Machine learning assisted convective wall heat transfer models for fire modeling along vertical walls, ceilings and floors
Creators: name: Tao, Jie
Publisher: name: Purdue University Graduate School
Year: 2024
Subjects: classificationCode: 400199
subject: Aerospace engineering not elsewhere classified

description: Fires cause significant casualties and property damage. As critical component of indoor and building fires, fires along a surface (vertical or horizontal) contribute significantly to fire spreading and resulted damage. Accurately predicting the interactions between a wall surface and fire is crucial to minimizing losses. Computational methods, such as large-eddy simulations (LES), can result in errors in fire modeling along a surface due to various model and numerical errors among which the error in the convective wall heat transfer models is an important source. The convective heat transfer model error grows when the grid resolution near a thermal boundary layer along a wall surface decreases. Traditional wall-function based heat transfer models, mostly developed for forced convection heat transfer problems, tend to fail in the buoyancy-driven fire wall heat transfer. It is imperative to develop accurate and efficient convective wall heat transfer models for fire modeling.In this study, machine learning is employed as an alternative to traditional physics-based modeling approach for wall heat transfer in fire modeling. A significant advantage of machine learning over physics-based modeling is that machine learning does not require thorough knowledge of fire wall heat transfer which is generally hard to acquire due to the complexity of the problem. A machine-learning assisted convective wall heat transfer model, aiming to enhance wall fire predictions, is developed in this work. The objective is to improve predictions of convective heat flux to a wall in under-resolved LES of wall fires. An amplification factor ($\beta$) is introduced to compensate the under-prediction of temperature gradients normal to a wall surface in coarse grid simulations. Machine learning is then employed to assist the construction of models for $\beta$ with the training data obtained directly from fine-resolution LES. Extensive studies are conducted to identify suitable machine learning architecture, input features, training data generation strategies, training procedure, and testing and validation approaches.A vertical wall fire test case is considered first to develop a baseline machine learning model. The focus is on identifying suitable input features and training strategies for machine learning of convective wall fire heat transfer. A four-parameter (input) machine learning model for $\beta$ is constructed. Both \textit{a priori} and \textit{a posteriori} testing are developed in the vertical wall fire case to provide preliminary model performance assessment. The fully tested model is also examined in an intermediate-scale parallel-wall fire spreading case that was not seen in the model training to assess the applicability of the developed machine learning model. In general, excellent model performance is observed in the vertical wall fire case.The established machine learning approach for the vertical wall case is then extended to horizontal surfaces like floor fires and ceiling fires to expand the training scope of the machine learning model. The unique challenges in these new fire scenarios are investigated separately to identify the need of additional input features and training strategies. It is found that a fifth input parameter, in addition to the four parameters identified in the vertical wall, is generally needed in order to correctly identify different fire scenarios. Data augmentation techniques are also found to be a useful technique to handle data sparsity during model training. Different machine learning architectures like random forest and deep neural network are also compared.The above studies are finally integrated into a unified machine learning model suitable for both vertical and horizontal surfaces. Extensive testing shows that the unified model reproduces the model performance of the separately trained models. The work is significant in demonstrating the feasibility of using machine learning approaches to enhance fire simulations. The developed machine learning modeling techniques improve predictions in various fire scenarios by using relatively coarse grid to maintain low computational cost, a critical consideration when simulation approaches are employed in real fire simulations.