A new ANN driven MCMC method for multi-parameter estimation in two-dimensional conduction with heat generation

Balaji, C.; Padhi, Tamanna

doi:10.1016/j.ijheatmasstransfer.2010.05.064

Cited by 29 publications

(4 citation statements)

References 11 publications

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“…Training is accomplished using a set of network inputs for which the desired outputs are known. The selection of the number of hidden neurons is based on the values of some of the common performance metrics used [12], as follows. This is termed as neuron independence study as shown in table 5.…”

Section: Forward Modelmentioning

confidence: 99%

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A Bayesian inference approach: estimation of heat flux from fin for perturbed temperature data

Kumar

Gnanasekaran

2018

Sādhanā

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This paper reports the estimation of the unknown boundary heat flux from a fin using the Bayesian inference method. The setup consists of a rectangular mild steel fin of dimensions 250915096 mm 3 and an aluminium base plate of dimensions 250915098 mm 3. The fin is subjected to constant heat flux at the base and the fin setup is modelled using ANSYS14.5. The problem considered is a conjugate heat transfer from the fin, and the Navier-Stokes equation is solved to obtain the flow parameters. Grid independence study is carried out to fix the number of grids for the study considered. To reduce the computational cost, computational fluid dynamics (CFD) is replaced with artificial neural network (ANN) as the forward model. The Markov Chain Monte Carlo (MCMC) powered by Metropolis-Hastings sampling algorithm along with the Bayesian framework is used to explore the estimation space. The sensitivity analysis of the estimated temperature with respect to the unknown parameter is discussed to know the dependency of the temperature with the parameter. This paper signifies the effect of a prior model on the execution of the inverse algorithm at different noise levels. The unknown heat flux is estimated for the surrogated temperature and the estimates are reported as mean, Maximum a Posteriori (MAP) and standard deviation. The effect of a-priori information on the estimated parameter is also addressed. The standard deviation in the estimation process is referred to as the uncertainty associated with the estimated parameters.

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Section: Forward Modelmentioning

confidence: 99%

“…Balaji and Tamanna Padhi [12] used ANN in conjunction with MCMC method to solve an inverse heat conduction problem. They considered steady-state two-dimensional heat conduction from a square slab with uniform volumetric internal heat generation.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian inference approach: estimation of heat flux from fin for perturbed temperature data

Kumar

Gnanasekaran

2018

Sādhanā

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“…With the advent of high performance computing facilities, it is now easy to solve the 3-D Navier Stokes equation along with the energy equation to obtain the necessary temperature distribution of the fin. Recent times, artificial neural network (ANN) is used as forward model that develops a good correlation between the input and output parameters (Balaji and Padhi, 2010;Deng and Hwang, 2006). The use of ANN is found to be useful for the estimation of unknown quantities like heat flux and heat transfer coefficients during various heat transfer problems (Sablani et al, 2005, Ghadimi et al, 2015Zhang et al, 2010, Najafi andWoodbury, 2015).…”

Section: Introductionmentioning

confidence: 99%

3D coupled conduction-convection problem using in-house heat transfer experiments in conjunction with hybrid inverse approach

Vishweshwara

Kumar

Gnanasekaran

et al. 2019

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Purpose Many a times, the information about the boundary heat flux is obtained only through inverse approach by locating the thermocouple or temperature sensor in accessible boundary. Most of the work reported in literature for the estimation of unknown parameters is based on heat conduction model. Inverse approach using conjugate heat transfer is found inadequate in literature. Therefore, the purpose of the paper is to develop a 3D conjugate heat transfer model without model reduction for the estimation of heat flux and heat transfer coefficient from the measured temperatures. Design/methodology/approach A 3 D conjugate fin heat transfer model is solved using commercial software for the known boundary conditions. Navier–Stokes equation is solved to obtain the necessary temperature distribution of the fin. Later, the complete model is replaced with neural network to expedite the computations of the forward problem. For the inverse approach, genetic algorithm (GA) and particle swarm optimization (PSO) are applied to estimate the unknown parameters. Eventually, a hybrid algorithm is proposed by combining PSO with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method that outperforms GA and PSO. Findings The authors demonstrate that the evolutionary algorithms can be used to obtain accurate results from simulated measurements. Efficacy of the hybrid algorithm is established using real time measurements. The hybrid algorithm (PSO-BFGS) is more efficient in the estimation of unknown parameters for experimentally measured temperature data compared to GA and PSO algorithms. Originality/value Surrogate model using ANN based on computational fluid dynamics simulations and in-house steady state fin experiments to estimate the heat flux and heat transfer coefficient separately using GA, PSO and PSO-BFGS.

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“…This method quantify various uncertainties in the system and enhances the accuracy of estimation. Efforts are also made to combine BIT with artificial neural network (ANN) [16,17]. Apart from claiming several other advantages of supplementing BIT in ANN, both the investigations suggested its use particularly when the forward model is computationally too expensive.…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian Inference Technique for the reconstruction of an isothermal hot spot inside a circular disc from peripheral temperature measurement – A critical assessment

Chakraborty

2015

International Journal of Heat and Mass Transfer

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A new ANN driven MCMC method for multi-parameter estimation in two-dimensional conduction with heat generation

Cited by 29 publications

References 11 publications

A Bayesian inference approach: estimation of heat flux from fin for perturbed temperature data

A Bayesian inference approach: estimation of heat flux from fin for perturbed temperature data

3D coupled conduction-convection problem using in-house heat transfer experiments in conjunction with hybrid inverse approach

Application of Bayesian Inference Technique for the reconstruction of an isothermal hot spot inside a circular disc from peripheral temperature measurement – A critical assessment

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