Establishing Best Practices for X-57 Maxwell CFD Database Generation

Duensing, Jared C.; Yoo, Seung Y.; Maldonado, Daniel; Housman, Jeffrey A.; Jensen, James C.; Kiris, Cetin C.

doi:10.2514/6.2019-0274

Cited by 10 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lower fidelity flight dynamics model uses Bechamo LLC's developed VLM plus propeller effects low-fidelity tool, BLOFI. This tool was developed initially to model the NASA Tiltwing aircraft and was validated against the X-57 data [41]. The validation plots can be seen in fig.…”

Section: B Low Fidelity Modelmentioning

confidence: 99%

“…BLOFI was developed at Bechamo LLC and in order to prove its usefulness in comparison to other available VLM solvers that do not account for blown wing effects. It has been previously validated using CFD data [41] generated by NASA for the X-57. Just as a flat plate model was created for the Aerobeacon with the appropriate propellers for the developments of this paper, a flat plate model with the corresponding propellers was earlier created for the X-57 as part of the validation study on BLOFI.…”

Section: Prior Analysis Of Blofi Performance On Other Data-setsmentioning

confidence: 99%

See 1 more Smart Citation

Physics Infused Machine Learning Based Prediction of VTOL Aerodynamics with Sparse Datasets

Oddiraju

Amin²,

Piedmonte³

et al. 2023

AIAA AVIATION 2023 Forum

View full text Add to dashboard Cite

Complex optimal design and control processes often require repeated evaluations of expensive objective functions and consist of large design spaces. Data-driven surrogate models such as neural networks and Gaussian processes provide an attractive alternative to expensive simulations and are utilized frequently to represent these objective functions in optimization. However, pure data-driven models, due to a lack of adherence to basic physics laws and constraints, are often poor at generalizing and extrapolating. This is particularly the case, when training occurs over sparse high-fidelity datasets. A class of Physicsinfused machine learning (PIML) models integrate ML models with low-fidelity partial physics models to improve generalization performance while retaining computational efficiency. This paper presents two potential approaches for Physics infused modelling of aircraft aerodynamics which incorporate Artificial Neural Networks with a low-fidelity Vortex Lattice Method model with blown wing effects (BLOFI) to improve prediction performance while also keeping the computational cost tractable. This paper also develops an end-to-end auto differentiable open-source framework that enables efficient training of such hybrid models. These two PIML modelling approaches are then used to predict the aerodynamic coefficients of a 6 rotor eVTOL aircraft given its control parameters and flight conditions. The models are trained on a sparse high-fidelity dataset generated using a CHARM model. The trained models are then compared against the vanilla low-fidelity model and a standard pure data-driven ANN. Our results show that one of the proposed architecture outperforms all the other models and at a nominal increase in execution time. These results are promising and pave way to PIML frameworks which are able generalize over different aircraft and configurations thereby significantly reducing costs of design and control.

show abstract

Section: B Low Fidelity Modelmentioning

confidence: 99%

Section: Prior Analysis Of Blofi Performance On Other Data-setsmentioning

confidence: 99%