Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2573664

Flexible PCB Windings Size Optimization for Winding AC Resistance Minimization Under the Sinusoidal Voltage Regime

Wojda, Rafal P. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000286609268)
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this paper, optimization of the winding AC resistance of the flexible printed circuit board (FPCB) wire windings is performed. A one-dimensional model of the FPCB winding is introduced and an equation for the FPCB winding AC resistance and its low and medium frequency approximation are derived. The approximate FPCB winding AC resistance equation is used to derive optimum thickness of the FPCB winding conductor thickness at which minimum of winding AC resistance (global optimum) is achieved. The Finite Element Method analysis and experimental verification of derived equations (winding resistance, impedance, power loss, and temperature measurements) is performed in order validate derived model and winding resistance equation.

Explore related subjects

42 ENGINEERINGconductorscoppereddy currentsflexible printed circuit boardinductorsinductors and transformersinsulationmagnetic componentsmagnetic resonancemathematical modelsresistancewinding losswinding optimizationwinding resistancewindingswire

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wojda, Rafal P. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000286609268). 2025-07-15. Flexible PCB Windings Size Optimization for Winding AC Resistance Minimization Under the Sinusoidal Voltage Regime. https://doi.org/10.1109/access.2025.3589166

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗