The Thermal Properties of a Prototype Insulation with a Gyroid Structure-Optimization of the Structure of a Cellular Composite Made Using SLS Printing Technology

Abstract

This paper focuses on the search for novel insulating structures, and the generation of them by means of a state-of-the-art manufacturing method-3D printing. Bionic structures, which are successfully used in many branches of technology, were chosen as the source of inspiration for the research. The paper presents a design of spatial structures with a gyroid infill (e.g., TPMS), the shape of which reflects the bionic structure of the inside of a bone. For SLS printed single- and multi-layered structures, the design value of the thermal conductivity coefficient was determined through measurements and calculations. A statistical analysis was carried out to determine the effect of the direction of heat flow, as well as the internal structure and layering of the prototype materials, on the values of the thermal conductivity coefficient and the thermal resistance coefficient. On the basis of the multicriteria analysis, the composite's optimal composition according to the adopted optimization criteria was determined. The lowest possible thermal conductivity of the insulation was equal to 0.033 W/(m·K). The highest possible thermal resistance was equal to 0.606 m²·K/W. Thermal insulation made of the prototype insulating partitions with a gyroidal structure is characterized by good insulating parameters.

Keywords: 3D printed; TPMS; closure; structure; thermal insulation.

Figure 1

Elementary cells with TPMS architecture: (a) Gyroid, (b) Neovilius, (c) Schwarz-D.

Figure 2

Effect of changes in the value of a, b and c on the shape of the gyroid structure: (a) the size of the cell (a = b = c = 2π); (b) the size of the cell (a = b = 3π and c = 2π); (c) the size of the cell (a = b = 2π and c = 3π).

Figure 3

Effect of changes in the value of t on the shape of the gyroid structure: (a) wall thickness (t = 0.2), (b) wall thickness (t = 0.6), (c) wall thickness (t = 1.0).

Figure 4

Grasshopper algorithm for creating gyroidal structures.

Figure 5

Printing result in the form of ready to use prototype plates: (a) three-layer sample (n = 3); (b) single-layer sample (n = 1).

Figure 6

The Poensgen apparatus used by the author: (a) schematic diagram, (b) photo of the Poensgen apparatus [1,26,27,28].

Figure 7

Graphical interpretation of the experimental data that determine the influence of the input factors (independent variables) and their mutual interactions on the value of the thermal conductivity coefficient of a composite with a gyroid structure.

Figure 8

Graphical interpretation of the experimental data that determine the influence of the input factors (independent variables) and their mutual interactions on the value of the thermal resistance coefficient of a composite with a gyroid structure.

Figure 9

Impact of particular factors on the thermal conductivity (λ) and the thermal resistance coefficient (R).

Figure 10

Graphs of the dependence of the thermal conductivity coefficient (a) and the thermal resistance coefficient (b) from input factor: type of cooling.

Figure 11

Graphs of the dependence of the thermal conductivity coefficient (a) and the thermal resistance coefficient (b) from input factor: size of air cells (a,b_c).

Figure 12

Graphs of the dependence of the thermal conductivity coefficient (a) and the thermal resistance coefficient (b) from input factor: thickness of the wall in the composite (t).

Figure 13

Graphs of the dependence of the thermal conductivity coefficient (a) and the thermal resistance coefficient (b) from input factor: number of layers (n).

Figure 14

Graphical interpretation of the optimization of the composite structures with regards to the determined thermal conductivity coefficient (λ).

Figure 15

Graphical interpretation of the optimization of the composite structures with regards to the thermal resistance coefficient (R).