Dynamic Load Identification using Optimal Sensor Placement and Dynamic Condensation Methods
Bibliographic record
Abstract
Knowledge of excitation loads that structures experience during their service life is pivotal in different engineering fields, not only from a structural design optimization point of view but also as prevention of possible damages to the structures themselves. However, in case of dynamic events such as tornadoes, structures subjected to impulsive load due to their vicinity of explosions, the excitation load cannot be directly determined through direct measurements. In these scenarios, the inverse problem is used, and it is called load identification. This type of problem tries to determine the excitation load knowing the system response through a series of sensors placed on the structure. Most of the time, the number of sensors and their locations are randomly selected causing errors in the load estimation. The proposed mathematical method provides the appropriate number of sensors and their optimal locations. It combines the Craig-Brampton condensation method with the normal-mode method and D-optimal design technique. This method is employed and verified on simple structural members such as beams and plates and then it is applied on more complex structures such as a wind-turbine tower, wind-turbine blade, and an aircraft wing, which lead to more complexity in the procedures. Finite element models of these simple and complex structures are made in the general-purpose software ABAQUS. Then, a free-vibration analysis is carried out on each structure and the natural frequencies and mode shapes are extracted. Different load shapes are applied on the structures at different frequencies and noise conditions. The dynamic load is reconstructed by measuring the transient response of the structure at the optimum sensor locations. The results reveal that the proposed method can reconstruct a dynamic load with a high level of accuracy. Furthermore, the implementation of different parameters, such as noise effects, does not cause amplified errors in the final load estimation making the proposed method more robust.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".