NTSB CAROL · Event
Event ANC24LA047
Registry · N2358A
FAA Aircraft Registry record.
Make / Model
PIPER PA-20-135
Year of manufacture
1952 · 72 years old at event
Engine
LYCOMING 0-290 SERIES (140 hp)
Seats / Engines
4 seats · 1 engine
Last airworthiness date
19570515
ADS-B equipped
Yes — Mode-S A21E84
Registrant of record
BEDELL SAMUEL G
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot providing instruction’s delayed remedial action, resulting in a loss of control during landing roll.
Factual narrative
During an instructional flight, the pilot providing instruction, seated on the right side, reported that, on landing roll while performing a touch-and-go, the pilot receiving instruction, seated on the left side, began to drift left. The pilot providing instruction attempted to correct with right rudder but was unable to overcome the drift. The airplane was equipped with brake pedals only on the left side, so the pilot providing instruction subsequently told the pilot receiving instruction to correct with right brake. However, the pilot receiving instruction did not apply adequate corrective measures, which resulted in a ground loop. The airplane sustained substantial damage to the left wing and fuselage. The pilot providing instruction reported no pre-impact mechanical malfunctions or failures with the airplane that would have precluded normal operation. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
Hierarchical cause / factor breakdown from the FAA bulk avdata database. Each finding tagged C (Cause) or F (Factor).
- — Personnel issues-Action/decision-Action-Delayed action-Instructor/check pilot
- — Personnel issues-Task performance-Use of equip/info-Aircraft control-Student/instructed pilot
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Directional control-Not attained/maintained
Verbatim from NTSB's published report. Source file
NTSB_2024_ANC24LA047.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket 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 or causal vocabulary (loss of control). Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- Embry-Riddle Scholarly Commons 2025 · Journal article (JAAER)
A Scoping Review of Aviation Loss of Control Inflight Research
Loss of control – inflight (LOC-I) contributes to aircraft accidents at unacceptably high rates. Significant industry efforts and research have aimed to improve LOC-I prevention, detection, and recove…
- SKYbrary (Eurocontrol) 2024 · SKYbrary article
Loss of Control In-Flight (LOC-I) — SKYbrary Knowledge Base
SKYbrary comprehensive knowledge-base entry on Loss of Control In-Flight — definitions, contributing factors, accident case studies (Air France 447, Colgan 3407), and prevention strategies.
- NTSB Aircraft Accident Reports 2022 · Accident report
Loss of Control on Takeoff in Icing Conditions — Citation 560XL
Cessna Citation 560XL fatal takeoff icing accident, March 2018. Investigation of a Citation 560XL loss-of-control takeoff accident in icing conditions.
- Semantic Scholar 2021 · Article (Aviation)
ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH
Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source…
- NASA NTRS 2021 · Presentation
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
- NASA NTRS 2021 · Conference Paper
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
Browse the full corpus — academia portal ↗