Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimization Using a Gaussian Process

Alhazmi, Nahla; Ghazi, Yousef; Aldosari, Mohammed N.; Tezaur, Radek; Farhat, Charbel

doi:10.2514/6.2021-0893

Cited by 4 publications

(3 citation statements)

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“…The combustor improved the rate of air utilization to improve fuel efficiency, and to significantly reduce NO X and soot emissions. In 2021, Alhazmi et al 17 used the Gaussian process to train a neural network-based surrogate model to optimize aerodynamic design, and used data on the NACA airfoil to verify that it can be used to predict the lift-to-drag ratio.…”

Section: Introductionmentioning

confidence: 99%

High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

Liang

Tian

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper considers optimizing the performance of high-temperature combustion chamber of an aero-engine based on a concentric and hierarchical model. First, sample data for the design variables are obtained based on Latin hypercube sampling method, and a one-dimensional program is used to obtain the true values of combustion efficiency and total pressure loss corresponding to each group of variables. The obtained data are then pre-processed to establish a dataset. Second, a multi-layer artificial neural network (ANN) architecture is designed and a surrogate model of the combustion-related performance of the combustor is established using a data-driven method. The results of global sensitivity analysis based on variance show that ratio of fuel flow to air flow (fuel–air ratio) and the total inlet pressure are the most important factors influencing the two objective functions. Finally, we optimize the multi-objective combustion-related performance of the surrogate model by applying the particle swarm optimization algorithm to it. The results of experiments show that the ANN-based model could accurately predict the efficiency of combustion and total pressure loss of the chamber, yielding root mean-squared errors of 0.0107 and 0.3032%, respectively. It also had better generalization ability than the cubic polynomial surrogate model. Compared with the cubic polynomial model, it generated an optimal Pareto solution set as prediction that had higher values in both objective functions. The proposed model might require better data that can be obtained using intelligent sampling methods so that deeper neural networks can be designed to reduce error and improve its optimization design.

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Section: Introductionmentioning

confidence: 99%

High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

Liang

Tian

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Yu and Hesthaven [6] presented a novel approach to reconstructing the flow field using an artificial NN. To improve the training effectiveness of an NN surrogate model of high-fidelity and highdimension, an adaptive sampling method based on the Gaussian process was proposed and applied in the aerodynamic design of the prediction of the airfoil lift-to-drag ratio [7]. For structural designs, a deep convolutional NN-based surrogate model was proposed to perform topological optimization for two dimensional and three dimensional structures [8].…”

Section: Introductionmentioning

confidence: 99%

Training Sample Pattern Optimization Based on a Swarm Intelligence Algorithm for Tiltrotor Flight Dynamics Model Approximation

Wen,

Song,

Wang

et al. 2023

Aerospace

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Neural networks have been widely used as compensational models for aircraft control designs and as surrogate models for other optimizations. In the case of tiltrotor aircraft, the total number of aircraft states and controls is much greater than that of both traditional fixed-wings and helicopters. This requires, in general, a huge amount of training data for the network to reach a satisfactory approximation precision and makes the network size rise considerably. To solve the practical problem of reducing the size of the approximating network, efforts have to be made in the efficient utilization of the limited amount of training data. This work presents the methodology of optimizing the sample pattern of the training data set by adopting the metaheuristic algorithm of the particle swarm optimizer improved by the fourth-order Runge–Kutta algorithm. A 6-degree-of-freedom nonlinear flight dynamics model of the tiltrotor aircraft is derived, along with its approximation radial basis function neural network. An example case of approximating a highly nonlinear function is studied to illustrate the principle and main parameters of the optimizer, and the approximation performance of the time-domain response of the unstable nonlinear system is revealed by the study of a Van der Pol oscillator. Then, the presented method is applied to the modeled tiltrotor aircraft for its early and late conversion modes. The optimization scheme shows great improvement in both cases, as the function approximation error is reduced significantly.

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“…SL aims to establish an optimal model based on existing knowledge and hence requires a sufficient amount of representative labeled data. Recent SL applications in AFC have made use of either artificial neural networks (ANNs), genetic algorithms 20,21 (GAs) or Gaussian process 22,23 (GP) to develop algorithms for modelling complex patterns, flow prediction and control problems.…”

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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Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimization Using a Gaussian Process

Cited by 4 publications

References 0 publications

High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

Training Sample Pattern Optimization Based on a Swarm Intelligence Algorithm for Tiltrotor Flight Dynamics Model Approximation

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

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