1. Overview:

Title: Multi-Objective Optimization of Plate-Fin Heat Exchangers via Non-Dominated Sequencing Genetic Algorithm (NSGA-II)
Authors: Shengchen Li, Zixin Deng, Jian Liu, Defu Liu
Year of Publication: 2022
Journal/Conference: Applied Sciences

2. Research Background:

Plate-fin heat exchangers are widely used for heat dissipation in automotive engines due to their compact and lightweight structure, excellent heat transfer performance, and low production cost. Serrated staggered fins are commonly employed to enhance the heat exchange surface. Previous studies have applied novel algorithms, such as genetic algorithms, simulated annealing algorithms, and model search algorithms, to heat exchanger optimization design research. However, these optimization algorithms have rarely been applied in engineering practice, and there is a lack of computational procedures to guide engineering applications. Furthermore, research on plate-fin heat exchangers, widely used for automotive engine heat dissipation, is limited. Traditional methods like the logarithmic mean temperature difference (LMTD) method and the effectiveness-number of transfer units (η-NTU) method for optimal heat exchanger design are costly and time-consuming. This study leverages advancements in computational fluid dynamics (CFD) and computer technology to achieve optimal plate-fin heat exchanger performance through multi-objective optimization based on CFD.

3. Research Objectives and Questions:

Research Objective: To perform multi-objective optimization based on CFD and the NSGA-II algorithm to obtain the optimal performance of a plate-fin heat exchanger for an extended-range hybrid vehicle engine.
Key Research Questions: Using the serrated staggered fin angle, oil flow rate, and water flow rate as input parameters, and heat transfer quantity, oil pressure drop, and oil outlet temperature as objective functions, how can the optimal solution for the heat exchanger be found?
Research Hypothesis: The combination of the NSGA-II algorithm and the TOPSIS method can effectively optimize the performance of the plate-fin heat exchanger.

4. Research Methodology:

Research Design: A multi-objective optimization research design based on CFD simulation and the NSGA-II algorithm.
Data Collection Method: 45 numerical simulation test cases were designed using commercial CFD software Fluent. The heat transfer quantity, oil pressure drop, and oil outlet temperature were measured by varying the serrated staggered fin angle, oil flow rate, and water flow rate. A porous media model was used to reduce the computational burden of the simulations.
Analysis Method: Support Vector Regression (SVR) was used to establish regression models for heat transfer quantity, oil pressure drop, and oil outlet temperature. The NSGA-II algorithm was employed for multi-objective optimization. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to determine the optimal solution from the Pareto optimal solution set.
Research Subjects and Scope: Plate-fin heat exchanger for an extended-range hybrid vehicle engine. Variables included fin angle (30°–90°), oil flow rate (5–15 L/min), and water flow rate (5–15 L/min).

5. Main Research Findings:

Key Findings: The optimal performance of the heat exchanger was achieved at a fin angle of 63.01°, an oil flow rate of 9.7 L/min, and a water flow rate of 6.45 L/min. At this point, the heat transfer quantity was 9.79 kW, the oil pressure drop was 13.63 kPa, and the oil outlet temperature was 65.11 °C. Oil pressure drop was significantly affected by oil flow rate, and showed a trend of decreasing and then increasing as the fin angle decreased. Heat transfer quantity was influenced by fin angle, oil flow rate, and water flow rate. When the oil flow rate was less than or equal to 10 L/min, changes in oil flow rate had a greater impact on heat transfer quantity; above 10 L/min, changes in water flow rate had a greater impact. Oil outlet temperature showed an inverse relationship with fin angle.
Statistical/Qualitative Analysis Results: SVR was used to analyze the 45 simulation results to create regression models for heat transfer quantity, oil pressure drop, and oil outlet temperature. The NSGA-II algorithm yielded a Pareto optimal solution set containing 2000 solutions. TOPSIS was used to select the optimal solution. The error between simulation and optimization results was 0.31% for heat transfer quantity, 2.64% for oil pressure drop, and 0.17% for oil outlet temperature.
Data Interpretation: The results obtained using the porous media model-based CFD simulation, along with the SVR, NSGA-II, and TOPSIS multi-objective optimization techniques, were consistent, validating the proposed optimization approach.
Figure List and Description: (Detailed descriptions of all figures would be included here, referencing their respective numbers and providing concise explanations.)

Figure 1. The heat exchangers are modeled from bottom to top as layers 1 to 14, where the odd-numbered layers are water-side fins and the even-numbered layers are oil-side fins: (a) Heat exchangers model, (b) Simplified model of heat exchangers.

Figure 9. Simulation results of optimal parameters.

6. Conclusion and Discussion:

This study performed multi-objective optimization of a plate-fin heat exchanger for an extended-range hybrid vehicle engine using CFD and the NSGA-II algorithm. The TOPSIS method was used to select the optimal solution from the Pareto optimal solution set, resulting in the identification of optimal fin angle, oil flow rate, and water flow rate. The high consistency between simulation and optimization results validates the proposed methodology. This research provides practical guidelines for optimizing heat exchanger design.

7. Suggestions for Future Research:

Future research could validate the simulation results through experimental testing using a physically constructed heat exchanger. Further optimization studies under various operating conditions are also warranted.
The research could be extended to investigate different fin structures or fluids.
More sophisticated optimization algorithms or machine learning techniques could be employed to improve efficiency.
Future work should incorporate assessments of heat exchanger durability and reliability.

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Copyright and Source Material:

This summary is based on the paper "Multi-Objective Optimization of Plate-Fin Heat Exchangers via Non-Dominated Sequencing Genetic Algorithm (NSGA-II)" by Shengchen Li, Zixin Deng, Jian Liu, and Defu Liu.