Methods and Approaches for Identifying and Evaluating Physical Quantities and Process Parameters in Physical Modelling of Prototypes

Abstract

Physical modelling of prototypes relies on the correct identification of physical quantities and process parameters to reproduce  operational behaviour and support engineering decisions. Prototype experiments frequently suffer from limited sample sizes,  uncertain boundary conditions, and heterogeneous sensor systems, making uncertainty evaluation and parameter identifiability’s critical concerns. Recent research highlights that uncertainty estimation under small-sample conditions constitutes major barrier for decision-grade prototype evidence [1]. Current paper reviews methods and approaches for identifying and evaluating physical  quantities and process parameters in prototype physical modelling. Key techniques include measurement-system design, uncertainty budgeting, operational modal analysis, inverse modelling, Bayesian parameter estimation, and experiments’ model-based design to increase parameter precision [2 - 4]. A structured workflow is proposed from measurand definition to validated parameter estimates with quantified uncertainty. Four submission-ready tables and an academic workflow figure concept are provided.