DLRK Vortrag 2021 - Klebeverbindungen der Primärstruktur innerhalb des HAP-Projektes

In the HAP (High Altitude Platform) project, the German Aerospace Center (DLR) is developing a solar-powered long-range aircraft, various payloads for Earth observation missions, the ground control station and operating concept. The mission conditions and objectives pose major challenges to the aircraft structure, especially with respect to lightweight design. This presentation intrudces the design process of bonded joints for the primary structure on the example of the wing spar, as the most critical component. The wing, with an overall span of 27m, is divided in three wing sections, with the tubular wing spar being assembled from several segments for manufacturing reasons. Considering the demanding lightweight design expectations, adhesively bonded scarfed tubular joints are selected. A manufacturing concept is developed to minimize effort for joint preparation. The dimensioning is based on global (GFEM) and detailed finite element models (DFEM) to fine-tune the design (stiffness discontinuities, surface toughness) to increase the load carrying capacity. This is flanked by physical tests for model validation and material characterization, such as adhesive characterization, to increase DFEM accuracy

PETcofold: predicting conserved interactions and structures of two multiple alignments of RNA sequences

Motivation: Predicting RNA–RNA interactions is essential for determining the function of putative non-coding RNAs. Existing methods for the prediction of interactions are all based on single sequences. Since comparative methods have already been useful in RNA structure determination, we assume that conserved RNA–RNA interactions also imply conserved function. Of these, we further assume that a non-negligible amount of the existing RNA–RNA interactions have also acquired compensating base changes throughout evolution. We implement a method, PETcofold, that can take covariance information in intra-molecular and inter-molecular base pairs into account to predict interactions and secondary structures of two multiple alignments of RNA sequences

Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper

RNAalifold: improved consensus structure prediction for RNA alignments

<p>Abstract</p> <p>Background</p> <p>The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.</p> <p>Results</p> <p>We show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets.</p> <p>Conclusion</p> <p>The new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers.</p

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500–1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation

DIMENSIONIERUNG UND DURCHFÜHRUNG EINES ULTIMATE-LOAD TESTS DER FLÜGELSTRUKTUR DES HAP-ALPHA

In the HAP (High Altitude Platform) project, the German Aerospace Center (DLR) is developing a solar-powered long-range aircraft, various payloads for Earth observation missions, the ground control station and operating concept. The mission conditions and objectives pose major challenges to the aircraft structure, especially with respect to lightweight design. This paper presents the dimensioning, the set-up and procedure of an Ultimate Load test of the HAP alpha wing structure. The wing, with an overall span of 27m, is divided in three wing sections, with the tubular wing spar being assembled from several segments for manufacturing reasons. The dimensioning is based on global (GFEM) and detailed finite element models (DFEM) considering numerous flight and ground load cases. Out of the load case envelope, two main test load cases are extracted, a pure torsion load test and a combined bending and torsion load case. In addition, stiffness tests are performed beforehand without and with wing covering. Due to the design phase, the tests are used for validation of the finite element models. It will be presented the test set-up, load application to the fragile structure, measurement equipment, the test procedure and results of the test