1. Ph.D Theses
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/11
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Item Inverse Estimation of Multi- Parameters Using Bayesian Framework Combined with Evolutionary Algorithms for Heat Transfer Problems(National Institute of Technology Karnataka, Surathkal, 2020) S, Vishweshwara P.; Gnanasekaran, N.; M, Arun.This thesis focuses on the estimation of unknown parameters using various inverse methods for the heat transfer problems. The first class of problem elaborately discusses about the estimation of interfacial heat transfer coefficients during the solidification of casting. To accomplish this, a prevalent one dimensional transient horizontal directional solidification of Sn-5%wtPb alloy with temperature dependent thermophysical properties and latent heat is considered to be the mathematical model/forward model and numerically solved using Explicit Finite Difference Method to obtain temperature distribution from the known boundary and initial conditions. The temperatures from the forward model is validated with the literature and an absolute error of 5% from the actual measurements was observed. In order to mimic the real time experiments, the temperatures are added with σ=0.01Tmax, σ=0.02Tmax and σ=0.03Tmax Gaussian white noise (simulated measurements) and compared with two different objective functions: (i) Least Squares and (ii) Bayesian Framework. Meantime, to expedite the solution of the inverse problem, the numerical model is then replaced with Artificial Neural Network (ANN), which acts as a fast forward model to estimate the unknown constants present in the correlation of interfacial heat transfer coefficient. A total of 473 data sets of inputs and corresponding outputs were used to create a trained artificial neural network which produced temperatures with an accuracy less than 0.1◦C temperature difference from the exact temperature data. Genetic Algorithm (GA) was implemented as an inverse method and it was found that ANN-GA-Bayesian framework was more effective compared to ordinary least squares for noise added data with an overall average error of 2%. Furthermore, an extended study on the advantage of Bayesian framework for the estimation of multi-parameters during Al-4.5wt%Cu alloy solidification is also discussed in detail. The main aim is to retrieve more information with less available simulated measurements. A sensitivity analysis is performed to understand the dependency of the unknown parameters like modeling error, latent heat and heat transfer coefficient parameters on the solution. It showed that the values of constants of the IHTC correlation and latent heat affect the temperature distribution in casting significantly. For iiithe solution of inverse estimation, the use of two different metaheuristic algorithms (i) Genetic Algorithm (GA) and (ii) Particle Swarm Optimization (PSO) is illustrated. A careful examination of the mentioned algorithms is performed to fix the algorithm parameters. The results revealed that PSO combined with Bayesian framework provides a better computational solution compared to GA-Bayesian with an overall absolute error less than 6%. Also, the study on the effect of multiple sensors revealed that using two sensor the average % error for the estimation of a ,b and latent heat was 0.247, 0.3 and 0.45 respectively and suggesting that two sensors were sufficient for the present analysis. The second class of problem is extended to retrieve the unknown heat flux and heat transfer coefficient for a 3-D steady state conjugate fin heat transfer problem. A mild steel fin with dimensions 150x250x6 mm3 is placed centrally on to an aluminium base of dimensions 150x250x8 mm3 and experiments are conducted for different heat flux values of 305, 544, 853 and 1232 W/m2 and corresponding temperature distribution along the vertical fin is recorded. Navier-Stokes equation is solved to obtain the necessary temperature distribution of the fin. Heat flux with the range between 305W/m2 and 3300 W/m2 and its corresponding temperature distribution of the fin is obtained using commercial software. A total of 24 Computational Fluid Dynamics (CFD) simulations are performed to create a neural network model that can surrogate the forward problem in order to expedite the computational process. The estimation of the heat flux and heat transfer coefficient using GA, PSO and PSO- Broyden Fletcher Goldfarb Shanno (BFGS) is carried out for both simulated and experimental data. A detailed comparison study on the effect of algorithm parameters on the solution is demonstrated in order to examine the performance of the algorithms. For simulated temperature measurements, all the mentioned algorithms proved to be effective but PSO-BFGS estimated the heat flux with an absolute % error of 0.86 and heat transfer coefficient with 0.105% for experimental temperatures. The results show that the PSO-BFGS method outperforms GA and PSO and is observed to be a formidable approach in the estimation of the unknown parametersItem Investigation of flow boiling heat transfer and friction coefficient on compact plate-fin heat exchanger surfaces for R134a(National Institute of Technology Karnataka, Surathkal, 2017) Raju, Muppala Amaranatha; Ashok Babu, T. P.; Ranganayakulu, C.Compact plate-fin heat exchangers are extensively used in refrigeration industry, cryogenics and various process plants. Intensification of industrial thermal processes on one side as well as energy efficiency considerations on the other side has led to considerable interest in compact heat exchangers for applications of evaporation and condensation, which call for a low temperature difference between the fluids and thus for high heat transfer coefficients. In addition, compact heat exchangers are being used in aircraft industry for all electric ECS (Environmental Control System), utilizing phase change for design of evaporator and condenser. The hydraulic diameter of flow passages is usually less than 3 mm in compact heat exchangers. The two-phase flow regimes which, occur in these passages differ from those in general heat exchangers. In phase change heat transfer, in addition to fluid properties and geometrical parameters, fluid flow parameters are also affecting the heat transfer and frictional coefficients. Present study aims to extend the knowledge of performance of compact evaporator’s and to develop a model which can be used for evaluating the heat transfer and pressure drop over a wide range of operating conditions as possible. In the present study the two-phase phase frictional pressure drop and heat transfer performance characteristics of compact plate fin heat exchangers used as evaporators over R134a were investigated. Two-phase heat transfer coefficient and friction coefficient of the finned surfaces constitute the most important parameters for design of compact evaporator. These parameters are functions of fin geometry, mass flux, heat flux and vapour quality. An experimental test facility has been constructed to study the 2 offset strip and 2 wavy fin surfaces of plate fin heat exchangers and for generation of two-phase heat transfer and friction data. A cross flow heat exchanger of specified dimension (150 x150 mm) has been designed and manufactured using vacuum brazing technique. It serves as the experimental test section/test evaporator. One channel of the test section, R134a refrigerant is passed and another channel of test section is passed with water. The heat is exchanged between these fluids. R134a absorbs the heat from water and gets evaporated due to latent heat of evaporation. Water gets cooled. iiiExperiments were carried out on evaporator test sections under two-phase flow conditions using R134a on one side and water on another side of the test section to investigate the two-phase heat transfer coefficients and friction coefficients on the wavy and offset strip fin surfaces. Refrigerant flow boiling heat transfer and twophase pressure drop data were obtained over a range of refrigerant mass flux from 30 to 100 kg/m2s, heat flux from 11 to 24 kW/m2 , outlet vapour quality from 0.24 to 0.9 and saturation temperatures from -5 to 5 °C. The data was obtained under steady state conditions during evaporator performance tests. Inlet and exit temperatures, pressures as well as refrigerant flow rates, water flow rates, pressure drops across the test section has been measured and recorded. Experimental data was reduced and analysed for effect of quality, mass flux and heat flux and presented in the report. The correlations were developed in terms of Reynolds number factor (F) and Martenelli parameter (X) for flow boiling heat transfer and in terms two-phase frictional multiplier �� and Martenelli parameter (X) for frictional pressure drop using the regression analysis. Two-phase forced convective heat transfer coefficient is a multiplication of single-phase heat transfer coefficient, hl by Reynolds number factor (F). Before conducting two-phase heat transfer experiments single-phase flow and heat transfer experiments were conducted on these fin surfaces to validate the test facility and testing procedure and also to find out single-phase heat transfer coefficient hl and frictional factor f. The measured single-phase flow and heat transfer data for each fin surface is estimated in terms of the Colburn j factor and Fanning friction factor f as a function of Reynolds number. Single phase flow and heat transfer analysis of R134a refrigerant (liquid phase) has also been carried out using Computational fluid dynamics (CFD) approach for wavy and offset strip fin surfaces. The results were validated with the single phase experimental results. Colburn j factor and Fanning friction factor f are predicted for both the fins. The correlations are developed at Reynolds number range of 100-15000. The effects of fin geometry on the enhanced heat transfer and pressure drops were investigated.