Data-Driven Techniques for Fluid Mechanics and Heat Transfer

Sammanfattning: One of the main challenges in fluid mechanics and heat transfer is the need for detailed studies andfast computational speed to monitor and optimise a system. These fluid/heat flows comprise time-dependent velocity, multi-scale, pressure, and energy fluctuations. Although there has been major advancements in computational power and technology, modelling detailed physical problems is currently falling short. The fluid mechanics and heat transfer domains are rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatio temporal scales. Such an increase in the volume of data unlocks the possibility of using techniques like machine learning. These machine learning algorithms offer a wealth of techniques to extract information from data that can be translated into knowledge about the underlying physics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimisation. A significant milestone in the area of machine learning is the rise of deep learning, which is a powerful tool which can handle large data sets describing complex nonlinear dynamics that are commonly encountered in heat transfer and fluidflows.Therefore, this thesis aims to investigate data obtained from numerical simulations with deep learning techniques to reproduce the underlying physics present in data and considerably speed up the process. In this study, subcooled boiling transfer data has been used to train the deep neural network model then the trained model is validated using a validation dataset. The performance of the model is further evaluated using a set of interpolation and extrapolation datasets for different operating conditions outside the training and validation data. Furthermore, to highlight the robustness and reliability of the deep learning model, uncertainty quantification techniques such as Monte Carlo dropout and Deep Ensemble are implemented.This study demonstrates how a data-driven model can be used for subcooled boiling heat transfer and highlights why uncertainty quantification is important for such a model. The analysis and discussion in this thesis serve as the basis for further extending the potential use of data-driven methods for system optimisation, control and monitoring, diagnostic, and industrial applications.