Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics

This paper presents a structure-exploiting nonlinear model reduction method for systems with general nonlinearities. First, the nonlinear model is lifted to a model with more structure via variable...

Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition

We propose a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields while preserving the contribution order of the latent vectors. As preliminary tests, the proposed method is first applied to a cylinder wake at $Re_D$ = 100 and its transient process. It is found that the proposed method can extract the features of these laminar flow fields as the latent vectors while keeping the order of their energy content. The present hierarchical autoencoder is further assessed with a two-dimensional $y-z$ cross-sectional velocity field of turbulent channel flow at $Re_{\tau}$ = 180 in order to examine its applicability to turbulent flows. It is demonstrated that the turbulent flow field can be efficiently mapped into the latent space by utilizing the hierarchical model with a concept of ordered autoencoder mode family. The present results suggest that the proposed concept can be extended to meet various demands in fluid dynamics including reduced order modeling and its combination with linear theory-based methods by using its ability to arrange the order of the extracted nonlinear modes.

Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

Exploration of reduced order models for rocket combustion applications

A multifidelity framework is established and demonstrated for the prediction of combustion instabilities in rocket engines. The main idea is to adapt appropriate fidelity modeling approaches for di...

Jiayang Xu

Papers

Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics

Exploration of reduced order models for rocket combustion applications

Reduced-Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training

Reduced-Order Modeling of Model Rocket Combustors

Simulation of Maritime Helicopter Dynamics During Approach to Landing With Time-Accurate Wind-Over-Deck