Corpus ID: 236428405

Neural Differential Equations for Inverse Modeling in Model Combustors

@inproceedings{Su2021NeuralDE,
  title={Neural Differential Equations for Inverse Modeling in Model Combustors},
  author={Xingyu Su and Weiqi Ji and Long Zhang and Wantong Wu and Zhuyin Ren and Sili Deng},
  year={2021}
}
  • Xingyu Su, Weiqi Ji, +3 authors Sili Deng
  • Published 2021
  • Physics
Monitoring the dynamics processes in combustors is crucial for safe and efficient operations. However, in practice, only limited data can be obtained due to limitations in the measurable quantities, visualization window, and temporal resolution. This work proposes an approach based on neural differential equations to approximate the unknown quantities from available sparse measurements. The approach tackles the challenges of nonlinearity and the curse of dimensionality in inverse modeling by… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 19 REFERENCES
Arrhenius.jl: A Differentiable Combustion SimulationPackage
Combustion kinetic modeling is an integral part of combustion simulation, and extensive studies have been devoted to developing both high fidelity and computationally affordable models. Despite theseExpand
Inverse estimation of inlet parameters in an axisymmetric cylindrical combustor with thermal radiation effect
The major objective of the present study is to extend the application of an inverse analysis to a more realistic engineering problem with complex combustion process than traditional simple heatExpand
Analysis and neural network prediction of combustion stability for industrial gases
Abstract Combustion of industrial gases has gained increasing attention in the past decades. Great challenges for reliable industrial operation are posed by combustion instability. In this study, theExpand
Inverse identification of boundary conditions in a scramjet combustor with a regenerative cooling system
Abstract Accurately determining boundary conditions in a scramjet combustor is of great importance for modeling the coupled process of fuel burning, fluid flow and heat transfer in the scramjetExpand
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Abstract We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinearExpand
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
TLDR
This work first investigates the performance of PINN in solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs), and employs Quasi-Steady-State-Assumptions (QSSA) to reduce the stiffness of the ODE systems, suggesting stiffness could be the major reason for the failure of the regular PINN. Expand
Heat flux evaluation in a multi-element CH4/O2 rocket combustor using an inverse heat transfer method
Abstract Heat load measurements in experimental lab-scale rocket combustors are essential in order to obtain information about the mixing and energy release of the propellants, the injector/injectorExpand
Combustion stability analysis for non-standard low-calorific gases: Blast furnace gas and coke oven gas
Abstract The effective and efficient utilization of byproduct fuels has gained increasing attention in all industrial fields. In this study, flame stabilization mechanisms for non-standardExpand
Neural Ordinary Differential Equations
TLDR
This work shows how to scalably backpropagate through any ODE solver, without access to its internal operations, which allows end-to-end training of ODEs within larger models. Expand
DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia
TLDR
DifferentialEquations.jl offers a unified user interface to solve and analyze various forms of differential equations while not sacrificing features or performance, and is an algorithm testing and benchmarking suite which is feature-rich and highly performant. Expand
...
1
2
...