On the importance of graph search algorithms for DRGEP-based mechanism reduction methods

TL;DR

This paper evaluates various graph search algorithms within the DRGEP mechanism reduction method, finding Dijkstra's algorithm offers the most consistent, compact, and computationally efficient skeletal mechanisms for n-heptane and larger n-alkanes.

Contribution

It systematically compares different graph search algorithms in DRGEP, highlighting Dijkstra's algorithm as superior in producing order-independent, compact, and efficient skeletal mechanisms.

Findings

Dijkstra's algorithm and RBFS produce order-independent results.

Dijkstra's algorithm yields the most compact skeletal mechanisms.

Dijkstra's algorithm is two orders of magnitude more efficient computationally.

Abstract

The importance of graph search algorithm choice to the directed relation graph with error propagation (DRGEP) method is studied by comparing basic and modified depth-first search, basic and R-value-based breadth-first search (RBFS), and Dijkstra's algorithm. By using each algorithm with DRGEP to produce skeletal mechanisms from a detailed mechanism for n-heptane with randomly-shuffled species order, it is demonstrated that only Dijkstra's algorithm and RBFS produce results independent of species order. In addition, each algorithm is used with DRGEP to generate skeletal mechanisms for n-heptane covering a comprehensive range of autoignition conditions for pressure, temperature, and equivalence ratio. Dijkstra's algorithm combined with a coefficient scaling approach is demonstrated to produce the most compact skeletal mechanism with a similar performance compared to larger skeletal mechanisms resulting from the other algorithms. The computational efficiency of each algorithm is also compared by applying the DRGEP method with each search algorithm on the large detailed mechanism for n-alkanes covering n-octane to n-hexadecane with 2115 species and 8157 reactions. Dijkstra's algorithm implemented with a binary heap priority queue is demonstrated as the most efficient method, with a CPU cost two orders of magnitude less than the other search algorithms.