The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
P G Tucker - One of the best experts on this subject based on the ideXlab platform.
-
trends in Turbomachinery turbulence treatments
Progress in Aerospace Sciences, 2013Co-Authors: P G TuckerAbstract:Abstract General forms of turbulence models are outlined along with their defects and palliatives for these in relation to Turbomachinery. The turbulence modelling hierarchy available in Turbomachinery is set out, moving from RANS (Reynolds Averaged Navier–Stokes) to the eddy resolving DNS (Direct Numerical Simulation) approach. New vistas for techniques are discussed. A modular RANS turbulence modelling strategy is outlined. Simple scaling arguments for Unsteady RANS (URANS) spectral gaps in Turbomachinery are presented and the presence of such gaps shown not always to be guaranteed. The power of computers continues to steadily rise. Hence, the use of eddy resolving simulations in their various forms is expected to increase and also their use for the refinement of lower order models. Current examples for the latter are given. The use of eddy resolving simulations in the coupled and sometimes multi-physics Turbomachinery environment is considered. The need for improved measurements with well defined boundary conditions that have Reynolds stress and even spectral information, at Reynolds and Mach numbers that connect with typically powerful Turbomachinery systems is identified. This is necessary to refine both RANS and eddy resolving strategies. Most available ‘Best Practices’ are centred on RANS. Hence, new guidance needs to be developed for eddy resolving methods. Expert systems, based around flow taxonomies, that can assist with for example making initial grid estimates and guiding aerodynamicists through the eddy resolving simulation process are discussed. The need for more Turbomachinery relevant strategies for generating turbulence inflow is identified.
-
computation of unsteady Turbomachinery flows part 2 les and hybrids
Progress in Aerospace Sciences, 2011Co-Authors: P G TuckerAbstract:Abstract The choice of turbulence model can have a strong impact on results for many Turbomachinery zones. Palliative corrections to them and also transition modeling can have a further profound solution impact. The spectral gaps necessary for theoretically valid URANS solutions are also lacking in certain Turbomachinery zones. Large Eddy Simulation (LES) alleviates the serious area of turbulence modeling uncertainty but with an extreme increase in computational cost. However, there seems a lack of validation data to explore in depth the performance of LES and thus strategies to refine it. LES best practices are needed. Although LES is, obviously, much less model dependent than RANS, grids currently used for more practical simulations are clearly insufficiently fine for the LES model and numerical schemes not to be playing an excessively strong role. Very few Turbomachinery simulations make use of properly constructed, correlated turbulence inflow. Even if this is attempted, most measurement sets are incomplete and lack an adequate basis for modeling this inflow. Gas turbines are highly complex coupled systems and hence inflow and outflow boundary condition specification needs to go beyond just synthesizing turbulent structures and preventing their reflection. Despite the strong limitations of the dissipative Smagorinsky model, it still sees the most wide spread use, generally, in excessively dissipative flow solvers. Monotone Integrated LES (MILES) related approaches, hybrid LES–RANS and more advanced LES models seem to have an equal but subservient frequency of use in Turbomachinery applications. Clearly the introduction of a RANS layer can have a substantial accuracy penalty. However, it does allow LES to be rationally used, albeit in a diluted sense for industrial applications. The Reynolds numbers found in Turbomachinery are substantial. However, in certain areas evidence suggests they will not be enough to ensure a long inertial subrange and hence the use of standard LES modeling practices. Despite the excessively coarse grids used in much of the LES work reviewed, with essentially RANS based codes, meaningful results are often gained. This can perhaps be attributed to the choice of cases, these being ones for which RANS modeling gives extremely poor performance. It is a concern that for practical Turbomachinery LES studies grid densities used tend to have an Reynolds number scaling to a strong negative power.
Michael Adams - One of the best experts on this subject based on the ideXlab platform.
-
Turbomachinery Active Subspace Performance Maps
Journal of Turbomachinery, 2018Co-Authors: Pranay Seshadri, Geoffrey T Parks, Shahrokh Shahpar, Paul G. Constantine, Michael AdamsAbstract:Turbomachinery active subspace performance maps are two-dimensional (2D) contour plots that illustrate the variation of key flow performance metrics with different blade designs. While such maps are easy to construct for design parameterizations with two variables, in this paper, maps will be generated for a fan blade with twenty-five design variables. Turbomachinery active subspace performance maps combine active subspaces—a new set of ideas for dimension reduction—with fundamental Turbomachinery aerodynamics and design spaces. In this paper, contours of (i) cruise efficiency, (ii) cruise pressure ratio (PR), (iii) maximum climb flow capacity, and (iv) sensitivity to manufacturing variations are plotted as objectives for the fan. These maps are then used to infer pedigree design rules: how best to increase fan efficiency; how best to desensitize blade aerodynamics to the impact of manufacturing variations? In the present study, the former required both a reduction in PR and flow capacity—leading to a reduction of the strength of the leading edge bow wave—while the latter required strictly a reduction in flow capacity. While such pedigree rules can be obtained from first principles, in this paper, these rules are derived from the active subspaces. This facilitates a more detailed quantification of the aerodynamic trade-offs. Thus, instead of simply stating that a particular design is more sensitive to manufacturing variations; or that it lies on a hypothetical “efficiency cliff,” this paper seeks to visualize, quantify, and make precise such notions of Turbomachinery design.
-
Turbomachinery Active Subspace Performance Maps
Volume 2A: Turbomachinery, 2017Co-Authors: Pranay Seshadri, Geoffrey T Parks, Shahrokh Shahpar, Paul G. Constantine, Michael AdamsAbstract:Turbomachinery active subspace performance maps are 2D contour plots that illustrate the variation of key flow performance metrics with different blade designs. While such maps are easy to construct for design parameterizations with two variables, in this paper maps will be generated for a fan blade with twenty-five design variables. Turbomachinery active subspace performance maps combine active subspaces — a new set of ideas for dimension reduction — with fundamental Turbomachinery aerodynamics and design spaces. In this paper, contours of (i) cruise efficiency, (ii) cruise pressure ratio, (iii) maximum climb flow capacity and (iv) sensitivity to manufacturing variations, are plotted as objectives for the fan. These maps are then used to infer pedigree design rules: how best to increase fan efficiency; how best to desensitize blade aerodynamics to the impact of manufacturing variations? In the present study, the former required both a reduction in pressure ratio and flow capacity — leading to a reduction of the strength of the leading edge bow wave — while the latter required strictly a reduction in flow capacity. While such pedigree rules can be obtained from first principles, in this paper these rules are derived from the active subspaces. This facilitates a more detailed quantification of the aerodynamic trade-offs. Thus, instead of simply stating that a particular design is more sensitive to manufacturing variations; or that it lies on a hypothetical ‘efficiency cliff’, this paper seeks to visualize, quantify and make precise such notions of Turbomachinery design.Copyright © 2017 by Rolls-Royce plc
Pranay Seshadri - One of the best experts on this subject based on the ideXlab platform.
-
Turbomachinery Active Subspace Performance Maps
Journal of Turbomachinery, 2018Co-Authors: Pranay Seshadri, Geoffrey T Parks, Shahrokh Shahpar, Paul G. Constantine, Michael AdamsAbstract:Turbomachinery active subspace performance maps are two-dimensional (2D) contour plots that illustrate the variation of key flow performance metrics with different blade designs. While such maps are easy to construct for design parameterizations with two variables, in this paper, maps will be generated for a fan blade with twenty-five design variables. Turbomachinery active subspace performance maps combine active subspaces—a new set of ideas for dimension reduction—with fundamental Turbomachinery aerodynamics and design spaces. In this paper, contours of (i) cruise efficiency, (ii) cruise pressure ratio (PR), (iii) maximum climb flow capacity, and (iv) sensitivity to manufacturing variations are plotted as objectives for the fan. These maps are then used to infer pedigree design rules: how best to increase fan efficiency; how best to desensitize blade aerodynamics to the impact of manufacturing variations? In the present study, the former required both a reduction in PR and flow capacity—leading to a reduction of the strength of the leading edge bow wave—while the latter required strictly a reduction in flow capacity. While such pedigree rules can be obtained from first principles, in this paper, these rules are derived from the active subspaces. This facilitates a more detailed quantification of the aerodynamic trade-offs. Thus, instead of simply stating that a particular design is more sensitive to manufacturing variations; or that it lies on a hypothetical “efficiency cliff,” this paper seeks to visualize, quantify, and make precise such notions of Turbomachinery design.
-
Turbomachinery Active Subspace Performance Maps
Volume 2A: Turbomachinery, 2017Co-Authors: Pranay Seshadri, Geoffrey T Parks, Shahrokh Shahpar, Paul G. Constantine, Michael AdamsAbstract:Turbomachinery active subspace performance maps are 2D contour plots that illustrate the variation of key flow performance metrics with different blade designs. While such maps are easy to construct for design parameterizations with two variables, in this paper maps will be generated for a fan blade with twenty-five design variables. Turbomachinery active subspace performance maps combine active subspaces — a new set of ideas for dimension reduction — with fundamental Turbomachinery aerodynamics and design spaces. In this paper, contours of (i) cruise efficiency, (ii) cruise pressure ratio, (iii) maximum climb flow capacity and (iv) sensitivity to manufacturing variations, are plotted as objectives for the fan. These maps are then used to infer pedigree design rules: how best to increase fan efficiency; how best to desensitize blade aerodynamics to the impact of manufacturing variations? In the present study, the former required both a reduction in pressure ratio and flow capacity — leading to a reduction of the strength of the leading edge bow wave — while the latter required strictly a reduction in flow capacity. While such pedigree rules can be obtained from first principles, in this paper these rules are derived from the active subspaces. This facilitates a more detailed quantification of the aerodynamic trade-offs. Thus, instead of simply stating that a particular design is more sensitive to manufacturing variations; or that it lies on a hypothetical ‘efficiency cliff’, this paper seeks to visualize, quantify and make precise such notions of Turbomachinery design.Copyright © 2017 by Rolls-Royce plc
Eric S Hendricks - One of the best experts on this subject based on the ideXlab platform.
-
meanline analysis of turbines with choked flow in the object oriented Turbomachinery analysis code
54th AIAA Aerospace Sciences Meeting, 2016Co-Authors: Eric S HendricksAbstract:The prediction of Turbomachinery performance characteristics is an important part of the conceptual aircraft engine design process. During this phase, the designer must examine the effects of a large number of Turbomachinery design parameters to determine their impact on overall engine performance and weight. The lack of detailed design information available in this phase necessitates the use of simpler meanline and streamline methods to determine the Turbomachinery geometry characteristics and provide performance estimates prior to more detailed CFD (Computational Fluid Dynamics) analyses. While a number of analysis codes have been developed for this purpose, most are written in outdated software languages and may be difficult or impossible to apply to new, unconventional designs. The Object-Oriented Turbomachinery Analysis Code (OTAC) is currently being developed at NASA Glenn Research Center to provide a flexible meanline and streamline analysis capability in a modern object-oriented language. During the development and validation of OTAC, a limitation was identified in the code's ability to analyze and converge turbines as the flow approached choking. This paper describes a series of changes which can be made to typical OTAC turbine meanline models to enable the assessment of choked flow up to limit load conditions. Results produced with this revised model setup are provided in the form of turbine performance maps and are compared to published maps.
Geoffrey T Parks - One of the best experts on this subject based on the ideXlab platform.
-
Turbomachinery Active Subspace Performance Maps
Journal of Turbomachinery, 2018Co-Authors: Pranay Seshadri, Geoffrey T Parks, Shahrokh Shahpar, Paul G. Constantine, Michael AdamsAbstract:Turbomachinery active subspace performance maps are two-dimensional (2D) contour plots that illustrate the variation of key flow performance metrics with different blade designs. While such maps are easy to construct for design parameterizations with two variables, in this paper, maps will be generated for a fan blade with twenty-five design variables. Turbomachinery active subspace performance maps combine active subspaces—a new set of ideas for dimension reduction—with fundamental Turbomachinery aerodynamics and design spaces. In this paper, contours of (i) cruise efficiency, (ii) cruise pressure ratio (PR), (iii) maximum climb flow capacity, and (iv) sensitivity to manufacturing variations are plotted as objectives for the fan. These maps are then used to infer pedigree design rules: how best to increase fan efficiency; how best to desensitize blade aerodynamics to the impact of manufacturing variations? In the present study, the former required both a reduction in PR and flow capacity—leading to a reduction of the strength of the leading edge bow wave—while the latter required strictly a reduction in flow capacity. While such pedigree rules can be obtained from first principles, in this paper, these rules are derived from the active subspaces. This facilitates a more detailed quantification of the aerodynamic trade-offs. Thus, instead of simply stating that a particular design is more sensitive to manufacturing variations; or that it lies on a hypothetical “efficiency cliff,” this paper seeks to visualize, quantify, and make precise such notions of Turbomachinery design.
-
Turbomachinery Active Subspace Performance Maps
Volume 2A: Turbomachinery, 2017Co-Authors: Pranay Seshadri, Geoffrey T Parks, Shahrokh Shahpar, Paul G. Constantine, Michael AdamsAbstract:Turbomachinery active subspace performance maps are 2D contour plots that illustrate the variation of key flow performance metrics with different blade designs. While such maps are easy to construct for design parameterizations with two variables, in this paper maps will be generated for a fan blade with twenty-five design variables. Turbomachinery active subspace performance maps combine active subspaces — a new set of ideas for dimension reduction — with fundamental Turbomachinery aerodynamics and design spaces. In this paper, contours of (i) cruise efficiency, (ii) cruise pressure ratio, (iii) maximum climb flow capacity and (iv) sensitivity to manufacturing variations, are plotted as objectives for the fan. These maps are then used to infer pedigree design rules: how best to increase fan efficiency; how best to desensitize blade aerodynamics to the impact of manufacturing variations? In the present study, the former required both a reduction in pressure ratio and flow capacity — leading to a reduction of the strength of the leading edge bow wave — while the latter required strictly a reduction in flow capacity. While such pedigree rules can be obtained from first principles, in this paper these rules are derived from the active subspaces. This facilitates a more detailed quantification of the aerodynamic trade-offs. Thus, instead of simply stating that a particular design is more sensitive to manufacturing variations; or that it lies on a hypothetical ‘efficiency cliff’, this paper seeks to visualize, quantify and make precise such notions of Turbomachinery design.Copyright © 2017 by Rolls-Royce plc
-
biobjective design optimization for axial compressors using tabu search
AIAA Journal, 2008Co-Authors: Timoleon Kipouros, D M Jaeggi, W N Dawes, Geoffrey T Parks, Mark A Savill, John P ClarksonAbstract:At present, optimization is an enabling technology in innovation. Multi-objective and multidisciplinary optimization tools are essential in the design process for real-world applications. In Turbomachinery design, these approaches give insight into the design space and identify the tradeoffs between the competing performance measures. This paper describes the application of a novel multi-objective variant of the tabu search algorithm to the aerodynamic design optimization of Turbomachinery blades. The aim is to improve the performance of a specific stage and eventually of the whole engine. The integrated system developed for this purpose is described. It combines the optimizer with an existing geometry parameterization scheme and a well-established computational fluid dynamics package. Its performance is illustrated through a case study in which the flow characteristics most important to the overall performance of Turbomachinery blades are optimized.
