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Record W4205788710 · doi:10.3390/en15010320

Design and Analysis of a 35 GHz Rectenna System for Wireless Power Transfer to an Unmanned Air Vehicle

2022· article· en· W4205788710 on OpenAlexafffund
Muttahid Ull Hoque, Deepak Kumar, Yves Audet, Yvon Savaria

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversité de SherbrookePolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsRectennaDirectivityMicrowaveElectrical engineeringMicrostripMaximum power transfer theoremMicrostrip antennaAntenna (radio)Microwave transmissionPower (physics)Antenna gainWireless power transferRadio frequencyEnergy conversion efficiencyEngineeringVoltageElectronic engineeringAntenna efficiencyPhysicsTelecommunicationsRectification

Abstract

fetched live from OpenAlex

In this article, the concept of a 22-kW microwave-powered unmanned aerial vehicle is presented. Its system architecture is analyzed and modeled for wirelessly transferring microwave power to the flying UAVs. The microwave system transmitting power at a 35 GHz frequency was found to be suitable for low-cost and compact architectures. The size of the transmitting and receiving systems are optimized to 108 m2 and 90 m2, respectively. A linearly polarized 4 × 2 rectangular microstrip patch antenna array has been designed and simulated to obtain a high gain, high directivity, and high efficiency in order to satisfy the power transfer requirements. The numerically simulated gain, directivity, and efficiency of the proposed patch antenna array are 13.4 dBi, 14 dBi, and 85%, respectively. Finally, a rectifying system (rectenna) is optimized using the Agilent advanced design system (ADS) software as a microwave power receiving system. The proposed rectenna at the core of the system has an efficiency profile of more than 80% for an RF input power range of 9 to 18 dBm. Moreover, the RF-to-DC conversion efficiency and DC output voltage of the proposed rectenna are 80% and 3.5 V, respectively, for a 10 dBm input power at 35 GHz with a load of 1500 Ω.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.206
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2022
Admission routes2
Has abstractyes

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