Influence of Italian Climatic Conditions on Performance of a 3D Printed Building Second-Skin Façade with 20% Gyroid Filling: Simulation Assessment

In this section, the procedures and results for the experimental characterization of the 3D-printed sample are described, together with the details of the TRNSYS simulation models and the methodology for the energy, environmental and visual analyses.

2.1 Material characterization

The analyzed sample consists of a couple of identical 3D-printed specimens, two disks with a diameter of 6 cm and a thickness of 1.5 cm, made of ASA (Acrylonitrile Styrene Acrylate), a UV-resistant 3D-printable plastic polymer [22]. The sample was realized by using the 3D printer Stratasys F900 with the Fused Deposition Modeling (FDM) technology [23], considering a 20% Gyroid filling. The dimensions of the specimens were defined considering the guidelines suggested by the manufacturer of the Hot Disk Thermal Constants Analyser (TPS 1000) [18] used to measure the thermal conductivity, the thermal diffusivity and the volumetric specific heat of the sample through the Transient Plane Source (TPS) method [24]: in particular, considering the radius of the sensor (r_sensor= 1.5 cm), a bifilar nickel spiral sealed in a thin film of Kapton tape, the specimens have been realized considering the minimum and maximum suggested thickness (minimum=r_sensor; maximum=2*r_sensor) and diameter (minimum=3*r_sensor; maximum=4*r_sensor), in order to minimize the variances caused by the anisotropy of the 3D-printed geometries and the edge effects [25]. The infill percentage of 20% was set considering the results of previous studies [20, 26-28].

The measurements were carried out in the RIAS Laboratory of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli [29].

During the measurements, the sensor is stacked between the two identical 3D-printed specimens, and then a small electrical current (90 mW) is applied during a test time (640 s) correlated to the thermal diffusivity of the sample. The heat generated by the sensor diffuses into the sample at a rate dependent on the thermal transport characteristics of the material. The time-dependent temperature increase in the sensor (ΔT) is recorded during the test, which allows resolving l, α and c_p of the material by iterative fitting to the corresponding mathematical model [24, 30]. The measurements were performed under controlled boundary conditions (ambient temperature = 23 ± 2℃, and relative humidity = 50 ± 10%) monitored via a Pt100 thermocouple and covering the samples with a steel cover to avoid a temperature drift. Between two consecutive measurements, a long relaxation time has been accounted to let the setup reach thermal equilibrium [18].

Figure 1 shows the two specimens clamped in the TPS 1000, with the sensor in-between and the Pt100 thermocouple on the side.

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Figure 1. The sample set in the TPS 1000 without the steel cover

The measured thermal properties of the ASA 3D-printed sample are reported in Table 1.

Table 1. Thermal properties of the sample

2.2 Simulation model

The simulation study has been carried out in four different Italian cities to evaluate the performance of the proposed 3D-printed solution when integrated into a SSF upon varying climatic conditions. In particular, the four considered cities are: Palermo (38°06′56.37″N 13°21′40.54″E, HDD=751), Napoli (40°50′09″N 14°14′55″E, HDD=1034), Pisa (43°43′N 10°24′E, HDD=1694), and Milano (45°28′01″N 9°11′24″E, HDD =2404). The office building is modelled referencing a typical office structure from IEA Annex 27 activities [31] and consists of 7 floors, each having a floor space of 661 m² and a height of 4.13 m. The geometrical model was realized using SketchUp 3D-modeling software, dividing each floor into five thermal zones (TZ) upon varying the space typology, office (Of1 and Of2) or stairs (St), and the orientation, east (E) or west (W). Figure 2 reports an axonometric view of the reference office building model, while Figure 3 shows the third-floor plan as an example of the TZ subdivision per floor.

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Figure 2. Axonometric view of the building model and the TZ subdivision

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Figure 3. Scheme of the thermal zone division per floor

Each floor has continuous windows on the east and west facades, sized based on the appropriate Windows-to-Wall Ratio (WWR) for each orientation [32]. The WWR ratios of the east and west façades are equal to 33% and 34%, respectively. TRNSYS Type 56 is used to import the geometric model in TRNSYS 18 and define in detail the building envelope, the internal gains (people, equipment, and artificial lighting systems), and the target value for cooling and heating systems.

Table 2 outlines the simulation parameters common to all the simulation cases in this study, namely the occupancy schedule, the temperature setpoint for heating and cooling, the thermal gains associated with the occupants, lighting systems, and general equipment, and the air infiltration rate [32-36].

Table 2. Common simulation parameters

The thermal transmittance (U-value) of the opaque and transparent surfaces of the building envelope was defined according to Schimschar et al. [37]. Considering the characteristics of buildings constructed during the 1980-90 decades, the construction typology most common in the country and in need of renovation [37]. In particular, the following U-values for the reference building were used: 0.80 W/m²K for vertical walls and roof, 0.50 W/m²K for the floor, and 4.20 W/m²K for the window.

For the refurbishment of the building, a SSF system has been installed on all the exterior walls of the reference building. The SSF system comprises an outer layer made of 3D-printed ASA panels, a 10 cm air cavity, and an insulation layer. The thickness of the insulation layer (Expanded Polystyrene - EPS, l = 0.041 W/mK) has been set differently to reach the U-value threshold suggested by the Italian legislation for each climatic zone [38].

The SSF system is implemented in TRNSYS through the Type 1230, which simulates the performance of the SSF system by taking into account the following factors: (i) solar radiation, longwave radiation and air convection on the external surface of the outside layer; (ii) thermal stored energy and conduction in the outside layer; (iii) radiation exchange between the outside layer and the air cavity; (iv) convective exchanges from all the surfaces facing the air cavity; (v) conduction through the interface layer [39].

The thermal conductivity of the 3D-printed ASA panels has been set equal to 0.0249 W/mK, as returned by the experimental results, while the panel thickness has been assumed equal to 1 cm. Two variations of the ASA panels have been considered to be installed on the opaque and transparent surfaces of the building’s envelope: full panel (Figure 4(a)) and perforated panel with a porosity of 28% [10, 16]. The perforated panels can be moved to cover or uncover the windows (Figure 4(b)) with independent control for each thermal zone based on the incident vertical solar irradiation threshold (50 W/m²) on the façades.

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(a)$~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~$(b)

Figure 4. (a) Building model with a schematic view of the proposed SSF, (b) Model of the dynamic shading system

Figure 5 reports the layout of the 34 horizontal illuminance measurement points placed at 0.80 m height from the floor to evaluate visual comfort on each story.

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Figure 5. Layout of the illuminance measurement points

The SSF system is provided with shutters at the air cavity’s inlet and outlet, controlled to keep the cavity open only when the outdoor air temperature is higher than 20℃ to maximize natural inner ventilation. Table 3 summarizes all the simulation case studies, including the specific characteristics of the electric air-to-air vapor compression heat pumps (EHPs) for the offices’ zones [35], the insulation thicknesses, and the U-values.

Table 3. Summary of the simulation case studies

Whatever the location of the simulation cases, the thermal zones of the stairs are served by the same EHPs with COP equal to 2.62 and EER equal to 2.75 [35].

2.3 Energy, environmental and visual analysis

The analysis of energy consumption involves assessing primary energy usage, which is determined by evaluating the Primary Energy Saving (PES) as reported in the study by Carlucci et al. [40].

PES $=\left( 1-\frac{E_{el,B}^{PC}/\eta PP}{E_{el,B}^{RR}/\eta PP} \right)\cdot 100$        (1)

where, $\text{E}_{\text{el,B}}^{\text{RC}}$ represents the overall electric energy consumed by the reference cases (RC) and associated to the operation of equipment, lighting systems, EHPs, while $\text{E}_{\text{el,B}}^{\text{PC}}$ is the overall electric energy associated to the proposed cases (PC) and due to the operation of equipment, lighting systems, EHPs, and ${{\eta }_{PP}}$ is the power plants’ average efficiency assumed equal to 0.465 [28]. A positive PES index indicates that the implemented passive retrofit measures reduce primary energy consumption in comparison to the reference case.

The environmental comparison is conducted by assessing the reduction of carbon dioxide equivalent emissions (ΔCO₂), defined according to Herzanita et al. [14] and reported below:

Δ$\text{C}{{\text{O}}_{\text{2}}}\text{= }\!\!~\!\!\text{ m}_{\text{C}{{\text{O}}_{\text{2}}}\text{,eq}}^{\text{RC}}\text{- }\!\!~\!\!\text{ m}_{\text{C}{{\text{O}}_{\text{2}}}\text{,eq}}^{\text{PC}}=~\alpha \cdot \left( E_{el,B}^{RC}-E_{el,B}^{PC} \right)$              (2)

where, $\text{m}_{\text{C}{{\text{O}}_{\text{2}}}\text{,eq}}^{\text{RC}}$ represents the mass of carbon dioxide equivalent emissions for the reference cases and $\text{m}_{\text{C}{{\text{O}}_{\text{2}}}\text{,eq}}^{\text{PC}}$ represents the mass of carbon dioxide equivalent emissions for each of the four proposed cases, and $\alpha $ is the carbon dioxide equivalent emission factor linked to electricity production in Italy and assumed equal to 0.324 kgCO_2,eq/kWh_el [28]. Consequently, ΔCO₂ index signifies the capacity of the implemented passive retrofit measures to decrease the carbon dioxide equivalent emissions in the renovated case compared to the reference case.

Visual comfort has been evaluated considering the Continuous Daylight Autonomy (CDA) and the Useful Daylight Illuminance (UDI) [40]. The CDA represents the amount of natural light available at a given point of the space during occupied hours with an illuminance value equal to 300 lux:

$\text{CDA}=\frac{{{\sum }_{\text{i}}}\left( \text{w}{{\text{f}}_{1}}\cdot {{\text{t}}_{\text{i}}} \right)}{{{\sum }_{\text{i}}}{{\text{t}}_{\text{i}}}}\epsilon \left[ 0,1 \right]$           (3)

with $\text{w}{{\text{f}}_{\text{i}}}=\left\{ \begin{matrix}   1 & \text{ }\!\!~\!\!\text{ if }\!\!~\!\!\text{ }{{\text{E}}_{\text{daylight }\!\!~\!\!\text{ }}}\ge {{\text{E}}_{\text{limit }\!\!~\!\!\text{ }}}  \\   \frac{{{\text{E}}_{\text{daylight }\!\!~\!\!\text{ }}}}{{{\text{E}}_{\text{limit }\!\!~\!\!\text{ }}}} & \text{ }\!\!~\!\!\text{ if }\!\!~\!\!\text{ }{{\text{E}}_{\text{daylight }\!\!~\!\!\text{ }}}<{{\text{E}}_{\text{limit }\!\!~\!\!\text{ }}}  \\ \end{matrix} \right.$

where, ${{\text{E}}_{\text{daylight}}}$ is the daylight illuminance calculated at each simulation timestep i, and ${{\text{E}}_{\text{limit}}}$ is the illuminance limit equal to 300 lux.

The UDI consists of these three different fractions (Eq. (4)):

$\text{UDI=}\frac{\mathop{\sum }_{\text{i}}\left( \text{w}{{\text{f}}_{\text{i}}}\text{ }\!\!~\!\!\text{ }\text{ }\!\!~\!\!\text{ }~~~~~{{\text{t}}_{\text{i}}} \right)}{\mathop{\sum }_{\text{i}}{{\text{t}}_{\text{i}}}}\epsilon \left[ \text{0, }\!\!~\!\!\text{ 1} \right]\left\{ \begin{matrix}

   ~\text{UD}{{\text{I}}_{\text{overlit}}}\text{ }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{ }  \\

   \text{ }\text{with }\!\!~\!\!\text{ w}{{\text{f}}_{\text{i}}}\text{=}\left\{ \begin{matrix}

   \text{}\!\!~\!\!\text{ if }\!\!~\!\!\text{ }{{\text{E}}_{\text{daylight}}}\text{}{{\text{E}}_{\text{upper }\!\!~\!\!\text{ limit}}}  \\

   \text{0 }\!\!~\!\!\text{ if }\!\!~\!\!\text{ }{{\text{E}}_{\text{daylight}}}\le {{\text{E}}_{\text{upper }\!\!~\!\!\text{ limit}}}  \\

\end{matrix} \right.

 \\

   \text{UD}{{\text{I}}_{\text{useful}}}\text{ }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{ }  \\

   \text{ }\!\!~\!\!\text{ with }\!\!~\!\!\text{ w}{{\text{f}}_{\text{i}}}\text{=}\left\{ \begin{matrix}

   \text{ }\!\!~\!\!\text{ if }\!\!~\!\!\text{ }{{\text{E}}_{\text{lower }\!\!~\!\!\text{ limit}}}\le {{\text{E}}_{\text{daylight}}}\le {{\text{E}}_{\text{upper }\!\!~\!\!\text{ limit}}}\text{ }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{  }\!\!~\!\!\text{ }  \\

   \text{0 }\!\!~\!\!\text{ if }\!\!~\!\!\text{ }{{\text{E}}_{\text{daylight}}}\text{}{{\text{E}}_{\text{lower }\!\!~\!\!\text{ limit}}}\vee {{\text{E}}_{\text{daylight}}}\text{}{{\text{E}}_{\text{upper }\!\!~\!\!\text{ limit}}}\text{ }\!\!~\!\!\text{  }\!\!~\!\!\text{ }  \\

\end{matrix} \right.

 \\

   \text{UD}{{\text{I}}_{\text{underlit}}}\text{ }\!\!~\!\!\text{ }  \\

   \text{with }\!\!~\!\!\text{ w}{{\text{f}}_{\text{i}}}\text{=}\left\{ \begin{matrix}

   \text{1 }\!\!~\!\!\text{ if }\!\!~\!\!\text{ }{{\text{E}}_{\text{daylight}}}\text{}{{\text{E}}_{\text{lower }\!\!~\!\!\text{ limit}}}  \\

   \text{0 }\!\!~\!\!\text{ if }\!\!~\!\!\text{ }{{\text{E}}_{\text{daylight}}}\ge {{\text{E}}_{\text{lower }\!\!~\!\!\text{ limit}}}  \\

\end{matrix} \right.\text{ }\!\!~\!\!\text{ }

 \\

\end{matrix} \right.$           (4)

where, ${{\text{t}}_{\text{i}}}$ is each occupied hour in a year; $\text{w}{{\text{f}}_{\text{i}}}\text{ }\!\!~\!\!\text{ }$is a weighting factor depending on values of ${{\text{E}}_{\text{daylight}}}$ and the illuminance limit value (upper or lower), UDI_overlit is the percentage of time of discomfort due to daylight supply, calculated at each simulation timestep i, above the limit (2000 lux), UDI_underlit is the percentage of time of discomfort due to daylight supply, calculated at each simulation timestep i, under the limit (100 lux), and UDI_useful is the percentage of time with appropriate illuminance levels.

This study explores the energy, environmental, and visual comfort performances of a SSF system integrating dynamic 3D-printed panels as retrofit action for a typical office building in four Italian cities characterized by different climatic zones. The numerical simulation returned good results in terms of primary energy saving (up to 17.8% in Palermo), reduction of carbon dioxide equivalent emissions (up to 30.8 t_CO2,eq in Napoli), and visual comfort (UDI_useful values up to 95.4% in Palermo). Future research will focus on further optimizing the characteristics of the 3D-printed panels, particularly their design and operational states. In particular, (i) additional experimental tests of the 3D-printed material upon varying the filling percentage will be carried out, (ii) further optimization of the SSF control logic considering different operation strategies will be developed, and (iii) a 3D-printed panel will be tested in real operating conditions.