Control of flow behavior in complex fluids using automatic differentiation

• 成果类型: Article
• 署名作者: Alhashim, Mohammed G.; Hausknecht, Kaylie; Brenner, Michael P.
• 署名单位: Harvard University; Saudi Aramco; Harvard University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11769
• DOI: 10.1073/pnas.2403644122
• 发表日期: 2025-02-25
• 关键词: immersed-boundary method; reynolds-number; periodic arrays; simulation; dispersion

摘要

Inverse design of complex flows is notoriously challenging because of the high cost of high dimensional optimization. Usually, optimization problems are either restricted to few control parameters, or adjoint-based approaches are used to convert the optimization problem into a boundary value problem. Here, we show that the recent advances in automatic differentiation (AD) provide a generic platform for solving inverse problems in complex fluids. To demonstrate the versatility of the approach, we solve an array of optimization problems related to active matter motion in Newtonian fluids, dispersion in structured porous media, and mixing in journal bearing. Each of these problems highlights the advantages of AD in ease of implementation and computational efficiency to solve high-dimensional optimization problems involving particle-laden flows.