The specific heat of fifteen stainless steels in the temperature range 4K-30K

Corsan, J.M.; Mitchem, N.I.

doi:10.1016/0011-2275(79)90100-0

Cited by 18 publications

(4 citation statements)

References 13 publications

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“…Presumably, only accurate parameter estimates for the Hill Equation, Einstein Solid, Debye Model, and Multilinear model match these equations to experimental data. To assess heat capacity prediction by each formula, parameters were fit to data for 53 inorganic solids, alloys, and technical ceramics from information in literature publications and the NIST and DIPPR databanks [13,14,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Fitted parameters were discovered through minimization of the L2 norm between model predictions and experimental heat capacity results from temperatures that spanned absolute zero to 3000 Kelvin.…”

Section: Methodsmentioning

confidence: 99%

A Hill Equation for Solid Specific Heat Capacity Calculation

2022

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The Hill Equation and Hill Coefficient have been used extensively in biochemistry for the description of noncovalent binding. Previously, the Hill Coefficient was correlated with the Gibbs free energy, which suggests that the Hill Equation might be extensible to covalent binding phenomena. To evaluate this possibility, the Hill Equation was compared to the Debye Model and Einstein Solid in the calculation of heat capacity for 53 covalent solids, which included stainless steels and refractory ceramics. Hill Equation specific heat predictions showed a standard error of 0.37 J/(mole⋅Kelvin), whereas errors from the Debye Model and Einstein Solid were higher at 0.45 J/(mole⋅Kelvin) and 0.81 J/(mole⋅Kelvin), respectively. Furthermore, the Hill Equation is computationally efficient, a feature that can accelerate industrial chemical process simulation(s). Given its speed, simplicity, and accuracy, the Hill Equation likely offers an alternative means of specific heat calculation in chemical process models.

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Section: Methodsmentioning

confidence: 99%

A Hill Equation for Solid Specific Heat Capacity Calculation

2022

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“…Heat capacity and mass density data values of Nb 3 Sn superconductor, stainless steel and copper are taken from [22], together with copper electrical resistivity. In particular, the specific heat for stainless steel was originally taken from [23][24][25] while the same property for copper was obtained from [26]. Electrical resistivity properties for copper were included in [27].…”

Section: Hotspot Temperature Analysismentioning

confidence: 99%

A methodological approach for the optimal design of the toroidal field coils of a tokamak device using artificial intelligence

Tomassetti¹,

Marzi²,

Zignani³

et al. 2021

Supercond. Sci. Technol.

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As prototypes of future commercial tokamaks, DEMOs nuclear fusion power plants are expected to be able to produce cost-effective electrical power. In this view, an optimized design becomes crucial in the whole engineering workflow. Up to now, the design of one of the most critical components, the cross-section of each of the toroidal field coils inner leg winding pack, was performed using a sequential trial-and-error procedure. In this work, a novel comprehensive approach is proposed to include all the main design aspects into a unified tool taking advantage of artificial neural networks for faster computation in finding optimal design configurations. This procedure overcomes several difficulties including dealing with both real-valued and discrete design variables, the significant CPU-time of magneto-structural analysis and also guarantees the optimality for the winding pack configuration. The proposed methodology was demonstrated for the 2019 ENEA DEMO configuration which includes 16 toroidal field coils, made-up of 6 Nb3Sn double layers and a Wind and React manufacturing technique.

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“…In 0 Q < Fig. 2, is plotted for stainless steel EN 1.4429, assuming 0 Q = η =1, the heat capacity as best fit function for austenitic 18-14 % nickel-chrome steel from [4], and fitting measured yield curves to a Ramberg-Osgood curve…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Serrated Yielding at Cryogenic Temperatures in Structural Components of Wendelstein 7-X

Fellinger

Bykov

Schauer

2012

IEEE Trans. Appl. Supercond.

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-Structural materials like stainless steels are prone to serrated yielding at cryogenic temperatures. The serration effect is the unstable yielding resulting in a zigzag shape of the stress-strain curve observed in displacement controlled tensile tests. Moreover, a local temperature rise in the plastic zone is observed.In the current paper, the theories explaining the serration effect are briefly discussed. With a coupled thermal-mechanical FE model of a tensile test set up the influence of the test conditions is demonstrated on the observed shape of the stressstrain curve. The FE model is extended to quantify the temperature rise and significance of the serration effect in critical support structures of the magnet system of W7-X.For W7-X loading rates the temperature rise due to yielding is shown to be moderate and yielding does not localize. As a simple and safe approach, plastic collapse of critical components to be operated at 4K can be judged with a pure mechanical analysis neglecting the thermal-mechanical interaction, using yield curves obtained at 77K instead.Index Terms -serrated yielding, intermittent yielding, discontinuous flow, cryogenic material properties, coupled thermal-mechanical FEA, W7-X

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The specific heat of fifteen stainless steels in the temperature range 4K-30K

Cited by 18 publications

References 13 publications

A Hill Equation for Solid Specific Heat Capacity Calculation

A Hill Equation for Solid Specific Heat Capacity Calculation

A methodological approach for the optimal design of the toroidal field coils of a tokamak device using artificial intelligence

Serrated Yielding at Cryogenic Temperatures in Structural Components of Wendelstein 7-X

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