A finned tube heat exchanger is a key component used as a condenser or an evaporator in residential air-conditioning (AC) and heat pump systems. The overall cycle performance of these systems is significantly affected by the heat exchanger’s geometric design. This study investigates outdoor heat exchanger designs with varying geometric parameters such as the fin pitch, number of tube rows, and tube length, and their effect on system performance based on seasonal energy efficiency ratio (SEER) and seasonal coefficient of performance (SCOP). Air face velocity profiles for each operating condition along the outdoor heat exchangers are determined using CFD, with subsequent cycle simulations for 10 different operating conditions. Results have been validated with the available experimental data. The number of tube rows, fin pitch, and length of tube have been varied from 2–10, 1.4–2.5 mm, and 800–2800 mm respectively. The numerical results reveals that SEER increases 3.21% while SCOP increases 5.32% up to fourth and fifth tube row respectively and remain unaffected thereafter. Similarly, SEER increases by 3.55% as the tube length is increased from 800–1800 mm, while it increases only 0.67% for 1800–2800 mm and the maximum variation of 4.32% has been found for SCOP. Moreover, increasing the fin pitch reduces SEER and SCOP (except for fin pitch from 1.4 to 1.8 mm). Finally, the performance of the system with four different fin configurations have also been investigated and it has been found that slit fins are more effective.

While extensive research has focused on improving the efficiency and performance of heat exchangers (HXs), identifying the underlying causes of performance degradation remains equally important. Flow and temperature non-uniformities are among the most critical factors affecting performance, often reducing thermo-hydraulic efficiency by approximately 5–10%. These non-uniformities commonly manifest as thermal inconsistencies, airflow maldistribution, and uneven refrigerant distribution. Researchers have observed a notable performance degradation—up to 27%—due to flow maldistribution. Therefore, a clear understanding of their causes and effects is essential for developing effective mitigation strategies to enhance system performance. Despite the notable progress in this area, few studies have systematically classified the dominant non-uniformities associated with specific HX types. This article presents a two-decade review of the causes, impacts, and mitigation approaches related to non-uniformities across different HX configurations. The primary objective is to identify the most critical form of non-uniformity affecting performance in each category. This review specifically examines plate heat exchangers (PHXs), finned and tube heat exchangers (FTHXs), microchannel heat exchangers (MCHXs), and printed circuit heat exchangers (PCHXs). It also discusses mathematical models designed to account for non-uniformities in HXs. This article concludes by identifying key research gaps and outlining future directions to support the development of more reliable and energy-efficient HXs.

This study focuses on developing and optimizing of a microchannel gas cooler model for evaluating the performance of a transcritical CO₂ mobile air-conditioning system. A simulation model is developed with the aid of MATLAB R2022a. A segment-by-segment modeling approach is utilized by applying the effectiveness-NTU method. State-of-the-art heat transfer and pressure drop correlations are used to obtain air and refrigerant side heat transfer coefficients and friction factors. The developed model is validated through a wide range of available experimental data and is able to predict a gas cooler capacity and pressure drop within an acceptable range of accuracy. The average errors for a gas cooler capacity and pressure drop are 3.79% and 10.24%, respectively. Furthermore, a parametric optimization method is applied to obtain optimal microchannel heat exchanger dimensions, including the number of tubes, microchannel ports, and passes. Different combinations were selected within the practical range to obtain optimal dimensions while keeping the total core volume constant. The simultaneous effect of the number of tubes, the number of ports in each tube, and the number of passes is determined. The objective of the current optimization technique is to minimize the pressure drop for the specific design capacity under different operating conditions without changing the overall volume of the gas cooler. The average pressure drop reduction for the optimal geometry as compared with the baseline geometry under all operating conditions is about 15%. The results from this study can be used to select an optimal geometric design for the required design capacity with a minimal pressure drop without the need for expensive prototype development and testing.

In this study, a numerical model of a microchannel gas cooler was developed using a segment-by-segment approach for thermohydraulic performance evaluation. State-of-the-art heat transfer and pressure drop correlations were used to determine the air and refrigerant side heat transfer coefficients and friction factors. The developed model was validated against a wide range of experimental data and was found to accurately predict the gas cooler capacity (Q) and pressure drop (ΔP) within an acceptable margin of error. Furthermore, advanced machine learning algorithms such as extreme gradient boosting (XGB), random forest (RF), support vector regression (SVR), k-nearest neighbors (KNNs), and artificial neural networks (ANNs) were employed to analyze their predictive capability. Over 11,000 data points from the numerical model were used, with 80% of the data for training and 20% for testing. The evaluation metrics, such as the coefficient of determination (R², 0.99841–0.99836) and mean squared error values (0.09918–0.10639), demonstrated high predictive efficacy and accuracy, with only slight variations among the models. All models accurately predict the Q, with the XGB and ANN models showing superior performance in ΔP prediction. Notably, the ANN model emerges as the most accurate method for refrigerant and air outlet temperatures predictions. These findings highlight the potential of machine learning as a robust tool for optimizing thermal system performance and guiding the design of energy-efficient heat exchange technologies.

The regasification of liquefied natural gas (LNG) is a crucial process that involves certain challenges created by the low temperature of LNG and the risk of ice formation on the external surfaces of the tubes of heat exchangers, which can hinder heat transfer and increase flow resistance. This study presents a numerical model for ice formation on the external surface of the U-bend tube of shell-and-tube heat exchangers. The numerical model has been further enhanced by applying a custom user-defined function. The numerical results were validated using experimental data and demonstrated excellent predictive capability, particularly for the surface temperature of the tubes and the thickness of the ice layer. Hence, this model can reliably capture the overall behavior of the ice formation on the external surfaces of the tubes of shell-and-tube heat exchangers. By highlighting the importance of maintaining stable heat transfer conditions to prevent freezing, this study offers valuable insights that can guide the optimization of heat exchanger designs for LNG regasification.

The automotive industry is increasingly focused on developing more energy-efficient and eco-friendly air-conditioning systems. In this context, CO₂ microchannel gas coolers (MCGCs) have emerged as promising alternatives due to their low global warming potential (GWP) and environmental benefits. This paper explores the application of machine learning (ML) algorithms to predict the thermohydraulic performance of MCGCs in automotive air-conditioning systems. Using data generated from an experimentally validated numerical model, this study compares various ML techniques, including both linear and nonlinear regression models, to forecast key performance metrics such as refrigerant outlet temperature, pressure drop, and heat transfer rate. Spearman’s correlation was employed to develop performance maps, whereas the R² and MSE metrics were used to evaluate the models’ predictive accuracy. The linear models gave around 70% forecasting accuracy for pressure drop across the gas cooler and 97% accuracy for refrigerant outlet temperature, whereas the nonlinear models achieved more accurate predictions, with an accuracy ranging from 71% to 99%. This implies that nonlinear regression generally performs better than linear regression models in assessing the overall thermohydraulic performance of microchannel gas coolers. This research brings forth new ideas on how ML methods can be applied to enhance efficiency and effectiveness in gas coolers, contributing to the development of more eco-friendly automotive air-conditioning systems.