Agenda

Abstract

This short course introduces the core components necessary to build an effective and efficient simulation capability. The course then provides a framework for building and implementing a simulation strategy targeted at achieving the organisations product and business goals. ? Goals for Modelling and Simulation [M&S] ? Leadership Questions for M&S ? Trends and Challenges Affecting your M&S ? The Essential Elements for Effective and Efficient M&S ? Engaging Stakeholders ? Strategy Framework ? Building your Strategy ? The Essential Elements Mini Deep Dive (Process '“ Methods '“ Tools) ? What's your Status? - Maturity Assessment ? Improvement Actions ? Strategy Implementation ? Strategy Governance Andy is a Fellow of the Institution of Mechanical Engineers. He has over 30 yrs experience in Automotive Engineering at Jaguar Land Rover, with 20 years at Senior Management level, including 10 years as Head of Engineering Simulation. In 2019 formed PHRONESIM Ltd to provide consulting in all aspects of Engineering Simulation Strategy helping multinational organisations with; simulation capability maturity assessment, simulation strategy development, improvement roadmaps, and simulation strategy training and coaching. Andy is a member of the NAFEMS UK Steering Committee, Assess Business Theme Committee, and the Simulation Governance and Management Working Group .

Abstract

Engineering Simulation is a crucial capability for developing, refining and optimizing products. It is therefore, vital that organisations engineering simulation capability is fast and efficient, but can also be trusted to provide reliable results to enable important engineering decisions to be made with confidence. This is especially true with the increased use of AI techniques, as simulation is a critical source of trustworthy data used to train AI models. Is your engineering simulation capability ready? Engineering Simulation is like a complex machine. It depends on many components working together in harmony to be effective, efficient, and reliable. To assess the status of their Engineering Simulation, organisations can benefit from conducting a Maturity Assessment. This enables them to identify their strengths, but importantly to identify areas to target improvement across. This mini workshop will present an approach and framework that organisations can use to assess the status of their organisations engineering simulation capability, addressing the following topics: - Organisation Goals - Core Components of Engineering Simulation - Simulation Maturity Assessment Types - Approach to conducting a Maturity Assessment - An Example Maturity Framework - Some Practicalities - Taking Action - The Benefits of Conducting a Maturity Assessment Share your experiences and learn from others via interactive mini surveys and workshop discussions: - Your goals and challenges - Your experiences and lessons learnt - Benefits of maturity assessment Andy is a Fellow of the Institution of Mechanical Engineers. He has over 30 yrs experience in Automotive Engineering at Jaguar Land Rover, with 20 years at Senior Management level, including 10 years as Head of Engineering Simulation. In 2019 formed PHRONESIM Ltd to provide consulting in all aspects of Engineering Simulation Strategy helping multinational organisations with; simulation capability maturity assessment, simulation strategy development, improvement roadmaps, and simulation strategy training and coaching. Andy is a member of the NAFEMS UK Steering Committee, Assess Business Theme, and the Simulation Governance and Management Working Group .

Abstract

In engineering, the implementation of artificial intelligence methods can be a useful tool to break the boundaries of data silos and efficiently process data in the field of simulation-based design. In addition to implementing comprehensible and efficient data management systems, identifying and capturing the relationships between the respective engineering data elements provides usable context that sustainably enriches the data used. Contextualization makes the data highly structured, making it ideal for feeding into AI models to gain deeper insights, identify patterns and support informed decision-making. This is also the idea behind the concept of the Data Context Hub (DCH): which was developed as a research project over 6 years at Virtual Vehicle Research (Graz) together with automotive and rail OEMs. The platform brings together information from R&D and manufacturing data sources as well as from telemetry data streams or storage locations like data lakes. The DCH then creates an explorable context map in the shape of knowledge graphs from area-specific data models. These are essential for streamlining processes, reducing risks and identifying new opportunities in the data-driven development of innovative products. The use of state-of-the-art AI models also supports developers in gaining deeper insights from the data, predicting trends and automating tasks. In the joint presentation by Context64.ai and GNS Systems, the experts will present the findings from applied research on contextual graph databases. Using two practical examples from the automotive and manufacturing industries, the experts will demonstrate how Data Context Hub helps companies transform complex data into clear, actionable insights. Use case 1 concerns buildability studies in the automotive industry, where simulation times have been significantly reduced. Due to the high complexity of the product configurations, an exponential number of possible combinations and the associated slow development cycles, conventional tools and methods proved insufficient for the company. The Data Context Hub's AI-based platform served as a virtual buildability checker by providing a solution specifically for capturing, simplifying and querying complex configuration dependencies. The implemented solution presents the configuration rules to the user as nodes in a directed graph structure that can map the relationships and dependencies between different components. By consistently capturing dependencies between assembly constraints, configuration rules and special manufacturing processes, far-reaching insights can be gained that give engineers in the production process of innovative vehicles, for example, conclusions about the impairment of the rear axle by a selected battery pack. This approach allows a faster and more precise determination of the configurations that can be built, thereby shortening development times. In a second use case, it becomes clear that the platform optimally transforms data-intensive AI applications by creating a structured, context-aware basis for information retrieval and decision-making. The use of Large Language Models (LLM) as a chatbot provides precise, relevant answers to specific user queries about certain data contexts in technical use cases. Like the currently best-known ChatGPT, the chatbot uses language queries to independently generate content in the form of correlations between the data. For example, the chatbot would respond to a request for a summary of all simulations carried out with a specific material in the last month with an interpretation of the underlying knowledge graphs. To do this, the trained model uses the internal 'œMemory for Your AI' (M4AI) module to search through the linked data relationships and find relevant information based on semantic similarity. This works as follows: In the first phase, a defined data model searches a large dataset for relevant information. In the following phase, retrieval augmented generation models (RAG) are used to optimize the initial search. The model then identifies relevant information based on semantic similarity and not by searching for exact matches. The information obtained in this way flows into the subsequent generation process. This enables users to better understand the answers provided and make decisions more quickly. The concept behind the Data Context Hub combines the ability to retrieve information from a large database with the generative capabilities of state-of-the-art AI technologies such as LLMs in a pioneering way in the field of simulation-driven design.

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.

Abstract

Finite element analysis (FEA) has been a pillar of computer-aided engineering (CAE) since the 1960s. The longevity of this simulation technology presents both opportunities as well as unique challenges for leveraging generative AI for both new and experienced users. While there are mushrooming large language model (LLM) based assistants available for such users, many of the assistants operate at a superficial level based on training documents and manuals; they are not sufficient for supporting a user intent on understanding, debugging and quickly resolving issues that lie at the input file level, the gateway to the FEA solver. In this context, there are three unique challenges to leveraging generative AI. First, the inputs to FEA solvers are text-based with syntaxes, definitions and descriptions exposed through keywords that follow a non-intuitive, unique taxonomy and can be documented in manuals that can have thousands of pages. Next, these input file formats never adapted beyond the original implementation intended for punch cards which allow input to be unsorted but create the burden of ID management. This approach is in direct conflict with modern input decks that are geared towards HPC and can easily be gigabytes in file size. The sorting of such a file could be specific to a pre-processor, company best practices or simply the historical build-up of a model. The unsorted nature of this directly contracts with the natural flow of human language. In this work, we present a unique approach to solving the problem by establishing a graph representation of the input file, establishing edges based on cross-referencing ID schemes and traversing this graph. This approach enables input-file specific document retrieval and provides users of every skill-level the ability to obtain prompts and responses at a higher level (pure documentation) and deeper level (input file). Future ongoing work explores optimizing the developed algorithms that reduce token consumptions and help users leverage their compute resources in a more cost-effective manner. In this proof-of-concept work, we combine a parser, graph, a graph traversal method, and documentation retrieval (based on the graph) to deliver an in-context Generative AI experience relative to the exact features of an input file a user is interrogating. In ongoing trials, we expect reduced onboarding times, reduced debugging times, and reduced touches of traditional documentation.

Abstract

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