1. SSIA: A sensitivity-supervised interlock algorithm for high-performance microkinetic solving
- Author
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Zhuangzhuang Lai, Peijun Hu, Jianfu Chen, Haifeng Wang, and Menglei Jia
- Subjects
010304 chemical physics ,Computer science ,Complex system ,Stability (learning theory) ,General Physics and Astronomy ,Solver ,010402 general chemistry ,01 natural sciences ,0104 chemical sciences ,Orders of magnitude (time) ,Robustness (computer science) ,Ordinary differential equation ,0103 physical sciences ,Sensitivity (control systems) ,Physical and Theoretical Chemistry ,Interlock ,Algorithm - Abstract
Microkinetic modeling has drawn increasing attention for quantitatively analyzing catalytic networks in recent decades, in which the speed and stability of the solver play a crucial role. However, for the multi-step complex systems with a wide variation of rate constants, the often encountered stiff problem leads to the low success rate and high computational cost in the numerical solution. Here, we report a new efficient sensitivity-supervised interlock algorithm (SSIA), which enables us to solve the steady state of heterogeneous catalytic systems in the microkinetic modeling with a 100% success rate. In SSIA, we introduce the coverage sensitivity of surface intermediates to monitor the low-precision time-integration of ordinary differential equations, through which a quasi-steady-state is located. Further optimized by the high-precision damped Newton's method, this quasi-steady-state can converge with a low computational cost. Besides, to simulate the large differences (usually by orders of magnitude) among the practical coverages of different intermediates, we propose the initial coverages in SSIA to be generated in exponential space, which allows a larger and more realistic search scope. On examining three representative catalytic models, we demonstrate that SSIA is superior in both speed and robustness compared with its traditional counterparts. This efficient algorithm can be promisingly applied in existing microkinetic solvers to achieve large-scale modeling of stiff catalytic networks.
- Published
- 2021