These slides were presented at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

In the dynamic field of Computational Fluid Dynamics (CFD), the integration of Generative AI and Large Language Model (LLM) agents presents significant opportunities for workflow automation and enhancing efficiency. By employing specialized approaches for storing and querying source data, such as vector databases and graph-based databases, overall precision can be further improved. This presentation explores the innovative application of Generative AI combining it with advanced data retrieval mechanisms from vector databases and graph-based databases to automate the CFD workflow. It specifically focuses on the use case of employing LLM agents for Java macro generation within the Simcenter STAR-CCM+ tool. By leveraging traditional Retrieval-Augmented Generation (RAG) techniques in conjunction with the GraphRAG technique, we can significantly enhance the accuracy of CFD workflow processes in a CFD tool. Extraction of sources into structured data plays an important role in this integration, providing a structured and formalized source of engineering knowledge. These structures capture and interlink diverse data points, enabling access to relevant information and facilitating the generation of Java macros, which stand behind a workflow process in a CFD tool. The combination of knowledge graphs with AI-driven techniques accelerates and brings more precision to the retrieval process. Our presentation will begin by outlining the fundamental concepts of the RAG technique, knowledge graph technology, and its application in the CFD workflow automation. We will demonstrate how AI, when fed with structured data, can rewrite deprecated code, generate, modify and extend Java macros for a CFD specific tool. This not only improves the efficiency of the workflow but also ensures that the generated macros are up-to-date with the latest releases. Additionally, we will showcase real-world examples, where these techniques have shown the promises. We will discuss the implications of this approach, focusing on the quality of data and the preprocessing stage, and its impact on the accuracy and reliability of the final result. Emphasizing the importance of data integrity, we will explore how ultracareful preprocessing can prevent errors and enhance the overall performance of the AI models. Finally, we will share feedback from users of our very first Copilot prototype, highlighting their experiences and the tangible benefits observed in their CFD workflows. This feedback will provide valuable insights into the practical applications and future potential of integrating GenAI with workflow automation processes.