Search

In the past decades, natural rubber (NR) foams became popular in the automotive, construction and aerospace industries because of their lightweight, flexibility and shock-absorbing properties. The selection of optimal formulation and processing parameters is critical to produce foam with specific properties depending on the application. In this study, the effect of foaming agent concentration, foaming temperature and time on the morphological and mechanical properties of NR foams was investigated. First, increasing the foaming agent content from 5 to 9 phr (parts per hundred rubber) increased the cell size (16%), while decreasing the compression modulus (28%). In the second part, increasing the foaming temperature (145 to 155°C) resulted in larger cell size (163%); while decreasing the cell density (28%), compression modulus (2%), and hardness (1%). In the third part, increasing the foaming time (25 to 45 min) led to smaller cell size (63%) combined with higher cell density (100%), compression modulus (16%), and hardness (3%). Based on all the results obtained, the best NR foam was obtained with 7 phr of foaming agent and produced at 150°C for 35 min leading to superior morphological and mechanical performance: the smallest cell size (25 µm) and the most uniform cell size distribution (Đ = 1.03) generating the highest compression modulus (3.36 MPa). Finally, the experimental compression results were combined to build a nonlinear regression model to optimize the formulation and processing conditions leading to 6.5 phr of OBSH molded at 150°C for 36 min. The model showed good agreement with a validation test with less than 2% deviation observed for both compression modulus and strength. [ABSTRACT FROM AUTHOR]

• A deep learning technique was developed to identify a two-phase flow using multi-class segmentation. • The effects of the bubble length, velocity, and acceleration on the flow pattern are described. • A flow regime map for CLPHP was developed. • Bubbly, slug-plug, elongated plug, and annular/semi-annular flows were studied. Accurate prediction of the flow patterns is important for understanding the behavior and operation of thermo-fluidic phenomena in two-phase systems. Considering the importance of thermally induced flows in capillaries and their use in various processes, the thermo-hydrodynamics of two-phase flows in pulsating heat pipes (PHPs) have been studied. In the present research, flow regimes were identified in capillaries where the mass flow rate is difficult to deal with. A deep learning technique has been carried out to classify the flow patterns and extract their attributes in a particular capillary using multi-spectral segmentation. The modified Weber, Froude, and Bond numbers are used by considering the absolute velocities, accelerations, and bubble lengths for classifying the flow regime. Flow regime maps were plotted based on the proposed non-dimensional numbers by analyzing experimentally measured data sets and visual observation for methanol as a working fluid at six different heat levels (18,38,44,50,60, and 70 W). Four different flow regimes have been identified and classified as a bubbly flow, a slug flow, an elongated plug flow (transitional flow), and an annular/semi-annular flow, and a new flow regime map for thermally induced two-phase flows in a PHP is presented. The flow regime map gives quantifiable criteria for the calculation of bubbly, slug, and annular flow patterns. The present study will be useful for researchers analyzing a large number of two-phase flow images and to design two-phase passive heat transfer devices based on the flow regime map. [ABSTRACT FROM AUTHOR]