5th Leibniz MMS Days
April 25 - April 27, 2022
Potsdam

Ambient Forcing: Probabilistic Stability of Power Grids with constraint Phase Spaces

Anna Büttner (PIK)

Ambient Forcing is a novel method to sample random local perturbations of nodes in power systems with loads. These initial states enable the calculation of probabilistic stability measures of power systems with loads, which was not yet possible, but is important as these measures have become a crucial tool in studying power systems. We find that loads fundamentally influence the dynamical stability of power systems.

https://iopscience.iop.org/article/10.1088/1367-2630/ac6822

BeLuVa, Determination of air exchange rates in naturally ventilated barns bases on numerical simulation results

Moustapha Doumbia (ATB)

The air exchange rate (AER) of naturally ventilated dairy barns is a crucial characteristic for the evaluation of animal welfare and environmental impact. Its accurate determination represents an unsolved problem. The essential reason for this is that the fluxes that cause the AER are subject to high levels of spatial and temporal fluctuations and are therefore insufficiently monitored by measurements. BeLuVa investigated with computational fluids dynamics (CFD) modeling and data science how different factors, such as the barn's aspect ratio, weather conditions, configurations of the openings or cows acting as heat sources and flow obstacles, affect air flow and the resulting local gas concentration and AER.

Decentralized Open Science: Content first as guiding principle to collaboratively maintenant Research environments

Moritz Schubotz (FIZ-KA)

Traditionally research papers, research software and research data is being provided by publishers that assign persistent identifiers to each research item. A few years ago software heritage proposed a schema to address research software by a hash value that can be derived from the source code. While this allows to verify that the software code referenced is unchanged since it was referenced it does not provide a mechanism to retrieve the data without being depend on the existence of the software heritage service. In this poster we outline the idea how to drive the idea of content adressable research items further and outline how decentralized technology such as libp2p can be used to provide research data and services without to rely on a singe service.

Finding power grid (control) topologies resistant to frequency perturbations using MCMC methods

Anna Reckwitz (PIK)

Designing and controlling power grids in a way that ensures stability of the system concerning frequency perturbations is becoming increasingly relevant, especially in the context of the energy transition. We model this critical infrastructure through coupled Kuramoto oscillators and introduce distributed averaging proportional integral (DAPI) control dynamics as a second layer in a multiplex network. The network topology is optimized concerning transient performance measures (survivability and/or synchronization norm) of the system using simulated annealing. We show that for the droop controlled systems, survivability and synchronization norm could be improved simultaneously by about 100% and 60%, respectively. For the DAPI controlled systems, a trade-off between a high survivability and a low synchronization norm was found. The resulting network properties and their connection to high transient performance remain to be interpreted in further studies.

Fire prevents the regrowth of the Amazon rainforest after complete deforestation in a fire-enabled Earth system model

Markus Drüke, Werner von Bloh, Boris Sakschewski, Wolfgang Lucht and Kirsten Thonicke (PIK)

The terrestrial biosphere is exposed to land use and anthropogenic climate change, which not only affects vegetation dynamics but also changes land-atmosphere feedbacks. In particular, tropical rainforests are endangered by anthropogenic activities and are recognized as one of the terrestrial tipping elements. An ecosystem regime change to a new state could have profound impacts on regional and global climate, once the biome has transitioned from a forest into a savanna or grassland state. Fire is a potentially major driver in the position of the transition boundary and could hence impact the dynamic equilibrium between these possible vegetation states under a changing climate. However, systematic tests of fire-controlled tipping points and hysteretic behavior using comprehensive Earth system models are still lacking.
Here, we specifically test the recovery of the Amazon rainforest after complete deforestation at different atmospheric CO2 levels, by using the Earth system model CM2Mc-LPJmL v1.0 with a state-of-the-art representation of vegetation dynamics and fire. We find that fire prevents large-scale forest regrowth after complete deforestation and locks large parts of the Amazon in a stable grassland state. While slightly elevated atmospheric CO2 values had beneficial effects on the forest regrowth efficiency due to the fertilization effect, larger CO2 amounts further hampered the regrowth due to increasing heat stress. In a no-fire control experiment, the Amazon rainforest recovered after 250 years to nearly its original extent at various atmospheric CO2 forcing levels. This study highlights the potential of comprehensive fire-enabled Earth system models to investigate and quantify tipping points and their feedback on regional and global climate.

How does nitrogen limitation affect fire regimes in a dynamic global vegetation model?

Maik Billing, Markus Drüke, Luke Oberhagemann, Werner von Bloh, Boris Sakschewski & Kirsten Thonicke (PIK)

Wildfires are an essential component of the Earth system. Previous research has shown that fires are projected to increase as a result of anthropogenic climate change and altered land-use patterns, causing large environmental and economic impacts (Bowman et al., 2020). Fire-enabled dynamic global vegetation models (DGVMs) aim to assess changes in future fire regimes, helping fire managers and policy makers to anticipate and prepare. However, there are still great uncertainties in the projection of future fire regime trajectories due to missing processes and constraints in fire-enabled DGVMs, which might influence global patterns and fire carbon emissions. Most fire-enabled DGVMs do not consider constraints from nutrient limitation that might alter, e.g., net primary productivity, vegetation biomass and hydrology. In this study, we show how nitrogen limitation changes simulated fire regimes in a state-of-the-art DGVM. We apply the fire-enabled LPJmL 5.3 dynamic vegetation model including the nitrogen cycle (von Bloh et al., 2018). We compare model experiments with and without nitrogen limitation in terms of fire behaviour and vegetation dynamics for a historic period.

Influence of the composition, strain and temperature on the optical properties of GeSn microstructures

Ignatii Zaitsev (IHP)

Over the last decade, GeSn optoelectronic devices have shown promise due to their high carrier mobility and the band structure that can be tuned through control of strain and Sn concentration [1, 2]. In our work, we examine the impact of a combination of thermal, mechanical and compositional factors on optical performance of GeSn microstructures. For that purpose, we have investigated the mechanical and optical properties of strained GeSn microdisks on Ge pedestals, for samples of varying composition and strain under varying temperatures. The same structures were also modeled using 3D finite element simulations, accounting for the thermo-mechanical properties of each sample. The obtained estimation of surface strain is verified by the results determined through μ-Raman spectroscopy and then used to calculate the band landscape throughout the microdisk. Those calculations are compared to experimentally obtained photoluminescence spectra, allowing to extensively model the properties of a realistic GeSn mictostructure, accounting for contributions of structure's mechanical parameters, its composition, and temperature both through induced strain and directly. This model can also be used to simulate more complex geometries and predict their lasing characteristics, which would be welcome for the study and development of optoelectronic microstructures.

Integrative programming for simulation of packaging headspace and shelf life of fresh produce

Ali Jalali (ATB)

The real time measurements of storage conditions including temperature and relative humidity, enables estimating cumulative microbial deterioration and mass loss under dynamic storage conditions. Both estimated parameters were related to shelf life of fresh produce and validated with minimum product quality accepted by the consumer and satisfying the safety requirements of packaged and unpackaged fresh produce.

Machine-learned Integrators for Chemical Mechanisms

Levin Rug (TROPOS)

A beginner-level approach to predict concentrations over time of an arbitrary mechanism (in our case: RACM with about 100 species, 300 reactions) will be presented. Some important aspects and possibilities of what to predict in particular and how to train will be mentioned and, depending on our progress, maybe even results of more advanced network structure can be shown. An outview and various advanced approaches by others could be mentioned.

ParMooN - recent developments and applications

Ulrich Wilbrandt (WIAS)

ParMooN is a finite element research code written in C++. Its main applications include convection--diffusion, incompressible Navier--Stokes, turbulent flows, as well as coupled problems. Many different stabilizations are implemented together with a variety of solvers for the arising linear systems, in particular geometric multigrid. Parallel computations are possible on notebooks, workstations, as well as super computer clusters via MPI.

Poroelastic and thermo-poroelastic effects in an Enhanced Geothermal Reservoir - Case study of deep Upper Jurassic carbonates in the North Alpine Foreland Basin

Ernesto Meneses Rioseco (LIAG)

Optimal geothermal energy production from deep, low-permeability hot rocks, where a pre-existing fault and fracture system predominantly controls the fluid flow and heat transfer mechanisms, requires a deep understanding of the poro-elastic and thermo-poroelastic behaviour of the petrothermal reservoir under different injection and production profiles. Mining the geothermal energy from deeper sections of the Upper Jurassic formation in the Bavarian Molasse Basin in an optimal and sustainable way needs novel numerical approaches to properly model and simulate the complex behavior of fracture deformation and related fracture permeability changes. Geoscientific data at different scales have been collected within the Dolomitkluft and ZoKrateS projects at the Geretsried geothermal site, upon reservoir modelling and simulation is based in this work. Results show the coupled thermo-hydro-mechanical reservoir processes resulting from considered doublet schemes of highly deviated geothermal wells.

Predicting Dynamic Stability of Power Grids using Graph Neural Networks

Christian Nauck (PIK)

The prediction of dynamical stability of power grids becomes more important and challenging with increasing shares of renewable energy sources due to their decentralized structure, reduced inertia and volatility. We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure. To do so, we generate two synthetic datasets for grids with 20 and 100 nodes respectively and estimate SNBS using Monte-Carlo sampling. Those datasets are used to train and evaluate the performance of eight different GNN-models. All models use the full graph without simplifications as input and predict SNBS in a nodal-regression-setup. We show that SNBS can be predicted in general and the performance significantly changes using different GNN-models. Furthermore, we observe interesting transfer capabilities of our approach: GNN-models trained on smaller grids can directly be applied on larger grids without the need of retraining.

Probabilistic Behavioral Distance and Tuning - Reducing and aggregating complex systems

Ekaterina Zolotarevskaia (PIK)

Given a complex system with a given interface to the rest of the world, what does it mean for a the system to behave close to a simpler specification describing the behavior at the interface? We give several definitions for useful notions of distances between a complex system and a specification by combining a behavioral and probabilistic perspective. These distances can be used to tune a complex system to a specification. We show that our approach can successfully tune non-linear networked systems to behave like much smaller networks, allowing us to aggregate large sub-networks into one or two effective nodes.

Statistical Physics of the Mobility Transition

Alexander Schmaus (PIK)

While some progress has been made at decarbonizing electricity production, a large part of the discussion on sustainable mobility is centered around electric vehicles. Here we want to focus instead on the potential of on-demand ride pooling for reducing the driven distance necessary to satisfy individual transport wishes. This complex question requires approaches from economics, traffic science and physics to untangle the policy influences and the emergent effects of multi-agent movement. We analyze how the number of vehicles impacts the system-wide reduction in energy demand, how request pattern and street network influence the efficiency of pooling and what number of stops results in the fastest overall travel times.

Synchronization patterns in globally coupled Stuart-Landau oscillators

Alexander Gerdes (WIAS)

We study clusterized states in globally coupled Stuart-Landau oscillators, a paradigmatic model for patterning processes. To investigate 2-Cluster states, we set up a reduced model using collective variables, in which the cluster size ratio is an additional bifurcation parameter. We analyse longitudinal instabilities leading to complex 2-Cluster behaviour in the reduced system. By including test oscillators, we study instabilities transversal to the Cluster manifold, i.e. changes of the cluster type. Using numerical bifurcation analysis we find stability regions of cluster solutions of different types. In these, solitary states serve as primary patterns for clustering processes and allow an analytical treatment. The identified instabilities can be seen as building blocks of pathways to complex behaviour such as chimeras and extensive chaos as well as splay states occuring for varying parameters. With the analytical and numerical approach presented here we identify different transition scenarios from synchrony to complex behaviour by reducing the coupling strength. We locate each of these scenarios in regions in the plane of shear parameters.