Numerical solutions to differential equations are at the core of computational fluid dynamics calculations. As the size and complexity of the simulations grow, so does the need for computational power and time. As the size and complexity of the simulations grow, so does the need for computational power and time. Solving the equations in parallel can dramatically reduce the time to solution. While traditionally done on CPU, unlocking the massive number of computational cores on GPU is highly desirable. Many efforts have been made to implement stiff chemistry solvers on GPUs but have not been highly successful because of the logical divergence in traditional stiff algorithms like CVODE or LSODE. This study will demonstrate a machine learned hybrid algorithm implemented in TensorFlow for stiff problems and the speed gains relative to the traditional LSODE solver used in the Multiphase Flow with Interphase eXchanges (MFiX) Computational Fluid Dynamics (CFD) code. The results will show a dramatic decrease in total simulation time while maintaining the same degree of accuracy.
翻译:计算流体动态计算的核心是不同方程式的数值解决方案。 随着模拟的大小和复杂性的增大, 计算力和时间的需求也随之增加。 随着模拟的大小和复杂性的增大, 计算力和时间的需求也随之增加。 平行解析方程式可以大大缩短解决问题的时间。 虽然传统上在 CPU 上完成, 解开 GPU 上的大量计算核心非常可取 。 许多努力在 GPU 上实施硬化化学解析器, 但是由于 CVODE 或 LSODE 等传统硬算法的逻辑差异而没有取得很大成功 。 此研究将展示在TensorFlow 实施的机器学习混合算法, 解决棘手的问题, 以及与多阶段 电子Xchanges (MFIX) Computational Fluid Dynations (CFD) 代码中所使用的传统 LSODE解算器的速度增速。 其结果将显示整个模拟时间将大幅下降, 同时保持同样的精确度 。