Investigation of Sampling Strategies for Reduced-Order Models of Rocket Combustors

Wentland, Christopher R.; Huang, Cheng; Duraisamy, Karthik

doi:10.2514/6.2021-1371

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…Towards this end, though these ROMs have been demonstrated to be truly predictive in problems with challenging features, the formulation can be improved in several directions: 1) While the authors have realized between 3-4 orders of magnitude cost reduction using static basis ROMs, current adaptive formulations yield between one and two orders of magnitude cost reduction. Thus more detailed investigations are required on the strategies to select sampling points and in hyper-reduction, which has been shown to be important for ROM development [113]; 2) scalable implementation of the adaptive ROM algorithm for large-scale engineering problems requires further development, especially in developing an effective load-balancing strategy to accommodate the adapted sampling points; 3) in the current formulation, the input parameters such as the dimension of the reduced space, the initial training window size, and the number of sampling points are fixed while it can be more beneficial to have them adapted over time.…”

Section: Discussionmentioning

confidence: 99%

Predictive Reduced Order Modeling of Chaotic Multi-scale Problems Using Adaptively Sampled Projections

Huang¹,

Duraisamy²

2023

Preprint

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An adaptive projection-based reduced-order model (ROM) formulation is presented for model-order reduction of problems featuring chaotic and convection-dominant physics. An efficient method is formulated to adapt the basis at every time-step of the on-line execution to account for the unresolved dynamics. The adaptive ROM is formulated in a Least-Squares setting using a variable transformation to promote stability and robustness. An efficient strategy is developed to incorporate non-local information in the basis adaptation, significantly enhancing the predictive capabilities of the resulting ROMs. A detailed analysis of the computational complexity is presented, and validated. The adaptive ROM formulation is shown to require negligible offline training and naturally enables both future-state and parametric predictions. The formulation is evaluated on representative reacting flow benchmark problems, demonstrating that the ROMs are capable of providing efficient and accurate predictions including those involving significant changes in dynamics due to parametric variations, and transient phenomena. A key contribution of this work is the development and demonstration of a comprehensive ROM formulation that targets predictive capability in chaotic, multi-scale, and transport-dominated problems.

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Section: Discussionmentioning

confidence: 99%

Predictive Reduced Order Modeling of Chaotic Multi-scale Problems Using Adaptively Sampled Projections

Huang¹,

Duraisamy²

2023

Preprint

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“…Local stability is enhanced using physical limiters. Additional details of the MP-LSVT method in terms of performance and the hyper reduction achievable for 3D problems can be found in previous works 51,73 . MP-LSVT allows one to represent the system state as a function of primitive variables q p .…”

Section: Reduced-order Modeling (Rom)mentioning

confidence: 99%

Large-eddy simulation and challenges for projection-based reduced-order modeling of a gas turbine model combustor

Arnold-Medabalimi

Huang

Duraisamy

2022

International Journal of Spray and Combustion Dynamics

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Computationally efficient modeling of gas turbine combustion is challenging due to the chaotic multi-scale physics and the complex non-linear interactions between acoustic, hydrodynamic, and chemical processes. A large-eddy simulation, referred to as the full order model (FOM), is performed for a gas turbine model combustor with turbulent combustion effects modeled using a flamelet-based method. Modal analysis reveals a high degree of correlation with averaged and instantaneous high-frequency particle image velocimetry fields. The dynamics of the precessing vortex core is quantitatively characterized using dynamic mode decomposition. The governing equations of the FOM are projected onto a low-dimensional linear manifold to construct a reduced-order model (ROM). A discretely-consistent least squares projection is used to guarantee global stability. The ROM provides an accurate reconstruction of the combustion dynamics within the training region, but faces a significant challenge in future state predictions. This limitation is mainly due to the increased projection error, which in turn is a direct consequence of the highly chaotic nature of the flow field, involving a wide range of dispersed coherent structures. This shortcoming is overcome using an adaptive basis method which yields accurate predictions of dynamics beyond the training region consistent with the FOM. Formal projection-based ROMs have not been applied to a problem of this scale and complexity, and achieving accurate and efficient ROMs is a grand challenge problem. A production-ready ROM method will significantly decrease the computational cost of the flame dynamics as well as the portability of this prediction to smaller-scale computers.

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Reduced Models with Nonlinear Approximations of Latent Dynamics for Model Premixed Flame Problems

Uy,

Wentland,

Huang

et al. 2024

Lecture Notes in Computational Science and Engineering

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Investigation of Sampling Strategies for Reduced-Order Models of Rocket Combustors

Cited by 8 publications

References 42 publications

Predictive Reduced Order Modeling of Chaotic Multi-scale Problems Using Adaptively Sampled Projections

Predictive Reduced Order Modeling of Chaotic Multi-scale Problems Using Adaptively Sampled Projections

Large-eddy simulation and challenges for projection-based reduced-order modeling of a gas turbine model combustor

Reduced Models with Nonlinear Approximations of Latent Dynamics for Model Premixed Flame Problems

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