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DIGITAL ENGINEERING - Toolkit to Produce Common Adaptive Mesh for Virtual Reality-based Multidisciplinary Interactive Design of Naval Aircraft

Description:

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Computing and Software OBJECTIVE: Develop an innovative tool that can autonomously generate a common mesh from Computer-Aided Design (CAD) geometry with adaptive global and local refinement capabilities for coupled aero-thermal-structural analysis and optimization to enable Virtual Reality (VR)-based real-time interactive designs. DESCRIPTION: Digital engineering for aircraft development can be accelerated by multidisciplinary design, analysis, and optimization (MDAO). The core component of MDAO for hypersonic aircraft is the multi-physics simulations involving the interplay between high-speed aerodynamics, structural dynamics, and thermodynamics. Aero-structure-thermal simulations could dramatically reduce the ground-based and in-flight tests as more capable high-performance computing (HPC) hardware can afford higher resolutions of geometrical and physical complexities — i.e., if a 10 cm accuracy in the 1980s for an aircraft was the standard, 1 mm for the geometry and 1 µm in the boundary layer resolution is now commonplace. However, these increasing geometrical accuracy requirements and physical complexities pose grand challenges in mesh generation [Refs 1–2]. According to the NASA CFD Vision 2030 [Ref 3], mesh generation and adaptivity persist as significant bottlenecks in computational fluid dynamics (CFD) workflow. On the one hand, autonomous and geometry-aware mesh generation techniques are still lacking. Generating high-quality meshes by existing approaches [Refs 4–7] from complex CAD models of aircraft still involves time-consuming human intervention, and the resulting meshes do not retain the parameterization of the geometry. The geometric discrepancy can lead to significant errors in the prediction of critical physics, such as shock-boundary layer interaction and fatigue/damage in structures. On the other hand, mesh adaption can substantially save CPU time, memory requirement, and storage space. However, controlling the error and generating the optimal mesh for a given accuracy is challenging [Ref 9]. Automatic mesh adaption with local and global refinements in critical regions without prior knowledge of the problem is also a difficult task [Ref 1]. Resolving these challenges, which have been hampering automatic and adaptive mesh generation for complex geometries, will dramatically facilitate simulation-based aircraft design and optimization and have a far-reaching impact on Navy’s missions. An adaptive, common mesh generation tool is needed to facilitate the MDAO to accelerate aircraft development. The effectiveness of the tool is measured by reducing the time, and therefore cost, required to develop the multi-physics meshes. The tool should enable autonomously-generated geometry-aware mesh generation and adaption, which can be integrated into the simulation tools of aircrafts involving CFD (e.g., hypersonic flows), fluid-structure interaction (FSI), fatigue/damage, and thermodynamics. The developed tool should be flexible, i.e., able to handle various air vehicle geometries. The tool should allow automatic mesh adaption with no or minimal dependence on prior knowledge in multi-physics simulations for critical quantities at critical locations, such as shock and turbulence in the fluid domain, as well as mechanical and thermal variables in the structural domain. The mesh generation and adaption tool should have quantification metrics, control strategies, and error estimates to assist the user in obtaining reliable simulation results with the first mesh. It should also address both body-fitted meshes and nonbody-fitted meshes, as the latter meshes are essential for the feasibility, as well as efficiency of MDAO problems where the geometry undergoes large shape and/or topological changes, and the multidisciplinary simulations involve large structural displacements, rotations and/or deformations [Ref 10]. The toolkit should enhance the user’s experience with virtual reality by improving visual understanding of the mesh with respect to the geometry through the interaction of the various physics. PHASE I: Demonstrate the capability of the mesh generation tool and its integration with a prototype simulation toolkit design. Illustrate a workflow for a multidisciplinary analysis along with the local-global coupling for a representative aircraft structural component subjected to a given operational profile. Demonstrate the effectiveness and cost saving in design iteration in comparison with a conventional approach. Show the autonomous capability of meshing complex CAD models and adaption upon mesh generation, capturing critical features such as shocks or stress concentrations. The Phase I effort will include prototype plans to be developed under Phase II. PHASE II: Develop, demonstrate, and validate the prototype design from Phase I. Fully integrate the mesh generation technique with multi-physics simulation tools of aero-structure-thermal analysis for aircraft development. Quality metrics for the fully functional software product should include the automatic meshing adaptivity to capture the local physics and meshing transformation between different physical problem descriptions. Incorporate virtual reality to enhance the user’s understanding of the mesh and how it relates to the geometry. The virtual reality should also enhance understanding of the solution and the interactions of various physics. Show the applicability of the tool using commercially available or open literature CAD data for the original design and specifications. Demonstrate its advantages in terms of cost, accuracy, and robustness in the context of MDAO to reach an optimized design under fluid-thermal-mechanical loading. PHASE III DUAL USE APPLICATIONS: Demonstrate capability to model a flight test event with inclusion of fluid-thermal-mechanical loading and show it provides risk reduction for the test event. The use of multidisciplinary simulations is becoming more common for commercial products. Beyond the natural need of the commercial aerospace industry, many other industries are interested in multi-physics simulations. Civil engineering needs to consider fluid-structures interactions. Fluid-thermal interactions are critical for electronics. The automotive industry is interested in fluid-thermal-structural analysis, with rapid turnaround. These industries will benefit from more capable and quicker multidisciplinary mesh generation. REFERENCES: 1. Alauzet, F., & Loseille, A. (2016). A decade of progress on anisotropic mesh adaptation for computational fluid dynamics. Computer-Aided Design, 72, 13-39. https://doi.org/10.1016/j.cad.2015.09.005 2. Karman, S. L., Wyman, N., & Steinbrenner, J. P. (2017). Mesh generation challenges: A commercial software perspective. In 23rd AIAA Computational Fluid Dynamics Conference (p. 3790). https://doi.org/10.2514/6.2017-3790 3. Slotnick, J. P., Khodadoust, A., Alonso, J., Darmofal, D., Gropp, W., Lurie, E. and Mavriplis, D. J. (2014). CFD vision 2030 study: a path to revolutionary computational aerosciences. https://ntrs.nasa.gov/citations/20140003093 4. Aftosmis, M., Berger, M., & Murman, S. (2004, May). Applications of space-filling-curves to cartesian methods for cfd. In 42nd AIAA Aerospace Sciences Meeting and Exhibit (p. 1232). https://doi.org/10.2514/6.2004-1232 5. Löhner, R., & Parikh, P. (1988). Generation of three-dimensional unstructured grids by the advancing-front method. International Journal for Numerical Methods in Fluids, 8(10), 1135-1149. https://doi.org/10.1002/fld.1650081003 6. Weatherill, N. P., & Hassan, O. (1994). Efficient three-dimensional Delaunay triangulation with automatic point creation and imposed boundary constraints. International journal for numerical methods in engineering, 37(12), 2005-2039. https://doi.org/10.1002/nme.1620371203 7. Yerry, M. A., & Shephard, M. S. (1984). Automatic three-dimensional mesh generation by the modified-octree technique. International Journal for Numerical Methods in Engineering, 20(11), 1965-1990. https://doi.org/10.1002/nme.1620201103 8. Hughes, T. J., Cottrell, J. A., & Bazilevs, Y. (2005). Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement. Computer methods in applied mechanics and engineering, 194(39-41), 4135-4195. https://doi.org/10.1016/j.cma.2004.10.008 9. Park, M. A., Kleb, W. L., Jones, W. T., Krakos, J. A., Michal, T. R., Loseille, A., Haimes, R., & Dannenhoffer, J. (2019). Geometry modeling for unstructured mesh adaptation. In AIAA Aviation 2019 Forum (p. 2946). https://doi.org/10.2514/6.2019-2946 10. Ho, J. B., & Farhat, C. (2021). Aerodynamic shape optimization using an embedded boundary method with smoothness guarantees. In AIAA Scitech 2021 Forum (p. 0280). https://doi.org/10.2514/6.2021-0280 KEYWORDS: Mesh Generation; Adaptive Meshing; Global-local modeling; Multidisciplinary Analysis; multi-disciplinary design, analysis, and optimization; virtual reality
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