2021/04/05 by Rishikesh Ranade, Ranade, Rishikesh, Kevin M. Gitushi +4
Chemical Engineering · Computer Science · Engineering · Physics and Astronomy · #Advanced Combustion Engine Technologies #Combustion and flame dynamics #FOS: Computer and information sciences #FOS: Physical sciences #Fire dynamics and safety research #Fluid Dynamics (physics.flu-dyn) #Machine Learning (cs.LG) #cs.LG #physics.flu-dyn
paper · pdf · doi:10.48550/arxiv.2104.01996
arxiv created 2021/04/05 · openalex publication_date 2021/04/05 · arxiv updated 2021/04/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Joint probability density function (PDF)-based models in turbulent combustion provide direct closure for turbulence-chemistry interactions. The joint PDFs capture the turbulent flame dynamics at different spatial locations and hence it is crucial to represent them accurately. The jointPDFs are parameterized on the unconditional means of thermo-chemical state variables, which can be high dimensional. Thus, accurate construction of joint PDFs at various spatial locations may require an exorbitant amount of data. In a previous work, we introduced a framework that alleviated data requirements by constructing joint PDFs in a lower dimensional space using principal component analysis (PCA) in conjunction with Kernel Density Estimation (KDE). However, constructing the principal component (PC) joint PDFs is still computationally expensive as they are required to be calculated at each spatial location in the turbulent flame. In this work, we propose the concept of a generalized joint PDF model using the Deep Operator Network (DeepONet). The DeepONet is a machine learning model that is parameterized on the unconditional means of PCs at a given spatial location and discrete PC coordinates and predicts the joint probability density value for the corresponding PC coordinate. We demonstrate the accuracy and generalizability of the DeepONet on the Sandia flames, D, E and F. The DeepONet is trained based on the PC joint PDFs observed inflame E and yields excellent predictions of joint PDFs shapes at different spatial locations of flamesD and F, which are not seen during training