Gas permeation through a polymer network

We study the diffusion of gas molecules through a two-dimensional network of polymers with the help of Monte Carlo simulations. The polymers are modeled as non-interacting random walks on the bonds of a two-dimensional square lattice, while the gas particles occupy the lattice cells. When a particle attempts to jump to a nearest-neighbor empty cell, it has to overcome an energy barrier which is determined by the number of polymer segments on the bond separating the two cells. We investigate the gas current $J$ as a function of the mean segment density $\rho$, the polymer length $\ell$ and the probability $q^{m}$ for hopping across $m$ segments. Whereas $J$ decreases monotonically with $\rho$ for fixed $\ell$, its behavior for fixed $\rho$ and increasing $\ell$ depends strongly on $q$. For small, non-zero $q$, $J$ appears to increase slowly with $\ell$. In contrast, for $q=0$, it is dominated by the underlying percolation problem and can be non-monotonic. We provide heuristic arguments to put these interesting phenomena into context.Comment: Dedicated to Lothar Schaefer on the occasion of his 60th birthday. 11 pages, 3 figure

Simulation studies of permeation through two-dimensional ideal polymer networks

We study the diffusion process through an ideal polymer network, using numerical methods. Polymers are modeled by random walks on the bonds of a two-dimensional square lattice. Molecules occupy the lattice cells and may jump to the nearest-neighbor cells, with probability determined by the occupation of the bond separating the two cells. Subjected to a concentration gradient across the system, a constant average current flows in the steady state. Its behavior appears to be a non-trivial function of polymer length, mass density and temperature, for which we offer qualitative explanations.Comment: 8 pages, 4 figure

Road Network Simulation Using FLAME GPU

Demand for high performance road network simulation is increasing due to the need for improved traffic management to cope with the globally increasing number of road vehicles and the poor capacity utilisation of existing infrastructure. This paper demonstrates FLAME GPU as a suitable Agent Based Simulation environment for road network simulations, capable of coping with the increasing demands on road network simulation. Gipps’ car following model is implemented and used to demonstrate the performance of simulation as the problem size is scaled. The performance of message communication techniques has been evaluated to give insight into the impact of runtime generated data structures to improve agent communication performance. A custom visualisation is demonstrated for FLAME GPU simulations and the techniques used are described

Calibrating Car-Following Models using Trajectory Data: Methodological Study

The car-following behavior of individual drivers in real city traffic is studied on the basis of (publicly available) trajectory datasets recorded by a vehicle equipped with an radar sensor. By means of a nonlinear optimization procedure based on a genetic algorithm, we calibrate the Intelligent Driver Model and the Velocity Difference Model by minimizing the deviations between the observed driving dynamics and the simulated trajectory when following the same leading vehicle. The reliability and robustness of the nonlinear fits are assessed by applying different optimization criteria, i.e., different measures for the deviations between two trajectories. The obtained errors are in the range between~11% and~29% which is consistent with typical error ranges obtained in previous studies. In addition, we found that the calibrated parameter values of the Velocity Difference Model strongly depend on the optimization criterion, while the Intelligent Driver Model is more robust in this respect. By applying an explicit delay to the model input, we investigated the influence of a reaction time. Remarkably, we found a negligible influence of the reaction time indicating that drivers compensate for their reaction time by anticipation. Furthermore, the parameter sets calibrated to a certain trajectory are applied to the other trajectories allowing for model validation. The results indicate that ``intra-driver variability'' rather than ``inter-driver variability'' accounts for a large part of the calibration errors. The results are used to suggest some criteria towards a benchmarking of car-following models

How much multilateralism do we need? Effectiveness of unilateral agricultural mitigation efforts in the global context

Achieving climate neutrality in the European Union (EU) by 2050 will require substantial efforts across all economic sectors, including agriculture. At the same time, an ambitious unilateral EU agricultural mitigation policy is likely to have adverse effects on the sector and may have limited efficiency at global scale due to emission leakage to non-EU regions. To analyse the competitiveness of the EU's agricultural sector and potential non-CO2 emission leakage conditional on mitigation efforts outside the EU, we apply three economic agricultural sector models. We find that an ambitious unilateral EU mitigation policy in line with efforts needed to achieve the 1.5 °C target globally strongly affects EU ruminant production and trade balance. However, since EU farmers rank among the most greenhouse gas efficient producers worldwide, if the rest of the world were to start pursuing agricultural mitigation efforts too, economic impacts of an ambitious domestic mitigation policy get buffered and EU livestock producers could even start to benefit from a globally coordinated mitigation policy

Recognition of Crowd Behavior from Mobile Sensors with Pattern Analysis and Graph Clustering Methods

Mobile on-body sensing has distinct advantages for the analysis and understanding of crowd dynamics: sensing is not geographically restricted to a specific instrumented area, mobile phones offer on-body sensing and they are already deployed on a large scale, and the rich sets of sensors they contain allows one to characterize the behavior of users through pattern recognition techniques. In this paper we present a methodological framework for the machine recognition of crowd behavior from on-body sensors, such as those in mobile phones. The recognition of crowd behaviors opens the way to the acquisition of large-scale datasets for the analysis and understanding of crowd dynamics. It has also practical safety applications by providing improved crowd situational awareness in cases of emergency. The framework comprises: behavioral recognition with the user's mobile device, pairwise analyses of the activity relatedness of two users, and graph clustering in order to uncover globally, which users participate in a given crowd behavior. We illustrate this framework for the identification of groups of persons walking, using empirically collected data. We discuss the challenges and research avenues for theoretical and applied mathematics arising from the mobile sensing of crowd behaviors