Finally, the optimized channel geometry (called optimizedML) is investigated numerically for a wide range of Reynolds numbers. Later, C-shaped channel geometry is optimized by coupling the trained ML model with the multi-objective genetic algorithm (MOGA). In this context, CFD analysis is carried out on 81 different channel configurations of the C-shaped channel geometry, and computed data is used to train three ML algorithms.

The present work is focused on enhancing the overall thermo-hydraulic performance of a previously proposed C-shaped printed circuit heat exchanger (PCHEs) using Machine Learning (ML) Algorithms.

The comparison revealed that XGBoost has solid advantages in modeling occupant window behavior, over Logistic Regression Analysis, and it is expecting that the same finding would be obtained for other behavioral types, such as blind control and air-conditioner operation. Both XGBoost and Logistic Regression Analysis were used to build window behavior models, after a feature selection work, and their prediction performances on an independent dataset were compared. Field data in terms of both occupant window behavior and relevant influential factors were collected from real residential buildings during transitional seasons. In this study, this algorithm has been adopted to model and predict occupant window behavior, aiming to further improve the modeling accuracy from a globally accepted modeling approach, namely, Logistic Regression Analysis. XGBoost is a recently introduced machine learning algorithm, which has been proven as very powerful in modeling complicated processes in other research fields. During this work, many mathematical and machine learning methods have been used, and some level of prediction accuracy has been achieved. Due to the lack of good behavioral models in current simulation packages, many researchers have tried to develop useful behavioral models to improve simulation accuracy, including window behavior models, using field data collected from real buildings. Buildings account for over 32% of total society energy consumption, and to make buildings more energy efficient dynamic building performance simulation has been widely adopted during the buildings’ design to help select most appropriate HVAC (Heating Ventilation and Air Conditioning) systems.

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