Design and implementation of low mass short backfire antennas using additive manufacturing
Bibliographic record
Abstract
This thesis presents research into the design of low-mass short backfire (SBF) antennas with enhanced performance. In the first section of this thesis, modern techniques that can be utilized to decrease the mass of the aluminum SBF antenna were introduced. Two different antenna designs were developed using additive manufacturing and perforation techniques. The first design was created by manufacturing the antenna using additive manufacturing techniques, resulting in a significant reduction in mass. Simulations were conducted on this design to analyze the impact of additive manufacturing on the antenna’s performance. The results indicated that the gain was significantly affected by high levels of surface roughness introduced during the manufacturing process. The second low-mass antenna design, the perforated 3D-printed SBF antenna, combines additive manufacturing and perforation techniques. Parametric studies were conducted on this antenna to determine the optimal size, shape, and arrangement of perforations to achieve the best mass reduction and gain results. Simulation studies found that the antenna with a 3x37 circular array of perforations on its rim, each with a radius of 4.5 mm, performed the best. The simulated results were validated by fabricating and measuring the antennas. The mass of the 3D-printed and perforated 3D-printed SBF antennas were approximately 70% and 80% lighter than the aluminum antenna, respectively, while maintaining minimal loss in gain. The second part of this thesis discusses the enhancement of gain and bandwidth in the SBF antenna. This was done by flaring the rim to increase the aperture size of the antenna. Simulation studies were conducted to examine the impact of rim flaring and rim height on antenna performance. The results of these studies indicate that this technique significantly improved both the gain and bandwidth of the antenna while having minimal effect on the cross-polarization ratio. To further enhance the bandwidth, an iris was introduced to the waveguide feed aperture to obtain better impedance matching. The antenna was then manufactured and tested to confirm the accuracy of the simulations. The measured and simulated results were in excellent agreement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".