This paper presents a simulation model for a system equipped with thermoelectric elements, considering the impact of independent thermal contact resistances on each side of the module. An analytical model was constructed, taking into account the asymmetry of thermal resistances between the generator/cooler and the respective heat source/heat sink. A comparative analysis of thermoelectric device operating indicators such as conducted heat, efficiency, and the electricity/cooling power produced was performed. The selection of electrical current in the circuit was analysed based on the maximization of power or efficiency. This paper discusses deviations from ideal conditions, specifically the absence of thermal resistance between the heat source/sink and the thermoelectric junction. The model accurately simulates the operating conditions of the thermoelectric system with a low computational cost. The results indicate that the total thermal resistance, rather than its location, predominantly affects the operation of the thermoelectric generator. However, in cooling operations, the influence of thermal resistance significantly depends on the cooling power demand and temperature.

This paper considers issues related to the assessment of the mechanical properties of elements made with 3D printing technology. To enable experimental testing, an automated test stand was built to perform amplitude and phase angle measurements of any point of the specimen. A contactless, optical measurement method was selected, as it is especially adequate when it comes to elements with small dimensions and masses. One innovative element of the test stand is the original method of phase angle measurement using a single vibration sensor fitted with a system forcing and ensuring full measurement synchronization and dynamic state repeatability. Additionally, numerical models of tested objects were produced and simulations of their oscillations were performed. Based on that, the properties of the tested material (PLA) were considered, with a special focus on the density, elastic modulus, and damping. The analyses were conducted for a few elements with different dimensions at different vibration frequencies.

This paper analyzes the potential application of Physics-Informed Neural Networks (PINNs) in solving equations that describe thermal–electrical processes in thermoelectric systems. Combining machine learning with the laws of physics, the PINN method can serve as an alternative to traditional numerical methods, particularly in the context of the miniaturization of cooling systems, heat pumps, and systems that convert thermal energy (heat flow) into electrical energy (e.g., heat recovery), as well as the implementation of models in embedded systems. The article presents a model of thermoelectric equations, explains how PINNs work, provides numerical results, and assesses the advantages and disadvantages of the proposed approach.