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

Abstract

The increasing reliance on simulation models in engineering design and validation presents both opportunities and challenges. While simulations offer the potential to reduce costs, accelerate development timelines, and optimize designs, their credibility must be rigorously established to ensure reliable and actionable insights. This is especially critical in industries such as aerospace, defense, automotive, and energy, where safety and performance are paramount, and testing opportunities are often constrained by time or resources. This presentation outlines a comprehensive "brick-by-brick" approach to building simulation model credibility, drawing on real-world case studies and advanced methodologies. We begin by addressing the fundamental question: what does it mean for a simulation model to be credible? Credibility involves demonstrating that a model accurately represents the physical system it aims to simulate, across multiple dimensions such as geometry, material behavior, boundary conditions, and operational loads. To achieve this, we leverage the Predictive Capability Maturity Model (PCMM), a framework developed to assess and enhance the maturity of computational modeling efforts. The PCMM provides a structured methodology for setting goals, identifying improvement areas, and aligning simulation activities with experimental data in a systematic manner. The presentation explores some of the key 'œbricks' of the PCMM framework, including - Representation and Geometric Fidelity: Ensuring the model accurately captures the physical structure'™s geometry, using case studies like lattice structures at IRT Saint-Exupéry, where complex geometries required novel approaches to boundary condition management and measurement. - Physics and Material Model Fidelity: Demonstrating how material properties and physical interactions are represented accurately. This includes the use of finite element model updating (FEMU) to calibrate material models from experimental data. - Validation via Experimental Comparison: Illustrating how simulation results are compared against physical tests, using advanced instrumentation like multi-camera Digital Image Correlation (DIC) systems to capture high-fidelity strain and displacement data. Each of these components is supported by examples from industrial practice. For instance, the collaboration with ArianeGroup on dual launch structures demonstrates how test-simulation data fusion can validate and enhance structural performance predictions, reducing reliance on exhaustive physical testing while increasing confidence in critical systems. The presentation also delves into tools and technologies such as Digital Image Correlation (DIC) and photogrammetry, which provide precise, spatially dense experimental data to align with simulation models. We conclude by discussing the broader implications of adopting a maturity-based framework like PCMM. Industrial implementations of these concepts enable engineers and managers to make evidence-based decisions about testing policies, prioritize model improvements, and establish clear internal benchmarks for simulation credibility. By integrating simulation and experimental data in a structured manner, organizations can achieve higher levels of model maturity, reduce development risks, and accelerate innovation cycles. This approach provides a roadmap for engineers and researchers seeking to bridge the gap between computational and physical domains, fostering more reliable and effective engineering solutions.