AI-aided process intensification of structures

Abstract

Process intensification achieved through novel structural designs plays a crucial role in this field. The integration of artificial intelligence (AI) with computational fluid dynamics (CFD) and advanced manufacturing represents a paradigm shift in structural process intensification (PI), transforming it from an art into a systematic science of discovering optimal geometric configurations. Recently, many works have been published in AI-aided structural design, however, a systematic review is still lacking. This review examines the transformation, highlighting how AI-driven approaches enable rapid exploration of high-dimensional design spaces by providing fast predictive capabilities and efficiently guiding the search toward optimal solutions that would be difficult to identify using conventional methods. Key applications include reactors, heat exchangers, and mixers, where geometry plays a critical role in performance. Studies show that AI-assisted design can reduce development time compared to traditional methods while achieving comparable or superior performance metrics. However, challenges remain in constraints in manufacturing complex geometries, scale effects, and computationally intensive simulations. Despite these challenges, AI methods have demonstrated significant potential in accelerating the initial design phase and identifying promising structural configurations that can be further refined. This review provides a critical assessment of current achievements, challenges, and future directions in this rapidly evolving field.