Aerodynamic optimization of high-lift devices using a 2D-to-3D optimization method based on deep reinforcement learning and transfer learning

Dai, Jiahua; Liu, Peiqing; Qu, Qing; Li, Ling; Niu, Tongzhi

doi:10.1016/j.ast.2022.107348

Cited by 20 publications

(2 citation statements)

References 24 publications

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“…It is necessary to scale the design space of uncertain parameters for the improvement of the efficiency of PCE used in uncertainty analysis. This strategy can considerably reduce calculation costs [40]. Therefore, the influence of all design variables on aerodynamic performance, which is calculated by the corrected DATCOM, is sorted by the sensitivity analysis.…”

Section: Sensitivity Analysis Of Uncertain Parametersmentioning

confidence: 99%

Aerodynamic Performance Uncertainty Analysis and Optimization of a Conventional Axisymmetric Vehicle Based on Parallel Polynomial Chaos Expansions

Peng

Zhu

et al. 2022

Aerospace

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In this study, the aerodynamic uncertainty analysis and optimization of a conventional axisymmetric vehicle with an aerodynamic configuration were investigated. The prediction precision of the typical aerodynamic performance estimating methods, namely, engineering estimation and numerical simulation, was compared using the wind tunnel test data of the vehicle. Then, using a modified missile data compendium (DATCOM) software, a high-efficiency and high-precision method was developed, which was applied to analyze and characterize the aerodynamic parameters of the axisymmetric vehicle. To enhance the robustness and reliability of aerodynamic performance, an uncertainty-based design optimization (UDO) framework was established. The design space was scaled by parameter sensitivity analysis, and improved computational efficiency was achieved by developing parallel polynomial chaos expansions (PCEs). The optimized results show that the modified method exhibits high accuracy in predicting aerodynamic performance. For the same constraints, the results of the deterministic design optimization (DDO) showed that compared with the initial scheme, the probability of the controllability-to-stability ratio satisfying the constraint decreased from 98.8% to 72.4%, and this value increased to 99.9% in the case of UDO. Compared with the results of the initial scheme and DDO, UDO achieved a considerable reduction in mean values and standard deviation of aerodynamic performances, which can ensure a higher probability of constraints meeting the design requirements, thereby, realizing a reliable and robust design.

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Section: Sensitivity Analysis Of Uncertain Parametersmentioning

confidence: 99%

Aerodynamic Performance Uncertainty Analysis and Optimization of a Conventional Axisymmetric Vehicle Based on Parallel Polynomial Chaos Expansions

Peng

Zhu

et al. 2022

Aerospace

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“…Two popular DRL methods, i.e. proximal policy optimization (PPO) 39 and twin delayed deep deterministic policy gradients (TD3) 40 , have been widely adopted in AFC to process continuous action space with high dimensions [41][42][43][44][45] .…”

Section: Introductionmentioning

confidence: 99%

Active flow control using deep reinforcement learning with time delays in Markov decision process and autoregressive policy

2022

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Classical active flow control (AFC) methods based on solving the Navier-Stocks equations are laborious and computationally intensive even with the use of reduced-order models. Data-driven methods offer a promising alternative for AFC and have been applied successfully to reduce the drag of two-dimensional bluff bodies using deep reinforcement learning (DRL) paradigms. However, the standard DRL method tends to result in large fluctuations in the unsteady forces acting on the cylinder as the Reynolds number increases. In this study, a Markov decision process (MDP) with time delays is introduced to quantify the action delays in the DRL process along with the use of a first-order autoregressive policy (ARP). This hybrid DRL method is applied to control the vortex shedding process from a two-dimensional circular cylinder with four synthetic jet actuators at a freestream Reynolds number of 400. Compared to the standard DRL method, this method is shown to reduce the magnitude of drag and lift fluctuations by approximately 90% at the same actuation frequency while achieving a similar level of drag reduction. Although both methods suppress the vortex-induced forces by extending the recirculation zone behind the circular cylinder, the hybrid method leads to a steadier and more elongated recirculation zone, and hence a weaker vortex shedding process. This study demonstrates the necessity of accurately quantifying the delay in the MDP and the benefits of introducing ARPs to achieve a smooth control of unsteady forces in AFC.

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Reinforcement learning based approach for the optimization of mechanical properties of additively manufactured specimens

Mishra

Jatti

2023

Int J Interact Des Manuf

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Aerodynamic optimization of high-lift devices using a 2D-to-3D optimization method based on deep reinforcement learning and transfer learning

Cited by 20 publications

References 24 publications

Aerodynamic Performance Uncertainty Analysis and Optimization of a Conventional Axisymmetric Vehicle Based on Parallel Polynomial Chaos Expansions

Aerodynamic Performance Uncertainty Analysis and Optimization of a Conventional Axisymmetric Vehicle Based on Parallel Polynomial Chaos Expansions

Active flow control using deep reinforcement learning with time delays in Markov decision process and autoregressive policy

Reinforcement learning based approach for the optimization of mechanical properties of additively manufactured specimens

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