Time-Resolved Visualization of Instability Waves in a Hypersonic Boundary Layer

LAMINAR-TURBULENT transition in hypersonic boundary layers remains a challenging subject. This is especially true of the hypervelocity regime, in which an intriguing phenomenon is the possible damping of second-mode disturbances by chemical and vibrational nonequilibrium processes. To generate flows with sufficiently high enthalpy to investigate such effects, the use of shock-tunnel facilities is necessary; furthermore, it is now generally accepted that direct measurements of the instability mechanisms active within the boundary layer, together with a characterization of the freestream disturbance environment, are required, as simple measurements of transition locations can lead to ambiguous conclusions. However, as difficult as the accurate measurement of instability waves in conventional hypersonic facilities can be, in shock tunnels it is appreciably more so. For identical unit Reynolds numbers, the higher stagnation temperature in a shock tunnel means that the dominant second-mode disturbances lie at even higher frequencies (typically hundreds of kHz or higher); moreover, because of the destructive testing environment, hot-wire techniques, a staple for instability measurements in conventional tunnels, cannot be used. Fast-response pressure transducers are an obvious alternative, but recent experiments have highlighted the challenging nature of interpreting data from mechanically sensitive sensors in the high-noise environment of a shock tunnel, especially without accompanying stability computations. Measurements with recently developed atomic-layer thermopile (ALTP) heat-flux sensors show promise, though their use has yet to be demonstrated in shocktunnel facilities

Driving Big Data – Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis

This paper contributes to the issue of big data analysis and data quality with the specific field of time synchronization. As a highly relevant use case, big data analysis of work stress and strain factors for driving professions is outlined. Drivers experience work stress and strain due to trends like traffic congestion, time pressure or worsening work conditions. Although a large professional group with 2.5 million (US) and 3.5 million (EU) truck drivers, scientific analysis of work stress and strain factors is scarce. Driver shortage is growing into a large-scale economic and societal challenge, especially for small businesses. Empirical investigations require big data approaches with sources like physiological and truck, traffic, weather, planning or accident data. For such challenges, accurate data is required, especially regarding time synchronization. Awareness among researchers and practitioners is key and first solution approaches are provided, connecting to many further Machine Learning and big data applications

Optimal spatial scales for seasonal forecasts over Africa

The availability of seasonal weather forecast information in Africa provides advanced early warning of rainfall variability, informing preparedness actions to minimise adverse impacts. Obtaining accurate forecast information for the spatial scales at which decisions are made is vital. Here we examine the impact of spatial scales on the utility of seasonal rainfall forecasts in Africa. Using observations alongside seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), we combine measures of local representativity and skill to assess optimal spatial scales for local monitoring. The results reveal regions where spatial aggregation of gridded forecast data improves the quality of information provided at the local scale, and regions where forecasts have useful skill without aggregation. More generally this study presents a novel approach for evaluating the utility of forecast information which is applicable both globally and at all timescales

Fragmentation-driven divergent trends in burned area in Amazonia and Cerrado

The two major Brazilian biomes, the Amazonia and the Cerrado (savanna), are increasingly exposed to fires. The Amazonian Forest is a fire sensitive ecosystem where fires are a typically rare disturbance while the Cerrado is naturally fire-dependent. Human activities, such as landscape fragmentation and land-use management, have modified the fire regime of the Cerrado and introduced fire into the Amazonian Forest. There is limited understanding of the role of landscape fragmentation on fire occurrence in the Amazonia and Cerrado biomes. Due to differences in vegetation structure, composition, and land use characteristics in each biome, we hypothesize that the emerging burned area (BA) patterns will result from biome-specific fire responses to fragmentation. The aim of this study was to test the general relationship between BA, landscape fragmentation, and agricultural land in the Amazonia and the Cerrado biomes. To estimate the trends and status of landscape fragmentation a Forest Area Density (FAD) index was calculated based on the MapBiomas land cover dataset for both biomes between 2002 and 2018. BA fraction was analyzed within native vegetation against the FAD and agricultural land fraction. Our results showed an increase in landscape fragmentation across 16% of Amazonia and 15% of Cerrado. We identified an opposite relationship between BA fraction, and landscape fragmentation and agricultural fraction contrasting the two biomes. For Amazonia, both landscape fragmentation and agricultural fraction increased BA fraction due to an increase of human ignition activities. For the Cerrado, on the other hand, an increase in landscape fragmentation and agricultural fraction caused a decrease in BA fraction within the native vegetation. For both biomes, we found that during drought years BA increases whilst the divergent trends driven by fragmentation in the two contrasting global biomes is maintained. This understanding will be critical to informing the representation of fire dynamics in fire-enable Dynamic Global Vegetation Models and Earth System Models for climate projection and future ecosystem service provision

Scientific Railway Signalling Symposium 2022 - Mehr Schiene – mehr Klimaschutz: Wie kann die Bahn das wachsende Verkehrsaufkommen nachhaltig meistern?

Aufgrund des Klimawandels wird eine massive Verkehrsverlagerung von der Straße auf die Schiene zwingend. Durch diesen Bedeutungszuwachs der Schiene muss die Eisenbahn bereits im Jahr 2030 ein Verkehrswachstum von 20 - 30 % bewältigen können. Bereits heute ist das Streckennetz jedoch vielerorts an der Belastungsgrenze. Im Rahmen des Scientific Railway Signalling Symposiums (SRSS) 2022 widmeten sich am 18. Mai daher ca. 90 Fachexpertinnen und Fachexperten aus Forschung und Praxis der Frage, wie die Bahn das wachsende Verkehrsaufkommen nachhaltig meistern kann. Nach dem erfolgreichen virtuellen SRSS 2021 konnte das fünfte SRSS 2022 wieder in Präsenz im Georg-Christoph-Lichtenberg-Haus in Darmstadt stattfinden. Die Beiträge der Konferenz zeigten, dass eine der größten Herausforderungen die Entwicklung und das reibungslose Zusammenspiel der verschiedenen Technologien darstellt, die das zukünftige Eisenbahnsystem bilden werden. In den Keynotes berichtete zu Beginn Bernd Elsweiler von der DB Netz AG über eine Vielzahl von Projekten und Programmen, die zur Umsetzung des Zielbildes der Digitalen Schiene Deutschland beitragen sollen. Roman Treydel von der DB Netz AG erläuterte, wie Initiativen wie Shift2Rail und RCA in ERJU zu einem vereinheitlichten europäischen Bahnsystem führen sollen. Durch die Diskussion in Kleingruppen wurde bereits der Blick auf die übernächsten Innovationen geworfen. Der Nachmittag widmete sich detaillierteren Themen. Neben wissenschaftlichen Vorträgen aus den Bereichen Zugvollständigkeitserkennung und digitaler automatisierter Infrastrukturplanung standen Themen aus der Praxis im Vordergrund. In dieser Publikation sollen ausgewählte Arbeiten zu den Praxis-Vortragsthemen veröffentlicht werden. Sonja-Lara Bepperling von Nextrail berichtete von modernen Lösungsansätzen zur Strukturierung der Zulassungs- und Sicherheitsbewertungen, da der Sicherheitsnachweis für alle Innovationen im Bereich der Sicherungstechnik eine zentrale Rolle spielt. Neben funktionaler Sicherheit ist auch die Gewährleistung der Sicherheit gegen Angriffe (Security) ein zentrales Thema. Dabei ist ein schnelles Erkennen des Angriffs und der Angriffsart und darauffolgend ein zielgerichtetes Handeln essentiell. Damit die schnelle Bewertung von Angriffen gewährleistet werden kann, wurden von Markus Heinrich von der INCYDE GmbH Angriffsgraphen zur Unterstützung der Risikoanalyse sowie zur expliziten Zuordnung von Bedrohungen vorgestellt. Um Kapazitätsvorteile im Bereich der LST zu erzielen, sind viele innovative Technologien auf eine präzise Ortung der Fahrzeuge angewiesen. Einige dieser Technologien basieren auf dem Prinzip von Fiber Optic Sensing (FOS), bei dem die akustischen Ereignisse entlang der Strecke mit Hilfe der verlegten Glasfaserkabel in Echtzeit lokalisiert und durch Methoden der Mustererkennung klassifiziert werden. Roman Wilhelm von AP Sensing gab hierzu einen Einblick in neuartige Lösungsansätze zur zuverlässigen KI-basierten Zugortung in Echtzeit. Neben der Entwicklung innovativer Technologien muss auch der Rollout dieser Technologien effizient und zeitnah umsetzbar sein. Hierfür ist ein effizienter Planungsprozess erforderlich. In diesem Zusammenhang stellte Volker Uminski von WSP die Vorteile der Verknüpfung des LST-Planungstools PlanPro mit der BIM-Planung der Infrastruktur vor

Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change

Tropical secondary forests sequester carbon up to 20 times faster than old-growth forests. This rate does not capture spatial regrowth patterns due to environmental and disturbance drivers. Here we quantify the influence of such drivers on the rate and spatial patterns of regrowth in the Brazilian Amazon using satellite data. Carbon sequestration rates of young secondary forests (<20 years) in the west are ~60% higher (3.0 ± 1.0 Mg C ha−1 yr−1) compared to those in the east (1.3 ± 0.3 Mg C ha−1 yr−1). Disturbances reduce regrowth rates by 8–55%. The 2017 secondary forest carbon stock, of 294 Tg C, could be 8% higher by avoiding fires and repeated deforestation. Maintaining the 2017 secondary forest area has the potential to accumulate ~19.0 Tg C yr−1 until 2030, contributing ~5.5% to Brazil’s 2030 net emissions reduction target. Implementing legal mechanisms to protect and expand secondary forests whilst supporting old-growth conservation is, therefore, key to realising their potential as a nature-based climate solution

Mind the gap: reconciling tropical forest carbon flux estimates from earth observation and national reporting requires transparency

Background: The application of different approaches calculating the anthropogenic carbon net flux from land, leads to estimates that vary considerably. One reason for these variations is the extent to which approaches consider forest land to be “managed” by humans, and thus contributing to the net anthropogenic flux. Global Earth Observation (EO) datasets characterising spatio-temporal changes in land cover and carbon stocks provide an independent and consistent approach to estimate forest carbon fluxes. These can be compared against results reported in National Greenhouse Gas Inventories (NGHGIs) to support accurate and timely measuring, reporting and verification (MRV). Using Brazil as a primary case study, with additional analysis in Indonesia and Malaysia, we compare a Global EO-based dataset of forest carbon fluxes to results reported in NGHGIs. Results: Between 2001 and 2020, the EO-derived estimates of all forest-related emissions and removals indicate that Brazil was a net sink of carbon (− 0.2 GtCO2yr−1), while Brazil’s NGHGI reported a net carbon source (+ 0.8 GtCO2yr−1). After adjusting the EO estimate to use the Brazilian NGHGI definition of managed forest and other assumptions used in the inventory’s methodology, the EO net flux became a source of + 0.6 GtCO2yr−1, comparable to the NGHGI. Remaining discrepancies are due largely to differing carbon removal factors and forest types applied in the two datasets. In Indonesia, the EO and NGHGI net flux estimates were similar (+ 0.6 GtCO2 yr−1), but in Malaysia, they differed in both magnitude and sign (NGHGI: -0.2 GtCO2 yr−1; Global EO: + 0.2 GtCO2 yr−1). Spatially explicit datasets on forest types were not publicly available for analysis from either NGHGI, limiting the possibility of detailed adjustments. Conclusions: By adjusting the EO dataset to improve comparability with carbon fluxes estimated for managed forests in the Brazilian NGHGI, initially diverging estimates were largely reconciled and remaining differences can be explained. Despite limited spatial data available for Indonesia and Malaysia, our comparison indicated specific aspects where differing approaches may explain divergence, including uncertainties and inaccuracies. Our study highlights the importance of enhanced transparency, as set out by the Paris Agreement, to enable alignment between different approaches for independent measuring and verification