WPR11CA152
2011-02-13 · Lancaster, California, United States · None · 1 aircraft · Status: Completed
Airport WJF
Current FAA registration · N31FC
- Make / Model
- CESSNA 172
- Engine
- CONT MOTOR 0-300 SER (145 hp)
- Seats / Engines
- 4 seats · 1 engine
- Last airworthiness date
- 19581024
- ADS-B equipped
- Yes — Mode-S A344F8
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The student pilot's improper flare which resulted in a hard landing. Contributing to the accident was the certified flight instructor's delayed remedial action.
Factual narrative
The certificated flight instructor (CFI) reported that the student pilot was practicing short field procedures and had completed several uneventful landings. As they approached the runway, the CFI noted that the student had leveled the airplane too high. He instructed the student pilot to add power; however the student pilot reduced power and lowered the nose. Subsequently, the airplane landed hard. The CFI stated that he immediately took control of the airplane after touchdown, bringing it to a stop on the runway. The CFI reported no preimpact mechanical malfunctions or failures with the airframe or engine that would have precluded normal operation. The airplane sustained substantial damage to the firewall during the accident sequence. The certified flight instructor (CFI) reported that the student pilot was practicing short field procedures and had completed several uneventful landings. As they approached the runway, the CFI noted that the student had leveled the airplane too high. He instructed the student pilot to add power; however, the student pilot reduced power and lowered the nose. The airplane landed hard and the CFI immediately took control of the airplane, bringing it to a stop on the runway. The CFI reported no preimpact mechanical malfunctions or failures with the airframe or engine that would have precluded normal operation. The airplane sustained substantial damage to the firewall during the accident sequence. 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-Landing flare-Incorrect use/operation - C
- F Personnel issues-Action/decision-Action-Delayed action-Instructor/check pilot - F
- C Personnel issues-Task performance-Use of equip/info-Aircraft control-Student pilot - C
Verbatim from NTSB's published report. Source file
NTSB_2011_WPR11CA152.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.
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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
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- 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.
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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…