Physikalisches Praktikum

Schenk, Wolfdieter A.; Kremer, Friedrich; Beddies, G.; Franke, Thomas; Galvosas, Petrik; Rieger, Peter

doi:10.1007/978-3-658-00666-2

Cited by 6 publications

(6 citation statements)

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“…Jamali et al proposed a way calculating the viscosity of the mixture based on the slope of the linear regression, so the regression was constrained assuming an identical slope a : G 0.25em ( D 1 ∞ , D 2 ∞ , a ) = prefix∑ i = 1 n false( D 1 i − ( D 1 ∞ + a · x i ) false) 2 + prefix∑ i = 1 n false( D 2 i − ( D 2 ∞ + a · x i ) false) 2 G is the function that has to be optimized, D 1 ∞ , D 2 ∞ are the extrapolated diffusion coefficients of two components, and ( D 1 i , x i ) and ( D 2 i , x i ) are pairs of values of diffusion coefficients and inverse box sizes (Figure S5). The standard deviations σ D 1 and σ D 2 were calculated as described previously …”

Section: Methodsmentioning

confidence: 99%

Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML

et al. 2022

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In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiments and simulations was demonstrated for two binary mixtures, methanol–water and glycerol–water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data were extracted manually from the literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of nonideality of methanol–water and glycerol–water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters, and the experimentally determined or simulated properties as Python data classes. The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. PyThermoML enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible and thus FAIR.

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Section: Methodsmentioning

confidence: 99%

Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML

et al. 2022

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“…Accordingly, two formulas are needed to describe the 14 C-calibration. The Fourier transform is widely applied in quantum physics (e.g., Feynman 2010;Fließbach, Walliser 2012;Hermann 1970;Messiah 1976;Schenk et al 2014;Wichmann 1971)…”

Section: Acknowledgements [1] Translation Of Variablesmentioning

confidence: 99%

Bayesian 14C-rationality, Heisenberg Uncertainty, and Fourier Transform

Weninger¹,

Edinborough

2020

Doc. praeh.

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Following some 30 years of radiocarbon research during which the mathematical principles of 14C-calibration have been on loan to Bayesian statistics, here they are returned to quantum physics. The return is based on recognition that 14C-calibration can be described as a Fourier transform. Following its introduction as such, there is need to reconceptualize the probabilistic 14C-analysis. The main change will be to replace the traditional (one-dimensional) concept of 14C-dating probability by a two-dimensional probability. This is entirely analogous to the definition of probability in quantum physics, where the squared amplitude of a wave function defined in Hilbert space provides a measurable probability of finding the corresponding particle at a certain point in time/space, the so-called Born rule. When adapted to the characteristics of 14C-calibration, as it turns out, the Fourier transform immediately accounts for practically all known so-called quantization properties of archaeological 14C-ages, such as clustering, age-shifting, and amplitude-distortion. This also applies to the frequently observed chronological lock-in properties of larger data sets, when analysed by Gaussian wiggle matching (on the 14C-scale) just as by Bayesian sequencing (on the calendar time-scale). Such domain-switching effects are typical for a Fourier transform. They can now be understood, and taken into account, by the application of concepts and interpretations that are central to quantum physics (e.g. wave diffraction, wave-particle duality, Heisenberg uncertainty, and the correspondence principle). What may sound complicated, at first glance, simplifies the construction of 14C-based chronologies. The new Fourier-based 14C-analysis supports chronological studies on previously unachievable geographic (continental) and temporal (Glacial-Holocene) scales; for example, by temporal sequencing of hundreds of archaeological sites, simultaneously, with minimal need for development of archaeological prior hypotheses, other than those based on the geo-archaeological law of stratigraphic superposition. As demonstrated in a variety of archaeological case studies, just one number, defined as a gauge-probability on a scale 0–100%, can be used to replace a stacked set of subjective Bayesian priors.

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“…Furthermore its 1, ∩ 2, ̸ = 0 because of (25) and (26). The missing intersections are nonempty too, for , ∈ {1, .…”

Section: )mentioning

confidence: 99%

“…By inserting the pressure bounds, one can see that inequality (25) is always fulfilled and inequality (27) implies…”

Section: (96)mentioning

confidence: 99%

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Stationary Gas Networks with Compressor Control and Random Loads: Optimization with Probabilistic Constraints

Gugat

Schuster

2018

Mathematical Problems in Engineering

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We introduce a stationary model for gas flow based on simplified isothermal Euler equations in a non-cycled pipeline network. Especially the problem of the feasibility of a random load vector is analyzed. Feasibility in this context means the existence of a flow vector meeting these loads, which satisfies the physical conservation laws with box constraints for the pressure. An important aspect of the model is the support of compressor stations, which counteract the pressure loss caused by friction in the pipes. The network is assumed to have only one influx node; all other nodes are efflux nodes. With these assumptions the set of feasible loads can be characterized analytically. In addition we show the existence of optimal solutions for some optimization problems with probabilistic constraints. A numerical example based on real data completes this paper.

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Physikalisches Praktikum

Cited by 6 publications

References 0 publications

Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML

Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML

Bayesian 14C-rationality, Heisenberg Uncertainty, and Fourier Transform

Stationary Gas Networks with Compressor Control and Random Loads: Optimization with Probabilistic Constraints

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