Hydraulic Barrier Optimization Using Random‐Walk Particle Tracking in Heterogeneous Media: Effects of Dispersion, Particle Discretization and Algorithmic Initialization

Abstract

Hydraulic barriers (HBs) are commonly used for solute plume containment in polluted aquifers. Their long‐term sustainability depends on minimizing the cumulative pumping rate, , required to ensure plume capture. Lagrangian random‐walk particle tracking (RWPT) is an effective approach for simulating advective‐dispersive transport to wells. However, as this method typically requires a large number of particles, , RWPT‐based simulation–optimization (SO) can be computationally demanding. This work presents an SO framework that couples a warm‐start genetic algorithm with parallel RWPT to identify minimal pumping rates for HB containment in a computationally efficient manner. The framework was applied in a Monte Carlo study using stochastic hydraulic conductivity fields, with ranging from to . Results showed that optimal values were jointly conditioned by transport processes, particle discretization, and algorithmic initialization. Under advection‐only transport, was nearly insensitive to . In contrast, when local dispersion was included, and its variability increased with . The influence of particle discretization depended strongly on the adopted recovery criterion, becoming substantially weaker when incomplete particle recovery was allowed. Neglecting local dispersion can therefore underestimate pumping requirements, particularly under stringent recovery criteria and increasing particle resolution. Warm‐start initialization generally identified lower solutions than cold initialization while also reducing computational time. These findings demonstrate that particle discretization, local dispersion and the recovery criterion should be considered explicitly when optimizing HBs, and that warm‐start strategies can substantially improve computational efficiency in RWPT‐based SO workflows.