NTSB CAROL · Event
Event FTW96LA097
Registry · N111UF
FAA Aircraft Registry record.
Make / Model
U-FLY-IT LIGHT SPORT ACFT LLC CHALLENGER II
Engine
ROTAX 582DCDI (65 hp)
Seats / Engines
2 seats · 1 engine
Last airworthiness date
20150123
ADS-B equipped
Yes — Mode-S A031B2
Registrant of record
PAYNE JOSEPH C
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot's improper flare, improper recovery from a bounced landing, and failure to maintain directional control during the landing. The soft terrain was a related factor.
Factual narrative
On January 13, 1996, at 1045 central standard time, a Cessna 152, N111UF, was substantially damaged following a loss of control while landing near Spring, Texas. The student pilot was not injured. The airplane was operated by United Flight Services of Spring, Texas, under Title 14 CFR Part 91. Visual meteorological conditions prevailed for the solo local instructional flight. According to the operator, the pilot had been cleared for a solo flight to practice landings at the David Wayne Hooks Airport. During an intended touch and go landing on Runway 17L, the airplane landed hard, porpoise a couple of times, followed by a loss of directional control. The airplane departed to the left of the runway, the nose wheel sank into the soft ground, nosed over, coming to rest in the inverted position. The 65-hour student pilot had been cleared for a solo flight to practice touch and go landings. While landing on runway 17L, the airplane landed hard, bounced, and porpoised a couple of times. The airplane then departed the left side of the runway. When the airplane departed the runway, the nose wheel sank in the soft ground and the airplane nosed over to the inverted position. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_1996_FTW96LA097.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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