Preparation of microencapsulated phase change materials based on expanded polystyrene foam wastes

2018 ◽  
Vol 13 (7) ◽  
pp. 998-1000 ◽  
Author(s):  
Yifei Shao ◽  
Haifeng Jiang ◽  
Shu Li ◽  
Jiaqi Yang ◽  
Mingyue Zhang ◽  
...  
Keyword(s):  
Phase Change ◽  
Phase Change Materials ◽  
Expanded Polystyrene ◽  
Polystyrene Foam ◽  
Expanded Polystyrene Foam ◽  
Microencapsulated Phase Change Materials

A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

2021 ◽  
pp. 0021955X2110210
Author(s):  
Alejandro E Rodríguez-Sánchez ◽  
Héctor Plascencia-Mora
Keyword(s):  
Neural Network ◽  
Energy Absorption ◽  
Mechanical Energy ◽  
Compressive Loading ◽  
Ground Truth ◽  
Expanded Polystyrene ◽  
Polystyrene Foam ◽  
Stress Strain ◽  
Ground Truth Data ◽  
Expanded Polystyrene Foam

Traditional modeling of mechanical energy absorption due to compressive loadings in expanded polystyrene foams involves mathematical descriptions that are derived from stress/strain continuum mechanics models. Nevertheless, most of those models are either constrained using the strain as the only variable to work at large deformation regimes and usually neglect important parameters for energy absorption properties such as the material density or the rate of the applying load. This work presents a neural-network-based approach that produces models that are capable to map the compressive stress response and energy absorption parameters of an expanded polystyrene foam by considering its deformation, compressive loading rates, and different densities. The models are trained with ground-truth data obtained in compressive tests. Two methods to select neural network architectures are also presented, one of which is based on a Design of Experiments strategy. The results show that it is possible to obtain a single artificial neural networks model that can abstract stress and energy absorption solution spaces for the conditions studied in the material. Additionally, such a model is compared with a phenomenological model, and the results show than the neural network model outperforms it in terms of prediction capabilities, since errors around 2% of experimental data were obtained. In this sense, it is demonstrated that by following the presented approach is possible to obtain a model capable to reproduce compressive polystyrene foam stress/strain data, and consequently, to simulate its energy absorption parameters.

Theoretical and experimental study of width effects on horizontal flame spread over extruded and expanded polystyrene foam surfaces

2013 ◽  
Vol 32 (3) ◽  
pp. 193-209 ◽  
Author(s):  
Lin Jiang ◽  
Huahua Xiao ◽  
Yang Zhou ◽  
Weiguang An ◽  
Weigang Yan ◽  
...  
Keyword(s):  
Experimental Study ◽  
Flame Spread ◽  
Expanded Polystyrene ◽  
Polystyrene Foam ◽  
Expanded Polystyrene Foam

scholarly journals Energy Performance Assessment of Waste Materials for Buildings in Extreme Cold and Hot Conditions

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3131 ◽  
Author(s):  
Yasir Rashid ◽  
Fadi Alnaimat ◽  
Bobby Mathew
Keyword(s):  
Date Palm ◽  
Concrete Block ◽  
Climatic Conditions ◽  
Expanded Polystyrene ◽  
Waste Materials ◽  
Geopolymer Concrete ◽  
Polystyrene Foam ◽  
Expanded Polystyrene Foam ◽  
Rubber Tire ◽  
By Products

In this article, thermal performance of different waste materials and by-products of industrial processes is investigated experimentally. A geopolymer concrete block with 7.5 cm thickness and cross-sectional area of 5 × 5 cm was considered as a reference model to measure heat transmission across the two opposite surfaces while all four remnant surfaces were perfectly insulated. For all other samples, a sandwich concrete block was developed by taking two pieces of the geopolymer concrete with 2.5 cm thickness each on either side and insulation material of 2.5 cm thickness in between. The sandwich materials investigated were air cavity, expanded polystyrene foam, polyurethane foam, rubber tire, date palm, PCM-30, and PCM-42. Experimental investigations revealed that the investigated green materials and industrial by-products have comparable insulation performance with respect to the traditional insulations such as expanded polystyrene foam. It is found that polyurethane foam and date palm can reduce indoor cooling demand by 46.6% each in hot conditions while rubber tire can reduce indoor heating demand by 59.2% in cold climatic conditions at the maximum. The research results confirm and encourage the effective utilization of waste materials in building walls for reducing indoor air-conditioning demand in the extreme climatic conditions.

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