Ermittlung der Leistungsverfügbarkeit zellularer Intralogistiksysteme mit Hilfe von Simulations- und VR-Techniken

Die Bestimmung der Leistungsverfügbarkeit als Maß für den Erfüllungsgrad logistischer Prozesse erfolgt gewöhnlich während des Betriebes einer logistischen Anlage. Wir haben ein Simulationssystem entwickelt, um die Leistungsverfügbarkeit einer zellularen intralogistischen Anlage schon im Vorfeld ermitteln zu können. Die detailgenaue Simulation erfasst dabei nicht nur Einflüsse statischer Parameter wie Dimensionierung der Anlage sondern auch dynamische Parameter, wie Fahrzeugverhalten oder Auftragszusammensetzung. Aufgrund der Echtzeit- und VR-Fähigkeit des vorgestellten Systems, ist eine Präsentation in einer VR-Umgebung möglich. Intuitive Interaktionsmechanismen und Visualisierungs-Metaphern bieten einen intuitiven Zugang zur Leistungsverfügbarkeit des Systems und den Größen, die sie beeinflussen.

Supramolecular Organization of Heptapyrenotide Oligomers—An in Depth Investigation by Molecular Dynamics Simulations

We present a molecular modeling study based on ab initio and classical molecular dynamics calculations, for the investigation of the tridimensional structure and supramolecular assembly formation of heptapyrenotide oligomers in water solution. Our calculations show that free oligomers self-assemble in helical structures characterized by an inner core formed by π- stacked pyrene units, and external grooves formed by the linker moieties. The coiling of the linkers has high ordering, dominated by hydrogen-bond interactions among the phosphate and amide groups. Our models support a mechanism of longitudinal supramolecular oligomerization based on interstrand pyrene intercalation. Only a minimal number of pyrene units intercalate at one end, favoring formation of very extended longitudinal chains, as also detected by AFM experiment. Our results provide a structural explanation of the mechanism of chirality amplification in 1:1 mixtures of standard heptapyrenotides and modified oligomers with covalently linked deoxycytidine, based on selective molecular recognition and binding of the nucleotide to the groove of the left-wound helix.

Impact of volcanic stratospheric aerosols on diurnal temperature range in Europe over the past 200 years: Observations versus model simulations

We analyze the impact of stratospheric volcanic aerosols on the diurnal temperature range (DTR) over Europe using long-term subdaily station records. We compare the results with a 28-member ensemble of European Centre/Hamburg version 5.4 (ECHAM5.4) general circulation model simulations. Eight stratospheric volcanic eruptions during the instrumental period are investigated. Seasonal all- and clear-sky DTR anomalies are compared with contemporary (approximately 20 year) reference periods. Clear sky is used to eliminate cloud effects and better estimate the signal from the direct radiative forcing of the volcanic aerosols. We do not find a consistent effect of stratospheric aerosols on all-sky DTR. For clear skies, we find average DTR anomalies of −0.08°C (−0.13°C) in the observations (in the model), with the largest effect in the second winter after the eruption. Although the clear-sky DTR anomalies from different stations, volcanic eruptions, and seasons show heterogeneous signals in terms of order of magnitude and sign, the significantly negative DTR anomalies (e.g., after the Tambora eruption) are qualitatively consistent with other studies. Referencing with clear-sky DTR anomalies to the radiative forcing from stratospheric volcanic eruptions, we find the resulting sensitivity to be of the same order of magnitude as previously published estimates for tropospheric aerosols during the so-called “global dimming” period (i.e., 1950s to 1980s). Analyzing cloud cover changes after volcanic eruptions reveals an increase in clear-sky days in both data sets. Quantifying the impact of stratospheric volcanic eruptions on clear-sky DTR over Europe provides valuable information for the study of the radiative effect of stratospheric aerosols and for geo-engineering purposes.

Distance-based kriging relying on proxy simulations for inverse conditioning

Let us consider a large set of candidate parameter fields, such as hydraulic conductivity maps, on which we can run an accurate forward flow and transport simulation. We address the issue of rapidly identifying a subset of candidates whose response best match a reference response curve. In order to keep the number of calls to the accurate flow simulator computationally tractable, a recent distance-based approach relying on fast proxy simulations is revisited, and turned into a non-stationary kriging method where the covariance kernel is obtained by combining a classical kernel with the proxy. Once the accurate simulator has been run for an initial subset of parameter fields and a kriging metamodel has been inferred, the predictive distributions of misfits for the remaining parameter fields can be used as a guide to select candidate parameter fields in a sequential way. The proposed algorithm, Proxy-based Kriging for Sequential Inversion (ProKSI), relies on a variant of the Expected Improvement, a popular criterion for kriging-based global optimization. A statistical benchmark of ProKSI’s performances illustrates the efficiency and the robustness of the approach when using different kinds of proxies.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.

How can computer simulations produce new knowledge?

It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some objections and reject the view that computer simulations produce knowledge because they are experiments. I conclude by comparing thought experiments and computer simulations, assuming that both are arguments.

Evaluating Learner Perceptions of Use of Simulations for New nurses – A Collaboration Between the UT SON and the Methodist Hospital

The purpose of this evaluation project was to describe the integration of simulation into a nursing internship program and to help prepare new graduate nurses for patient care. Additionally, learning styles and perceptions of active learning, collaboration among peers, ways of learning, expectation of simulation, satisfaction, self-confidence, and design of simulation were examined. [See PDF for complete abstract]

Glacial–interglacial dynamics of Antarctic firn columns: comparison between simulations and ice core air-δ15N measurements

Correct estimation of the firn lock-in depth is essential for correctly linking gas and ice chronologies in ice core studies. Here, two approaches to constrain the firn depth evolution in Antarctica are presented over the last deglaciation: outputs of a firn densification model, and measurements of δ15N of N2 in air trapped in ice core, assuming that δ15N is only affected by gravitational fractionation in the firn column. Since the firn densification process is largely governed by surface temperature and accumulation rate, we have investigated four ice cores drilled in coastal (Berkner Island, BI, and James Ross Island, JRI) and semi-coastal (TALDICE and EPICA Dronning Maud Land, EDML) Antarctic regions. Combined with available ice core air-δ15N measurements from the EPICA Dome C (EDC) site, the studied regions encompass a large range of surface accumulation rates and temperature conditions. Our δ15N profiles reveal a heterogeneous response of the firn structure to glacial–interglacial climatic changes. While firn densification simulations correctly predict TALDICE δ15N variations, they systematically fail to capture the large millennial-scale δ15N variations measured at BI and the δ15N glacial levels measured at JRI and EDML – a mismatch previously reported for central East Antarctic ice cores. New constraints of the EDML gas–ice depth offset during the Laschamp event (~41 ka) and the last deglaciation do not favour the hypothesis of a large convective zone within the firn as the explanation of the glacial firn model–δ15N data mismatch for this site. While we could not conduct an in-depth study of the influence of impurities in snow for firnification from the existing datasets, our detailed comparison between the δ15N profiles and firn model simulations under different temperature and accumulation rate scenarios suggests that the role of accumulation rate may have been underestimated in the current description of firnification models.

Large-scale temperature response to external forcing in simulations and reconstructions of the last millennium

Understanding natural climate variability and its driving factors is crucial to assessing future climate change. Therefore, comparing proxy-based climate reconstructions with forcing factors as well as comparing these with paleoclimate model simulations is key to gaining insights into the relative roles of internal versus forced variability. A review of the state of modelling of the climate of the last millennium prior to the CMIP5–PMIP3 (Coupled Model Intercomparison Project Phase 5–Paleoclimate Modelling Intercomparison Project Phase 3) coordinated effort is presented and compared to the available temperature reconstructions. Simulations and reconstructions broadly agree on reproducing the major temperature changes and suggest an overall linear response to external forcing on multidecadal or longer timescales. Internal variability is found to have an important influence at hemispheric and global scales. The spatial distribution of simulated temperature changes during the transition from the Medieval Climate Anomaly to the Little Ice Age disagrees with that found in the reconstructions. Thus, either internal variability is a possible major player in shaping temperature changes through the millennium or the model simulations have problems realistically representing the response pattern to external forcing. A last millennium transient climate response (LMTCR) is defined to provide a quantitative framework for analysing the consistency between simulated and reconstructed climate. Beyond an overall agreement between simulated and reconstructed LMTCR ranges, this analysis is able to single out specific discrepancies between some reconstructions and the ensemble of simulations. The disagreement is found in the cases where the reconstructions show reduced covariability with external forcings or when they present high rates of temperature change.

Transient simulations of the carbon and nitrogen dynamics in northern peatlands: from the Last Glacial Maximum to the 21st century

The development of northern high-latitude peatlands played an important role in the carbon (C) balance of the land biosphere since the Last Glacial Maximum (LGM). At present, carbon storage in northern peatlands is substantial and estimated to be 500 ± 100 Pg C (1 Pg C = 1015 g C). Here, we develop and apply a peatland module embedded in a dynamic global vegetation and land surface process model (LPX-Bern 1.0). The peatland module features a dynamic nitrogen cycle, a dynamic C transfer between peatland acrotelm (upper oxic layer) and catotelm (deep anoxic layer), hydrology- and temperature-dependent respiration rates, and peatland specific plant functional types. Nitrogen limitation down-regulates average modern net primary productivity over peatlands by about half. Decadal acrotelm-to-catotelm C fluxes vary between −20 and +50 g C m−2 yr−1 over the Holocene. Key model parameters are calibrated with reconstructed peat accumulation rates from peat-core data. The model reproduces the major features of the peat core data and of the observation-based modern circumpolar soil carbon distribution. Results from a set of simulations for possible evolutions of northern peat development and areal extent show that soil C stocks in modern peatlands increased by 365–550 Pg C since the LGM, of which 175–272 Pg C accumulated between 11 and 5 kyr BP. Furthermore, our simulations suggest a persistent C sequestration rate of 35–50 Pg C per 1000 yr in present-day peatlands under current climate conditions, and that this C sink could either sustain or turn towards a source by 2100 AD depending on climate trajectories as projected for different representative greenhouse gas concentration pathways.

Greenland accumulation and its connection to the large-scale atmospheric circulation in ERA-Interim and paleoclimate simulations

Changes in Greenland accumulation and the stability in the relationship between accumulation variability and large-scale circulation are assessed by performing time-slice simulations for the present day, the preindustrial era, the early Holocene, and the Last Glacial Maximum (LGM) with a comprehensive climate model. The stability issue is an important prerequisite for reconstructions of Northern Hemisphere atmospheric circulation variability based on accumulation or precipitation proxy records from Greenland ice cores. The analysis reveals that the relationship between accumulation variability and large-scale circulation undergoes a significant seasonal cycle. As the contributions of the individual seasons to the annual signal change, annual mean accumulation variability is not necessarily related to the same atmospheric circulation patterns during the different climate states. Interestingly, within a season, local Greenland accumulation variability is indeed linked to a consistent circulation pattern, which is observed for all studied climate periods, even for the LGM. Hence, it would be possible to deduce a reliable reconstruction of seasonal atmospheric variability (e.g., for North Atlantic winters) if an accumulation or precipitation proxy were available that resolves single seasons. We further show that the simulated impacts of orbital forcing and changes in the ice sheet topography on Greenland accumulation exhibit strong spatial differences, emphasizing that accumulation records from different ice core sites regarding both interannual and long-term (centennial to millennial) variability cannot be expected to look alike since they include a distinct local signature. The only uniform signal to external forcing is the strong decrease in Greenland accumulation during glacial (LGM) conditions and an increase associated with the recent rise in greenhouse gas concentrations.

Hybrid stochastic simulations of intracellular reaction-diffusion systems.

With the observation that stochasticity is important in biological systems, chemical kinetics have begun to receive wider interest. While the use of Monte Carlo discrete event simulations most accurately capture the variability of molecular species, they become computationally costly for complex reaction-diffusion systems with large populations of molecules. On the other hand, continuous time models are computationally efficient but they fail to capture any variability in the molecular species. In this study a hybrid stochastic approach is introduced for simulating reaction-diffusion systems. We developed an adaptive partitioning strategy in which processes with high frequency are simulated with deterministic rate-based equations, and those with low frequency using the exact stochastic algorithm of Gillespie. Therefore the stochastic behavior of cellular pathways is preserved while being able to apply it to large populations of molecules. We describe our method and demonstrate its accuracy and efficiency compared with the Gillespie algorithm for two different systems. First, a model of intracellular viral kinetics with two steady states and second, a compartmental model of the postsynaptic spine head for studying the dynamics of Ca+2 and NMDA receptors.

Why Monte Carlo Simulations are Inferences and not Experiments

Monte Carlo simulations arrive at their results by introducing randomness, sometimes derived from a physical randomizing device. Nonetheless, we argue, they open no new epistemic channels beyond that already employed by traditional simulations: the inference by ordinary argumentation of conclusions from assumptions built into the simulations. We show that Monte Carlo simulations cannot produce knowledge other than by inference, and that they resemble other computer simulations in the manner in which they derive their conclusions. Simple examples of Monte Carlo simulations are analysed to identify the underlying inferences.

More than just Good Luck. How to Explain the Success of Monte Carlo Simulations

Monte Carlo simulation is a powerful method in many natural and social sciences. But what sort of method is it? And where does its power come from? Are Monte Carlo simulations experiments, theories or something else? The aim of this talk is to answer these questions and to explain the power of Monte Carlo simulations. I provide a classification of Monte Carlo techniques and defend the claim that Monte Carlo simulation is a sort of inference.