NTSB CAROL · Event
Event ATL96LA082
Aircraft involved
Probable cause & findings
The pilot's improper use of the flight control during a crosswind landing that resulted in a loss of control of the airplane.
Factual narrative
On April 26, 1996, at 1245 central daylight time, a Aero Commander 100-180, N4090X, nosed over during an attempted crosswind landing at the Huntington Carrol County Airport in McKenzie, Tennessee. The personal flight was being operated under the provisions of 14 CFR Part 91 with no flight plan filed. Visual weather conditions prevailed at the time of the accident. The airplane was substantially damaged. The private pilot and passenger were not injured. The flight departed Millington, Tennessee, at 1145. Upon arriving at Huntington Carrol County Airport, the pilot stated that the winds were out of the west-northwest at thirteen knots. The airport wind sock showed that the prevailing winds favored runway 19. The pilot entered the traffic pattern for runway 19. After the touchdown on the runway, the pilot stated that a gust of wind caused the right wing to come up quickly. He also stated that when the right main gear came back down on the runway, it sustained damage. The aircraft departed the runway, and nosed over in the sod area adjacent to the runway. No mechanical problems were reported by the pilot. Upon arriving at destination airport, the pilot noticed that the winds were out of the west-northwest at thirteen knots. Since the winds favored runway 19, he entered the traffic pattern for runway 19. After a touchdown on the runway, the pilot stated that a gust of wind caused the right wing to come up. He also stated that when the right main gear came back down on the runway, it sustained damage. The aircraft departed the runway, and nosed over in the sod area adjacent to the runway. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_1996_ATL96LA082.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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