Machine-Learning-Assisted Prediction of Port-Flow Distribution and Multi-Objective Parametric Optimization for Navigation Lock Manifolds

Abstract

Navigation lock manifolds are key components of filling-and-emptying systems, and port-flow distribution affects chamber flow stability and filling efficiency. Under unsteady filling conditions, port-flow distribution is governed by discharge variation and manifold geometry, making rapid prediction and engineering-constrained screening challenging. This study develops a surrogate-assisted prediction and Pareto-screening framework for a large-scale navigation lock manifold. Three-dimensional computational fluid dynamics (CFD) simulations were used to examine unsteady port-flow evolution. The peak-flow condition was selected as a representative control condition, and the flow non-uniformity coefficient α and system resistance coefficient ξ were used as performance indicators. Based on 243 parametric CFD samples and 144 independent external test samples, artificial neural network (ANN), Gaussian process regression (GPR), and support vector regression (SVR) models were evaluated. ANN performed best, with independent-test R2 values of 0.9999 and 0.9928 for α and ξ. Feature-attribution analysis identified port width, culvert height, and port number as dominant variables. Pareto screening within a predefined engineering design space identified representative candidates with CFD verification errors below 1.1%. The TOPSIS-based candidate reduced ξ by 32.2% while maintaining α nearly unchanged.