Uncertainty propagation and sensitivity analysis of thermo-physical properties of phase change materials (PCM) in the energy demand calculations of a test cell with passive latent thermal storage

Mazo, Javier; Badry, Abdallah Tarek El; Carreras, Joan; Delgado, Mónica; Boer, Dieter; Zalba, Belén

doi:10.1016/j.applthermaleng.2015.07.047

Cited by 32 publications

(12 citation statements)

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“…Whereas, Foglia et al (2009) explored different types of SA and found out that local SA provides sufficient information to justify the results and global SA methods do not provide additional information. Pannell (1997) (Karaoğlu and Secgin 2008;Amidpour et al 2015;Mazo et al 2015;Pastore et al 2015). However, SA on machine tool selection and manufacturing area has received less attention from the researchers.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis for justification of utilising special purpose machine tools in the presence of uncertain parameters

Vafadar

Hayward

Tolouei‐Rad

2017

International Journal of Production Research

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Decision-makers in manufacturing area frequently face machine tool selection problem under uncertainty due to competitive market changes. Special purpose machines (SPMs), a relatively new class of reconfigurable machine tools, are used to react quickly to changes. Justification of utilising these machines vs. other machine tools requires a technique to investigate the sources of uncertainties. In this work, sensitivity analysis is utilised to investigate the sources of these uncertainties and errors which may reveal new insights for evaluating a machine tool. An illustrative example is provided to show the sensitivity of parameters on the economic performance of SPMs compared to the other alternatives. The results show that this analysis provides additional information and moves the decision closer to the optimum alternative

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Section: Introductionmentioning

confidence: 99%

Sensitivity analysis for justification of utilising special purpose machine tools in the presence of uncertain parameters

Vafadar

Hayward

Tolouei‐Rad

2017

International Journal of Production Research

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“…From analyses with simple analytical models, Günter et al [24] drew some basic conclusions regarding the effects of uncertain material data. The effect of differential scanning calorimetry (DSC) results that were achieved with different heating rates were investigated numerically [25,26], and the influence of the width of the melting range on the numerical results was studied by several authors [9,10,15,21,27,28]. Interestingly, it is reported that a larger melting range may either lead to a faster phase change [9,21] or a slower one [9].…”

Section: Introductionmentioning

confidence: 99%

“…The authors claim that the model with different material properties in the solid and liquid phase has better potential to reduce the discrepancy between simulation and experiment than the model with constant material properties. The effect of the uncertainty of the material properties data was analyzed in detail with purely diffusive phase change models by Dolado et al [27], Zsembinszki et al [30], and Mazo et al [28]. In all three studies, the melting temperature is the most important, or one of the most important, material properties.…”

Section: Introductionmentioning

confidence: 99%

“…In all three studies, the melting temperature is the most important, or one of the most important, material properties. Interestingly, the detailed uncertainty analyses performed in Dolado et al [27] and Mazo et al [28] allow to propose a certain set of allowed uncertainties in the material properties to achieve a predefined maximum error in the numerical results. Possible variances in the start and boundary conditions, as well as in the geometric data, are investigated by Dolado et al [27] and Zsembinszki et al [30].…”

Section: Introductionmentioning

confidence: 99%

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Basic Analysis of Uncertainty Sources in the CFD Simulation of a Shell-and-Tube Latent Thermal Energy Storage Unit

et al. 2020

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Computational Fluid Dynamics (CFD) simulations are increasingly employed in the development of latent thermal energy storage units. Yet there are often strong deviations between the experiments and numerical results. To unveil the sources of the deviations for the CFD simulation of a vertical shell-and-tube latent thermal energy storage unit, a basic analysis of different uncertainties is undertaken in this paper. Consequently, the effect of a variation of 10 material properties, six initial and boundary conditions, as well as a displacement of the temperature measuring points in the simulation, are examined. The results depict that the influence of the substance data depend on the output variable under consideration. Beside material properties, which have almost no influence, there are some properties that influence the power and the global liquid phase fraction over time, and a third group, which also has an influence on the mean power. Partly in contrast to results found in literature, the highest influence on the mean power occurs for the heat losses (which are varied in an on/off manner), the density, and the melting enthalpy (both varied by ±10%).

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“…The statistical moments are important characteristics of a system response's probability distribution. Thus, solving the statistical moments of the system response has become a key issue in UP [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty propagation analysis by an extended sparse grid technique

Jia

Jiang

et al. 2018

Front. Mech. Eng.

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In this paper, an uncertainty propagation analysis method is developed based on an extended sparse grid technique and maximum entropy principle, aiming at improving the solving accuracy of the high-order moments and hence the fitting accuracy of the probability density function (PDF) of the system response. The proposed method incorporates the extended Gauss integration into the uncertainty propagation analysis. Moreover, assisted by the Rosenblatt transformation, the various types of extended integration points are transformed into the extended Gauss-Hermite integration points, which makes the method suitable for any type of continuous distribution. Subsequently, within the sparse grid numerical integration framework, the statistical moments of the system response are obtained based on the transformed points. Furthermore, based on the maximum entropy principle, the obtained first four-order statistical moments are used to fit the PDF of the system response. Finally, three numerical examples are investigated to demonstrate the effectiveness of the proposed method, which includes two mathematical problems with explicit expressions and an engineering application with a black-box model.

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Uncertainty propagation and sensitivity analysis of thermo-physical properties of phase change materials (PCM) in the energy demand calculations of a test cell with passive latent thermal storage

Cited by 32 publications

References 50 publications

Sensitivity analysis for justification of utilising special purpose machine tools in the presence of uncertain parameters

Sensitivity analysis for justification of utilising special purpose machine tools in the presence of uncertain parameters

Basic Analysis of Uncertainty Sources in the CFD Simulation of a Shell-and-Tube Latent Thermal Energy Storage Unit

Uncertainty propagation analysis by an extended sparse grid technique

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