System-level multi-objective optimization of a magnetic air conditioner through coupling of artificial neural networks and genetic algorithms

Peixer, Guilherme Fidelis; Nakashima, Alan T.D.; Lozano, Jaime; Barbosa, Jader R.

doi:10.1016/j.applthermaleng.2023.120368

Cited by 5 publications

(1 citation statement)

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“…We employed the polynomial features technique coupled to a Ridge regression to prevent the overfitting of the data. We coupled governing equations based on geometric and operating interactions (Nakashima et al, 2022; Peixer et al, 2023a). For instance, there is the AMR and MC coupling.…”

Section: The Development Of a Magnetic Refrigeration Unitmentioning

confidence: 99%

Model-based trade-off curves to support the set-based concurrent engineering of highly innovative projects

Silvestre De Oliveira,

Fidelis Peixer,

Forcellini

et al. 2023

Concurrent Engineering

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The literature acknowledges the advantages of Set-Based Concurrent Engineering (SBCE), but there is a lack of models for its adoption and documented cases of its implementation, mostly on products with consolidated technology, which raises the possibility of expanding SBCE for highly innovative products. These projects often have extensive resource limitations, leading to computational tools and mathematical modelling as fundamental sources of information. This paper proposes a method for model-based Trade-off Curves (ToC) generation to support SBCE. We adopted it to develop a magnetic air conditioner. The use of model-based ToC enabled the narrowing of the design space and monitoring of the design parameters and performance metrics and enabled SBCE adoption in the design process. The main contributions of this research are presenting the state-of-the-art in ToC generation and application, proposing the model, and demonstrating SBCE in highly innovative projects, while its importance lies in the opportunity to further disseminate SBCE to different development environments.

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Section: The Development Of a Magnetic Refrigeration Unitmentioning

confidence: 99%