Design Validation of Ceramic Radome Subjected to Combined Thermal and Structural Loads
Bibliographic record
Abstract
Abstract Radome is the foremost component of missiles that houses avionic systems and is meant to protect them as they operate within a limited temperature range. During flight radome is subjected to structural loads and severe thermal loads. The thermal loads on radome are due to aerodynamic or kinetic heating, and structural loads are due to drag, normal forces caused by the surrounding fluid. Materials used in radome are generally Ceramics and are selected based on operational point of view of Seeker to transmit electro-magnetic radiation, ability to withstand applied thermo-structural loads without any failure. The ceramic radome is attached to metallic bulkhead using a special adhesive. This paper discusses the methodology used to carry out design of Radome using classical principles and use of numerical tools finite element analysis. The design is carried out for combined thermal and structural loads acting on the missile. The design is further validated with an experiment that simulates equivalent effect of external pressure, temperature. Structural loads are applied using hydraulic oil as pressurizing medium, thermal loads are applied using short wave infra-red radiation heater systems. The subsystems involved in carrying out thermos-structural test, instrumentation used to apply loads, measure response in order to capture the realistic behavior during test are discussed. Comparison of measured strains, deflections and temperatures with predicted values is presented in this manuscript thereby validating the numerical model and ensuring structural integrity of designed Radome and its deploy ability for operation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".