DEN02LA021
2002-01-17 · Logan, Utah, United States · Minor · 1 aircraft · Status: Completed
Airport LGU
Aircraft involved
Probable cause & findings
the pilot's failure to maintain aircraft control during landing roll. Contributing factors were the runway's patchy snow and ice conditions and a snow bank along the side of the runway.
Factual narrative
On January 17, 2002, at approximately 1515 mountain standard time, a Cessna 152, N68737, operated by Utah State University, was substantially damaged following loss of control during landing roll at Logan-Cache Airport, Logan, Utah. The private pilot, the sole occupant on board, was not injured. Visual meteorological conditions prevailed, and no flight plan was filed for this local instructional flight being conducted under Title 14 CFR Part 91. The flight originated at approximately 1500. According to the student pilot, during landing roll on runway 35, the airplane hit several patches of snow and ice that were located left of the centerline. He attempted to add a little power and right rudder to avoid the ice. The airplane veered to the left, the nose landing gear struck a snow bank, and the airplane nosed over. The airplane sustained substantial damage to the right wing spar, vertical stabilizer and rudder. The pilot reported that his total flight time was 85.5 hours in all aircraft, and this was his first flight in the last 90 days. At 1551, the recorded weather conditions at Logan-Cache Airport, were, wind, calm; visibility, 3 statute miles; temperature, minus 10 degrees Celsius; dew point, minus 12 degrees Celsius; and an altimeter setting, 30.16. During landing roll, the airplane hit several patches of snow and ice that were located left of the centerline. The pilot attempted to add a little power and right rudder to avoid the ice. The airplane veered to the left, the nose landing gear struck a snow bank, and the airplane nosed over. The winds at the time of the accident were calm. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_2002_DEN02LA021.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.