Multiparameter equations of state for selected siloxanes

Colonna, Piero; Nannan, N.R.; Guardone, Alberto; Lemmon, Eric W.

doi:10.1016/j.fluid.2006.04.015

Cited by 134 publications

(114 citation statements)

References 39 publications

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“…In addition to the preceding EOS, we consider, for calibration and validation purposes as discussed later, a more complex and accurate thermodynamic model, namely, the multiparameter technical equation of state introduced by Span and Wagner (SW) [15] with coefficients adjusted to D5 in [24]. This is a complex technical equation of state, which is considered as the most accurate and complete thermodynamic model presently available for this fluid, provided that a sufficiently large set of high-quality experimental data is available for a reliable fitting of the model coefficients, i.e.…”

Section: Equations Of Statementioning

confidence: 99%

“…Material-dependent coefficients for D5 have been taken from Ref. [24], to which we refer for more details. The thermal and caloric EOS are derived from the following thermodynamic relations:…”

Section: Sw Modelmentioning

confidence: 99%

“…where the polynomial coefficients c i depend again on the substance under consideration and are given for D5 in [24]. This model is considered as more accurate than the preceding ones.…”

Section: Sw Modelmentioning

confidence: 99%

“…the transonic flow of a silicon oil, namely cyclopentasiloxane (D5), past an airfoil, as in [13]. Experimental data being not available for this case, and since the scope of the present work is assessing the proposed calibration approach and not solving a realistic flow configuration, "synthetic" calibration data are generated by numerically computing the pressure distribution around the airfoil using a reference EOS based on Helmholtz free energy [24], which is considered as the best available thermodynamic model for the selected fluid.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian quantification of thermodynamic uncertainties in dense gas flows

Merle

Cinnella

2015

Reliability Engineering & System Safety

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. for a dense gas flow over a wing section. Flow thermodynamic conditions are such that the gas thermodynamic behavior strongly deviates from that of a perfect gas. In the aim of assessing the proposed methodology, synthetic calibration data -specifically, wall pressure data-are generated by running the numerical solver with a more complex and accurate thermodynamic model. The statistical model used to build the likelihood function includes a model-form inadequacy term, accounting for the gap between the model output associated to the best-fit parameters, and the true phenomenon. Results show that, for all of the relatively simple models under investigation, calibrations lead to informative posterior probability density distributions of the input parameters and improve the predictive distribution significantly. Nevertheless, calibrated parameters strongly differ from their expected physical values. The relationship between this behavior and model-form inadequacy is discussed.

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Section: Equations Of Statementioning

confidence: 99%

Section: Sw Modelmentioning

confidence: 99%

“…where the polynomial coefficients c i depend again on the substance under consideration and are given for D5 in [24]. This model is considered as more accurate than the preceding ones.…”

Section: Sw Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian quantification of thermodynamic uncertainties in dense gas flows

Merle

Cinnella

2015

Reliability Engineering & System Safety

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show abstract

“…Aby uzyskać wiarygodne wyniki w obliczeniach zarówno 1D, jak i 3D czynnik MDM był opisywany równaniem stanu Helmholtza dedykowanym dla tej cieczy. Model ten został zaimplementowany w bibliotece NIST Refprop 9.0 [10,11]. Moc wewnętrzna turbiny wyznaczona z obliczeń CFD wynosi 1.08 MW, co odpowiada sprawności 93%.…”

Section: Opisunclassified

Optimization of blade profiles of axial turbine working with MDM

Witanowski¹,

Klonowicz²,

Lampart³

2015

Mechanik

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Digitalization in Thermodynamics

Forte

Jirasek

Bortz

et al. 2019

Chemie Ingenieur Technik

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Digitalization is about data and how they are used. This has always been a key topic in applied thermodynamics. In the present work, the influence of the current wave of digitalization on thermodynamics is analyzed. Thermodynamic modeling and simulation is changing as large amounts of data of different nature and quality become easily available. The power and complexity of thermodynamic models and simulation techniques is rapidly increasing, and new routes become viable to link them to the data. Machine learning opens new perspectives, when it is suitably combined with classical thermodynamic theory. Illustrated by examples, different aspects of digitalization in thermodynamics are discussed: strengths and weaknesses as well as opportunities and threats.

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Multiparameter equations of state for selected siloxanes

Cited by 134 publications

References 39 publications

Bayesian quantification of thermodynamic uncertainties in dense gas flows

Bayesian quantification of thermodynamic uncertainties in dense gas flows

Optimization of blade profiles of axial turbine working with MDM

Digitalization in Thermodynamics

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