RECORD
Artificial Hair Sensor Placement Optimization on Airfoils for Angle of Attack Prediction
Fluid Dynamics · Submitted 21 Dec 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim For two or more sensors this algorithm finds a set of sensor locations (design point) which predicts angle of attack to within 0.10 degrees and ranks within the top 1% of all possible design points found by brute force search.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Arrays of bioinspired artificial hair-cell airflow velocity sensors can enable flight-by-feel of small, unmanned aircraft. Natural fliers - bats, insects, and birds - have hundred or thousands of velocity sensors distributed across their wings. Aircraft designers do not have this luxury due to size, weight, and power constraints. The challenge is to identify the best locations for a small set of sensors to extract relevant information from the flow field for the prediction of flight control parameters. In this paper, we introduce the data-reducing Sparse Sensor Placement Optimization for Prediction algorithm which locates near-optimal sensor placement on airfoils and wings. For two or more sensors this algorithm finds a set of sensor locations (design point) which predicts angle of attack to within 0.10 degrees and ranks within the top 1% of all possible design points found by brute force search. We demonstrate this algorithm on several variations of airfoil sections of infinite and finite wings in clean and noisy data, evaluate model sensitivities, and show that the algorithm can be used to identify an appropriate number of sensors for a given accuracy requirement. Applications for this algorithm are explored for aircraft design and flight-by-feel control.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Arrays of bioinspired artificial hair-cell airflow velocity sensors can enable flight-by-feel of small, unmanned aircraft. | background 0.98 | 0.09 | 0.05 | hedged possibility 0.90 |
| s1 | Natural fliers - bats, insects, and birds - have hundred or thousands of velocity sensors distributed across their wings. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | Aircraft designers do not have this luxury due to size, weight, and power constraints. | background 0.93 | 0.04 | 0.02 | asserted 1.00 |
| s3 | The challenge is to identify the best locations for a small set of sensors to extract relevant information from the flow field for the prediction of flight control parameters. | background 0.64 | 0.07 | 0.04 | asserted 0.96 |
| s4 | In this paper, we introduce the data-reducing Sparse Sensor Placement Optimization for Prediction algorithm which locates near-optimal sensor placement on airfoils and wings. | contribution 0.82 | 0.94 | 0.17 | asserted 1.00 |
| s5 | For two or more sensors this algorithm finds a set of sensor locations (design point) which predicts angle of attack to within 0.10 degrees and ranks within the top 1% of all possible design points found by brute force search. | result 1.00 | 0.95 | 0.66 | asserted 1.00 |
| s6 | We demonstrate this algorithm on several variations of airfoil sections of infinite and finite wings in clean and noisy data, evaluate model sensitivities, and show that the algorithm can be used to identify an appropriate number of sensors for a given accuracy requirement. | contribution 0.74 | 0.96 | 0.24 | asserted 1.00 |
| s7 | Applications for this algorithm are explored for aircraft design and flight-by-feel control. | future work 0.66 | 0.54 | 0.07 | asserted 0.49 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Arrays of bioinspired artificial hair-cell airflow velocity sensors can enable flight-by-feel of small, unmanned aircraft. Natural fliers - bats, insects, and birds - have hundred or thousands of velocity sensors distributed across their wings. Aircraft designers do not have this luxury due to size, weight, and power constraints. The challenge is to identify the best locations for a small set of sensors to extract relevant information from the flow field for the prediction of flight control parameters. In this paper, we introduce the data-reducing Sparse Sensor Placement Optimization for Prediction algorithm which locates near-optimal sensor placement on airfoils and wings. For two or more sensors this algorithm finds a set of sensor locations (design point) which predicts angle of attack to within 0.10 degrees and ranks within the top 1% of all possible design points found by brute force search. We demonstrate this algorithm on several variations of airfoil sections of infinite and finite wings in clean and noisy data, evaluate model sensitivities, and show that the algorithm can be used to identify an appropriate number of sensors for a given accuracy requirement. Applications for this algorithm are explored for aircraft design and flight-by-feel control.
Human review
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.