Estimation of time-dependent heat flux using temperature distribution at a point in a two layer system

AbstractIn this paper, the conjugate gradient method, coupled with the adjoint problem, is used in order to solve the inverse heat conduction problem and estimation of the time-dependent heat flux, using temperature distribution at a point in a two layer system. Also, the effect of noisy data on the final solution is studied. The numerical solution of the governing equations is obtained by employing a finite-difference technique. For solving this problem, the general coordinate method is used. The irregular region in the physical domain (r,z) is transformed into a rectangle in the computational domain (ξ,η). The present formulation is general and can be applied to the solution of boundary inverse heat conduction problems over any region that can be mapped into a rectangle. The obtained results for few selected examples show the good accuracy of the presented method. Also, the solutions have good stability even if the input data includes noise. The problem is solved in an axisymmetric case. Applications of this model are in the thermal protect systems (t.p.s.) and heat shield systems

Selection and optimization of marine oil spill response operations using artificial intelligence and soft computing techniques

Marine oil spill incidents are detrimental to both natural environment and human health. Water quality, marine ecosystems, and shoreline conditions can be deteriorated by the spilt oil. Swift and efficient response to an oil spill is crucial to minimize the adverse consequences. However, the oily waste generated from response operations may also become a challenge, requiring careful waste management strategies. Widely used oil spill response methods (OSRMs) include mechanical containment and recovery (MCR), in-situ burning, and the use of chemical dispersants. Choosing the most suitable method is a complex process depending on various factors. Among OSRMs, MCR is the most effective in removal of spilt oil from the marine environment. The management of oily wastewater generated during MCR requires careful attention, as it comprises a significant portion of overall oily waste. This study developed multiple tools to aid selecting OSRMs in harsh and remote offshore waters. These selection tools employ machine learning techniques and historical response data to predict appropriate OSRMs for new incidents. The tools were developed in MATLABTM using various artificial intelligence and soft computing techniques, such as fuzzy decision tree (FDT), Gaussian process regression (GPR), and artificial neural network, individually or in combination. FDT-based tools were also integrated with regression analysis techniques and an optimization algorithm to enhance their performance. Optimized FDTs integrated with regression analysis and GPR were found to be the most effective techniques based on the prediction power. Furthermore, this study developed an integrated optimization tool to efficiently manage the mechanical response process. This tool aims to minimize the time and cost associated with MCR and oily wastewater management (OWM) and the volume of weathered oil during the operation. The tool encompasses three components of multi-objective optimization, oil weathering process, and MCR and OWM operational agents, simulating detailed response procedures. Applying the tool to a case study in Canada led to a notable reduction in the time and cost of the entire response, and a considerable increase in the volume of recovered oil. It provides an effective approach to manage response process, and significantly reduces the environmental and socio-economic impacts of oil spill incidents.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat

Fabrication and characterization of form-stable FA ternary eutectic mixture/GO nanocomposite for thermal energy storage

The thermal conductivity of commonly used phase change materials (PCM) for thermal energy storage (TES), such as, fatty acids, paraffin etc., is relatively poor, which is one of the main drawbacks for limiting their utility. In the recent past, few attempts have been made to enhance the thermal conductivity of PCM by mixing different additives in the appropriate amount. Graphene nanoparticles, having higher thermal conductivity may be a potential candidate for the same, when mixed appropriately with different PCM. In present study, acetic acid , tristearin and stearic acid (CA-PA-SA) ternary eutectic mixture was impregnated into nano-graphene oxide (nano-GO) to prepare a form-stable composite PCM (CAPA-SA/nano-SiO2). The phase change temperature range of the composite PCM is 17.2 ◦C–26 ◦C, which is suitable for indoor thermal environment. The high latent heat value and thermal conductivity of the composite PCM are 169.43 kJ kg-1 and 0.68239 W (m K)-1, which is helpful to stabilize the indoor temperature for a long time and improve human comfort. Furthermore, after 500 heating/cooling cycles, the composite PCM showed good thermal and chemical stability. All the results indicated that the composite PCM is suitable for storing excess solar radiation and reducing the amount and rate of heat loss of buildings in the winter, which will help reduce building energy consumption

Numerical Investigation of Channel Geometry on the Performance of a Pem Fuel Cell

A complete three-dimensional and single phase model for proton exchange membrane (PEM) fuel cells was used to investigate the effect of using different channels geometry on the performances, current density and gas concentration. The proposed model was a full cell model, which includes all the parts of the PEM fuel cell, flow channels, gas diffusion electrodes, catalyst layers and the membrane. Coupled transport and electrochemical kinetics equations were solved in a single domain; therefore no interfacial boundary condition was required at the internal boundaries between cell components. This computational fluid dynamics code was employed as the direct problem solver, which was used to simulate the three-dimensional mass, momentum, energy and species transport phenomena as well as the electron- and proton-transfer process taking place in a PEMFC. The results showed that the predicted polarization curves by using this model were in good agreement with the experimental results and a high performance was observed by using circle geometry for the channels of anode and cathode sides. Also, the results showed that the performance of the fuel cell improved when a rectangular channel was used

Comparison of the Experimental and Predicted Data for Thermal Conductivity of Fe3O4/water Nanofluid Using Artificial Neural Networks

Objective(s): This study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ANN) and correlation using experimental data. Methods: Two-layer perceptron feedforward artificial neural network and backpropagation Levenberg-Marquardt (BP-LM) training algorithm are used to predict the thermal conductivity of the nanofluid. Fe3O4 nanoparticles are prepared by chemical co-precipitation method and thermal conductivity coefficient is measured using 2500TPS apparatus. Results: Fe3O4 nanofluids with particle size of 20-25 nm are used to test the effectiveness of ANN. Thermal conductivity of Fe3O4 /water nanofluid at different temperatures of 25, 30 and 35℃ and volume concentrations, ranging from 0.05% to 5% is employed as training data for ANN. The obtained results show that the thermal conductivity of Fe3O4 nanofluid increases linearly with volume fraction and temperature. Conclusions: the artificial neural network model has a reasonable agreement in predicting experimental data. So it can be concluded the ANN model is an effective method for prediction of the thermal conductivity of nanofluids and has better prediction accuracy and simplicity compared with the other existing theoretical methods

Numerical Analysis of Thermal Conductivity of Non-Charring Material Ablation Carbon-Carbon and Graphite with Considering Chemical Reaction Effects, Mass Transfer and Surface Heat Transfer

Nowadays, there is little information, concerning the heat shield systems, and this information is not completely reliable to use in so many cases. for example, the precise calculation cannot be done for various materials. In addition, the real scale test has two disadvantages: high cost and low flexibility, and for each case we must perform a new test. Hence, using numerical modeling program that calculates the surface recession rate and interior temperature distribution is necessary. Also, numerical solution of governing equation for non-charring material ablation is presented in order to anticipate the recession rate and the heat response of non-charring heat shields. the governing equation is nonlinear and the Newton- Rafson method along with TDMA algorithm is used to solve this nonlinear equation system. Using Newton- Rafson method for solving the governing equation is one of the advantages of the solving method because this method is simple and it can be easily generalized to more difficult problems. The obtained results compared with reliable sources in order to examine the accuracy of compiling code

Axisymmetric stagnation-point flow and heat transfer of a viscous, compressible fluid on a cylinder with constant heat flux

AbstractExisting solutions of the problem of axisymmetric stagnation-point flow and heat transfer on either a cylinder or flat plate are for incompressible fluid. Here, fluid with temperature dependent density is considered in the problem of axisymmetric stagnation-point flow and heat transfer on a cylinder with constant heat flux. The impinging free stream is steady and with a constant strain rate, k̄. An exact solution of the Navier–Stokes equations and energy equation is derived in this problem. A reduction of these equations is obtained by use of appropriate transformations introduced for the first time. The general self-similar solution is obtained when the wall heat flux of the cylinder is constant. All the solutions above are presented for Reynolds numbers, Re=k̄a2/2υ, ranging from 0.01 to 1000, selected values of compressibility factors, and different values of Prandtl number, where a is cylinder radius and ν is the kinematic viscosity of the fluid. For all Reynolds numbers and surface heat flux, as the compressibility factor increases, both components of the velocity field, the heat transfer coefficient and the shear-stresses increase, and the pressure function decreases

Numerical Analysis of Thermal Conductivity of Non-Charring Material Ablation Carbon-Carbon and Graphite with Considering Chemical Reaction Effects, Mass Transfer and Surface Heat Transfer

Nowadays, there is little information, concerning the heat shield systems, and this information is not completely reliable to use in so many cases. for example, the precise calculation cannot be done for various materials. In addition, the real scale test has two disadvantages: high cost and low flexibility, and for each case we must perform a new test. Hence, using numerical modeling program that calculates the surface recession rate and interior temperature distribution is necessary. Also, numerical solution of governing equation for non-charring material ablation is presented in order to anticipate the recession rate and the heat response of non-charring heat shields. the governing equation is nonlinear and the Newton- Rafson method along with TDMA algorithm is used to solve this nonlinear equation system. Using Newton- Rafson method for solving the governing equation is one of the advantages of the solving method because this method is simple and it can be easily generalized to more difficult problems. The obtained results compared with reliable sources in order to examine the accuracy of compiling code