Open Access

This study investigates loudness perception in real-world contexts using predictors related to the sound, situation, or person. In the study, 105 participants recorded 6594 sound environments in their homes, which were then evaluated based on the Experience Sampling Method. Hierarchical linear regressions using a loudness level based on ISO 532-1 allowed for obtaining the best model fits for predicting perceived loudness and explaining the highest variance. LAeq and LAF5 provided comparable results and may require less computational effort. However, the analysis shows that only one-third of the variance explained by fixed effects was attributable to the loudness level. Sixteen percent stemmed from perceived properties of the soundscape; 1% were attributable to relatively temporally stable, person-related predictors like participants' age; non-auditory situational predictors made no additional contribution. The results thus did not confirm previous findings on loudness perception under laboratory conditions, emphasizing the importance of the situational context. Along with the current paper, a comprehensive dataset, including the assessed person-related, situational, and sound-related measures as well as LAeq time-series and third-octave spectrograms, is provided to enable further research on sound perception, indoor soundscapes, and emotion.

Solar chimney power plants (SCPPs) are encouraging sustainable energy sources due to their low cost, abundance, low maintenance, and eco-friendliness. However, despite significant efforts to optimize SCPP design, their efficiency and power generation capabilities remain limited. Researchers have explored modifications in plant geometry and hybridization to improve efficiency. Despite extensive work in this area, commercialization of SCPPs has not yet been achieved. Most of the research is numerical and may differ from real-world practical use. The number of experimental studies is also relatively small. To facilitate commercialization, further investigation with practical and feasible dimensions is required. This comprehensive review paper aims to provide an in-depth analysis of experimental approaches and advancements in the field of SCPPs. The paper begins with an introduction, highlighting the background, significance, and objectives of the review. It provides an overview of the plants, discussing their principles and operation as innovative renewable energy systems. The historical development and evolution of solar chimneys are explored, shedding light on their progression over time. Case studies of operational hybrid SCPPs are examined to showcase real-world applications and performance. The paper also addresses environmental impacts and sustainability considerations associated with SCPPs. Furthermore, recommendations for future research and development in this field are provided to guide researchers and industry professionals. This study focuses on the possibility of commercialization of both standalone and hybrid SCPPs.

Previous research suggests that noise sensitivity is related to inefficient auditory processing that might increase the mental load of noise and affect noise evaluation. This assumption was tested in an experiment using a dual-task paradigm with a visual primary task and an auditory secondary task. Results showed that participants' noise sensitivity was positively correlated with mental effort. Furthermore, mental effort mediated the effect of noise sensitivity on loudness and unpleasantness ratings. The results thus support the idea that noise sensitivity is related to increased mental effort and difficulties in filtering auditory information and that situational factors should be considered.

The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 minutes to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.

Die katalytische Methanisierung von Kohlendioxid (CO2) mit Hilfe von elektrolytisch erzeugtem Wasserstoff (H2) aus erneuerbaren Energiequellen kann einen wichtigen Beitrag zur Reduktion von CO2-Emissionen leisten. Aufgrund einer immer stärker fluktuierenden Energie- und Rohstoffbereitstellung spielt die Möglichkeit einer flexiblen, dynamischen Betriebsweise eine zunehmend große Rolle für zukünftige Technologien. Dies bedingt auch im Fall der katalytischen Methanisierung umfangreiche Untersuchungen, um zu ermitteln, inwieweit eine dynamische Fahrweise Auswirkungen auf Prozess und Katalysator besitzt. An diesem Punkt setzte das HiFF-Projekt Power2Gas an. So wurden verschiedene nickelbasierte Katalysatorsysteme synthetisiert, wobei sich der via Imprägnierung hergestellte Ni/Al2O3-Katalysator sowohl aufgrund seiner guten Leistung als auch der praktikablen und reproduzierbaren Herstellung als geeignetes Benchmark-System für weitere Untersuchungen - vor allem im kinetischen Bereich - erwies. Während umfassender Parametervariationen wurden kinetische Datensätze im stationären Zustand bestimmt, die zur Modellierung der katalysierten CO2-Methanisierung verwendet werden können. Darüber hinaus wurden in ausgewählten Versuchsreihen die isotherme Temperaturkontrolle und eine ausreichende Langzeitstabilität sichergestellt sowie makrokinetische Diffusionsbeschränkungen ausgeschlossen. Die Ergebnisse wurden in einer peer-review-Publikation veröffentlicht und auf zwei Konferenzen präsentiert. Sie waren zudem die Basis für eine erfolgreiche Masterarbeit und flossen in die Antragstellung im Rahmen des DFG-Programms „Großgeräteaktion für Hochschulen für Angewandte Wissenschaften“ ein.