arXiv subjects

Innyoung Kim

Publications and source records attributed to Innyoung Kim.

3 recordsLinked to original sources

arXiv2024

Optimal mesh generation for a non-iterative grid-converged solution of flow through a blade passage using deep reinforcement learning

An automatic mesh generation method for optimal computational fluid dynamics (CFD) analysis of a blade passage is developed using deep reinforcement learning (DRL). Unlike conventional automation techniques, which require repetitive tuning of meshing parameters for each new geometry and flow condition, the method developed herein trains a mesh generator to determine optimal parameters across varying configurations in a non-iterative manner. Initially, parameters controlling mesh shape are optimized to maximize geometric mesh quality, as measured by the ratio of determinants of Jacobian matrices and skewness. Subsequently, resolution-controlling parameters are optimized by incorporating CFD results. Multi-agent reinforcement learning is employed, enabling 256 agents to construct meshes and perform CFD analyses across randomly assigned flow configurations in parallel, aiming for maximum simulation accuracy and computational efficiency within a multi-objective optimization framework. After training, the mesh generator is capable of producing meshes that yield converged solutions at desired computational costs for new configurations in a single simulation, thereby eliminating the need for iterative CFD procedures for grid convergence. The robustness and effectiveness of the method are investigated across various blade passage configurations, accommodating a range of blade geometries, including high-pressure and low-pressure turbine blades, axial compressor blades, and impulse rotor blades. Furthermore, the method is capable of identifying the optimal mesh resolution for diverse flow conditions, including complex phenomena like boundary layers, shock waves, and flow separation. The optimality is confirmed by comparing the accuracy and the efficiency achieved in a single attempt with those from the conventional iterative optimization method.

physics.flu-dyn↗

arXiv2023

Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning

A method using deep reinforcement learning (DRL) to non-iteratively generate an optimal mesh for an arbitrary blade passage is developed. Despite automation in mesh generation using either an empirical approach or an optimization algorithm, repeated tuning of meshing parameters is still required for a new geometry. The method developed herein employs a DRL-based multi-condition optimization technique to define optimal meshing parameters as a function of the blade geometry, attaining automation, minimization of human intervention, and computational efficiency. The meshing parameters are optimized by training an elliptic mesh generator which generates a structured mesh for a blade passage with an arbitrary blade geometry. During each episode of the DRL process, the mesh generator is trained to produce an optimal mesh for a randomly selected blade passage by updating the meshing parameters until the mesh quality, as measured by the ratio of determinants of the Jacobian matrices and the skewness, reaches the highest level. Once the training is completed, the mesh generator create an optimal mesh for a new arbitrary blade passage in a single try without an repetitive process for the parameter tuning for mesh generation from the scratch. The effectiveness and robustness of the proposed method are demonstrated through the generation of meshes for various blade passages.

cs.LG↗

arXiv2021

Multi-condition multi-objective optimization using deep reinforcement learning

A multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed for the first time using deep reinforcement learning. Unlike the conventional methods which perform optimization at a single condition, the present method learns the correlations between conditions and optimal solutions. The exclusive capability of the developed method is examined in the solutions of a novel modified Kursawe benchmark problem and an airfoil shape optimization problem which include nonlinear characteristics which are difficult to resolve using conventional optimization methods. Pareto front with high resolution over a defined condition space is successfully determined in each problem. Compared with multiple operations of a single-condition optimization method for multiple conditions, the present multi-condition optimization method based on deep reinforcement learning shows a greatly accelerated search of Pareto front by reducing the number of required function evaluations. An analysis of aerodynamics performance of airfoils with optimally designed shapes confirms that multi-condition optimization is indispensable to avoid significant degradation of target performance for varying flow conditions.

cs.LG↗