ERA20CA168
2020-05-02 · Bowling Green, Kentucky, United States · None · 1 aircraft · Status: Completed
Airport BWG
Aircraft involved
Probable cause & findings
A primary control (aileron) system anomaly that progressively worsened during a test flight that resulted in a loss of control. The reason for the anomaly was not determined due to impact damage.
Factual narrative
The pilot reported that, during the first flight test of the antique airplane, which was in the process of being restored, he had difficulty controlling the airplane in the roll axis. After takeoff, he noticed that the airplane required some right aileron to keep the wings level. After turning crosswind, the problem worsened, and required that he apply more pressure to the control stick (in the right aileron direction). As he turned downwind, he used nearly full right aileron to maintain control, and the force on the control stick was so high that he was unable to hold it with one hand. When he removed his left hand from the control stick to adjust the throttle, the airplane would roll toward the left. As the airplane approached the base leg, the pilot was unable to keep the wings level; the airplane entered a slip and he was unable to prevent the airplane from turning left. The airplane descended, touched down in the grass to the right of the runway, and came to rest nose down. Examination of the airplane revealed that the aileron control system was continuous. However, due to the airplane damage, the rigging of the aileron system could not be evaluated. The airplane sustained substantial damage to the forward fuselage, lower wing, and horizontal stabilizer. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
FAA avdata. C = Cause, F = Factor.
- C Aircraft-Aircraft systems-Flight control system-Aileron control system-Malfunction - C
- C Not determined-Not determined-(general)-(general)-Unknown/Not determined - C
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Lateral/bank control-Attain/maintain not possible
Verbatim from NTSB's published report. Source file
NTSB_2020_ERA20CA168.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
Matched on aircraft type or causal vocabulary (loss of control). All research papers
- 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.
- Semantic Scholar 2024 · Article (International Conference on Networking and Services) Risk Assessment of Loss of Control In-Flight Trajectories for Urban Air Mobility Safety
In Urban Air Mobility (UAM), maintaining a minimum separation between aircraft is a safety requirement, which becomes challenging amidst congested air traffic and off-nominal conditions, such as Loss …
- 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.