Balaji Chakravarthy

Post shutdown decay heat removal in a fast reactor is one of the most important safety functions which must be accomplished with a very high reliability. To achieve high reliability, the fast breeder reactor design has emphasized on passive or near passive decay heat removal systems utilizing the natural convection in the heat removal path. A typical passive decay heat removal system used in recent designs of fast breeder reactors consists of a sodium to sodium heat exchanger and sodium to air heat exchanger which together remove heat directly from the hot pool to the final heat sink, which is air. Since these are safety systems, it is necessary to confirm the design with detailed numerical analysis. The numerical studies include pool hydraulics, natural convection phenomena in closed loops, flow through narrow gaps between SA, multi-scale modeling, etc. Toward understanding the evolution of thermal hydraulic parameters during natural convection decay heat removal, a three-dimensional CFD model for the primary system coupled with an appropriate one-dimensional model for the secondary system is proposed. The model has been validated against the results of natural convection test conducted in PHENIX reactor. Adopting the model for the Indian PFBR, six different decay heat removal cases have been studied which bring out the effect of safety grade decay heat removal system (SGDHRS) capacity, secondary sodium inventory and inter-wrapper flow heat transfer on the subassembly outlet temperatures that are important for safety evaluation of the reactor. From the results, it is concluded that the delay in initiation of SGDHRS, replacement of intermediate sodium in SGDHRS with NaK and a decrease in the AHX air inlet temperature do not change the temperatures of the primary circuit significantly. The secondary sodium inventory plays an important role in reducing the temperatures in the primary coolant. The beneficial effect of inter-wrapper flow heat transfer on primary temperatures is limited to about 20 K in the fissile zone and 50 K in the blanket zone. These results are very important and give direction for future designs of fast breeder reactors. © 2012 Elsevier B.V.

In this paper we present a novel, practical approach for carrying out distributed high temperature sensing in boilers. We have demonstrated distributed high temperature sensing using fiber Bragg gratings encapsulated inside a rugged mechanical structure. The encapsulation is designed to not only protect the optical fiber from the harsh environment of the boiler, but also to scale the temperature down to a range over which the gratings are relatively stable. A key aspect of our work is the use of a Bayesian inference technique to retrieve the temperature outside the encapsulation within < 1°C accuracy based on the temperature measured by the fiber Bragg grating (FBG).

Numerical results are presented for turbulent natural convection of a heat generating low Prandtl number fluid in a cylindrical enclosure with isothermal lateral walls and adiabatic upper and lower walls. Computations are carried out for three different liquid metals i.e sodium, lead- bismuth (44.5% Pb - 55.5% Bi) and mercury of Prandtl numbers 0.005, 0.0189 and 0.0248 respectively. Turbulence in the natural convection flow has been modeled by a modified k - E closure, wherein fine grids are employed close to the solid walls to enable numerical integration of the equations up to the walls without using wall functions. The computational model has been validated against experimental benchmarks for turbulent natural convection flows in enclosures. Velocity vectors and isotherms are analyzed to understand the fluid flow and heat transfer characteristics along with temperature profiles at various locations in the enclosure. The maximum values of the natural convection velocities for various heat generation rates are found to be in good agreement with natural convection scaling laws. The establishment of the boundary layers is found to be dominated by internal heat generation rate in the enclosure. Correlations for the mean convection Nusselt number and dimensionless temperature in terms of the Boussinesq number are proposed for design purposes.

This work reports a novel “divide and conquer” approach to estimate the principal thermal conductivities of an orthotropic material, specifically engineered with a view to demonstrate the potency of the inverse heat transfer method with unsteady temperature data. The sample is placed in a vacuum chamber maintained at a pressure of 8.6 (Formula presented.) mbar. The heat capacity of the engineered orthotropic material was determined via estimating the heat capacity of a solid SS304 in a sequential fashion. First steady-state experiments followed by a Bayesian estimation with the Metropolis Hastings-Markov Chain Monte Carlo method were done to obtain the thermal conductivity of a solid SS304 block. Using this as a prior, the heat capacity of solid SS304 was obtained through unsteady experiments followed by Bayesian estimation. The heat capacity of SS304 thus obtained is multiplied by the solidity of the engineered orthotropic material, and using this information, the three components of the orthotropic conductivity are estimated again using the Bayesian route. To expedite the estimation, a surrogate for the forward model was developed using artificial neural network. Finally, the retrieved parameters are used to determine the simulated temperatures through the forward model for the orthotropic material. These, when compared with the measured temperatures, gave excellent agreement.

This paper employs the Bayesian based Metropolis Hasting - Markov Chain Monte Carlo algorithm to solve inverse heat transfer problem of determining the spatially varying heat transfer coefficient from a flat plate with flush mounted discrete heat sources with measured temperatures at the bottom of the plate. The Nusselt number is assumed to be of the form Nu = aReb(x/l)c . To input reasonable values of 'a' and 'b' into the inverse problem, first limited two dimensional conjugate convection simulations were done with Comsol. Based on the guidance from this different values of 'a' and 'b' are input to a computationally less complex problem of conjugate conduction in the flat plate (15mm thickness) and temperature distributions at the bottom of the plate which is a more convenient location for measuring the temperatures without disturbing the flow were obtained. Since the goal of this work is to demonstrate the eficiacy of the Bayesian approach to accurately retrieve 'a' and 'b', numerically generated temperatures with known values of 'a' and 'b' are treated as 'surrogate' experimental data. The inverse problem is then solved by repeatedly using the forward solutions together with the MH-MCMC aprroach. To speed up the estimation, the forward model is replaced by an artificial neural network. The mean, maximum-a-posteriori and standard deviation of the estimated parameters 'a' and 'b' are reported. The robustness of the proposed method is examined, by synthetically adding noise to the temperatures.

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The major objectives in the design of thermal systems are obtaining the information about thermophysical, transport and boundary properties. The main purpose of this paper is to estimate the unknown heat flux at the surface of a solid body. A constant area mild steel fin is considered and the base is subjected to constant heat flux. During heating, natural convection heat transfer occurs from the fin to ambient. The direct solution, which is the forward problem, is developed as a conjugate heat transfer problem from the fin and the steady state temperature distribution is recorded for any assumed heat flux. In order to model the natural convection heat transfer from the fin, an extended domain is created near the fin geometry and air is specified as a fluid medium and Navier Stokes equation is solved by incorporating the Boussinesq approximation. The computational time involved in executing the forward model is then reduced by developing a neural network (NN) between heat flux values and temperatures based on back propagation algorithm. The conjugate heat transfer NN model is now coupled with Genetic algorithm (GA) for the solution of the inverse problem. Initially, GA is applied to the pure surrogate data, the results are then used as input to the Levenberg- Marquardt method and such hybridization is proven to result in accurate estimation of the unknown heat flux. The hybrid method is then applied for the experimental temperature to estimate the unknown heat flux. A satisfactory agreement between the estimated and actual heat flux is achieved by incorporating the hybrid method.

Sodium to air heat exchangers (AHX) with finned tubes is used in fast breeder reactors for decay heat removal. The aim of decay heat removal is to maintain the fuel, clad, coolant, and structural temperatures within safety limits. To investigate the thermal hydraulic features of AHX, a robust porous body based computational fluid dynamics (CFD) model has been developed and validated against the experimental data obtained from a model AHX of 2MW capacity in Steam Generator Test Facility at the Indira Gandhi Centre for Atomic Research, Kalpakkam. In the present paper, the developed porous body model is used to study the sodium and air temperature distribution and the influence of various parameters that affect the heat removal rate and sodium outlet temperature in full-size AHX used in the fast breeder reactors. The parameters include mass flow rates and inlet temperatures of sodium and air. The focus of the study has been to identify conditions that can pose the risk of sodium freezing.

An experimental investigation of the interaction of surface radiation and natural convection in an L shaped corner is presented. Parametric studies to explore the influence of surface emissivity and aspect ratio on the total heat transfer rate from the isothermal vertical wall of the L corner, have been carried out. For an L corner with highly polished walls, the present experimental results are compared with results available in literature, and are found to be in good agreement. A new semi-experimental method for solving this class of problems is discussed, and a correlation for estimating the total Nusselt number is proposed. It is found that surface radiation has a significant effect on the total heat transferred from the vertical wall of the L-shaped corner.

This article reports the results of mixed convection heat transfer studies from five heat sources (aluminum) mounted at different positions on a substrate board (Bakelite). The goal is to determine the optimal arrangement, such that, the maximum temperature excess is minimum among all the possible configurations. For accomplishing this, a completely experimental driven hybrid optimization strategy, that combines Artificial neural network (ANN) with Genetic algorithm (GA) is used. Initial optimization studies are carried out by employing a heuristic non-dimensional geometric parameter λ, which is identified to be the key parameter to decide the maximum temperature in the system. © 2014 Taylor & Francis Group, LLC.