MIA07CA025
2006-12-04 · Miami, Florida, United States · None · 1 aircraft · Status: Completed
Airport KTMB
Aircraft involved
Probable cause & findings
The failure of the pilot to maintain directional control during the landing roll resulting in a ground loop, and subsequent wing contact with the runway.
Factual narrative
On December 4, 2006, about 1446 eastern standard time, a Waco Classic Aircraft Corporation YMF-F5C, N113KJ, registered to and operated by a private individual, experienced a loss of control during a touch-and-go landing at Kendall-Tamiami Executive Airport, Miami, Florida. Visual meteorological conditions prevailed at the time and no flight plan was filed for the 14 CFR Part 91 personal flight from Kendall-Tamiami Executive Airport. The airplane was substantially damaged and the private-rated pilot, the sole occupant, was not injured. The flight originated about 5 minutes earlier from Kendall-Tamiami Executive Airport. The pilot stated that after takeoff, the flight remained in the traffic pattern for an intended full-stop landing on runway 9L. The wind was from 020 degrees at 10 knots, and he reported that the wheel landing was "normal", but encountered a wind gust which caused the airplane to yaw to the left. He applied right brake and the airplane turned hard to the right causing the left wing to contact the runway. The airplane came to rest upright. He further reported it was a "...classic ground loop." The pilot stated that after takeoff, the flight remained in the traffic pattern for an intended full-stop landing on runway 9L. The wind was from 020 degrees at 10 knots, and he reported that the wheel landing was "normal", but encountered a wind gust which caused the airplane to yaw to the left. He applied right brake and the airplane turned hard to the right causing the left wing to contact the runway. The airplane came to rest upright. He further reported it was a "...classic ground loop." Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_2006_MIA07CA025.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.