Numerical study of nanoencapsulated phase change material inside double pipe heat exchanger

Nanoencapsulated phase change material (NPCM) slurry is a dispersion where the phase change material (PCM) is dispersed in fluid. Compared with fluid, these nanofluids have a higher heat capacity during the phase change and a possible enhancement, as a result of this phase change, in the heat transfer phenomenon. To appreciate the merits, in terms of energy, a numerical study has been carried out with fluid based on NPCM inside double pipe heat exchanger. The numerical simulation results have been validated using experimental heat transfer data. The Reynolds and Nusselt numbers have been determined using thermal conductivity and viscosity evaluated in the same conditions as those in numerical model. The results obtained show an improvement of this energetic criterion at low mass flow rate compared with the base fluid. Analysis of the numerical and analytical results reveal that higher inlet flow rate and NPCM concentration results in higher heat transfer rate. In addition, increasing NPCM slurry temperature decreases its performance due to fast melting of PCM inside the tube.     

Thermal Energy Storage Behavior of a Paraffin during Melting and Solidification

Abstract

In this study, experiments are conducted to investigate charging and discharging characteristics of a paraffin as a phase change material (PCM). A vertical tube-in-shell geometry is designed to store the PCM. The thermophysical properties of the paraffin examined are determined through the differential scanning calorimeter (DSC) analysis. A series of experiments are carried out to investigate the effect of increasing the inlet temperature and the mass flow rate of the heat transfer fluid (HTF) both on the charging and discharging processes (i.e., melting and solidification) of the PCM.     

Microencapsulated dimethyl terephthalate phase change material for heat transfer fluid performance enhancement

Micro‐phase change materials (micro‐PCMs) are proposed to increase the thermal conductivity and the thermal energy storage capacity of a heat transfer fluid (HTF). In this work, we have selected dimethyl terephthalate (DMT) to be used as a PCM for performance enhancement of a synthetic oil in the temperature range of approximately 100 to 170 °C. Silicon dioxide (SiO₂) was used as the microencapsulant, because of its desirable properties as containment material, including thermal stability. The SiO₂‐coated DMT micro‐PCM was characterized to determine relevant properties and its suitability for HTF performance enhancement. The SiO₂‐coated DMT was found to completely disperse in the synthetic oil, Therminol SP, silicone oil, at and above 100 °C. FTIR, thermal diffusivity and differential scanning calorimetry measurements were carried out on the materials, and these tests demonstrated that the coated particles can be used for HTF enhancement in the temperature range of 100–170 °C and potentially higher temperatures if pressurized pipes/vessels are utilized. Using the measured thermal diffusivity and known data for density and specific heat capacity, the thermal conductivity of the micro‐PCM was calculated. Our calculations indicate that both the thermal conductivity and the thermal energy storage heat capacity of the HTF would be enhanced by the addition of this micro‐PCM. It is expected that the thermal conductivity increase will enhance the heat transfer of the fluid when in use at temperatures above and below the melting temperature of the PCM. At the melting point, the latent heat of the PCM will increase the thermal energy storage capacity of the fluid. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimal performance assessment for a photo‐Fenton degradation pilot plant driven by solar energy using artificial neural networks

Artificial neural networks (ANN) were proposed as a multivariate experimental design tool for monitoring a photo‐Fenton treatment of wastewaters containing a synthetic mixture of pesticides. ANN and Nelder‐Mead simplex methods were used to find out the optimum operating parameters of a photo‐Fenton pilot plant. ANN was developed to predict the most important operating parameters (e.g., the total organic carbon and the initial mineralization kinetic rate constants of the reactions), which determine the photo‐catalytic degradation efficiency in photo‐Fenton processes. Experimental measurements of temperature, pH, hydrogen peroxide (H₂O₂) consumption, initial concentration of Fe²⁺, and the AE were used as input data for the ANN learning. A feed‐forward with one hidden layer, a Levenberg–Marquardt learning algorithm, a hyperbolic tangent sigmoidal transfer function and a linear transfer function were used to develop the ANN model. The best fitting of the training database was obtained with an ANN architecture constituted by seven neurons in the hidden layer. The simulated results were validated with experimental measurements, showing an acceptable agreement (R² > 0.99). The ANN was subsequently coupled with a Nelder–Mead simplex method to obtain the optimum operating parameters of the photo‐Fenton pilot plant. The H₂O₂ consumption was used as key variable for evaluating the optimization procedure. Errors less than 1% between simulated and experimental data were found. The obtained results showed that the use of ANN provides an excellent predictive performance tool with the additional capability to assess the influence of each operating parameter on the removal process of water pollutants. Copyright © 2011 John Wiley & Sons, Ltd.     

Prediction of compressive strength of geopolymer composites using an artificial neural network

Geopolymers are highly complex materials which involve many variables and make for which modelling the properties is very difficult. There is no systematic approach in mix design for geopolymers. Since the amounts of silica modulus, Na₂O content, w/b ratios and curing time have a great influence on the compressive strength, an ANN (artificial neural network) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of adapting ANN and artificial intelligence system for predicting the compressive strength have been studied. Consequently, a multilayer ANN by using back propagation architecture can be developed for geopolymer compressive strength prediction. In this study, the coefficient of determination (R²) has been used for investigating the proposed model accuracy. As a result, proposed ANN model can predict the compressive strength of geopolymer with R²?=?0.958.     

Hydrophilic expanded graphite–magnesium nitrate hexahydrate composite phase change materials: Understanding the effect of hydrophilic modification on thermophysical properties

Expanded graphite (EG) has shown excellent performances in compression resilience, thermal conductivity, and adsorption ability. EG can adsorb liquid phase change materials (PCMs) mainly because of capillary action; however, EG is hydrophobic, which makes it less compatible with hydrated salts. Herein, hydrophilic EG (HEG) was prepared with Triton X‐100 (TX‐100) as surface modifier. The HEG–magnesium nitrate hexahydrate (HEG‐MNH) composite as a PCM was investigated for thermal energy storage (TES) to understand the effect of hydrophilic modification on thermophysical properties. The powder‐state HEG is added into MNH to prepare HEG‐MNH composite PCM, which contains 1.71 wt% of TX‐100, 7.29 wt% of EG, and 91.00 wt% of MNH by control variable method. The melting point and latent heat of HEG‐MNH composite PCM were 89.05°C and 137.28 J/g, respectively. The endothermic enthalpy change of HEG‐MNH composite PCM only decreased by 0.90%, along with the exothermic values of HEG‐MNH composite that increased by 3.80% after 100 cycles. The thermal conductivity is higher 5.17 times than that of the pure MNH. Our work suggests that the HEG‐MNH composite PCM has a great potential to be used as a PCM for TES.     

An experimental optimization study on a tube-in-shell latent heat storage

Thermal energy storage (TES) using phase change materials (PCMs) has recently received considerable attention in the literature, due to its high storage capacity and isothermal behaviour during the storage (melting or charging) and removal (discharging or solidification). In this study, a novel modification on a tube-in-shell-type storage geometry is suggested. In the proposed geometry, the outer surface of the shell is inclined and it is the objective of this study to determine the optimum range for the inclination angle of the shell surface. Paraffin with a melting temperature of 58.06°C, which is supplied by the Merck Company, is used as the PCM. The PCM is stored in the vertical annular space between an inner tube through which the heat transfer fluid (HTF), hot water, is flowing and a concentrically placed outer shell. At first, the thermophysical properties of this paraffin are determined through the differential scanning calorimeter (DSC) analysis. Temporal behaviour of the PCM undergoing a non-isothermal solid–liquid phase change during its melting or charging by the HTF are determined for different values of the inlet temperature and the mass flow rate of the HTF. The new geometry is shown to respond well with the melting characteristics of the PCM and to enhance heat transfer inside the PCM for a specific range of the shell inclination angle. Copyright © 2006 John Wiley & Sons, Ltd.     

Development and thermal performance of wood-HPDE- PCM nanocapsule floor for passive cooling in building

Cooling demand in the building sector is growing rapidly; thermal energy storage systems using phase change materials (PCM) can be a very useful way to improve the building thermal performance. This work shows the benefits of PCM when incorporated in wood fiber-polymer composite as floor cooling system using nano-encapsulated PCMs. The wood-plastic-NPCM composites were produced using compression molding process and its mechanical and thermal properties were investigated. Two dynamic simulators were employed to investigate synthesized composites thermal performance. Increasing NPCM content in WPC showed that the fluctuations of the simulator temperature was decreased while the heat fluxes through the floor was increased. The variations of ambient maximum temperature have little effect on the air temperature of the simulator with 40% PCM which indicates that the amount of PCM was enough for studied environmental condition. Field experiments were performed using two medium-scale test houses located on Tehran-Iran. It can be concluded that using NPCM helps to reduce heating and cooling demand. Moreover, the natural night ventilation by opening windows reduced the number of hours that the temperature is above 23°C from 499 h/year in case1 (without opening) to 255 h/year in case 2(with opening). This means that natural night ventilation could help reduce the overheating period to about 50% with the use of NPCM.     

Numerical analysis and parameters optimization of shell-and-tube heat storage unit using three phase change materials

In this paper, a mathematical model of shell-and-tube latent heat thermal energy storage (LHTES) unit of two-dimension of three phase change materials (PCMs) named PCM1, PCM2 and PCM3 with different high melting temperatures (983 K, 823 K and 670 K, respectively) and heat transfer fluid (HTF: air) with flowing resistance and viscous dissipation based on the enthalpy method has been developed. Instantaneous solid–liquid interface positions and liquid fractions of PCMs as well as the effects of inlet temperatures of the air and lengths of the shell-and-tube LHTES unit on melting times of PCMs were numerically analyzed. The results show that melting rates of PCM3 are the fastest and that of PCM1 are the slowest both x, r directions. It is also found that the melting times of PCM1, PCM2 and PCM3 decrease with increase in inlet temperatures of the air. Moreover, with increase in inlet temperatures of the air, decreasing degree of their melting times are different, decreasing degree of the melting time of PCM1 is the biggest and that of PCM3 is the smallest. Considering actual application of solar thermal power, we suggest that the optimum lengths are L₁ = 250 mm, L₂ = 400 mm, L₃ = 550 mm (L = 1200 mm) which corresponds to the same melting times of PCM1, PCM2 and PCM3 are about 3230 s and inlet temperature of the air is about 1200 K. The present analysis provides theoretical guidance for designing optimization of the shell-and-tube LHTES unit with three PCMs for solar thermal power.     

Thermal performance of a eutectic mixture of lauric and stearic acids as PCM encapsulated in the annulus of two concentric pipes

Thermal performance characteristics of a eutectic mixture of lauric and stearic acids as phase change material (PCM) during the melting and solidification processes were determined experimentally in a vertical two concentric pipe-energy storage system. This study deals with three important subjects: The first one is to determine the eutectic composition ratio of the lauric acid (LA) and stearic acid (SA) binary system, and to measure its thermophysical properties by DSC. The second one is to establish the thermal characteristics of the mixture such as total melting and solidification times, the heat transfer modes in melted and solidified PCM, and the effect of Reynolds and Stefan numbers as inlet heat transfer fluid (HTF) conditions on the phase transition behaviors. The final one includes the calculations of the heat transfer coefficients between the outside wall of the HTF pipe and the PCM, and heat fractions during the melting and solidification processes of the mixture, and also the discussion of the effect of inlet HTF parameters on these characteristics. The LA–SA binary system in the mixture ratio of 75.5:24.5 wt % forms a eutectic, which melts at 37°C and has a latent heat of 182.7 J g⁻¹, and, thus, these properties make it an attractive phase change material used for passive solar space heating applications such as building and greenhouse heating with respect to the climate conditions. The experimental results indicated that the mixture encapsulated in the annulus of two concentric pipes has good thermal and heat transfer characteristics during the melting and solidification processes, and it has potential for heat storage in passive solar space heating systems.     

Phase change and heat transfer characteristics of a eutectic mixture of palmitic and stearic acids as PCM in a latent heat storage system

The phase change and heat transfer characteristics of a eutectic mixture of palmitic and stearic acids as phase change material (PCM) during the melting and solidification processes were determined experimentally in a vertical two concentric pipes energy storage system. This study deals with three important subjects. First is determination of the eutectic composition ratio of the palmitic acid (PA) and stearic acid (SA) binary system and measurement of its thermophysical properties by differential scanning calorimetry (DSC). Second is establishment of the phase transition characteristics of the mixture, such as the total melting and solidification temperatures and times, the heat transfer modes in the melted and solidified PCM and the effect of Reynolds and Stefan numbers as initial heat transfer fluid (HTF) conditions on the phase transition behaviors. Third is calculation of the heat transfer coefficients between the outside wall of the HTF pipe and the PCM, the heat recovery rates and heat fractions during the phase change processes of the mixture and also discussion of the effect of the inlet HTF parameters on these characteristics. The DSC results showed that the PA–SA binary system in the mixture ratio of 64.2:35.8 wt% forms a eutectic, which melts at 52.3 °C and has a latent heat of 181.7 J g⁻¹, and thus, these properties make it a suitable PCM for passive solar space heating and domestic water heating applications with respect to climate conditions. The experimental results also indicated that the eutectic mixture of PA–SA encapsulated in the annulus of concentric double pipes has good phase change and heat transfer characteristics during the melting and solidification processes, and it is an attractive candidate as a potential PCM for heat storage in latent heat thermal energy storage systems.     

Numerical simulation of high‐temperature phase change heat storage system

In this paper, numerical results pertaining to cyclic melting and freezing of an encapsulated phase‐change material (PCM) have been reported. The cyclic nature of the present problem is relevant to latent heat thermal energy storage system used to power solar Brayton engines in space. In particular, a physical and numerical model of the single‐tube phase change heat storage system was developed. A high‐temperature eutectic mixture of LiF‐CaF₂ was used as the PCM and dry air was used as the working fluid. Numerical results were compared with available experimental data. The trends were in close agreement. © 2003 Wiley Periodicals, Inc. Heat Trans Asian Res, 33(1): 32–41, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/htj.10132     

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

In this study, the effect of the amount of data used in the design of artificial neural networks (ANNs) on the predictive accuracy of ANNs was investigated. Five different ANNs were designed using the experimentally measured specific heat data of the Al₂O₃/water nanofluid prepared at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1 and 0.2 using the Al₂O₃ nanoparticle. The developed ANN is a multi‐layer perceptron, feedforward and backpropagation model. In each ANN with 15 neurons in the hidden layer, the volumetric concentration (φ) and temperature (T) values were nominated as input layer factors and the specific heat value was estimated as the output value. With the aim of survey the effect of the amount of data on the predicted results of the ANN, a different amount of datasets were used in each developed ANN. In this context, in total 260 data were used in the Model 1 ANN. Subsequently, the total amount of data was reduced by 20% in each developed neural network and 55 data were used in the ANN named Model#5. The results obtained show that ANNs are highly talented of predicting the specific heat values of Al₂O₃/water nanofluid. However, in the comparisons, it was evaluated that the amount of data used had a share on the prediction performance of the ANN and that the decrease in the amount of data with the prediction performance of the ANN decreased.     

Developing microencapsulated 12‐hydroxystearic acid (HSA) for phase change material use

Phase change materials (PCM) have an increasingly more important role as a thermal energy storage (TES) media. However, leakage problem of PCM causes limitation during their integration in TES systems. Therefore, the encapsulation of PCMs is attracting research interest to extend usage of PCMs in real TES applications in recent years. In this study, hydroxystearic acid (HSA) was encapsulated with polymethyl methacrylate (PMMA) and different PMMA comonomer shells via emulsion polymerization method for the first time in literature. HSA with high melting temperature range (74–78°C) can widen the scope of using PCMs, and the encapsulated form can make it more versatile. The chemical structures, morphologies, and thermophysical properties of capsules were determined by FT‐IR, SEM, DSC, TGA, and thermal infrared camera. Among the produced HSA capsule candidates, PMMA‐HEMA is the most promising with latent heat of 48.5 J/g with melting range of 47 to 85°C. SEM analysis indicated that the capsules have spherical shape with compact surface at nano‐micro (100–440 nm) size range; however, some capsules exhibited agglomeration.     

Experiment on heat storage characteristic of microencapsulated phase change material slurry

Heat storage experiment by natural convection in rectangular enclosures heated from bottom has been conducted with fluid slurry composed of microencapsulated phase change material (PCM). The microencapsulated PCM is prepared by in-situ polymerization method, where the core materials are composed of several kinds of n-paraffin waxes (mainly nonadecane) and the membrane is a type of melamine resin. Its slurry mixed with water is used in this study, and shows a peak value in the specific heat capacity with latent heat at the temperature of about T=31 °C. The influences of the phase change material on heat storage and the heat transfer process, as well as effects of PCM mass concentration C_m on the microcapsule slurry, temperature of heat storage T_H and a horizontal enclosure height H are also investigated. Transient heat transfer coefficient α, heat storage capacity Q and completion time of heat storage t_c are discussed.     

Natural convection heat transfer of microemulsion phase-change-material slurry in rectangular cavities heated from below and cooled from above

An experimental study has been conducted in dealing with natural convection heat transfer characteristics of microemulsion slurry in rectangular enclosures. The microemulsion slurry used in the present experiment was composed of water, surfactant, and fine particles of phase-change-material (PCM). The PCM mass concentration of the microemulsion slurry was varied from a maximum 30 mass% to a diluted minimum 5 mass%, and the experiments have been done separately in three subdivided temperature ranges of the dispersed PCM particles in a solid phase, two phases (coexistence of solid and liquid) and a liquid phase. The results showed that the Nusselt number increased slightly with the PCM mass concentration for the slurry in solid phase. In the phase change temperature range, the Nusselt number increased with an increase in PCM mass concentration of the slurry at low Rayleigh numbers, while it decreased with increasing PCM mass concentration of the slurry at high Rayleigh numbers. There was not much difference in natural heat transfer characteristics of the PCM slurry with low PCM concentrations (<10 mass%), however, the difference was getting greater with increasing the PCM concentration, especially for the enclosure at a lower aspect ratio (width/height of the rectangular enclosure). The enclosure height was varied from 5.5 to 24.6 mm under a fixed width and depth of 120 mm. Hence, the experiments were performed for a wide range of modified Rayleigh number from 3 × 10² to 1.0 × 10⁷. The correlation generalized for the PCM slurry in a single phase was derived in the form of Nu=0.22(1−C₁C_me^−C₂AR)Ra^1/(3n+1), where C₁ and C₂ were the optimum fitting constants obtained by the least square method. While the PCM was in a phase changing region, the correlation could be expressed as Nu=0.22(1−C₁C_me^−C₂AR)Ra^1/(3n+1)Ste^−0.25, where the Ste was the modified Stefan number.     

Modeling the effect of non-linear process parameters on the prediction of hydrogen production by steam reforming of bio-oil and glycerol using artificial neural network

Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R²) > 0.9. A low mean square error of 3.024 × 10⁻²⁴ and 6.22 × 10⁻¹⁵ for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.     

Gas permeation through H2-selective mixed matrix membranes: Experimental and neural network modeling

Gas permeability through synthesized polydimethylsiloxane (PDMS)/zeolite 4A mixed matrix membranes (MMMs) were investigated with the aid of artificial neural network (ANN) approach. Kinetic diameter and critical temperature of permeating components (e.g. H₂, CH₄, CO₂ and C₃H₈), zeolite content and upstream pressure as input variables and gas permeability as output were inspected. Collected data of the experimental operation was used to ANN training and optimum numbers of hidden layers and neurons were obtained by trial-error method. The selected ANN architecture (4:10:1) was used to predict gas permeability for different inputs in the domain of training data. Based on the results, the predicted values demonstrate an excellent agreement with the experimental data, with high correlation (R² = 0.9944) and less error (RMSE = 1.33E−4). Furthermore, using sensitivity analysis, kinetic diameter and critical temperature were found as the most significant effective variables on gas permeability. As a result, ANN can be recommended for the modeling of gas transport through MMMs.     

Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse

Artificial neural network inverse (ANNi) is applied to calculate the optimal operating conditions on the coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling. An artificial neural network (ANN) model is developed to predict the COP which was increased with energy recycling. This ANN model takes into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two pressures and LiBr + H₂O concentrations. For the network, a feedforward with one hidden layer, a Levenberg–Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validation data set, simulations and experimental data test were in good agreement (R > 0.99). This ANN model can be used to predict the COP when the input variables (operating conditions) are well known. However, to control the COP in the system, we developed a strategy to estimate the optimal input variables when a COP is required from ANNi. An optimization method (the Nelder–Mead simplex method) is used to fit the unknown input variable resulted from the ANNi. This methodology can be applied to control on-line the performance of the system.     

Heat transfer prediction for radiant floor heating/cooling systems using artificial neural network (ANN)

Radiant floor cooling and heating systems (RHC) are gaining popularity as compared with conventional space conditioning systems. An understanding of the heat transfer capacity of the radiant system is desirable to design a space conditioning system using RHC technology. In the present work, a simplified heat flux model for RHC is developed for both cooling and heating modes of operation. The Artificial Neural Network (ANN) technique is used for the development of the simplified model. Experimental data from literature covering a wide operating range of the RHC is considered for model development and validation. Operating parameters such as mass flow rate (m_f), heat resistance (R_s), mean temperature of water flowing through the pipe (T_m), and operative temperature (T_op) are considered independent variables influencing the heat flux (q_t). The neural network consists of four input layers, one output layer, and one hidden layer with a feed-forward-back-propagation algorithm. A study on the selection of the optimum number of neurons in the range of 1–9 for the hidden layer is also performed. On the basis of the performance parameters, namely, average-absolute-relative-deviation (AARD = 0.11283) percentage, mean-square-error (MSE = 0.00055), and the coefficient of determination (R² = 0.9984), a hidden layer is modeled with five neurons.