Periodic Reporting for period 1 - KAFKA (Efficient Computational Methods for Active Flow Control Using Adjoint Sensitivities)
Periodo di rendicontazione: 2020-09-01 al 2022-08-31
Designing an effective fluid flow system such as a wind turbine or an aircraft wing requires a detailed understanding of the sensitivity of the objective function (for example, efficiency, lift, etc,) to a host of input parameters (for example the parameters determining the aerodynamic shape). Adjoint methods provide the sensitivity of the objective function to any number of input parameters at a reasonable cost. They are usually based on Reynolds-Averaged Navier-Stokes (RANS) models, which can be very inaccurate, especially in the presence of complex flow features such as flow separation. Thus, when the flow prediction itself has a significant error, the sensitivity obtained from the RANS-based adjoint method may not be useful in designing optimal aerodynamic configurations.
Our first objective was to improve the accuracy of the RANS turbulence model for separated flows using high-fidelity LES data, that accurately captures the flow physics. The adjoint methodology was used to spatially vary the turbulence-model parameters such that the RANS flow matches the corresponding LES flow. Our next goal was to use the improved RANS turbulence model in shape optimization. The geometry considered was a 3D U-Bend geometry widely studied in literature and integral to gas-turbine cooling channels. The final shape obtained using the improved RANS model was seen to be distinctly different from a shape obtained using an uncorrected baseline RANS model clearly demonstrating the potential of our approach. Our third goal was to study the effect of a refined parametrization of the U-Bend geometry. To bring out the full potential of the KAFKA strategy, we formulated a four-fold increase in the design variables for U-Bend geometry creation and carried out a shape optimization using the LES-aided RANS model, which brought forth much richer design features unseen in a low-parameter design space.
Our first objective was to improve the accuracy of the RANS turbulence model for separated flows using high-fidelity LES data, that accurately captures the flow physics. The adjoint methodology was used to spatially vary the turbulence-model parameters such that the RANS flow matches the corresponding LES flow. Our next goal was to use the improved RANS turbulence model in shape optimization. The geometry considered was a 3D U-Bend geometry widely studied in literature and integral to gas-turbine cooling channels. The final shape obtained using the improved RANS model was seen to be distinctly different from a shape obtained using an uncorrected baseline RANS model clearly demonstrating the potential of our approach. Our third goal was to study the effect of a refined parametrization of the U-Bend geometry. To bring out the full potential of the KAFKA strategy, we formulated a four-fold increase in the design variables for U-Bend geometry creation and carried out a shape optimization using the LES-aided RANS model, which brought forth much richer design features unseen in a low-parameter design space.
The following are the main results obtained through KAFKA:
1) Successfully tuned the RANS Turbulence model such that the RANS data matched the corresponding high-fidelity LES flow field.
2) Performed single-parameter (turbulence production term) and multi-parameter tuning analysis (turbulence production and destruction terms) to gauge the best fit between RANS and LES flow field.
3) Performed detailed analysis of the effect of the objective function to be minimized (Velocity match between RANS and LES, pressure match, and combined velocity and pressure match) on the RANS-flow match with LES.
4) Executed a 3-D shape optimization of the U-Bend geometry using the baseline turbulence model and the LES-aided turbulence model to obtain distinct shapes.
5) Implemented a four-fold increase in the parametric design of the U-Bend geometry to bring out more complex features in shape optimization.
The work performed during the KAFKA phase was demonstrated at two conferences, ECCOMAS-WCCM-2022, Norway, and UK Fluids Conference 2022, Sheffield.
1) Successfully tuned the RANS Turbulence model such that the RANS data matched the corresponding high-fidelity LES flow field.
2) Performed single-parameter (turbulence production term) and multi-parameter tuning analysis (turbulence production and destruction terms) to gauge the best fit between RANS and LES flow field.
3) Performed detailed analysis of the effect of the objective function to be minimized (Velocity match between RANS and LES, pressure match, and combined velocity and pressure match) on the RANS-flow match with LES.
4) Executed a 3-D shape optimization of the U-Bend geometry using the baseline turbulence model and the LES-aided turbulence model to obtain distinct shapes.
5) Implemented a four-fold increase in the parametric design of the U-Bend geometry to bring out more complex features in shape optimization.
The work performed during the KAFKA phase was demonstrated at two conferences, ECCOMAS-WCCM-2022, Norway, and UK Fluids Conference 2022, Sheffield.
Although RANS model tuning has been applied in the literature for fairly simple geometries (ex: 2D airfoils), KAFKA’s objectives involved RANS-model tuning for complex 3D flows involving large separation.
Various strategies were explored and successful ones were identified that enable improving the RANS turbulence model for complex flows. KAFKA also clearly demonstrated the impact of an improved RANS turbulence model on shape optimization.
This research work has the potential to improve the design of various geometries (ex: wind turbine blades, hydro-electric turbines, etc. ) which could have significant implications on a wider section of society.
Various strategies were explored and successful ones were identified that enable improving the RANS turbulence model for complex flows. KAFKA also clearly demonstrated the impact of an improved RANS turbulence model on shape optimization.
This research work has the potential to improve the design of various geometries (ex: wind turbine blades, hydro-electric turbines, etc. ) which could have significant implications on a wider section of society.