NTSB CAROL · Event
Event LAX05CA121
Registry · N4101E
FAA Aircraft Registry record.
Make / Model
AERONCA 11CC
Seats / Engines
2 seats · 1 engine
ADS-B equipped
Yes — Mode-S A4D6A0
Registrant of record
MAIDENS JAMES M
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
the pilot's failure to maintain directional control.
Factual narrative
On March 22, 2005, about 1630 mountain standard time, a Piper PA-18-150, N4101E, ground looped during an attempted touch-and-go landing at Chandler Municipal Airport, Chandler, Arizona. Chandler Air Service, Inc., was operating the airplane under the provisions of 14 CFR Part 91. The private pilot, the sole occupant, was not injured; the airplane sustained minor damage. The local personal flight departed Chandler about 1615. Visual meteorological conditions prevailed, and no flight plan had been filed. The approximate global positioning system (GPS) coordinates of the primary wreckage were 33 degrees 16 minutes north latitude and 111 degrees 48 minutes west longitude. The pilot was conducting his second touch-and-go landing. Upon touching down, he stated that he lost control and the airplane veered to the left and spun around 180 degrees. The plane came to rest off the runway on the right wing tip with the right main gear collapsed. The pilot reported that the airplane had no mechanical failures or malfunctions prior to the loss of control. The airplane ground looped off the runway and collapsed the right main gear during a touch-and-go. The pilot reported that upon touchdown, he lost directional control and the airplane veered left and then ground looped off the runway. The right wing tip was damaged upon contact with the ground, and the right main landing gear collapsed. The pilot reported that the airplane had no mechanical failures or malfunctions prior to the loss of control. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_2005_LAX05CA121.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.
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