Active Simulation of Transient Wind Field in a Multiple-Fan Wind Tunnel via Deep Reinforcement Learning

Li, Shaopeng; Snaiki, Reda; Wu, Teng

doi:10.1061/(asce)em.1943-7889.0001967

Cited by 17 publications

(6 citation statements)

References 33 publications

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“…Li Shaopeng [51] et al proposed a spatiotemporal variation scheme for multi-fan wind farms based on deep reinforcement learning (RL), as shown in Figure 10, using the RL method to train a fully connected deep neural network (DNN) to perform active flow control multi-fan wind tunnel in the system. Compared to manual parameter adjustment, it can find the optimal control parameter more efficiently.…”

Section: Transient Air Supply Precise Control Problemmentioning

confidence: 99%

Research on integrated thermal management system test technology of electric vehicle

Fu,

Li,

Wang

2023

Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023)

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The development of integrated thermal management system for the improvement of environmental adaptability and comprehensive energy efficiency of new generation electric vehicles has become the development trend of the industry, but its coupling between systems is strong and development is difficult, so it needs system integration test to assist development. The integrated test technology of thermal management system is reviewed from the characteristics of integrated thermal management system, the research status of system integration test technology and the technical conditions of bench test. Focusing on meeting the technical requirements of real vehicle scenario in bench test environment and customization of test conditions, this paper analyzes in detail the research progress and technical application of key technologies such as the real-time calculation method of system simulation of real vehicle operation air intake and the accurate control of transient air supply and temperature stability control under low speed condition, puts forward the current problems of system integration test and predicts the development direction.

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Section: Transient Air Supply Precise Control Problemmentioning

confidence: 99%

Research on integrated thermal management system test technology of electric vehicle

Fu,

Li,

Wang

2023

Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023)

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“…Typical reinforcement learning models include value-based models (e.g., Q-learning or deep Q-learning) (Watkins and Dayan 1992), policy-based models (e.g., deep deterministic policy gradient) (Lillicrap et al, 2015) and hybrid models (e.g., actor-critic) (Williams 1992). Recently, the deep RL (with DNN-based policy) has been gaining attention in wind engineering community as an efficient way for dynamic control and shape optimization (Li et al, 2021a;2021b). The architecture of a typical deep RL is depicted in Figure 7.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Applications of Machine Learning to Wind Engineering

Snaiki

2022

Front. Built Environ.

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Advances of the analytical, numerical, experimental and field-measurement approaches in wind engineering offers unprecedented volume of data that, together with rapidly evolving learning algorithms and high-performance computational hardware, provide an opportunity for the community to embrace and harness full potential of machine learning (ML). This contribution examines the state of research and practice of ML for its applications to wind engineering. In addition to ML applications to wind climate, terrain/topography, aerodynamics/aeroelasticity and structural dynamics (following traditional Alan G. Davenport Wind Loading Chain), the review also extends to cover wind damage assessment and wind-related hazard mitigation and response (considering emerging performance-based and resilience-based wind design methodologies). This state-of-the-art review suggests to what extend ML has been utilized in each of these topic areas within wind engineering and provides a comprehensive summary to improve understanding how learning algorithms work and when these schemes succeed or fail. Moreover, critical challenges and prospects of ML applications in wind engineering are identified to facilitate future research efforts.

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“…Numerous control algorithms have been used in this case, including the proportional-integral-derivative (PID) controller [20], the linear quadratic integrator (LQI) [20], the gain-scheduled Proportional-Integral (GSPI) controller [19,20] and the H ∞ state feedback controller [21]. Recently, reinforcement learning has also gained increasing popularity which could be potentially applied to floating wind turbines [24][25][26][27][28]. However, the control problem for the FOWT is challenging since it should be designed based on the multiple-input multiple-output mechanism while accounting for several constraints and including the effects of large environmental disturbances (e.g., wind and wave).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Repositioning of Floating Wind Turbines Using Model Predictive Control for Position and Power Regulation

Jard

Snaiki

2023

Wind

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As offshore wind capacity could grow substantially in the coming years, floating offshore wind turbines (FOWTs) are particularly expected to make a significant contribution to the anticipated global installed capacity. However, FOWTs are prone to several issues due partly to environmental perturbations and their system configuration which affect their performances and jeopardize their structural integrity. Therefore, advanced control mechanisms are required to ensure good performance and operation of FOWTs. In this study, a model predictive control (MPC) is proposed to regulate FOWTs’ power, reposition their platforms to reach predefined target positions and ensure their structural stability. An efficient nonlinear state space model is used as the internal MPC predictive model. The control strategy is based on the direct manipulation of the thrust force using three control inputs, namely the yaw angle, the collective blade pitch angle, and the generator torque without the necessity of additional actuators. The proposed controller accounts for the environmental perturbations and satisfies the system constraints to ensure good performance and operation of the FOWTs. A realistic scenario for a 5-MW reference wind turbine, modeled using OpenFAST and Simulink, has been provided to demonstrate the robustness of the proposed MPC controller. Furthermore, the comparison of the MPC model and a proportional-integral-derivative (PID) model to satisfy the three predefined objectives indicates the superior performances of the MPC controller.

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Active Simulation of Transient Wind Field in a Multiple-Fan Wind Tunnel via Deep Reinforcement Learning

Cited by 17 publications

References 33 publications

Research on integrated thermal management system test technology of electric vehicle

Research on integrated thermal management system test technology of electric vehicle

Applications of Machine Learning to Wind Engineering

Real-Time Repositioning of Floating Wind Turbines Using Model Predictive Control for Position and Power Regulation

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