MACHINE LEARNING BASED SPECTRAL MODEL FOR PARTICIPATING MEDIUM FOR MONTE CARLO METHOD

Date

2024-01-01

Author

Dincer, Selim
Tarı, İlker
Ertürk, Hakan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In many engineering applications, radiative heat transfer occurs in the presence of participating media, which means there is an interaction between thermal radiation with absorbing, emitting, and scattering medium. In the last few decades, numerous methods have been developed and implemented to solve the radiative transfer equation. Among these, the Monte Carlo Method (MCM) is found to be one of the most accurate and versatile approaches to model radiative heat transfer in engineering applications. The MCM relies on representing physical events by statistical sampling based on probability distributions. Once a large enough sample is used, it enables the accurate modelling of many complex phenomena with ease [Howell, 1998]. In radiative heat transfer applications, the MCM is directly applied to simulate the physical nature of radiative heat exchange by tracing a finite number of energy packages often referred to as photon bundles, from their emission points to their final absorption points, representing all intermediate physical events relying on pseudo-random numbers and cumulative distribution functions derived based on probability density functions of each physical event.

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85204037437&origin=inward
https://hdl.handle.net/11511/111088

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Department of Mechanical Engineering, Conference / Seminar