Heat exchangers are critical components in various industrial applications, requiring efficient thermal management to enhance thermal performance and energy efficiency. Longitudinal vortex generators (LVGs) have emerged as a potent mechanism to enhance heat transfer within these devices. A precise knowledge of the thermal performance enhancement of HE through LVGs is missing in the literature. Therefore, this study aims to provide a critical review of both numerical simulations and experimental studies focusing on the enhancement of heat transfer through LVGs to further enhance the knowledge of the field. It begins with elucidating the fundamental principles behind LVGs and delineating their role in manipulating flow patterns to augment heat transfer. This is followed by an exploration of the various numerical methods employed in the field, including computational fluid dynamics techniques such as Reynolds-Averaged Navier–Stokes (RANS) models, Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS). Various experimental methods are then summarised, including differential pressure measuring instruments, temperature measurements, velocity measurements, heat transfer coefficient measurements, and flow visualisation techniques. The effectiveness of these methods in capturing the complex fluid dynamics and thermal characteristics induced by LVGs is critically assessed. The review covers a wide range of LVG configurations, including their geometry, placements, and orientations, and their effects on the thermal performance of heat exchangers. Different from previous reviews that mainly focus on classical configurations and historical studies, this review also emphasizes recent developments in computational fluid dynamics and progress in interdisciplinary fields such as innovative materials, additive manufacturing, surface finishing, and machine learning. By bridging the gap between fluid dynamics, thermal enhancement, and emerging manufacturing technologies, this paper provides a forward-looking, comprehensive analysis that is valuable for both academic and industrial innovations.

Missing value imputation is essential in data pre-processing for statistical model building and data mining. The problem with using a machine learning model to impute missing values is that it is hard to evaluate the performance of imputation and choose the optimal model and tuning parameters. Although many missing values imputation models have been proposed, the precise understanding of the imputation performance evaluation is missing in the literature. Therefore, this study aims to develop a new method named ‘imputation validation mean square error’ which would potentially improve the knowledge of the missing values imputation. The imputation validation dataset is obtained by randomly assigning some attributes as NA in the complete dataset according to the information on the percentage of missing values for each attribute and how often each combination of missing values occurs. This literature compares four imputation models (Clustering, KNN, PCA, and MCMC) for multivariate missing values imputation on the Pima Indian Diabetes dataset. The imputation validation MSE range is from 0 to 1. These four methods with imputation validation MSE equal to 0.1447, 0.1360, 0.1129, and 0.1312 are better than the mean (0.2270) and median (0.2306) imputation methods. The performance of different imputation models can be compared clearly based on this method.