Optimization of Nano-Additive Characteristics to Improve the Efficiency of a Shell and Tube Thermal Energy Storage System Using a Hybrid Procedure: DOE, ANN, MCDM, MOO, and CFD Modeling

Abstract

Using nano-enhanced phase change material (NePCM) rather than pure PCM significantly affects the melting/solidification duration and the stored energy, which are two critical design parameters for latent heat thermal energy storage (LHTES) systems. The present article employs a hybrid procedure based on the design of experiments (DOE), computational fluid dynamics (CFD), artificial neural networks (ANNs), multi-objective optimization (MOO), and multi-criteria decision making (MCDM) to optimize the properties of nano-additives dispersed in a shell and tube LHTES system containing paraffin wax as a phase change material (PCM). Four important properties of nano-additives were considered as optimization variables: volume fraction and thermophysical properties, precisely, specific heat, density, and thermal conductivity. The primary objective was to simultaneously reduce the melting duration and increase the total stored energy. To this end, a five-step hybrid optimization process is presented in this paper. In the first step, the DOE technique is used to design the required simulations for the optimal search of the design space. The second step simulates the melting process through a CFD approach. The third step, which utilizes ANNs, presents polynomial models for objective functions in terms of optimization variables. MOO is used in the fourth step to generate a set of optimal Pareto points. Finally, in the fifth step, selected optimal points with various features are provided using various MCDM methods. The results indicate that nearly 97% of the Pareto points in the considered shell and tube LHTES system had a nano-additive thermal conductivity greater than 180 Wm−1K−1. Furthermore, the density of nano-additives was observed to be greater than 9950 kgm−3 for approximately 86% of the optimal solutions. Additionally, approximately 95% of optimal points had a nano-additive specific heat of greater than 795 Jkg−1K−1.

Mathematical Analysis of a Low Cost Mechanical Ventilator Respiratory Dynamics Enhanced by a Sensor Transducer (ST) Based in Nanostructures of Anodic Aluminium Oxide (AAO)

“…Consequently, an algorithm was also designed to obtain the airflow, volume and pressure from the pressure difference, which were obtained by the transduction process of the transducer joined to the designed sensor. Moreover, the ST system was analyzed in dynamical and transient behavior for ranges of work that depended on geometrical parameters and physical values of airflow, pressure, and volume of mechanical ventilators for artificial human breathing, This is a novel proposed sensor design such as other new proposals [22][23][24] because of the multiple variable correlation also variables considered like disturbances (temperature and vibration) and considering the effect of nanostructures in this objective.…”

“…The controller's parameters need to be obtained by different methodologies such as a stability analysis and a comparison with the theoretical model of dynamic systems given by Equation ( 24) [21], in which ω 0 is the natural frequency for the system, is the damping effect, and α is the coefficient as the auxiliary connector between Equations ( 23) and (24).…”

Numerical Approach toward Ternary Hybrid Nanofluid Flow Using Variable Diffusion and Non-Fourier’s Concept

“…During energy transfers, a hybrid nanofluid surpasses conventional fluids such as acetone, water, nanofluids, and acetylene. The capacity to freeze at high temperatures is one of the many thermal features of hybrid nanofluids. − Power production, heat exchange, heating systems, air conditioners, the automobile sector, electronic equipment, generators, reactors, and energy transmission in spacecraft are all applications of hybrid nanocomposites. − The working fluid in this study contained titanium dioxide (TiO 2 ), magnesium oxide (MgO), and cobalt ferrite (CoFe 2 O 4 ) NPs. TiO 2 is an inorganic substance that has been utilized in several products for a long period.…”

Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

“…The self-organizing feature of the GMDH method is regarded as a significant advantage because, during the modeling process, only submodels that improve the final model's accuracy are retained. In recent years, the use of this method has increased significantly, particularly in research requiring the presentation of mathematical relationships between dependent and independent variables [76][77][78] . To describe a system with M datasets, a complex function such as f is needed, which can connect inputs x = (x 1 , x 2 , .…”