Combustion Dynamics in a Single-Element Lean Direct Injection Gas Turbine Combustor

Huang, Cheng; Gejji, Rohan; Anderson, William E.; Yoon, Changjin; Sankaran, Venkateswaran

doi:10.1080/00102202.2019.1646732

Cited by 22 publications

(4 citation statements)

References 73 publications

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“…The computational infrastructure used for the full-and reduced-order models solves conservation equations for mass, momentum, energy and species transport in a fully coupled way using the in-house CFD code, the General Mesh and Equation Solver (GEMS). GEMS has been used to model a variety of complex, practical reacting flow problems [102,103]. More details of the FOM equations can be found in Appendix A.…”

Section: Numerical Results and Analysismentioning

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: Numerical Results and Analysismentioning

confidence: 99%

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

Huang¹,

Duraisamy²

2023

Preprint

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“…The computational infrastructure used for the full-and reduced-order models solves conservation equations for mass, momentum, energy and species transport [58,61] in a fully coupled manner, which has been used to model a variety of complex, practical reacting flow problems. More details of the FOM equations can be found in Appendix B of [14].…”

Section: Numerical Resultsmentioning

confidence: 99%

Component-Based Reduced Order Modeling of Large-Scale Complex Systems

2022

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Large-scale engineering systems, such as propulsive engines, ship structures, and wind farms, feature complex, multi-scale interactions between multiple physical phenomena. Characterizing the operation and performance of such systems requires detailed computational models. Even with advances in modern computational capabilities, however, high-fidelity (e.g., large eddy) simulations of such a system remain out of reach. In this work, we develop a reduced‐order modeling framework to enable accurate predictions of large-scale systems. We target engineering systems which are difficult to simulate at a high-enough level of fidelity, but are decomposable into different components. These components can be modeled using a combination of strategies, such as reduced-order models (ROM) or reduced-fidelity full-order models (RF-FOM). Component-based training strategies are developed to construct ROMs for each individual component. These ROMs are then integrated to represent the full system. Notably, this approach only requires high-fidelity simulations of a much smaller computational domain. System-level responses are mimicked via external boundary forcing during training. Model reduction is accomplished using model-form preserving least-squares projections with variable transformation (MP-LSVT) (Huang et al., Journal of Computational Physics, 2022, 448: 110742). Predictive capabilities are greatly enhanced by developing adaptive bases which are locally linear in time. The trained ROMs are then coupled and integrated into the framework to model the full large-scale system. We apply the methodology to extremely complex flow physics involving combustion dynamics. With the use of the adaptive basis, the framework is demonstrated to accurately predict local pressure oscillations, time-averaged and RMS fields of target state variables, even with geometric changes.

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“…The computational infrastructure used for the full-and reduced-order models solves conservation equations for mass, momentum, energy and species transport in a fully coupled way using the in-house CFD code, the General Mesh and Equation Solver (GEMS). GEMS has been used to model a variety of complex, practical reacting flow problems [55,65]. More details of the FOM equations can be found in Appendix B.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

Model Reduction for Multi-Scale Transport Problems using Model-form Preserving Least-Squares Projections with Variable Transformation

Huang¹,

Wentland²,

Duraisamy³

et al. 2020

Preprint

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A projection-based formulation is presented for non-linear model reduction of problems with extreme scale disparity. The approach allows for the selection of an arbitrary, but complete, set of solution variables while preserving the conservative form of the governing equations. Least-squares-based minimization is leveraged to guarantee symmetrization and discrete consistency with the full-order model (FOM) at the sub-iteration level. Two levels of scaling are used to achieve the conditioning required to effectively handle problems with extremely disparate physical phenomena, characterized by extreme stiffness in the system of equations. The formulation -referred to as structure-preserving least-squares with variable transformation (SP-LSVT) -provides global stabilization for both implicit and explicit time integration schemes. To achieve computational efficiency, a pivoted QR decomposition is used with oversampling, and adapted to the SP-LSVT method. The framework is demonstrated in representative two-and three-dimensional reacting flow problems, and the SP-LSVT is shown to exhibit improved stability and accuracy over standard projection-based ROM techniques. Physical realizability is promoted by enforcing limiters in both temperature and species mass fractions, thus contributing to local stability enhancement. These limiters are demonstrated to be important in eliminating regions of spurious burning, thus enabling the ROMs to provide accurate representations of the heat release rate and flame propagation speed. In the 3D application, it is shown that more than two orders of magnitude acceleration in computational efficiency can be achieved, while also providing reasonable future-state predictions. A key contribution of this work is the development and demonstration of a comprehensive ROM formulation that targets highly challenging multi-scale transport-dominated problems.

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Combustion Dynamics in a Single-Element Lean Direct Injection Gas Turbine Combustor

Cited by 22 publications

References 73 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

Component-Based Reduced Order Modeling of Large-Scale Complex Systems

Model Reduction for Multi-Scale Transport Problems using Model-form Preserving Least-Squares Projections with Variable Transformation

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