TY - JOUR
T1 - Adjoint-based optimization for understanding and suppressing jet noise
AU - Freund, Jonathan B.
N1 - Funding Information:
This work is currently supported by NASA Glenn Research Center. Workpreviously supported by AFOSR is also reported. The author is grateful for input and figures from Prof. Mingjun Wei, Mr. Randy Kleinman, Mr. Jeonglae Kim, and Prof. Daniel Bodony.
PY - 2010
Y1 - 2010
N2 - Advanced simulation tools, particularly large-eddy simulation techniques, are becoming capable of making quality predictions of jet noise for realistic nozzle geometries and at engineering relevant flow conditions. Increasing computer resources will be a key factor in improving these predictions still further. Quality prediction, however, is only a necessary condition for the use of such simulations in design optimization. Predictions do not of themselves lead to quieter designs. They must be interpreted or harnessed in some way that leads to design improvements. As yet, such simulations have not yielded any simplifying principals that offer general design guidance. The turbulence mechanisms leading to jet noise remain poorly described in their complexity. In this light, we have implemented and demonstrated an aeroacoustic adjoint-based optimization technique that automatically calculates gradients that point the direction in which to adjust controls in order to improve designs. This is done with only a single flow solutions and a solution of an adjoint system, which is solved at computational cost comparable to that for the flow. Optimization requires iterations, but having the gradient information provided via the adjoint accelerates convergence in a manner that is insensitive to the number of parameters to be optimized.
AB - Advanced simulation tools, particularly large-eddy simulation techniques, are becoming capable of making quality predictions of jet noise for realistic nozzle geometries and at engineering relevant flow conditions. Increasing computer resources will be a key factor in improving these predictions still further. Quality prediction, however, is only a necessary condition for the use of such simulations in design optimization. Predictions do not of themselves lead to quieter designs. They must be interpreted or harnessed in some way that leads to design improvements. As yet, such simulations have not yielded any simplifying principals that offer general design guidance. The turbulence mechanisms leading to jet noise remain poorly described in their complexity. In this light, we have implemented and demonstrated an aeroacoustic adjoint-based optimization technique that automatically calculates gradients that point the direction in which to adjust controls in order to improve designs. This is done with only a single flow solutions and a solution of an adjoint system, which is solved at computational cost comparable to that for the flow. Optimization requires iterations, but having the gradient information provided via the adjoint accelerates convergence in a manner that is insensitive to the number of parameters to be optimized.
KW - Adjoint-based optimization
KW - Computational aeroacoustics
KW - Jet noise
KW - Optimal control
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U2 - 10.1016/j.proeng.2010.09.007
DO - 10.1016/j.proeng.2010.09.007
M3 - Article
AN - SCOPUS:78649673080
VL - 6
SP - 54
EP - 63
JO - Procedia Engineering
JF - Procedia Engineering
SN - 1877-7058
ER -