Employing phase-change materials (PCM) is considered a very efficient and cost-effective option for addressing the mismatch between the energy supply and the demand. The high storage density, little temperature degradation, and ease of material processing register the PCM as a key candidate for the thermal energy storage system. However, the sluggish response rates during their melting and solidification processes limit their applications and consequently require the inclusion of heat transfer enhancers. This research aims to investigate the potential enhancement of circular fins on intensifying the PCM thermal response in a vertical triple-tube casing. Fin arrays of non-uniform dimensions and distinct distribution patterns were designed and investigated to determine the impact of modifying the fin geometric characteristics and distribution patterns in various spatial zones of the heat exchanger. Parametric analysis on the various fin structures under consideration was carried out to determine the most optimal fin structure from the perspective of the transient melting evolution and heat storage rates while maintaining the same design limitations of fin material and volume usage. The results revealed that changing the fin dimensions with the heat-flow direction results in a faster charging rate, a higher storage rate, and a more uniform temperature distribution when compared to a uniform fin size. The time required to fully charge the storage system (fully melting of the PCM) was found to be reduced by up to 10.4%, and the heat storage rate can be improved by up to 9.3% compared to the reference case of uniform fin sizes within the same fin volume limitations.

No abstract

In this research, a numerical analysis is accomplished aiming to investigate the effects of adding a new design fins arrangement to a vertical triplex tube latent heat storage system during the melting mechanism and evaluate the natural convection effect using Ansys Fluent software. In the triplex tube, phase change material (PCM) is included in the middle tube, while the heat transfer fluid (HTF) flows through the interior and exterior pipes. The proposed fins are triangular fins attached to the pipe inside the PCM domain in two different ways: (1) the base of the triangular fins is connected to the pipe, (2) the tip of the triangular fins is attached to the pipe and the base part is directed to the PCM domain. The height of the fins is calculated to have a volume equal to that of the uniform rectangular fins. Three different cases are considered as the final evaluation toward the best case as follows: (1) the uniform fin case (case 3), (2) the reverse triangular fin case with a constant base (case 12), (3) the reverse triangular fin case with a constant height (case 13). The numerical results show that the total melting times for cases 3 and 12 increase by 4.0 and 10.1%, respectively, compared with that for case 13. Since the PCM at the bottom of the heat storage unit melts slower due to the natural convection effect, a flat fin is added to the bottom of the heat storage unit for the best case compared with the uniform fin cases. Furthermore, the heat storage rates for cases 3 and 12 are reduced by 4.5 and 8.5%, respectively, compared with that for case 13, which is selected as the best case due to having the lowest melting time (1978s) and the highest heat storage rate (81.5 W). The general outcome of this research reveals that utilizing the tringle fins enhances the thermal performance and the phase change rate.

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

No abstract