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.Comment: 65 pages, 24 figures, and 1 tabl

Transcription factor NFAT5 promotes macrophage survival in rheumatoid arthritis

Defective apoptotic death of activated macrophages has been implicated in the pathogenesis of rheumatoid arthritis (RA). However, the molecular signatures defining apoptotic resistance of RA macrophages are not fully understood. Here, global transcriptome profiling of RA macrophages revealed that the osmoprotective transcription factor nuclear factor of activated T cells 5 (NFAT5) critically regulates diverse pathologic processes in synovial macrophages including the cell cycle, apoptosis, and proliferation. Transcriptomic analysis of NFAT5-deficient macrophages revealed the molecular networks defining cell survival and proliferation. Proinflammatory M1-polarizing stimuli and hypoxic conditions were responsible for enhanced NFAT5 expression in RA macrophages. An in vitro functional study demonstrated that NFAT5-deficient macrophages were more susceptible to apoptotic death. Specifically, CCL2 secretion in an NFAT5-dependent fashion bestowed apoptotic resistance to RA macrophages in vitro. Injection of recombinant CCL2 into one of the affected joints of Nfat5+/-mice increased joint destruction and macrophage infiltration, demonstrating the essential role of the NFAT5/CCL2 axis in arthritis progression in vivo. Moreover, after intra-articular injection, NFAT5-deficient macrophages were more susceptible to apoptosis and less efficient at promoting joint destruction than were NFAT5-sufficient macrophages. Thus, NFAT5 regulates macrophage survival by inducing CCL2 secretion. Our results provide evidence that NFAT5 expression in macrophages enhances chronic arthritis by conferring apoptotic resistance to activated macrophages.clos