Radiative heat transfer calculation for mixture of gases using full spectrum k-distribution method

The full spectrum k-distribution method is used to obtain radiative heat flux and divergence of radiative heat flux for two test cases, containing mixture of CO 2 and H 2 O at different concentration and temperature keeping pressure constant. The k-distribution for mixture of gases is obtained from individual gas k-distribution using three different mixing models, viz., superposition, multiplication and hybrid model. Further, the radiative transfer equation (RTE) is solved by the finite volume discrete ordinate method (FVDOM) to obtain the radiative flux and the radiation source term. The results obtained were compared with the FSK from spectral addition and LBL method. The multiplication mixing model provides better accuracy compared to other mixing models considered in the present study.

SIMULASI CFD PEMBAKARAN OXY-FUEL PADA PACKAGE BOILER MENGGUNAKAN MODEL RADIASI DISCRETE ORDINATE

Meningkatnya konsentrasi CO2 di atmosfer, mendorong umat manusia untuk melakukan penelitian tentang teknologi yang ramah lingkungan dan rendah emisi. Berbagai penelitian sudah dilakukan, salah satunya adalah pembakaran oxy-fuel. Pembakaran oxy-fuel merupakan salah satu teknologi penangkapan dan penyimpanan karbon (CCS), dimana bahan bakar dibakar dengan campuran oksigen murni dan daur ulang gas buang. Perbedaan kondisi tersebut dapat mempengaruhi suhu pembakaran dan perpindahan panas radiasi pada ruang pembakaran. Oleh sebab itu, penelitian ini bertujuan untuk mengetahui perbandingan suhu pembakaran dan perpindahan panas radiasi yang terjadi di dalam ruang bakar antara pembakaran udara dan pembakaran oxy-fuel. Objek penelitian ini adalah sebuah package boiler berjenis water tube boiler di salah satu unit pembangkit uap dengan kapasitas uap 100 ton/jam. Penelitian ini dilakukan dengan menggunakan simulasi CFD. Perpindahan panas radiasi dimodelkan dengan model radiasi Discrete Ordinate (DO) dan model Weighted Sum of Gray Gases (WSGG) yang digunakan untuk menghitung energi radiasi yang diserap dan dipancarkan oleh gas hasil pembakaran. Dari hasil simulasi CFD didapat bahwa suhu maksimum pembakaran pada pembakaran oxy-fuel yaitu sebesar 1.865,34℃ lebih tinggi dari pembakaran udara yaitu sebesar 1.837,01℃, begitupun dengan energi radiasi yang dipancarkan pada pembakaran oxy-fuel adalah sebesar 3,45 MW/m2 lebih tinggi dari pembakaran udara yaitu 2,75 MW/m2.

Accuracy Enhancement of the Two-Stream Radiative Transfer Model for Computing Absorption Bands at the Presence of Aerosols

The two-stream model is the fastest radiative transfer model among those based on the discrete ordinate method. Although its accuracy is not high enough to be used in applications, the two-stream model gets more attention in computationally demanding tasks such as line-by-line simulations in the gaseous absorption bands. For this reason, we designed the cluster low-streams regression (CLSR) technique, in which a spectrum computed with a two-stream model, is refined by using statistical dependencies between two- and multistream radiative transfer models. In this paper, we examine the efficiency of this approach for computing Hartley-Huggins, O2 A-, water vapour and CO2 bands at the presence of aerosols. The numerical results evidence that the errors of the CLSR method is not biased and around 0.05 %, while the performance enhancement is two orders of magnitude.

Optimization and parallelization of the discrete ordinate method for radiation transport simulation in OpenFOAM: Hierarchical combination of shared and distributed memory approaches

This paper describes the reduction in memory and computational time for the simulation of complex radiation transport problems with the discrete ordinate method (DOM) model in the open-source computational fluid dynamics platform OpenFOAM. Finite volume models require storage of vector variables in each spatial cell; DOM introduces two additional discretizations, in direction and wavelength, making memory a limiting factor. Using specific classes for radiation sources data, changing the store of fluxes and other minor changes allowed a reduction of 75% in memory requirements. Besides, a hierarchical parallelization was developed, where each node of the standard parallelization uses several computing threads, allowing higher speed and scalability of the problem. This architecture, combined with optimization of some parts of the code, allowed a global speedup of x15. This relevant reduction in time and memory of radiation transport opens a new horizon of applications previously unaffordable.

Discrete Ordinate Radiative Transfer Model With the Neural Network Based Eigenvalue Solver: proof Of Concept

Artificial neural networks are attracting increasing attention in various applications. They can be used as ‘universal approximations’, which substitute computationally expensive algorithms by relatively simple sequences of functions, which simulate a reaction of a set of neurons to the incoming signal. In particular, neural networks have proved to be efficient for parameterization of the computationally expensive radiative transfer models (RTMs) in atmospheric remote sensing. Although a direct substitution of RTMs by neural networks can lead to the multiple performance enhancements, such an approach has certain drawbacks, such as loss of generality, robustness issues, etc. In this regard, the neural network is usually trained for a specific application, predefined atmospheric scenarios and a given spectrometer. In this paper a new concept of neural-network based RTMs is examined, in which the neural network substitutes not the whole RTM but rather a part of it (the eigenvalue solver), thereby reducing the computational time while maintaining its generality. The explicit dependencies on geometry of observation and optical thickness of the medium are excluded from training. It is shown that although the speedup factor due to this approach is modest (around 3 times against 103 speed up factor of other approaches reported in recent papers), the resulting neural network is flexible and easy to train. It can be used for arbitrary number of atmospheric layers. Moreover, this approach can be used in conjunction with any RTMs based on the discrete ordinate method. The neural network is applied for simulations of the radiances at the top of the atmosphere in the Huggins band.