NTSB CAROL · Event
Event NYC65F0214
Registry · N50135
FAA Aircraft Registry record.
Make / Model
TAYLORCRAFT/COBURN DCO-65L2M
Seats / Engines
2 seats · 1 engine
ADS-B equipped
Yes — Mode-S A64086
Registrant of record
BERRY DARREL
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Historical record (pre-1982)
NTSB recorded this accident in the pre-1982 coded-field schema — structured fields rather than free-text narrative. Decoded codes use established NTSB single-letter taxonomies; cause factors remain verbatim pending the Form 6120.4 codebook lookup.
Aircraft
TAYLORCRAFT L2 · N50135
Damage
Substantial
Craft type
Airplane
Classification
Accident
Light condition
Night
Weather
VMC
Phase of flight
D1
Operator type
D
Airport
SALEM AIRFLD
Kind of flying
B1
Weather at impact
Sky
CLEAR
Aircraft history
Total time
1,424 hrs
Pilot
Certificate
Airline transport
Total hours
30
Age
37
Cause factors
- 74/A/G1 A POWERPLANT MUFFLERS Cause — pilot/personnel action
- 88/A/95 A MISCELLANEOUS ACTS,CONDITIONS MATERIAL FAILURE Cause — pilot/personnel action
- 68/A/D6 A PERSONNEL INADEQUATE MAINTENANCE AND INSPECTION Cause — pilot/personnel action
- 88/J/50 J MISCELLANEOUS ACTS,CONDITIONS FIRE IN CABIN,COCKPIT,BAGGAGE COMPARTMENT Joint cause
- 88/J/51 J MISCELLANEOUS ACTS,CONDITIONS FIRE IN ENGINE Joint cause
Decoded against the NTSB Form 6120.4 cause-factor codebook (ct_Pre1982 table). Each row shows the raw triplet, modifier (Cause / Factor / etc.), category, and specific code.
Source: NTSB pre-1982 historical archive. Docket
2 0223.
Source file
NTSB_1965_2_0223.txt.
Modern CAROL record on
data.ntsb.gov ↗.
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Related research
What the literature says.
Academic papers and agency reports matching this event's aircraft type. Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- arXiv 2025 · arXiv preprint
Quickest change detection for UAV-based sensing
This paper addresses the problem of quickest change detection (QCD) at two spatially separated locations monitored by a single unmanned aerial vehicle (UAV) equipped with a sensor.
- arXiv 2021 · arXiv preprint
$\mathcal{L}_1$ Adaptive Control with Switched Reference Models: Application to Learn-to-Fly
Learn-to-Fly (L2F) is a new framework that aims to replace the traditional iterative development paradigm for aerial vehicles with a combination of real-time aerodynamic modeling, guidance, and learni…
- arXiv 2021 · arXiv preprint
Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme
Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft System (UAS) carry out a wide variety of missions (e.g.
- arXiv 2020 · arXiv preprint
Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility
With increasing urban population, there is global interest in Urban Air Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS) execute missions in the airspace above cities.
- NASA NTRS 2019 · Presentation
Effect of Environmental Exposures on Fatigue Life of P/M Disk Superalloys
As the temperature capability of Ni-base superalloy powder metallurgy disks is steadily increased, environmental resistance and protection of advanced nickel-based turbine disk components are becoming…
- Embry-Riddle Scholarly Commons 2016 · Conference paper
Late Afternoon Concurrent Sessions: Training and Education: Presentation: Aviation Management Education Study (AMES): Two Years in Review
The Aviation Management Education Study (AMES) is a longitudinal effort consisting of a series of research papers covering various facets of aviation education as it pertains to managers in the field …
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