Mixing and detonation structure in a rotating detonation engine with an axial air inlet

Sato, Takuma; Chacon, Fabian; White, Logan; Raman, Venkat; Gamba, Mirko

doi:10.1016/j.proci.2020.06.283

Cited by 81 publications

(21 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detonation combustion with a gas fuelgas oxidizer has been reported to have worse performance than theoretically determined due to non-detonation combustion such as parasitic and commensal combustion. [22][23][24][25] The method of accelerating the vaporization of liquid fuels is believed to encourage other combustions than detonation combustion, like RDCs with gas fuels-gas oxidizers. In this study, using liquid fuel and a gas oxidizer at room temperature suppressed combustions other than detonation combustion, which might have increased the detonation speed compared to other studies.…”

Section: Resultsmentioning

confidence: 99%

“…They have shown that the detonation structure of the annular RDC with gas propellants appeared to be remarkably similar to the computational fluid dynamics results of RDEs. Sato et al 22) observed the inside of an annular RDC using gas hydrogenair as a propellant using the OH-PLIF technique and reported its structure in detail. Their study showed that parasitic and commensal combustion exists around the detonation wave.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Visualization and Performance Evaluation of a Liquid-Ethanol Cylindrical Rotating Detonation Combustor

Ishihara

Yoneyama

Sato

et al. 2023

Trans. Japan Soc. Aero. S Sci.

View full text Add to dashboard Cite

Rotating detonation combustors (RDCs) are among the combustors that use supersonic combustion waves known as detonation waves, and are expected to simplify engine systems and improve thermal efficiency due to their supersonic combustion and compression performance using shock waves. Research is also being actively conducted worldwide on a cylindrical RDC; a RDC without an inner cylinder, which is expected to simplify and downsize the combustor. However, most of the research was performed using gas propellants, and liquid propellants were rarely used. Since liquid propellants are used in many combustors, it is important to evaluate the performance of RDCs with liquid propellants. In this study, a cylindrical RDC with a liquid ethanol-gas oxygen mixture was constructed and tested at a flow rate of 31.5 « 5.0 g/s, an equivalence ratio of 0.46-1.39, and a back pressure of 14.5 « 2.5 kPa. The thrust was shown to depend strongly on the combustor bottom pressure history. In addition, the start-up process of the cylindrical RDC with liquid fuel was clarified by self-luminous and CH + radical visualizations. It was found that the detonation wavefront propagated at a distance of 2-3 mm from the combustor bottom, and the main combustion region was 10-15 mm in height.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visualization and Performance Evaluation of a Liquid-Ethanol Cylindrical Rotating Detonation Combustor

Ishihara

Yoneyama

Sato

et al. 2023

Trans. Japan Soc. Aero. S Sci.

View full text Add to dashboard Cite

show abstract

“…In some applications, accurate prediction of rare events is crucial when the model is deployed. For example, in non-premixed rotating detonation engines, detonations are heavily influenced by triple points and fuel mixing ahead of the detonation [43,45,46], which are very localized phenomena. For other combustion applications, the lack of data in some parts of phase-space has even required the addition of synthetic data [47].…”

Section: Problem Statement and Justificationmentioning

confidence: 99%

Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows

Hassanaly¹,

Perry²,

Müeller³

et al. 2021

Preprint

View full text Add to dashboard Cite

Figure 1: Graphical illustration of the data selection method. Starting from a large dataset (top left scatter plot), a reduced uniform-in-phase-space dataset is constructed (right scatter plot). The reduction method relies on the probability map which is iteratively computed (Step 1 and Step 1 correction). An acceptance probability constructed with the probability map is used to accept or reject each data point (Step 2).

show abstract

“…The 1-D detonation simulations were conducted with the OpenFOAM-based solver UMdetFOAM [36][37][38], which solves the governing equations of fluid flow consisting of mass, momentum, energy, and species conservation equations. UMdetFOAM is a compressible flow solver which contains shock-capturing numerics using the Monotonic Upwind Scheme for Conservation Laws (MUSCL)-based Harten-Lax-van Leer-Contact (HLLC) scheme [37,39], a second-order Runge-Kutta temporal discretization with minimal dissipation [40], and the Kurganov, Noelle, and Petrova (KNP) scheme [41] for diffusion terms.…”

Section: -D Channel Detonationmentioning

confidence: 99%

“…2 implemented in the CUDA and cuBLAS environments. This code has been extensively validated using experiments of detonation-containing flows [36][37][38][39].…”

Section: -D Channel Detonationmentioning

confidence: 99%

A Neural Network Inspired Formulation of Chemical Kinetics

Barwey,

Raman

2020

Preprint

Self Cite

View full text Add to dashboard Cite

A method which casts the chemical source term computation into an artificial neural network (ANN)inspired form is presented. This approach is well-suited for use on emerging supercomputing platforms that rely on graphical processing units (GPUs). The resulting equations allow for a GPU-friendly matrixmultiplication based source term estimation where the leading dimension (batch size) can be interpreted as the number of chemically reacting cells in the domain; as such, the approach can be readily adapted in high-fidelity solvers for which an MPI rank offloads the source term computation task for a given number of cells to the GPU. Though the exact ANN-inspired recasting shown here is optimal for GPU environments as-is, this interpretation allows the user to replace portions of the exact routine with trained, so-called approximate ANNs, where the goal of these approximate ANNs is to increase computational efficiency over the exact routine counterparts. Note that the main objective of this paper is not to use machine learning for developing models, but rather to represent chemical kinetics using the ANN framework. The end result is that little-to-no training is needed, and the GPU-friendly structure of the ANN formulation during the source term computation is preserved. The method is demonstrated using chemical mechanisms of varying complexity on both 0-D auto-ignition and 1-D channel detonation problems, and the details of performance on GPUs are explored.

show abstract

Mixing and detonation structure in a rotating detonation engine with an axial air inlet

Cited by 81 publications

References 29 publications

Visualization and Performance Evaluation of a Liquid-Ethanol Cylindrical Rotating Detonation Combustor

Visualization and Performance Evaluation of a Liquid-Ethanol Cylindrical Rotating Detonation Combustor

Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows

A Neural Network Inspired Formulation of Chemical Kinetics

Contact Info

Product

Resources

About