Low-Heat-Load-Vane Profile Optimization,  Part 1: Code Validation and Airfoil Redesign

Johnson, Jamie J.; King, Paul I.; Clark, John P.

doi:10.2514/1.28530

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Johnson et al [22] used a 2D RANS solver in concert with a Genetic Algorithm to optimise the Nozzle Guide Vane Mid-Span geometry for minimal heat load. Compared to the baseline results, the peak LE heat transfer was reduced by 15% and the SS transition was moved 24% closer to the TE for the optimised geometry.…”

Section: Genetic Algorithm Optimisationmentioning

confidence: 99%

Genetic Algorithm-Based Optimisation of a Double-Wall Effusion Cooling System for a High-Pressure Turbine Nozzle Guide Vane

van de Noort,

Ireland

2024

IJTPP

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Double-Wall Effusion Cooling schemes present an opportunity for aeroengine designers to achieve high overall cooling effectiveness and convective cooling efficiency in High-Pressure Turbine blades with reduced coolant usage compared to conventional cooling technologies. This is accomplished by combining impingement, pin-fin and effusion cooling. Optimising these cooling schemes is crucial to ensuring that cooling is achieved sufficiently at high-heat-flux regions and not overused at low-heat-flux ones. Due to the high number of design variables employed in these systems, optimisation through the use of Computational Fluid Dynamics (CFD) simulations can be a computationally costly and time-consuming process. This study makes use of a Low-Order Flow Network Model (LOM), developed, validated and presented previously, which quickly assesses the pressure, temperature, mass flow and heat flow distributions through a Double-Wall Effusion Cooling scheme. Results generated by the LOM are used to rapidly produce an ideal cooling system design through the use of an Evolutionary Genetic Algorithm (GA) optimisation process. The objective is to minimise the coolant mass flow whilst maintaining acceptable metal cooling effectiveness around the external surface of the blade and ensuring that the Backflow Margin for all film holes is above a selected threshold. For comparison, a Genetic Aggregation model-based optimisation using CFD simulations in ANSYS Workbench is also conducted. Results for both the reduction of coolant mass flow and the total optimisation runtime are analysed alongside those from the LOM, demonstrating the benefit of rapid low-order solving techniques.

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Section: Genetic Algorithm Optimisationmentioning

confidence: 99%

Genetic Algorithm-Based Optimisation of a Double-Wall Effusion Cooling System for a High-Pressure Turbine Nozzle Guide Vane

van de Noort,

Ireland

2024

IJTPP

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Genetic Algorithm Optimization of a High-Pressure Turbine Vane Pressure Side Film Cooling Array

Johnson¹,

King

Clark³

et al. 2013

Journal of Turbomachinery

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The following work is an in-depth investigation of the heat transfer characteristics and cooling effectiveness of a full-scale fully cooled modern high-pressure turbine (HPT) vane as a result of genetic algorithm (GA) optimization, relative to a modern baseline film cooling configuration. Individual designs were evaluated using 3D Reynolds-averaged Navier–Stokes (RANS) computational fluid dynamics (CFD) that modeled film cooling injection using a transpiration boundary condition and evaluated 10 cells from the wall with an isothermal surface condition. 1800 different cooling arrays were assessed for fitness within the optimization where film cooling parameters such as axial and radial hole location, hole size, injection angle, compound angle, and custom-designed row patterns were varied in the design space. The GA optimization terminated with a unique pressure side (PS) cooling array after only 13 generations. The fitness functions prescribed for the problem lowered the PS average near-wall surface temperature, lowered the near-wall maximum temperature, and maintained the level of near-wall average overall effectiveness. Results show how the optimization resulted in redistributed flow from overcooled areas on the vane PS to undercooled areas near the shroud. The optimized cooling array yielded a reduction of average near-wall gas temperature of 2 K, a reduction in the maximum near-wall gas temperature of 3 K, a reduction in maximum heat flux of 2 kW/m2 and a reduction in pressure loss over the vane, all while maintaining a constant level of surface-averaged overall effectiveness. Methods used to improve pressure side film cooling performance here are promising in terms of eliminating hot spots on individual HPT components in their proper operating environments as well as increasing the potential to use less air for cooling purposes in a gas turbine engine.

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Low-Heat-Load-Vane Profile Optimization, Part 2: Short-Duration Shock-Tunnel Experiments

Johnson

King

Clark

et al. 2008

Journal of Propulsion and Power

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Low-Heat-Load-Vane Profile Optimization, Part 1: Code Validation and Airfoil Redesign

Cited by 3 publications

References 17 publications

Genetic Algorithm-Based Optimisation of a Double-Wall Effusion Cooling System for a High-Pressure Turbine Nozzle Guide Vane

Genetic Algorithm-Based Optimisation of a Double-Wall Effusion Cooling System for a High-Pressure Turbine Nozzle Guide Vane

Genetic Algorithm Optimization of a High-Pressure Turbine Vane Pressure Side Film Cooling Array

Low-Heat-Load-Vane Profile Optimization, Part 2: Short-Duration Shock-Tunnel Experiments

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