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Record W3176843572 · doi:10.1117/12.2599547

Optical fiber sensor for atmospheric reentry experiments

2021· article· en· W3176843572 on OpenAlexaff
E. Haddad, Kamel Tagziria, Hongxin Chen, Florian Klinberg, Ali Guelhan, Brahim Aïssa, David Barba, I. McKenzie

Bibliographic record

VenueInternational Conference on Space Optics — ICSO 2020 · 2021
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsInstitut National de la Recherche ScientifiqueMPB Technologies & Communications (Canada)
Fundersnot available
KeywordsThermocoupleMaterials scienceTemperature measurementHypersonic speedOptical fiberWind tunnelMicroheaterTemperature cyclingVibrationFiberFiber optic sensorAcousticsThermalOpticsAerospace engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Reliable temperature measurements of hot structures of re-entry vehicles is one of the main challenges associated with atmospheric heating. During re-entry, several minutes at hypersonic velocities results in severe aerothermal loads and a resultant temperature increase of more than 1000°C. In contrast to single-point measurements provided by thermocouples, optical fiber sensors allow temperature measurement at multiple positions along the fiber line. Key challenges of this technique include packaging, integration and extraction of the temperature contribution from a signal that is also influenced by strain effects. MPBC developed optical fiber sensors for such temperatures with special packaging optimizing between protective capability and fast thermal conductivity. The fiber sensors were initially calibrated with thermocouples using a standard oven, then by means of a test in the DLR arc-heated wind tunnel L3K at a material temperature of 1000°C. To well monitor the fast heat fluxes in reentry two special ruggedized interrogation modules (Interrogator) was developed with a data acquisition at 100 Hz and 3.5 kHz. The Interrogators have large memory capacity to save data during 1 hour, and a USB memory stick as back up. The Interrogators was validated for vacuum, thermal cycling and vibrations, being completely functional during the tests. The vibrations tests were successful even an accurate sweeping part the piezo of the tunable Fabry-Perot Interferometer was sweeping during the vibrations in X, Y and Z-axis. DLR integrated both 100 Hz and 3.5 kHz into the hypersonic flight experiment ATEK for measuring temperature distribution of the motor case of the second stage motor and hybrid module structure, respectively. The 3.5 KHz Interrogator was integrated in the hybrid module, which was part of the launcher block equipped with a parachute. The second stage performed the return flight without parachute and allowed testing the impact resistance of the new DLR’s data acquisition system and some measurement techniques. The ATEK flight experiment was successfully launched on 13th July 2019 from the launch site Esrange in Kiruna. The second stage and the payload reached an apogee of approx. 240 km and continued the descent without any thrust and landed approx. 500 seconds after the take-off at a distance of approx. 67 km from the launch site. The Health Monitoring System allowed the measurement of aerothermal and mechanical loads on the hybrid payload structure and the motor case along the complete flight. Part of the data has been transmitted during flight to ground via telemetry at a low sampling rate of several Hertz. In addition, several impact-resistant data acquisition units could acquire the data at a high sampling rate of several Kilohertz and stored it onboard. The housing of interrogator and memory stick box was nearly completely undamaged. All four fiber optic connectors were still attached. MPB will recuperate and evaluate its functionality. The 100Hz Interrogator, without protection, impacted the ground with a velocity of about 95 m/s and was damaged due to the impact

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.364
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.058
GPT teacher head0.349
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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