What question did this study set out to answer?

The research aims to enhance shape design in fluid machinery by integrating advanced AI techniques for better performance evaluation.

March 26, 2026Open Access

A Study on Shape Design Agent for Fluid Machinery Using Large Language Model and Retrieval-Augmented Generation of Design Knowledge

Key Points

The research aims to enhance shape design in fluid machinery by integrating advanced AI techniques for better performance evaluation.
Developed a system using a Large Language Model (LLM) for knowledge generation and interpretation.
Implemented Reasoning and Acting (ReAct) for iterative shape modification and evaluation.
Utilized surrogate models for rapid performance evaluation in the design process.
Incorporated Retrieval-Augmented Generation (RAG) for extracting past design knowledge.
Achieved interactive shape generation based on user inputs and design criteria.
Increased efficiency in the trial-and-error process of shape design in fluid machinery.

Abstract

In the design of fluid machinery, shape is a crucial factor that influences performance. Shape design is a highly subjective task that requires going through a trial-and-error process of iterative shape modification and evaluation, as well as an empirical process based on past design knowledge. To address this challenge, we have developed a system that combines a Large Language Model (LLM) capable of generating and interpreting various types of knowledge with a method called Reasoning and Acting (ReAct). This approach incorporates trial-and-error behavior, rapid performance evaluation using surrogate models, and design knowledge extraction through Retrieval-Augmented Generation (RAG), allowing for interactive shape generation based on user instructions.

A Study on Shape Design Agent for Fluid Machinery Using Large Language Model and Retrieval-Augmented Generation of Design Knowledge

Key Points

Abstract

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