On the REM approximation of TAP free energies

The free energy of TAP-solutions for the SK-model of mean field spin glasses can be expressed as a nonlinear functional of local terms: we exploit this feature in order to contrive abstract REM-like models which we then solve by a classical large deviations treatment. This allows to identify the origin of the physically unsettling quadratic (in the inverse of temperature) correction to the Parisi free energy for the SK-model, and formalizes the $\textit{true}$ cavity dynamics which acts on TAP-space, i.e. on the space of TAP-solutions. From a non-spin glass point of view, this work is the first in a series of refinements which addresses the stability of hierarchical structures in models of evolving populations

Ridge-type roughness: from turbulent channel flow to internal combustion engine

While existing engineering tools enable us to predict how homogeneous surface roughness alters drag and heat transfer of near-wall turbulent flows to a certain extent, these tools cannot be reliably applied for heterogeneous rough surfaces. Nevertheless, heterogeneous roughness is a key feature of many applications. In the present work we focus on spanwise heterogeneous roughness, which is known to introduce large-scale secondary motions that can strongly alter the near-wall turbulent flow. While these secondary motions are mostly investigated in canonical turbulent shear flows, we show that ridge-type roughness—one of the two widely investigated types of spanwise heterogeneous roughness—also induces secondary motions in the turbulent flow inside a combustion engine. This indicates that large scale secondary motions can also be found in technical flows, which neither represent classical turbulent equilibrium boundary layers nor are in a statistically steady state. In addition, as the first step towards improved drag predictions for heterogeneous rough surfaces, the Reynolds number dependency of the friction factor for ridge-type roughness is presented

"Off-Season" - CO2_{2}-Austausch landwirtschaftlicher Flächen

Durch zunehmende CO2-Konzentrationen und Erwärmung hat sich sowohl die Senken- (Photosynthese) als auch die Quellenfunktion (Respiration) der terrestrischen Biosphäre intensiviert. Der Nettoeffekt entspricht derzeit einer Senke, die etwa ein Drittel der CO2-Emissionen aus fossilen Brennstoffen aufgenommen hat. Allerdings stellt zugleich der Landnutzungswandel eine Nettoquelle von etwa 14% dar (5. IPCC-Sachstandsbericht, WG I, Kap. 6, S. 471, 2013).Die Klimawirksamkeit der Landwirtschaft wird von allen drei Faktoren beeinflusst – einem steigenden Senkenpotential durch den CO2-Düngeeffekt, einer steigenden (Boden)respiration durch Erwärmung, und Landnutzungsentscheidungen. Die Wechselwirkung zwischen ihnen wird im Folgenden am Beispiel der zunehmenden Klimarelevanz von Entscheidungen über die Zwischennutzung landwirtschaftlicher Flächen demonstriert.In den letzten 50 Jahren haben sich die Aussaattermine für Winterweizen in Deutschland etwa um eine, die Erntetermine um zwei Wochen nach vorne verschoben, ähnliches gilt für vergleichbare Kulturen. Die nicht für den produktiven Anbau genutzte Zeit wird sowohl länger als auch wärmer – einerseits wegen ihrer zunehmenden Verschiebung in Richtung Sommer, andererseits wegen steigender Jahresmitteltemperaturen. Die Entscheidung über die Verwendung dieser Phasen wird somit klimarelevanter: Bei vegetationsfreiem Boden ist eine stärkere respirationsbedingten Quellenfunktion, bei einer Nutzung für den produktiven Anbau oder einer Einsaat von Zwischenfrüchten eine stärkere Senkenfunktion möglich.Durch die EU-Gesetzgebung unter dem Stichwort „Greening“ erscheint eine sprunghafte Zunahme von Zwischensaaten wie z.B. Ölrettich und Gelbsenf im Winter 2015/16 wahrscheinlich. Dies bietet eine gute Gelegenheit zur Quantifizierung des möglichen Einflusses von Landnutzungsentscheidungen auf diese „Off-Season“-Klimawirksamkeit. Auf dem Poster stellen wir Ergebnisse von CO2-Austauschmessungen in Zwischensaatbeständen und Pläne zur satellitengestützten Quantifizierung ihrer Anbaufläche vor. Die Messungen des CO2-Austauschs, der Verdunstung und der Bodenrespiration mit Hilfe von Eddy-Kovarianz-Stationen und zwei verschiedenen Haubensystemen sind ein Teil des BMBF-geförderten Projektes „IDAS-GHG“, dessen Gesamtkonzept im Vorjahr vorgestellt wurde

Multi-site Calibration and Validation of a Net Ecosystem Carbon Exchange Model for Croplands

Croplands play an important role in the carbon budget of many regions. However, the estimation of their carbon balance remains difficult due to diversity and complexity of the processes involved. We report the coupling of a one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) to predict the net ecosystem exchange (NEE) of carbon. The coupled model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was sufficient with a model efficiency above 0.78 and a correlation coefficient above 0.91 for NEE. In a second step, AgroC was optimized with eddy covariance NEE measurements to examine the effect of different objective functions, constraints, and data-transformations on estimated NEE. It was found that NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. In particular, both positive and negative day‑ and nighttime fluxes were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed substantially. Therefore, it is concluded that data-transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of eddy covariance measurements

Expression of Retinoid Acid Receptor-Responsive Genes in Rodent Models of Placental Pathology

In humans, retinoic acid receptor responders (RARRES) have been shown to be altered in third trimester placentas complicated by the pathologies preeclampsia (PE) and PE with intrauterine growth restriction (IUGR). Currently, little is known about the role of placental Rarres in rodents. Therefore, we examined the localization and expression of Rarres1 and 2 in placentas obtained from a Wistar rat model of isocaloric maternal protein restriction (E18.5, IUGR-like features) and from an eNOS-knockout mouse model (E15 and E18.5, PE-like features). In both rodent models, Rarres1 and 2 were mainly localized in the placental spongiotrophoblast and giant cells. Their placental expression, as well as the expression of the Rarres2 receptor chemokine-like receptor 1 (CmklR1), was largely unaltered at the examined gestational ages in both animal models. Our results have shown that RARRES1 and 2 may have different expression and roles in human and rodent placentas, thereby underlining immanent limitations of comparative interspecies placentology. Further functional studies are required to elucidate the potential involvement of these proteins in early placentogenesis

A comprehensive dataset of vegetation states, fluxes of matter and energy, weather, agricultural management, and soil properties from intensively monitored crop sites in western Germany

Data description paperThe development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) "Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modeling and Data Assimilation". Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).Peer reviewe

Cavity Polariton Condensate in a Disordered Environment

We report on the influence of disorder on an exciton-polariton condensate in a ZnO based bulk planar microcavity and compare experimental results with a theoretical model for a non-equilibrium condensate. Experimentally, we detect intensity fluctuations within the far-field emission pattern even at high condensate densities which indicates a significant impact of disorder. We show that these effects rely on the driven dissipative nature of the condensate and argue that they can be accounted for by spatial phase inhomogeneities induced by disorder, which occur even for increasing condensate densities realized in the regime of high excitation power. Thus, non-equilibrium effects strongly suppress the stabilization of the condensate against disorder, contrarily to what is expected for equilibrium condensates in the high density limit. Numerical simulations based on our theoretical model reproduce the experimental data.Comment: main article and supplementary, 13 pages, 8 figures (main article

Ridge-type roughness: from turbulent channel flow to internal combustion engine

While existing engineering tools enable us to predict how homogeneous surface roughness alters drag and heat transfer of near-wall turbulent flows to a certain extent, these tools cannot be reliably applied for heterogeneous rough surfaces. Nevertheless, heterogeneous roughness is a key feature of many applications. In the present work we focus on spanwise heterogeneous roughness, which is known to introduce large-scale secondary motions that can strongly alter the near-wall turbulent flow. While these secondary motions are mostly investigated in canonical turbulent shear flows, we show that ridge-type roughness — one of the two widely investigated types of spanwise heterogeneous roughness — also induces secondary motions in the turbulent flow inside a combustion engine. This indicates that large scale secondary motions can also be found in technical flows, which neither represent classical turbulent equilibrium boundary layers nor are in a statistically steady state. In addition, as the first step towards improved drag predictions for heterogeneous rough surfaces, the Reynolds number dependency of the friction factor for ridge-type roughness is presented

Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine

Machine learning (ML) models based on a large data set of in-cylinder flow fields of an IC engine obtained by high-speed particle image velocimetry allow the identification of relevant flow structures underlying cycle-to-cycle variations of engine performance. To this end, deep feature learning is employed to train ML models that predict cycles of high and low in-cylinder maximum pressure. Deep convolutional autoencoders are self-supervised-trained to encode flow field features in low dimensional latent space. Without the limitations ascribable to manual feature engineering, ML models based on these learned features are able to classify high energy cycles already from the flow field during late intake and the compression stroke as early as 290 crank angle degrees before top dead center (-290° CA) with a mean accuracy above chance level. The prediction accuracy from -290° CA to -10° CA is comparable to baseline ML approaches utilizing an extensive set of engineered features. Relevant flow structures in the compression stroke are revealed by feature analysis of ML models and are interpreted using conditional averaged flow quantities. This analysis unveils the importance of the horizontal velocity component of in-cylinder flows in predicting engine performance. Combining deep learning and conventional flow analysis techniques promises to be a powerful tool for ultimately revealing high-level flow features relevant to the prediction of cycle-to-cycle variations and further engine optimization