Design of Mounting Bracket for Avionics Units of High Speed Research Vehicle
Bibliographic record
Abstract
Abstract The present study focuses on design of mounting bracket for avionics units located in avionics bay of a high speed research vehicle. The avionics units are mounted on bulkheads, which are made of aluminium alloy. The objective of the current study is to design mounting bracket/bulkheads for the avionics units whose natural frequency is greater than the operating frequency of the avionics units and response grms value at mounting location is less than the acceptable level for the package. The acceptable grms level for Avionics unit-1 (AU-1) is 15g and for Avionics unit-2 (AU-2) is 12g. The operating frequency for AU-2 is 400-450 Hz. A finite element based approach has been carried out to design the mounting brackets for the avionics units. A finite element model of avionics bay along with bulkheads has been generated through a commercial finite element software and mass of all packages present in the avionics bay has been simulated as point masses. Mounting brackets are stiffened and sized such that their first natural frequency is above 500Hz. To see the effect of random vibration, a detailed response analysis was performed on the avionics bay. Modal analysis of the entire avionics bay with the simulated masses was performed and frequencies ranging from 0-2000Hz were extracted. Then the entire section is subjected to base excitation. The input vibration levels are as per MIL standard. Input vibrations was provided for a frequency range of 20-2000Hz. The input amplitude of power spectral density acceleration (PSDG) is 0.03 G2/Hz from 100 to 1000Hz frequency. From the response PSD plot the frequencies which get excited more to the input are understood and the output grms at the mounting locations of the avionics units are found out.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".