3D Predictions of the Primary Breakup of Fuel in Spray Nozzles for Aero Engines

Dauch, Thilo F.; Chaussonnet, Geoffroy; Keller, Marc C.; Okraschevski, Max; Ates, Cihan; Koch, Rainer; Bauer, Hans‐Jörg

doi:10.1007/978-3-030-80602-6_27

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…In fact, in the simpler use case without sublients, no modifications to user code are necessary at all because the MPI Consumer also integrates with the Go2, which is part of the traditional Geneva user interface. To use the MPI Consumer, just a different command line parameter must be specified at startup time 12 . The framework offered to the user already takes care of (1) the initialization of the network communication, (2) the coordination of processes and assigning them to different roles, (3) the invocation of the client and subclient code at the right time, (4) checking the clients' state to determine the end of the optimization and (5) shutting down the network connections at the end.…”

Section: New Features and User Experiencementioning

confidence: 99%

“…Parametric optimization is the process of searching for an optimal set of input parameters to an application-specific cost function, such that its output value is minimal. Parametric optimization problems are often encountered in physics [7] [8,[115][116], chemistry [9], medicine [10] [11,70], engineering [12,[419][420] [13,131] [14,12370] and economics [15].…”

Section: Introductionmentioning

confidence: 99%

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Parametric Optimization on HPC Clusters with Geneva

Weßner

Berlich²,

Schwarz

et al. 2023

Preprint

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Many challenges of today's science are parametric optimization problems that are extremely complex and computationally intensive to calculate. At the same time, the hardware for high-performance computing is becoming increasingly powerful. Geneva is a framework for parallel optimization of large-scale problems with highly nonlinear quality surfaces in grid and cloud environments. To harness the immense computing power of high-performance computing clusters, we have developed a new networking component for Geneva, the so-called MPI Consumer, which makes Geneva suitable for HPC. Geneva is most prominent for its evolutionary algorithm, which requires repeatedly evaluating a user-defined cost function. The MPI Consumer parallelizes the computation of the candidate solutions' cost functions by sending them to remote cluster nodes. By using an advanced multithreading mechanism on the master node and by using asynchronous requests on the worker nodes, the MPI Consumer is highly scalable. Additionally, it provides fault tolerance, which is usually not the case for MPI programs but becomes increasingly important for HPC. Moreover, the MPI Consumer provides a framework for the intuitive implementation of fine-grained parallelization of the cost function. Since the MPI Consumer conforms to the standard paradigm of HPC programs, it vastly improves Geneva's user-friendliness on HPC clusters. This article gives insight into Geneva's general system architecture and the system design of the MPI Consumer as well as the underlying concepts. Geneva, including the novel MPI Consumer, is publicly available as an open source project on GitHub and is currently used for fundamental physics research at GSI in Darmstadt, Germany.

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Section: New Features and User Experiencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parametric Optimization on HPC Clusters with Geneva

Weßner

Berlich²,

Schwarz

et al. 2023

Preprint

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“…In this setup, the primary atomization process is reduced to two dimensions in a necessary compromise between physical fidelity and computational cost. Despite its simplified two-dimensional approach, the SPH method has demonstrated its capability to accurately capture the spray characteristics [10,11,21,22,25,26]. Figure 1 displays two snapshots from the primary atomization process, one at OP1 and the other at OP4, clearly illustrating how the emerging ligament structures and, subsequently, the downstream droplet characteristics are impacted by the change in operating conditions with the decreasing air-fuel ratio.…”

Section: Description Of the Training Datamentioning

confidence: 99%

Modeling Multivariate Spray Characteristics with Gaussian Mixture Models

Wicker,

Ates,

Okraschevski

et al. 2023

Energies

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With the increasing demand for efficient and accurate numerical simulations of spray combustion in jet engines, the necessity for robust models to enhance the capabilities of spray models has become imperative. Existing approaches often rely on ad hoc determinations or simplifications, resulting in information loss and potentially inaccurate predictions for critical spray characteristics, such as droplet diameters, velocities, and positions, especially under extreme operating conditions or temporal fluctuations. In this study, we introduce a novel approach to modeling multivariate spray characteristics using Gaussian mixture models (GMM). By applying this approach to spray data obtained from numerical simulations of the primary atomization in air-blast atomizers, we demonstrate that GMMs effectively capture the spray characteristics across a wide range of operating conditions. Importantly, our investigation reveals that GMMs can handle complex non-linear dependencies by increasing the number of components, thereby enabling the modeling of more complex spray statistics. This adaptability makes GMMs a versatile tool for accurately representing spray characteristics even under extreme operating conditions. The presented approach holds promise for enhancing the accuracy of spray combustion modeling, offering an improved injection model that accurately captures the underlying droplet distribution. Additionally, GMMs can serve as a foundation for constructing meta models, striking a balance between the efficiency of low-order approaches and the accuracy of high-fidelity simulations.

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“…To model fuel jettison more generally for fixed-wing aircraft, a higher fidelity method is required that considers the continuum dynamics, such as finite volume CFD (via volume of fluid (VOF) or otherwise) [11] [12], or a Lagrangian approach such as smoothed particle hydrodynamics (SPH) [13] [14] [15] [16]. These methods have been used for detailed simulations of fuel injection with airblast atomizers, a scenario similar to that of fuel jettison, displaying an ability to represent the environment.…”

Section: Introductionmentioning

confidence: 99%

Modeling Aircraft Fuel Jettison Using Smoothed Particle Hydrodynamics on Finite-Volume Meshes

MacLeod,

Rendall,

Allen

2024

Journal of Aircraft

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Weakly compressible smoothed particle hydrodynamics is used to investigate aircraft fuel jettison using a single-phase model. Fuel simulations are coupled to the aircraft computational fluid dynamics flowfield using cell containment checks on the finite-volume mesh to locate any smoothed particle hydrodynamics (SPH) particle within the mesh, after which the local flow velocity vector is retrieved and then used to apply an approximate aerodynamic force to the SPH particle based on a continuum correction to discrete droplet calculations. Further downstream, the SPH simulation may be continued, or a switch may be made to implicit particle tracking (IPT) in order to accelerate the simulation. Comparison to IPT results shows that a fluid model of the initial continuum breakup of the jet is required, but following this transition to IPT is reasonable to reduce computation time. Models are validated against volume of fluid (VOF) simulations, with the runtime of the proposed model being approximately 100 times less than the VOF, and good qualitative agreement is also found compared to recorded flight results.

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3D Predictions of the Primary Breakup of Fuel in Spray Nozzles for Aero Engines

Cited by 6 publications

References 29 publications

Parametric Optimization on HPC Clusters with Geneva

Parametric Optimization on HPC Clusters with Geneva

Modeling Multivariate Spray Characteristics with Gaussian Mixture Models

Modeling Aircraft Fuel Jettison Using Smoothed Particle Hydrodynamics on Finite-Volume Meshes

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