Hybrid Large-Eddy Simulation Optimization of a Fundamental Turbine Blade Turbulated Cooling Passage

Zlatinov, Metodi; Laskowski, Gregory M.

doi:10.2514/1.b35713

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…Recently very targeted applications have been reported in this area. (Gourdoin et al, 2014;Rodebaugh et al, 2015;Ivanova and Laskowski, 2015;Zlatinov and Laskowski, 2015;Michelassi et al, 2014).…”

Section: I1 Motivation: the Problemmentioning

confidence: 91%

“…Optimization is then performed on top of the well-behaved metamodel. Zlatinov and Laskowski (2015) adopted this approach to optimize a turbulated square duct, using a hybrid largeeddy simulation turbulence modeling approach shown in Figure 29. The geometry of the turbulators is optimized for two competing aero-thermal performance metrics: heat transfer and pressure drop.…”

Section: Figure 28mentioning

confidence: 99%

“…WALE LES modeling clearly matched the wake. Hypothesizing that the miss was due to lack of diffusion in the SST model, a meta-model assisted optimization of the diffusion coefficients on a Bayesian response surface map based on the framework described in Zlatinov and Laskowski (2015) was developed. The diffusion coefficients of the SST model were tuned for the uncooled VKI S1N to match SST mass weighted turbulent kinetic energy through the passage and the wake to time averaged resolved scale turbulence of the wallresolved simulation.…”

Section: Physics and Boundary Condition Modelingmentioning

confidence: 99%

See 2 more Smart Citations

Future Directions of High Fidelity CFD for Aerothermal Turbomachinery Analysis and Design

Laskowski¹,

Kopriva²,

Michelassi³

et al. 2016

46th AIAA Fluid Dynamics Conference

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Computational Fluid Dynamics is a powerful tool used on a daily basis by designers and researchers in the advancement of propulsion technology. As hardware and software technology continue to evolve, the impact on propulsion systems has the potential to be disruptive. With continued development of High Performance Computing, Large Eddy Simulation, High-Order unstructured grid algorithms, optimization and uncertainty quantification it is conceivable that a new frontier of simulation based research, analysis and design capability is in the foreseeable future. In order to accelerate this development collaboration between industry, academia and government labs is required. NomenclatureBR = Bypass Ratio c = Chord Length CFD = Computational Fluid Dynamics DNS = Direct Numerical Simulation HLES = Hybrid Large Eddy Simulation HO = High-Order HPC = High Performance Computing HPC = High Pressure Compressor HPT = High Pressure Turbine IDDES = Improved Delayed Detached Eddy Simulation IDDES-T = Improved Delayed Detached Eddy Simulation with Transition LES = Large Eddy Simulation LPC = Low Pressure Compressor LPT = Low Pressure Turbine Ma = Mach Number 1 Principal Engineer high-fidelity CFD, Advanced Design Tools, 2 MDAO = Multi-Disciplinary Analysis and Optimization OPR = Overall Pressure Ratio Pt = Total Pressure RANS = Reynolds Averaged Navier Stokes Equations Re = Reynolds number Ro = Rossby Number S = Slot Height S1B = Stage 1 Blade S2B = Stage 2 Blade S1N = Stage 1 Nozzle S2N = Stage 2 Nozzle SFC = Specific Fuel Consumption St = Strouhal Number TIT = Turbine Inlet Temperature Tt = Total Temperature TVD = Total Variation Diminishing UQ = Uncertainty Quantification URANS = Unsteady Reynolds Averaged Navier Stokes Equations VKI = von Karman Institute WMLES = Wall-Modeled Large Eddy Simulation WRLES = Wall-Resolved Large Eddy Simulation

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Section: I1 Motivation: the Problemmentioning

confidence: 91%

Section: Figure 28mentioning

confidence: 99%

Section: Physics and Boundary Condition Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Future Directions of High Fidelity CFD for Aerothermal Turbomachinery Analysis and Design

Laskowski¹,

Kopriva²,

Michelassi³

et al. 2016

46th AIAA Fluid Dynamics Conference

View full text Add to dashboard Cite

show abstract

“…Modeling techniques must be robust enough to deliver converged solutions over a range of design conditions with minimal user intervention. Additionally, automated meshing for optimization is becoming more common for complex design features where unstructured meshing is required (Zlatinov and Laskowski, 2015).…”

Section: Approachmentioning

confidence: 99%

Hybrid Large Eddy Simulations of an Uncooled High Pressure Turbine Stator-Rotor Stage

Kopriva¹,

Laskowski²

2017

European Conference on Turbomachinery Fluid Dynamics and Hermodynamics

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Turbulence plays an important role on the aero-thermal performance of modern aircraft engine high pressure turbines. The role of the vane wake and passage turbulence on the downstream blade is an important consideration for both performance and durability. In this paper, we consider turbulence and transition modeling for RANS, hybrid RANS/LES and wall resolved LES simulations of a span-wise periodic representation of the uncooled vane of Arts and Rouvroit geometry to establish predictive capability. A fundamental blade has been designed to pair with the uncooled vane to evaluate the impact of passage turbulence and vane wake on the downstream blade boundary layer, wake formation and evolution. Finally, the learnings from the statistical 2D simulations are applied to a full 3D annular representation of the geometry to demonstrate impact of secondary flows on the overall aero-thermal performance of the stage.

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“…Forrester, Bressloff, and Keane 2006). For instance, Zlatinov and Laskowski (2015) employed several surrogate models to deal with design space nonlinearity and noisy data, in a turbine blade turbulator optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Application of a territorial-based filtering algorithm in turbomachinery blade design optimization

Bahrami

Khelghatibana

Tribes

et al. 2016

Engineering Optimization

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A territorial-based filtering algorithm (TBFA) is proposed as an integration tool in a multi-level design optimization methodology. The design evaluation burden is split between low-and high-cost levels in order to properly balance the cost and required accuracy in different design stages, based on the characteristics and requirements of the case at hand. TBFA is in charge of connecting those levels by selecting a given number of geometrically different promising solutions from the low-cost level to be evaluated in the high-cost level. Two test case studies, a Francis runner and a transonic fan rotor, have demonstrated the robustness and functionality of TBFA in real industrial optimization problems. ARTICLE HISTORY

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Hybrid Large-Eddy Simulation Optimization of a Fundamental Turbine Blade Turbulated Cooling Passage

Cited by 6 publications

References 10 publications

Future Directions of High Fidelity CFD for Aerothermal Turbomachinery Analysis and Design

Future Directions of High Fidelity CFD for Aerothermal Turbomachinery Analysis and Design

Hybrid Large Eddy Simulations of an Uncooled High Pressure Turbine Stator-Rotor Stage

Application of a territorial-based filtering algorithm in turbomachinery blade design optimization

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