Novel optimal sensor placement method towards the high-precision digital twin for complex curved structures

Tian, Kuo; Gao, Tianhe; Hu, Xuanwei; Xiao, Junyi; Liu, Yi

doi:10.1016/j.ijsolstr.2024.113003

International Journal of Solids and Structures

2024

DOI: 10.1016/j.ijsolstr.2024.113003

|View full text |Cite

Novel optimal sensor placement method towards the high-precision digital twin for complex curved structures

Kuo Tian,

Tianhe Gao,

Xuanwei Hu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimization of MOSFET Copper Clip to Enhance Thermal Management Using Kriging Surrogate Model and Genetic Algorithm

Cheon,

Jung,

et al. 2024

Mathematics

View full text Add to dashboard Cite

Metal–oxide–semiconductor field-effect transistors (MOSFETs) are critical in power electronic modules due to their high-power density and rapid switching capabilities. Therefore, effective thermal management is crucial for ensuring reliability and superior performance. This study used finite element analysis (FEA) to evaluate the electro-thermal behavior of MOSFETs with copper clip bonding, showing a significant improvement over aluminum wire bonding. The aluminum wire model reached a maximum temperature of 102.8 °C, while the copper clip reduced this to 74.6 °C. To further optimize the thermal performance, Latin Hypercube Sampling (LHS) generated diverse design points. The FEA results were used to select the Kriging regression model, chosen for its superior accuracy (MSE = 0.036, R2 = 0.997, adjusted R2 = 0.997). The Kriging model was integrated with a Genetic Algorithm (GA), further reducing the maximum temperature to 71.5 °C, a 4.20% improvement over the original copper clip design and a 43.8% reduction compared to aluminum wire bonding. This integration of Kriging and the GA to the MOSFET copper clip package led to a significant improvement in the heat dissipation and overall thermal performance of the MOSFET package, while also reducing the computational power requirements, providing a reliable and efficient solution for the optimization of MOSFET copper clip packages.

show abstract

Optimization of MOSFET Copper Clip to Enhance Thermal Management Using Kriging Surrogate Model and Genetic Algorithm

Cheon,

Jung,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Novel optimal sensor placement method towards the high-precision digital twin for complex curved structures

Cited by 1 publication

References 52 publications

Optimization of MOSFET Copper Clip to Enhance Thermal Management Using Kriging Surrogate Model and Genetic Algorithm

Optimization of MOSFET Copper Clip to Enhance Thermal Management Using Kriging Surrogate Model and Genetic Algorithm

Contact Info

Product

Resources

About