WPR11CA177
2011-03-31 · Republic, Washington, United States · None · 1 aircraft · Status: Completed
Airport R49
Current FAA registration · N7302A
- Make / Model
- CESSNA 172
- Year of manufacture
- 1956 · 55 years old at event
- Engine
- CONT MOTOR 0-300 SER (145 hp)
- Seats / Engines
- 4 seats · 1 engine
- Last airworthiness date
- 19560927
- ADS-B equipped
- Yes — Mode-S A9CDAA
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot’s failure to maintain directional control during the landing roll.
Factual narrative
The student pilot was practicing forward slips to a landing for his private pilot check ride, which was scheduled for the following day. He landed flaps up, and was rolling down the runway. As he applied the brakes, the airplane turned to the right, and departed the runway surface. The airplane collided with a winrow of rocks about a foot high. The nose wheel was knocked off, and the left wing sustained substantial damage. The pilot had a total time of 130 hours with 39 hours as pilot-in-command. The student pilot was practicing forward slips to a landing for his private pilot check ride, which was scheduled for the following day. He performed a no flap landing and when he applied the brakes, the airplane turned to the right, and departed the runway surface. The airplane collided with rocks about one foot high. The nose wheel separated, and the left wing sustained substantial damage. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
FAA avdata. C = Cause, F = Factor.
- C Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Directional control-Not attained/maintained - C
- — Environmental issues-Physical environment-Object/animal/substance-(general)-Effect on equipment
- C Personnel issues-Task performance-Use of equip/info-Aircraft control-Student pilot - C
Verbatim from NTSB's published report. Source file
NTSB_2011_WPR11CA177.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
Search this event elsewhere
External sources are reported, not agency: signal that something happened, not fact about what happened.
- TallyAero Live Wire Aviation press
- NTSB CAROL Agency ↗
- NTSB Docket Agency ↗
- Aviation Safety Network Aviation press ↗
- Kathryn's Report Aviation press ↗
- Aviation Herald Aviation press ↗
- AVweb Aviation press ↗
- Pilots of America Community ↗
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Related research
Matched on aircraft type or causal vocabulary (icing). All research papers
- NASA NTRS 2023 · Reprint (Version printed in journal) Finite Element Simulation of Three Full-Scale Crash Tests for Cessna 172 Aircraft
The NASA Emergency Locator Transmitter Survivability and Reliability project was initiated in 2013 to assess the crash performance standards for the next generation of emergency locator transmitter (E…
- NASA NTRS 2019 · Conference Paper Crash Testing and Simulation of a Cessna 172 Aircraft: Pitch Down Impact Onto Soft Soil
During the summer of 2015, NASA Langley Research Center conducted three full-scale crash tests of Cessna 172 (C-172) aircraft at the NASA Langley Landing and Impact Research (LandIR) Facility.
- NASA NTRS 2019 · Technical Memorandum (TM) Simulating the Impact Response of Three Full-Scale Crash Tests of Cessna 172 Aircraft
During the summer of 2015, a series of three full-scale crash tests were performed at the Landing and Impact Research Facility located at NASA Langley Research Center of Cessna 172 aircraft.
- arXiv 2026 · arXiv preprint Enabling Beyond-Visual-Line-of-Sight Drones Operation over Open RAN 5G Networks with Slicing
Among the foretold claims of the transition from 5G to 6G, Beyond-Visual-Line-of-Sight (BVLoS) drone operation has emerged as a prominent Internet-of-Robots enabler.
- NASA NTRS 2026 · Contractor Report (CR) Icing Physics Studies Using the 3D SIDRM Test Article: 2023 Icing Tests Analysis
In-flight icing is an important safety issue and is a factor that affects aircraft design and performance. Newer regulations are driving a need for improvements in airframe and engine icing simulation…
- arXiv 2025 · arXiv preprint Multi-Agent Deep Reinforcement Learning for UAV-Assisted 5G Network Slicing: A Comparative Study of MAPPO, MADDPG, and MADQN
The growing demand for robust, scalable wireless networks in the 5G-and-beyond era has led to the deployment of Unmanned Aerial Vehicles (UAVs) as mobile base stations to enhance coverage in dense urb…