| Goal-driven, multi-fidelity approaches for military vehicle system-level design|
|Applied Vehicle Technology|
design optimization, multidisciplinary analysis, Multifidelity analysis, multiphysics
Design-centric working groups (e.g., AVT-237 and AVT-252) have consistently shown that there is design benefit to coupling more engineering disciplines at higher levels of fidelity earlier in the development process. But, there is no mathematical framework to determine which disciplines, which level of coupling, and which level of fidelity is required to capture the physics most critical to a particular system’s design, or how to make the best possible design decision with constrained computing resources. Currently, these decisions are based solely on engineering experience. This approach works reasonably well for systems that are similar to previous designs, but can fail for unique and innovative vehicles and technologies.
The primary purpose of this task group is to identify and extend the current State of the Art associated with design frameworks for adaptive selection of sources of information of different fidelity, based on data and physics, for system-level vehicle design. A variety of test problems will be developed by which adaptive selection and the mixing of data produced by different information sources can be evaluated.
(1) Mathematically rigorous frameworks for synergistically fusing information sources of different fidelity, e.g., (a) efficient algorithms for utilizing high-fidelity methods to construct sufficiently accurate low-fidelity models; (b) efficient algorithms for low-fidelity models to determine when high-fidelity is needed; (c) surrogate models containing low- and high-fidelity data.
(2) Addressing system-level considerations, e.g. determining: (a) how physical interactions affect fidelity decisions; (b) what coupling is needed; (c) how fidelity needs adapt at different steps of the design process; (d) compatibility of multi-physics models of different fidelities.
(3) Basing fidelity decisions on system-level objectives and constrained by process resources.
(4) Mixing of test and computational data as information sources.
(5) Benchmark steady and transient test problems by which different relevant methods and frameworks can be compared and assessed.