Tae Young Kim

Transpiration cooling that utilizes the phase change of a liquid coolant is recognized as an effective thermal protection technique for extreme environments. However, the introduction of phase change within the porous structure brings about challenges, such as vapor blockage, pressure fluctuations, and temperature inversion, which critically influence system reliability. This study conducts numerical analyses of coupled processes of heat transfer, flow, and phase change in transpiration cooling using a Two-Phase Mixture Model. The simulation incorporates a Local Thermal Non-Equilibrium approach to capture the distinct temperature fields of the solid and fluid phases, enabling accurate prediction of the thermal response within two-phase and single-phase regions. The results reveal that under low heat flux, dominant capillary action suppresses dry-out and expands the two-phase region. Conversely, high heat flux causes vaporization to overwhelm the capillary supply, forming a superheated vapor layer and constricting the two-phase zone. The analysis also explains a paradoxical pressure drop, where an initial increase in flow rate reduces pressure loss by suppressing the high-viscosity vapor phase. Furthermore, a local temperature inversion, where the fluid becomes hotter than the solid matrix, is identified and attributed to vapor counterflow and its subsequent condensation.

This study proposes a vertically stacked thermoelectric generator (TEG) design to enhance output power per unit volume. While the proposed TEG achieved improved conversion efficiency, the high inertia of the exhaust gas leads to significant flow maldistribution across the channels, causing uneven thermal conditions on the TEM surfaces and reducing overall efficiency. To enhance waste heat recovery by improving flow uniformity in the exhaust gas channels, a perforated plate with porosity ranging from 0.15 to 0.75 was inserted. A multi-physics numerical model was developed to simulate the thermoelectric energy conversion phenomena, enabling for the accurate evaluation of both module- and system-wise performance. The insertion of the perforated plate with 0.45 porosity provided the most uniform flow distribution with only a 5% flow rate difference between the exhaust gas channels. This resulted in a system-level output power of 167.1 W, which is ~7% higher than the case without the perforated plate, along with electrical efficiency of 91.1% and conversion efficiency of 3.41%. Moreover, enhanced flow uniformity led to an improved volumetric power density of 20.8 kW/m³. When accounting for pumping losses, the perforated plate with 0.6 porosity maximized net output power, demonstrating how optimized flow distribution significantly enhances energy harvesting performance.

This study analyzes the physical principles and characteristics of thermoelectric energy conversion phenomena utilizing p-n semiconductor couples to propose a design methodology for thermoelectric generators (TEGs) capable of energy recovery in marine environments. To design TEGs with practical power generation and energy conversion efficiency, the development of a numerical analysis model that can predict and analyze the behavior characteristics of TEGs is required. We present the necessary conditions for a numerical analysis model that simulates the generation characteristics of TEGs at the system level, along with a comparison and analysis of experimental results. The electrical connection method of the thermoelectric modules (TEMs) significantly affects the power generation performance of TEGs; however, there is a lack of research on the electrical connections between TEMs in existing studies. This paper examines the reasons for performance changes due to electrical connections between TEMs and proposes an optimal electrical connection method. Additionally, it is demonstrated that improving the thermal flow distribution within the TEG can enhance system performance and maximize the effects of electrical connections between TEMs. We analyze the characteristics of technologies that can improve the level of thermal flow distribution within the TEG, such as finned and porous structures, and present optimal utilization strategies.

This paper presents the design optimization of a heat exchanger for a free-piston Stirling engine (FPSE) through an improved quasi-steady flow (iQSF) model and a central composite design. To optimize the tubular hot heat exchanger (HHX) design, a design set of central composite designs for the design factors of the HHX was constructed and the brake power and efficiency were predicted through the iQSF model. The iQSF model is improved because it adds various heat and power losses based on the QSF model and applies a heat transfer model that simulates the oscillating flow condition of an actual Stirling engine. Based on experimental results from the RE-1000, an FPSE developed by Sunpower, the iQSF model significantly improves the prediction error of the indicated power from 66.9 to 24.9% compared to the existing QSF model. For design optimization of the HHX, the inner diameter and the number of tubes with the highest brake power and efficiency were determined using a regression model, and the tube length was determined using the iQSF model. Finally, the brake output and efficiency of FPSE with the optimized HHX were predicted to be 7.4 kW and 36.4%, respectively, through the iQSF analysis results.

This study evaluated the potential of artificial neural networks (ANNs) to predict the system-level performance of a thermoelectric generator (TEG), whose performance depends on various variables including engine load, engine rotation speed, and external load resistance. Therefore, a Python code was developed to determine an optimal ANN structure by tracking the training/prediction errors of the ANN as a function of the number of hidden layers and nodes of hidden layers. The optimal ANN was trained using 484 output current (I)–load resistance (R) datasets obtained under three different engine rotation speeds and five different engine loads. The prediction accuracy of the ANN was validated by comparing 88 I–R datasets reproduced by the ANN using experimental data that were not used for training. In the validation procedure, differences of only 3.49% and 2.59% were observed in the experimental and ANN-predicted output power obtained for the 1000 rpm–0.8 MPa brake mean effective pressure (BMEP) and 1500 rpm–0.4 MPa BMEP scenarios, respectively. The exhaust gas flow characteristics were used for training and validation to predict the pumping loss caused by the installation of the TEG in the middle of the exhaust tailpipe with high accuracy. The results demonstrated that the ANN effectively reproduced datasets to fill the gaps between the discretized experimental results for all the experimental scenarios without any noticeable overfitting and underfitting. The net power gain obtained by the ANN exhibited a clear peak point for the engine rotation speed of 2000 rpm, which is difficult to obtain using experimental data.