Search Results (4)

This research proposes a numerical approach to improve the thermal performance of shape memory polymers (SMPs) while their mechanical properties remain intact. Sixteen different 3D minichannel structures were numerically designed to investigate the impact of embedded water flow in microchannel networks on the thermal response and shape recovery of SMPs. This work employs two approaches, each with different physics: approach A focuses on solid mechanics analysis and, accordingly, thermal analysis in solids without considering the fluid. approach B tackles solid and fluid mechanics analysis and thermal analysis in both solid and fluid subdomains, which inherently calls for fluid–structure coupling in a uniform procedure. Finally, the results of these two approaches are compared to predict the SMP’s thermal and mechanical behavior. The structural designs are then analyzed in terms of their shape recovery speed, recovery ratio, and recovery parameters. The results indicate that isotropic structures thermally outperform their anisotropic counterparts, exhibiting improved thermal characteristics and faster shape recovery. Additionally, it was observed that polymeric structures with a low volume fraction of embedded branches thermally perform efficiently. The findings of this study predict that the geometrical angle between the main branch and sub-branches of SMP favorably impacts the enhancement of thermal characteristics of the structure, accelerating its shape recovery. Approach B accelerates the shape recovery rate in SMPs due to fluid flow and uniform heat transfer within the structures. Full article

This paper proposes a methodology aiming at determining the most influent working variables and geometrical parameters over the pressure drop and heat transfer during the condensation process of several refrigerant gases using heat exchangers with pipes mini channels technology. A multi-criteria decision making (MCDM) methodology was used; this MCDM includes a mathematical method called SIMUS (Sequential Interactive Modelling for Urban Systems) that was applied to the results of 2543 tests obtained by using a designed refrigeration rig in which five different refrigerants (R32, R134a, R290, R410A and R1234yf) and two different tube geometries were tested. This methodology allows us to reduce the computational cost compared to the use of neural networks or other model development systems. This research shows six variables out of 39 that better define simultaneously the minimum pressure drop, as well as the maximum heat transfer, saturation pressure fluid entering the condenser being the most important one. Another aim of this research was to highlight a new methodology based on operation research for their application to improve the heat transfer energy efficiency and reduce the CO₂ footprint derived of the use of heat exchangers with minichannels. Full article

Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime. Full article

In the paper, a self-organizing map combined with the recurrence quantification analysis was used to identify flow boiling patterns in a circular horizontal minichannel with an inner diameter of 1 mm. The dynamics of the pressure drop during density-wave oscillations in a single pressure drop oscillations cycle were considered. It has been shown that the proposed algorithm allows us to distinguish five types of non-stationary two-phase flow patterns, such as bubble flow, confined bubble flow, wavy annular flow, liquid flow, and slug flow. The flow pattern identification was confirmed by images obtained using a high-speed camera. Taking into consideration the oscillations between identified two-phase flow patterns, the four boiling regimes during a single cycle of the long-period pressure drop oscillations are classified. The obtained results show that the proposed combination of recurrence quantification analysis (RQA) and a self-organizing map (SOM) in the paper can be used to analyze changes in flow patterns in non-stationary boiling. It seems that the use of more complex algorithms of neural networks and their learning process can lead to the automation of the process of identifying boiling regimes in minichannel heat exchangers. Full article