Open Access

The utilization of geothermal energy is crucial for achieving sustainable heating solutions during the transition to renewable energy sources.Geothermal probes, also known as borehole heat exchangers, are used to extract heat from the ground.These probes can operate individually or be combined into probe fields to meet larger heating demands.The design of these fields must account for potential undercooling or overheating over extended periods.G-functions are typically employed to calculate the thermal behavior of the ground with interconnected borehole heat exchangers.However, calculating numerical solutions for these functions can be very time-consuming.This study involves training artificial neural networks (\gls{ANN}) using pre-calculated g-functions.The accuracies and calculation times of the trained \gls{ANN} were then compared.The study created a dataset with over \num{\sizeOfData} g-functions for a rectangular probe field.Two different ANNs were trained using 80 \% of the dataset.The study then determined the accuracy of the g-functions generated by the ANN and the calculation speed.The results indicate that the mean absolute percentage error was below 2.3 \%.On average, the calculation time was less than 0.1 \% of the original time.Large probe fields performed better than average in terms of calculation time.The reduced calculation time enables the computation of complex tasks within a reasonable timeframe, including depth sizing and borefield design optimizations.

Ökobilanzen, die alle Phasen im Lebenszyklus eines Gebäudes berücksichtigen, gewinnen zunehmend an Bedeutung.Aufgrund ihrer langen Lebensdauer kommt bei Gebäuden mit strombasierten Wärmeversorgungssystemen, wie Wärmepumpen, der Interaktion mit dem Stromnetz eine herausragende Bedeutung zu, insbesondere im Zusammenhang mit der Dekarbonisierung der Stromerzeugung.Der Einfluss der Dekarbonisierung über die kommenden Jahrzehnte sowie die kurzfristige Variabilität der Emissionsfaktoren auf der Basis von stundenbasierten Werten für den Strommix auf die Ökobilanz von vier verschiedenen Gebäudetypen soll hier aufgezeigt werden.Die betrachteten Gebäudetypen umfassen ein Einfamilienhaus, ein Bürogebäude, ein Bildungsgebäude und eine Industriehalle.Ziel ist es den Einfluss der Dekarbonisierung und der Stundenauflösung auf die Auswahl der optimalen Dämmstärke für ein Wärmepumpen-basiertes System aufzuzeigen.Hierzu werden thermische und elektrische Lastprofile für die verschiedenen Gebäudetypen erzeugt.Dabei werden verschiedene Nutzungsprofile, Standorte, Dämmstärken, Fensteranteile, Ausrichtungen, Fensterverglasungen, Lüftungsvarianten und Sonnenschutzvarianten betrachtet.Für all diese Varianten werden verschiedene Szenarien in Bezug auf die Gebäudetechnik untersucht und hinsichtlich ihrer Dämmstärke sowie der Größe der gebäudetechnischen Komponenten mit GWP-Auswirkungen optimiert.Die Ergebnisse zeigen, dass die Berücksichtigung der Dekarbonisierung der Stromerzeugung die ökobilanziell optimale Dämmstärke im Vergleich zum statischen Ansatz mit heutigen Werten halbieren kann.Auch die Berücksichtigung stundenbasierter Stromemissionswerte führt zu einer Reduzierung der optimalen Dämmstärke um nahezu

Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Because physical urban water infrastructure has life expectancies of up to 100 years or more, contemporary urban drainage systems are strongly influenced by historical decisions and implementations. The current decisions taken in sewer asset management will, therefore, have a long-lasting impact on the functionality and quality of future services provided by these networks. These decisions can be supported by different approaches ranging from various inspection techniques, deterioration models to assess the probability of failure or the technical service life, to sophisticated decision support systems crossing boundaries to other urban infrastructure. This paper presents the state of the art in sewer asset management in its manifold facets spanning a wide field of research and highlights existing research gaps while giving an outlook on future developments and research areas.

Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.

A stabilized version of the fundamental solution method to catch ill-conditioning effects is investigated with focus on the computation of complex-valued elastic interior transmission eigenvalues in two dimensions for homogeneous and isotropic media. Its algorithm can be implemented very shortly and adopts to many similar partial differential equation-based eigenproblems as long as the underlying fundamental solution function can be easily generated. We develop a corroborative approximation analysis which also implicates new basic results for transmission eigenfunctions and present some numerical examples which together prove successful feasibility of our eigenvalue recovery approach.