This conference paper was submitted for presentation at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

In today'™s fast paced field of engineering analysis and simulations, the use of reduced models has proven to be crucial across various disciplines, particularly in aerospace and automotive for NVH and durability analyses. In numerical simulations, reduced models are computationally efficient mathematical representations derived from physical or analytical models using Model Order Reduction (MOR) techniques. Their primary purpose is to decrease the computational time and memory required to simulate complex analytical system models. Common types of reduced models in Finite Element Analysis (FEA) include modal models (from modal reduction), Nastran superelements, and Frequency Response Function (FRF) models, which can be derived from either analytical or measured data, among others. In practice, reduced models are implemented in a modular way, allowing analysts to focus on components of interest. These components are represented in detail during simulations, while the influence of surrounding components is accounted for using their computationally lighter reduced representations. This modular approach enables efficient structural and dynamic analyses by significantly reducing computational demands without compromising accuracy. Inherent challenges emerge with the development of reduced models. The definition of reduced models requires expert knowledge and is often the result of a try and error approach to achieve a balance between simplification and accuracy in encapsulating the complete model'™s main characteristics. Then, the creation of alternative representations of reduced models, as well as the creation of new reduced model versions for every modification of the detailed FE-representation of the model, unveil another inherent weakness of the standard practices; The lack of traceability, that completely hinders collaboration between simulation engineers. The implementation of a Simulation Process and Data Management (SPDM) system represents a transformative approach to managing simulation workflows and data. On the data management side, an SPDM system streamlines the creation and handling of reduced models within a collaborative environment. By providing structured 'œrecipes' for generating various reduced model types and maintaining traceability of source models and dependencies, it ensures end-to-end transparency. This enables seamless tracking from the digital mock-up to full FE-representations of sub-assemblies, and further, to reduced models and simulations. Such traceability simplifies the definition and analysis of simulation variations, allowing effortless transitions between full FE and reduced representations in the pre-processor. Moreover, the system significantly enhances "what-if" analyses and optimization processes by properly documenting simulation iterations, enabling clear and comprehensible comparisons between variations. Furthermore, during the early phases of product development, when detailed designs are not yet available, the system offers access to the complete library of reduced models from past models, that can be used for some early CAE verifications. On the process management front, SPDM systems facilitate the standardization and automation of CAE workflows. These capabilities optimize the generation of reduced models by automating the submission of reduction runs to the solvers on the HPC systems, followed by systematic storage of results linked to their respective reduced models. This work highlights the advantages of integrating reduced model methodologies within an SPDM framework. Real-world case studies illustrate how SPDM systems address critical challenges in simulation model preparation and analysis, including data sharing, integrity, traceability, and version control. Ultimately, the findings demonstrate that adopting SPDM systems not only accelerate the end-to-end simulation process but also fosters innovation and productivity across engineering teams.