propulsion powered by proton exchange membrane fuel cells emerging as a promising solution. These systems offer substantial reductions in greenhouse gas emissions and noise but face major challenges in thermal management. With typical fuel cell efficiencies of around 50%, a significant portion of input energy is released as waste heat. Ensuring stable operating conditions therefore requires compact and efficient thermal management systems (TMS), within which heat exchangers (HEXs) play a central role. The design of HEXs for electrified propulsion demands a delicate balance between high
thermal performance, low pressure drop, reduced weight, and manufacturability. Traditionally, HEX analysis and optimization have relied on high-fidelity computational fluid dynamics (CFD) simulations, which accurately capture flow and heat transfer phenomena but incur prohibitive computational costs. This limitation restricts their use for parametric sweeps, sensitivity analyses, or large-scale
design optimization. To overcome these barriers, this thesis develops a datadriven surrogate modeling framework that leverages deep learning to reproduce CFD-level accuracy at a fraction of the cost. Specifically, a geometry-adaptive convolutional neural network (CNN) based on U-Net architecture is introduced to predict the flow and thermal fields in staggered tube-bundle heat exchangers. A dataset of 400 two-dimensional Reynolds-Averaged Navier–Stokes (RANS)
CFD simulations was generated using the SU2 solver across systematically varied geometric parameters, including tube diameter, longitudinal spacing, and transverse spacing. Each simulation provided velocity components, pressure, temperature, and turbulent viscosity fields, which were resampled onto structured grids and paired with geometry encodings (binary masks and signed distance functions). Five independent U-Net models were trained on these inputs, with
specialized loss functions designed to capture both local flow features and global field consistency. The surrogate models achieved high predictive accuracy, with mean relative errors below 2% for velocity and temperature, and under 4% for pressure and turbulent viscosity fields. Importantly, the CNN-based framework reduced computational cost by approximately three orders of magnitude compared to CFD,
enabling rapid evaluation of new HEX designs in under one second. Sensitivity analyses further confirmed that increasing dataset size improved robustness and minimized prediction variability across different geometries. In conclusion, this work demonstrates the effectiveness of deep learning surrogates for accelerating HEX design in electrified aircraft propulsion. By uniting the physical fidelity of CFD with the efficiency of machine learning, the proposed framework provides a scalable tool for design-space exploration and optimization, paving the way for reliable and lightweight thermal management solutions in next-generation sustainable aviation
Uncertainty bounds for long-term causal effects of perturbations in spatiotemporal systems
Get PDFIn time-dependent systems, autoregressive models are frequently employed to investigate the interactions between variables of interest in fields such as climate science, macroeconomics, and neuroscience. Typically, these variables are aggregated from smaller-scale variables into large-scale variables, for instance, representing modes of climate variability in climate science. A key aspect of these models is estimating the long-term effects of external perturbations, once the system stabilizes. Our primary contribution is an explicit formula for quantifying these long-term effects on small-scale variables, which is directly estimable from the model’s linear coefficients and aggregation weights. This improves traditional autoregressive models by providing a localized understanding of the system behavior. We conduct a series of numerical experiments to evaluate the performance of various methods to estimate perturbation effects from data. Our second contribution is the derivation of the asymptotic properties of these estimators under suitable assumptions. These asymptotic properties can be leveraged for uncertainty quantification
Validation of a thermal storage model in the Virtual Solar Field software using operating data from the Évora Molten Salt Platform
No full textParabolic trough power plants use solar radiation to heat a heat transfer fluid (HTF) up to the design temperature. However, solar radiation often causes transient conditions in the solar field. For example, the solar field, which has cooled down overnight, has to be restarted every morning to bring the outlet temperature to the set point. Cloud passages can influence the spatial irradiation, which can lead to temperature fluctuations or different focusing of the collectors. Dynamic simulation tools that can cover these transient scenarios are used to develop operating strategies for the solar field. Such a simulation program, called Virtual Solar Field (VSF) [1], [2], has been developed at DLR. This simulation program makes it possible to carry out such simulations using spatially resolved irradiation data. The extension of the tool to include the thermal storage allows the representation and simulation of the entire HTF cycle. The aim of this work is to validate the thermal storage in VSF using real operating data from the Évora Molten Salt Platform (EMSP) [3]