Ein verteilter und agentenbasierter Ansatz für gekoppelte Probleme der rechnergestützten Ingenieurwissenschaften

Challenging questions in science and engineering often require to decouple a complex problem and to focus on isolated sub-problems first. The knowledge of those individual solutions can later be combined to obtain the result for the full question. A similar technique is applied in numerical modeling. Here, the software solver for subsets of the coupled problem might already exist and can directly be used. This thesis describes a software environment capable of combining multiple software solvers, the result being a new, combined model. Two important design decisions were crucial at the beginning: First, every sub-model keeps full control of its execution. Second, the source code of the sub-model requires only minimal adaptation. The sub-models choose themselves when to issue communication calls, with no outer synchronisation mechanism required. The coupling of heterogeneous hardware is supported as well as the use of homogeneous compute clusters. Furthermore, the coupling framework allows sub-solvers to be written in different programming languages. Also, each of the sub-models may operate on its own spatial and temporal scales. The next challenge was to allow the potential coupling of thousands software agents, being able to utilise today's petascale hardware. For this purpose, a specific coupling framework was designed and implemented, combining the experiences from the previous work with additions required to cope with the targeted number of coupled sub-models. The large number of interacting models required a much more dynamic approach, where the agents automatically detect their communication partners at runtime. This eliminates the need to explicitly specify the coupling graph a~priori. Agents are allowed to enter (and leave) the simulation at any time, with the coupling graph changing accordingly.Da viele Problemstellungen im Ingenieurwesen sehr komplex sind, ist es oft sinnvoll, sie in einzelne Teilprobleme aufzugliedern. Diese Teilbereiche können nun einzeln angegangen und dann zur Gesamtlösung kombiniert werden. Ein ähnlicher Ansatz wird bei der numerischen Modellierung verfolgt: Komplexe Software wird schrittweise erstellt, indem Software-Löser für einzelne Bereiche zuerst separat erarbeitet werden. In dieser Arbeit wird eine Software beschrieben, die eine Vielzahl von unabhängigen Software-Lösern kombinieren kann. Jedes Teilmodell verhält sich weiterhin wie ein selbständiges Programm. Hierfür wird es in einen Software-Agenten gehüllt. Zur Kopplung sind lediglich minimale Ergänzungen am Quellcode des Teilmodells nötig. Möglich wird dies durch die Struktur der Kommunikation zwischen den Teilmodellen. Sie lässt den Modellen die Kontrolle über die Kommunikationsaufrufe und benötigt zur Synchronisation keine Einflussnahme einer übergeordneten Instanz. Manche Teilmodelle sind für den Gebrauch mit einer speziellen Hardware optimiert. Daher musste das Zusammenspiel unterschiedlicher Hardware ebenso berücksichtigt werden wie homogene Rechencluster. Weiterhin ermöglicht das Kopplungs-Framework, dass unterschiedliche Programmiersprachen verbunden werden können. Wie schon der Programmablauf, so können auch die Modellparameter, etwa die räumliche und zeitliche Skala, von Teilmodell zu Teilmodell unterschiedlich bleiben. Weiter behandelt diese Arbeit eine Vorgehensweise um tausende von Software-Agenten zu einem Groß-Modell zu koppeln. Dies ist erforderlich, wenn die Ressourcen heutiger Petascale Rechencluster benutzt werden sollen. Hierzu wurde das bisherige Framework neu aufgelegt, da die große Anzahl von zu koppelnden Modellen einer wesentlich dynamischeren Kommunikationsstruktur bedarf. Die Agenten der Teilmodelle können einer laufenden Simulation hinzugefügt werden (oder diese verlassen) und die globalen Kopplungsbeziehungen passen sich dementsprechend an

Effects of initial boost with TGF-beta 1 and grade of intervertebral disc degeneration on 3D culture of human annulus fibrosus cells

Background: Three-dimensional (3D) culture in porous biomaterials as well as stimulation with growth factors are known to be supportive for intervertebral disc cell differentiation and tissue formation. Unless sophisticated releasing systems are used, however, effective concentrations of growth factors are maintained only for a very limited amount of time in in vivo applications. Therefore, we investigated, if an initial boost with transforming growth factor-beta 1 (TGF-beta 1) is capable to induce a lasting effect of superior cartilaginous differentiation in slightly and severely degenerated human annulus fibrosus (AF) cells. Methods: Human AF tissue was harvested during surgical treatment of six adult patients with lumbar spinal diseases. Grading of disc degeneration was performed with magnet resonance imaging. AF cells were isolated and expanded in monolayer culture and rearranged three-dimensionally in a porous biomaterial consisting of stepwise absorbable poly-glycolic acid and poly-(lactic-co-glycolic) acid and a supportive fine net of non-absorbable polyvinylidene fluoride. An initial boost of TGF-beta 1 or TGF-beta 1 and hyaluronan was applied and compared with controls. Matrix formation was assessed at days 7 and 21 by (1) histological staining of the typical extracellular matrix molecules proteoglycan and type I and type II collagens and by (2) real-time gene expression analysis of aggrecan, decorin, biglycan, type I, II, III, and X collagens as well as of catabolic matrix metalloproteinases MMP-2 and MMP-13. Results: An initial boost with TGF-beta 1 or TGF-beta 1 and hyaluronan did not enhance the expression of characteristic AF matrix molecules in our 3D culture system. AF cells showed high viability in the progressively degrading biomaterial. Stratification by grade of intervertebral disc degeneration showed that AF cells from both, slightly degenerated, or severely degenerated tissue are capable of significant up-regulations of characteristic matrix molecules in 3D culture. AF cells from severely degenerated tissue, however, displayed significantly lower up-regulations in some matrix molecules such as aggrecan. Conclusions: We failed to show a supportive effect of an initial boost with TGF-beta 1 in our 3D culture system. This underlines the need for further investigations on growth factor releasing systems

Simulations for CMIP6 With the AWI Climate Model AWI‐CM‐1‐1

The Alfred Wegener Institute Climate Model (AWI‐CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice‐ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max Planck Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI‐CM performs better than the average of CMIP5 models. AWI‐CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea‐ice extent in September over the past 30 years is 20–30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present‐day climate under the strong emission scenario SSP585 are similar to the multi‐model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2°C to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical, and Southern Ocean. Furthermore, in the northern middle latitudes in boreal summer and autumn as well as in the southern middle latitudes, a more zonal atmospheric flow is projected throughout the year

Spot farming – an alternative for future plant production

Das Ziel der nachhaltigen Intensivierung der Landwirtschaft ist die Steigerung der weltweiten Nahrungsmittelproduktion bei gleichzeitiger Reduzierung des Inputs sowie der Vermeidung von negativen Umwelteinflüssen. Wachsende Kritik an den derzeitigen Produktionssystemen sowie der demografische Wandel, der mit einem zunehmenden Arbeitskräftemangel in den ländlichen Räumen einhergeht, stellen weltweit eine zunehmende Herausforderung für die Landwirtschaft dar. Im Rahmen dieses Problemfeldes bieten die Digitalisierung und auto­nome Maschinensysteme neue Möglichkeiten um die Landwirtschaft an diese Herausforderungen anzupassen. Bisher ist nicht bekannt, welche Veränderungen zur Errei­chung einer nachhaltigen Intensivierung im Gesamt­system Pflanzenproduktion notwendig sind und wie die Landwirtschaft der Zukunft unter Einbeziehung neuer technologischer Möglichkeiten aussehen könnte.Im Rahmen dieser Arbeit wurde ein Konzept für zukünf­tige Pflanzenbausysteme unter Berücksichtigung der Kulturpflanzenansprüche und des Landschaftskontextes entwickelt. Hierbei werden die Agrarflächen nach unterschiedlichen teilflächenspezifischen Eigenschaften bewertet und darauf aufbauend in unterschiedlichen Spots reorganisiert. Das daraus abgeleitete Konzept des Spot-Farmings basiert auf einer vollständigen Bewirtschaftung mit autonomen Robotiksystemen auf Einzelpflanzenebene. Durch höhere Präzision bei Aussaat, Düngungs- und Pflanzenschutzmaßnahmen können Ressour­cen gespart und Erträge gesteigert werden. Kleine Robotersysteme können zudem einen Beitrag zum Boden­schutz leisten. Das Spot-Farming-Konzept berücksichtigt darüber hinaus die natürlichen Landschafts­eigenschaften, um gesellschaftlich erwünschte Neben­aspekte, wie vielfältigere Kulturlandschaften, mehr Biodiversität und Struktur in der Landschaft, zu berücksichtigen.Die Bewertung des Konzepts nach pflanzenbaulichen, technischen und ökonomischen Aspekten zeigt, dass die Kombination von modernen Technologien und einer Reorga­nisierung der Kulturlandschaften zum Ziel der nachhaltigen Intensivierung beitragen kann.Sustainable intensification is described as the desirable goal for agricultural production to increase agricultural productivity while using less input and without adverse environmental impacts. Increasing criticism on current agricultural production systems as well as demographic changes related with labour shortages in rural areas pose major challenges to agriculture all over the world. In this context, digitalization and autonomous machinery provide new opportunities to adapt agriculture to future demands. However, it is unknown what changes are necessary for a sustainable intensification of cropping systems and how future agriculture could look like under consideration of new technologies.Here we developed a concept for future cropping systems with focus on the requirements of crops and landscapes. In this concept, the agricultural area is classified into individual spots according to their site-specific characteristics. The resulting spot farming approach is completely managed by an autonomous robot system on the level of individual plants. High precision sowing, fertilization and pesticide application could reduce agronomic input and could increase yields. In addition, small robots contribute to soil protection. Furthermore, the spot farming approach considers landscape properties and has the potential for a higher biodiversity and more structural ele­ments as well as an increased social acceptance.The evaluation of the concept according to agronomical, technical and economic aspects showed that the combination of modern technologies and a reorganisation of agricultural landscapes could contribute to the goal of sustainable intensification

AWI-CM3 coupled climate model: Description and evaluation experiments for a prototype post-CMIP6 model

We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 has the multi-resolution functionality typical for unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of latest developments in the numerical weather prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable resolution (25–125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other on-going research activities with AWI-CM3. This includes the exploration of high and variable resolution, the development of a full Earth System Model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above CMIP6-average skills in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model

AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model

We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice-Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25-125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model

AWI-CM-1.1-HR model output prepared for CMIP6 HighResMIP: links to control-1950, spinup-1950, and hist-1950 simulations

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: HighResMIP HR simulations. These data include all datasets published for 'CMIP6.HighResMIP.AWI.AWI-CM-1-1-HR' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named AWI-CM 1.1 HR, released in 2018, includes the components: atmos: ECHAM6.3.04p1 (T127L95 native atmosphere T127 gaussian grid; 384 x 192 longitude/latitude; 95 levels; top level 80 km), land: JSBACH 3.20, ocean: FESOM 1.4 (unstructured grid in the horizontal with 1306775 wet nodes; 46 levels; top grid cell 0-5 m), seaIce: FESOM 1.4. The model was run by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany (AWI) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km. Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and the results will undoubtedly be relied on by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6

AWI-CM-1.1-MR model output prepared for CMIP6 CMIP: links to 1pctCO2, abrupt-4xCO2, historical, and piControl simulations

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: DECK (1pctCO2, abrupt-4xCO2, piControl simulations) and CMIP historical simulations. These data include all datasets published for 'CMIP6.CMIP.AWI.AWI-CM-1-1-MR' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named AWI-CM 1.1 MR, released in 2018, includes the components: atmos: ECHAM6.3.04p1 (T127L95 native atmosphere T127 gaussian grid; 384 x 192 longitude/latitude; 95 levels; top level 80 km), land: JSBACH 3.20, ocean: FESOM 1.4 (unstructured grid in the horizontal with 830305 wet nodes; 46 levels; top grid cell 0-5 m), seaIce: FESOM 1.4. The model was run by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany (AWI) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km. Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and the results will undoubtedly be relied on by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6

FESOM/fesom2: AWI-CM3_v3.2

Version of FESOM2 that was used for AWI-CM3.2 Includes all changes from FESOM 2.1, FESOM 2.1.1 and FESOM 2.5. In addition using this release FESOM2 is capable of writing grid corner points for OASIS, provided the mesh has no bad coastal nodes. #432 #45

AWI-CM-1.1-MR model output prepared for CMIP6 ScenarioMIP: links to SSP126, SSP245, SSP370, and SSP585 scenarios

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: ScenarioMIP. These data include all datasets published for 'CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named AWI-CM 1.1 MR, released in 2018, includes the components: atmos: ECHAM6.3.04p1 (T127L95 native atmosphere T127 gaussian grid; 384 x 192 longitude/latitude; 95 levels; top level 80 km), land: JSBACH 3.20, ocean: FESOM 1.4 (unstructured grid in the horizontal with 830305 wet nodes; 46 levels; top grid cell 0-5 m), seaIce: FESOM 1.4. The model was run by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany (AWI) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km. Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and the results will undoubtedly be relied on by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6