Affiliation:
1. Department of Mechanical, Aerospace, and Biomedical Engineering, The University of Tennessee, Knoxville, TN 37996, USA
Abstract
Complex engineering models are typically computationally demanding and defined by a high-dimensional parameter space challenging the comprehensive exploration of parameter effects and design optimization. To overcome this curse of dimensionality and to minimize computational resource requirements, this research demonstrates a user-friendly approach to formulating a reduced-dimension surrogate model that represents a high-dimensional, high-fidelity source model. This approach was developed specifically for a non-expert using commercially available tools. In this approach, the complex physical behavior of the high-fidelity source model is separated into individual, interacting physical behaviors. A separate reduced-dimension surrogate model is created for each behavior and then all are summed to formulate the reduced-dimension surrogate model representing the source model. In addition to a substantial reduction in computational resources and comparable accuracy, this method also provides a characterization of each individual behavior providing additional insight into the source model behavior. The approach encompasses experimental testing, finite element analysis, surrogate modeling, and sensitivity analysis and is demonstrated by formulating a reduced-dimension surrogate model for the damage tolerance of an aluminum plate reinforced with a co-cured bonded E-glass/epoxy composite laminate under four-point bending. It is concluded that this problem is difficult to characterize and breaking the problem into interacting mechanisms leads to improved information on influential parameters and efficient reduced-dimension surrogate modeling. The disbond damage at the interface between the resin and metal proved the most difficult mechanism for reduced-dimension surrogate modeling as it is only engaged in a small subspace of the full parameter space. A binary function was successful in engaging this damage mechanism when applicable based on the values of the most influential parameters.
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference88 articles.
1. Eldred, M., Giunta, A., and Collis, S. (September, January 30). Second-Order Corrections for Surrogate-Based Optimization with Model Hierarchies. Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, USA.
2. Dynamic data-driven reduced-order models;Peherstorfer;Comput. Methods Appl. Mech. Eng.,2015
3. Micro-mechanics and data-driven based reduced order models for multi-scale analyses of woven composites;Wu;Compos. Struct.,2021
4. Non-linear reduced order models for steady aerodynamics;Zimmermann;Procedia Comput. Sci.,2010
5. Vendl, A., and Faßbender, H. (2012, January 11–12). Projection-based model order reduction for steady aerodynamics. Proceedings of the Computational Flight Testing: Results of the Closing Symposium of the German Research Initiative ComFliTe, Braunschweig, Germany.