  Polyurethane (PU) possesses excellent thermal properties, making it an ideal
material for thermal insulation. Incorporating Phase Change Materials (PCMs)
capsules into Polyurethane (PU) has proven to be an effective strategy for
enhancing building envelopes. This innovative design substantially enhances
indoor thermal stability and minimizes fluctuations in indoor air temperature.
To investigate the thermal conductivity of the PU-PCM foam composite, we
propose a hierarchical multi-scale model utilizing Physics-Informed Neural
Networks (PINNs). This model allows accurate prediction and analysis of the
material's thermal conductivity at both the meso-scale and macro-scale. By
leveraging the integration of physics-based knowledge and data-driven learning
offered by PINNs, we effectively tackle inverse problems and address complex
multi-scale phenomena. Furthermore, the obtained thermal conductivity data
facilitates the optimization of material design. To fully consider the
occupants' thermal comfort within a building envelope, we conduct a case study
evaluating the performance of this optimized material in a single room.
Simultaneously, we predict the energy consumption associated with this
scenario. All outcomes demonstrate the promising nature of this design,
enabling passive building energy design and significantly improving occupants'
comfort. The successful development of this PINNs-based multi-scale model holds
immense potential for advancing our understanding of PU-PCM's thermal
properties. It can contribute to the design and optimization of materials for
various practical applications, including thermal energy storage systems and
insulation design in advanced building envelopes.
