Combined Conductive-Convective-Radiative Heat Transfer in Complex Geometry Using the Monte Carlo Method : Application to Solar Receivers

Ibarrart, Loris; Caliot, Cyril; Hafi, Mouna El; Fournier, Richard; Blanco, Stéphane; Dutour, Sébastien; Dauchet, Jérémi; Tregan, Jean-Marc; Eymet, Vincent; Forest, Vincent

doi:10.1615/ihtc16.pma.023662

Cited by 11 publications

(19 citation statements)

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“…As regards theoretical developments to extend the MC calculation and therefore, in the longer term the SMC method, work has been undertaken to take into account the heat advection term (added in Eq. ( 1) with a known velocity field) [Ibarrart, 2018] and on the implications of not linearizing radiative transfer when coupling to the heat transfer equation [Tregan, 2020].…”

Section: Discussionmentioning

confidence: 99%

“…Monte Carlo (MC) methods are well known for estimating the propagator with ease in case models requiring large refined geometries, and for solving radiation problems efficiently [Eymet, 2009]. Recent developments in Green's formulation and stochastic processes for combined heat transfer have led to the generation of conducto-convecto-radiative paths to solve such problems with MC algorithms [Fournier, 2016;Caliot, 2017;Ibarrart, 2018]. Random paths are generated from the probe location until a known temperature is reached.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, [Villefranque, 2019] showed the insensitivity of computation time to mesh refinements for the resolution of radiative transfer with the MC method in the field of atmospheric radiation. [Ibarrart, 2018] highlighted such insensitivity to geometrical complexity in the case of combined heat transfer resolution with the MC method. In the present paper, such insensitivity to mesh refinement and geometrical complexity is extended to the use of the SMC method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transfer function estimation with SMC method for combined heat transfer: insensitivity to detail refinement of complex geometries

Penazzi¹,

Blanco²,

Caliot³

et al. 2021

Proceeding of Proceedings of CHT-21 ICHMT International Symposium on Advances in Computational Heat Transfer

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The optimization of thermal transfers in engineering systems such as heat exchangers requires the analysis of the influence of heat sources upon the temperature at various positions of interest in the studied system. In order to achieve the resolution of the combined modes of heat transfer through these systems, their couplings and the complex 3D geometries involved need to be integrated with full accuracy. Recent developments in probabilistic formulations in the context of transient combined heat transfer (linearized conduction-radiation-convection) have opened a new route to solving such problems with Monte Carlo (MC) algorithms, using state-of-the-art computer graphics digital libraries to handle complex geometries. To estimate the temperature at a probe point of interest, random paths are generated from its position and propagated through the geometry until a known temperature is reached. From a single MC calculation to sample the path statistics, the Symbolic Monte Carlo (SMC) method is used to express the probe temperature as a linear function of the sources. This function can then be used to estimate the probe temperature for any source values, alleviating the need to repeat Monte-Carlo simulations for each source condition, resulting in greatly reduced computation time. This approach is applied to the case of an open-cavity porous medium and computation time insensitivity to the complexity and fineness of the geometry is demonstrated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transfer function estimation with SMC method for combined heat transfer: insensitivity to detail refinement of complex geometries

Penazzi¹,

Blanco²,

Caliot³

et al. 2021

Proceeding of Proceedings of CHT-21 ICHMT International Symposium on Advances in Computational Heat Transfer

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“…Today, Monte Carlo is used to carry out simulations of scope and complexity that surely could not have been imagined by Ulam, von Neumann, Metropolis, and Fermi nearly 80 years ago. Recent examples include radiative loading on clouds, which is important for climate change modeling [118][119][120], radiative transfer within complex heterogeneous [121,122] and graded media [108,[123][124][125][126][127], polarization [128][129][130][131], shape optimization [69][70][71], computer graphics rendering [118], large scale systems [132,133], manufacturing [134][135][136][137], combined-mode problems [138][139][140][141][142][143][144], and others .…”

Section: Recent Advances In the Monte Carlo Methodsmentioning

confidence: 99%

The Past and Future of the Monte Carlo Method in Thermal Radiation Transfer

Howell

Daun

2021

Journal of Heat Transfer

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“…Among these theoretical attempts, Fournier et al [5] proposed a statistical formulation starting from Green formalism that allows the extension to combined heat transfer of the computer graphics techniques used for rendering images of complex scenes. This thermal Monte Carlo method (involving a linearization with temperature of radiative transfer) was applied and validated numerically for combined heat-transfer in porous media by Caliot et al [6] and Ibarrart et al [7]. The authors of the present article are also intensively using this approach for engineering applications in contexts where the linearization of radiation is meaningful, e.g.…”

Section: Introductionmentioning

confidence: 93%

Toward the Use of Symbolic Monte Carlo for Conduction-Radiation Coupling in Complex Geometries

Penazzi

Blanco

Caliot

et al. 2019

Proceeding of Proceedings of the 9th International Symposium on Radiative Transfer, RAD-19

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We address the interest of using Symbolic Monte Carlo to obtain a reduced model for conduction-radiation coupling in complex geometries. Symbolic Monte Carlo was successfully used for radiative transfer in a decoupled manner, but no attempt has yet been reported to extend its use to radiation coupled with other modes. Here we show that from a unique Monte Carlo simulation of radiation coupled with conduction in a semi-transparent solid surrounded by a convective flow, it is possible to build a formulation of the local temperature as function of the convective heat transfer coefficient, for instance, including the evaluation of uncertainty. This reduced model (a transfer function) enables to decrease the computation time when the function needs to be evaluated plenty of times for different values of the parameters as in optimization or control algorithms.

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Combined Conductive-Convective-Radiative Heat Transfer in Complex Geometry Using the Monte Carlo Method : Application to Solar Receivers

Cited by 11 publications

References 0 publications

Transfer function estimation with SMC method for combined heat transfer: insensitivity to detail refinement of complex geometries

Transfer function estimation with SMC method for combined heat transfer: insensitivity to detail refinement of complex geometries

The Past and Future of the Monte Carlo Method in Thermal Radiation Transfer

Toward the Use of Symbolic Monte Carlo for Conduction-Radiation Coupling in Complex Geometries

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