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Recent Reports
| Title: | Fuel Tank Flammability Assessment Method User’s Manual | ||||
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| Author: | Steven M. Summer | ||||
| Abstract: | The Fuel Tank Flammability Assessment Method (FTFAM) is a Federal Aviation Administration-developed computer model designed as a comparative analysis tool to determine airplane fuel tank flammability as a requirement of Title 14 Code of Federal Regulations 25.981. The model uses Monte Carlo statistical methods to generate flammability data for certain unknown variables over known distributions for a large number of flights. The FTFAM iterates through each flight, calculating the flammability exposure time of each flight given the data input provided by the user. Calculating this flammability exposure time for a sufficiently large number of flights results in statistically reliable flammability exposure data. These calculations can be performed by the user for virtually any type of airplane fuel tank (body tank, wing tank, auxiliary tank, etc.) both with and without a flammability reduction method being employed. This report serves as a user’s manual for this computer model to assist the user in its operation and to discuss the permissible changes that may be made to this model specific to a particular fleet of aircraft. It is updated through version 10 of the FTFAM. The user should reference Advisory Circular 25.981-2A for additional guidance on when to use this model and for a discussion of interpretation of results. |
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| Report: | DOT/FAA/AR-05/8 | Pages: | 80 | Size: | 408 KB |
| Title: | Thermo-Kinetic Model of Burning | ||||
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| Author: | Stanislav I. Stoliarov and Richard E. Lyon | ||||
| Abstract: | One main obstacle in developing more effective passive fire protection for transportation is the lack of a quantitative understanding of the relations between the results of various materials fire tests used in this field. The need for multiple testing techniques arises from the complexity of fire phenomena and their sensitivity to environmental conditions. This study addressed this problem by developing a computational tool that predicts the behavior of materials exposed to fire. While it is not expected that this tool will eliminate the need for fire testing, the goal is to considerably reduce the number and complexity of the tests necessary for a comprehensive characterization of the materials of interest. The foundation of this tool is a mathematical model that describes transient thermal energy transport, chemical reactions, and the transport of gases through the condensed phase. The model also captures important aspects of a material’s behavior such as charring and intumescence. This technical note provides a detailed description of the one-dimensional version of this model and summarizes the results of the model’s verification. | ||||
| Report: | DOT/FAA/AR-TN08/17 | Pages: | 32 | Size: | 758 KB |
| Title: | Aircraft Cargo Compartment Multisensor Smoke Detection Algorithm Development | ||||
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| Author: | Adityanand Girdhari | ||||
| Abstract: | There is a need to effectively develop and test an advanced fire detection system for aircraft cargo compartments that significantly reduces false alarms and improves alarm time response. Title 14 Code of Federal Regulations Part 25.858 requires that aircraft detection systems alarm within 1 minute of the start of a fire. Gas concentrations, temperature fluctuations, and particulate levels are three main parameters representative of a complete fire signature. Current aircraft detection systems depend solely on one parameter, particulate levels, for the detection of this wide fire signature. Improved fire detection capabilities can be achieved by combining multiple fire signatures or parameters in specific algorithms. An advanced fire detection system combining an ionization smoke detector, thermocouple, smokemeter, and a carbon monoxide (CO)/carbon dioxide (CO2) gas probe was installed in a Boeing 707 forward cargo compartment. A broad spectrum of fire and nuisance sources were tested to produce a matrix of extreme detector levels from all four sensors. This matrix provided alarm threshold criteria that aided in the development of a multisensor algorithm based on fire signatures such as CO and CO2 gas concentrations, temperature, ionization chamber voltage, and percent light transmission per foot. Multiple algorithms were created to determine the most effective multisensor algorithm that responded the fastest to fires while providing nuisance immunity. A spatial distribution analysis was conducted by using a Computational Fluid Dynamic (CFD) model to specify the physical range of the multisensor detector subjected to the optimized algorithm. A multisensor algorithm combining CO2 gas concentrations, percent light transmission per foot, and ionization chamber voltage parameters produced a 100% success rate for detection of fires within 1 minute while providing nuisance immunity to those signatures tested. Comparison of computational and experimental alarm time, smokemeter, and ionization chamber results demonstrated the effectiveness of the CFD and provided strong evidence that the CFD can be used as a virtual detector to simulate fires with an average alarm time uncertainty of 2.57 seconds. Spatial distribution analysis from the CFD determined the physical range of the single multisensor detector to be at least 910 cubic feet, the volume of the Boeing 707 forward cargo compartment. |
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| Report: | DOT/FAA/AR-07/58 | Pages: | 98 | Size: | 1.1 MB |