A GPU-boosted high-performance multi-working condition joint analysis framework for predicting dynamics of textured axial piston pump
🤖 AI Summary
Axial piston pumps (APPs) with textured surfaces have long suffered from limited computational efficiency on CPUs and poor convergence of conventional solvers in high-fidelity, multi-cycle dynamic simulations. This work proposes a GPU-accelerated, multi-operating-condition co-simulation framework integrating global synchronization convergence control and parallel solution of coupled algebraic systems, enabling the first efficient, high-accuracy multi-cycle dynamic simulation of both smooth and textured-surface APPs. Innovatively, a GPU-optimized preconditioned conjugate gradient (PCG) method with an approximate symmetric SSOR preconditioner is adopted, substantially accelerating pressure-field solution and hydraulic force integration. Results demonstrate that surface texturing enhances load-carrying capacity and torsional stiffness; axial and circumferential hydrodynamic forces respond instantaneously to input pressure; normal pressure stabilizes rapidly, while viscous shear stress evolves periodically, revealing a distinct stepwise pressure distribution induced by surface textures.
Application Category
📝 Abstract
Accurate simulation to dynamics of axial piston pump (APP) is essential for its design, manufacture and maintenance. However, limited by computation capacity of CPU device and traditional solvers, conventional iteration methods are inefficient in complicated case with textured surface requiring refined mesh, and could not handle simulation during multiple periods. To accelerate Picard iteration for predicting dynamics of APP, a GPU-boosted high-performance Multi-working condition joint Analysis Framework (GMAF) is designed, which adopts Preconditioned Conjugate Gradient method (PCG) using Approximate Symmetric Successive Over-Relaxation preconditioner (ASSOR). GMAF abundantly utilizes GPU device via elevating computational intensity and expanding scale of massive parallel computation. Therefore, it possesses novel performance in analyzing dynamics of both smooth and textured APPs during multiple periods, as the establishment and solution to joint algebraic system for pressure field are accelerated magnificently, as well as numerical integral for force and moment due to oil flow. Compared with asynchronized convergence strategy pursuing local convergence, synchronized convergence strategy targeting global convergence is adopted in PCG solver for the joint system. Revealed by corresponding results, oil force in axial direction and moment in circumferential directly respond to input pressure, while other components evolve in sinusoidal patterns. Specifically, force and moment due to normal pressure instantly reach their steady state initially, while ones due to viscous shear stress evolve during periods. After simulating dynamics of APP and pressure distribution via GMAF, the promotion of pressure capacity and torsion resistance due to textured surface is revealed numerically, as several'steps'exist in the pressure field corresponding to textures.
Problem
Research questions and friction points this paper is trying to address.
Conventional CPU methods inefficiently simulate textured axial piston pump dynamics
Existing approaches cannot handle multi-period simulations with refined meshes
Limited computational capacity restricts analysis of complex textured surface cases
Innovation
Methods, ideas, or system contributions that make the work stand out.
GPU-accelerated framework for axial piston pump dynamics
Preconditioned Conjugate Gradient with ASSOR preconditioner
Massive parallel computation for multi-period synchronized analysis
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